Object recognition ability predicts episodic location memory, enhanced by meaningfulness

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R. Smithson, Isabel Gauthier This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7594990/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Feb, 2026 Read the published version in Psychological Research → Version 1 posted 8 You are reading this latest preprint version Abstract People differ in their ability to distinguish visually similar items, a domain-general ability known as o (Richler et al., 2019 ). While o typically involves extracting invariant object properties, we investigated whether it also relates to long-term memory for episodic information extrinsic to object identity, specifically, object location. We further examined whether this relationship is influenced by stimulus meaningfulness, a factor known to enhance long-term memory, by using both high- and low-meaning stimuli. Participants completed a location memory test, a series of visual object-recognition tasks assessing o , and other cognitive covariate measures. Results showed a positive correlation between o and location memory, which was stronger for high-meaning than for low-meaning stimuli. This suggests that semantic content may enhance the link between object recognition and episodic location memory. Importantly, these effects remained after controlling for age, gender, low-level visual perception, working memory, and general intelligence. Our findings indicate that domain-general object recognition ability contributes to episodic memory by supporting the binding of meaningful objects to their spatial context. This challenges traditional cognitive boundaries by integrating current knowledge about individual differences in perception and memory, with semantic meaning acting as a significant moderator. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Public Significance People differ in how precisely they tell similar objects apart (“object recognition ability”). In our experiments, individuals with stronger recognition ability more accurately remembered where objects appeared, especially for meaningful, familiar items, even after accounting for general intelligence, working memory, and basic visual acuity. These findings link perception to memory for specific experiences, suggesting that fine-grained object representations help anchor where things were seen in everyday life. Introduction Domain-general object recognition ability, or o , explains shared variance in performance across different object recognition tasks that span multiple domains (Richler et al., 2019; Smithson & Gauthier, 2026; Sunday et al., 2021). At its core, o reflects the ability to distinguish visually similar objects, such as differentiating between two bird species at the subordinate level (Richler et al., 2019). It is, however, usually measured with novel objects so that semantic knowledge and individual experience with familiar categories do not complicate its interpretation (Richler et al., 2017; Smithson et al., 2024). As such, an important characteristic of o is its domain-generality: an o factor measured with several categories of familiar objects (e.g., birds and planes) correlates perfectly at the latent level with an o factor measured using novel, unfamiliar objects (Sunday et al., 2021). This consistency across familiar and novel stimuli suggests that o represents a general perceptual ability rather than accumulated expertise. In recent work, we found that o can be measured using tasks that vary substantially in their cognitive demands - some tasks make substantial recognition-memory demands while others primarily stress perception, yet both types of tasks also tap a common underlying object recognition ability (Smithson & Gauthier, 2025). Thus, even when we measure o in memory tasks, the memory demands are incidental to the core ability, which involves distinguishing visually similar objects regardless of task format. Here, we explore for the first time whether o relates to long-term memory for a property extrinsic to object shape or identity, specifically its location - a key facet of episodic memory (Ranganath, 2010; Tulving, 1972). Although the theoretical definition of o does not directly predict whether it should support better episodic memory for non-shape features, addressing this question is important for integrating current knowledge about individual differences in perception and their potential relationship with memory. Moreover, o is thought to reflect perceptual skills that do not require linkage to semantic knowledge, which is why it can be estimated with novel objects. Yet in the real world, the semantic meaning of objects can often support visual perception through increased familiarity and/or top-down knowledge. Although the processing of both shape and location primarily relies on perceptual abilities, semantic information influences both visual expertise (Gauthier et al., 2003) and episodic memory (Renoult et al., 2019). We will therefore further examine whether, and how, the semantic meaning of real-world objects potentially interacts with the effects of high perceptual ability ( o ) on location memory. Success on o tasks depends on identifying diagnostic features while maintaining invariant representations that abstract away from instance-specific details. This emphasis on invariance would allow someone to recognize a warbler regardless of its pose, lighting conditions or location on a specific branch, focusing on identity-relevant features while ignoring contextual variations. But what of a bird watcher with a high- o? Should we expect that she would also be better able to remember this contextually-related episodic information? While these skills - visual object recognition and episodic memory - are typically studied separately, both abilities are useful, and may interact in many real world applications. For instance, a crime scene investigator must quickly identify objects despite occlusions but also remember their exact placement. Similarly, a field biologist needs to recognize animal tracks regardless of substrate conditions but also remember the specific timing and location of each sighting. To study interactions between perceptual abilities and meaning for the memory of object location, we asked individuals with varied perceptual abilities ( o ) to encode objects low or high in meaningfulness, and later, to report the objects’ location. Our first prediction is that location memory will be better for objects that are more meaningful. As mentioned above, our study included two kinds of objects, low- and high-meaning objects, as defined by independent ratings (Shoval et al., 2023). Prior work found that both object identity and their arbitrary location are better remembered in long-term memory when items are more meaningful (Gronau et al., 2024; see also Taevs et al., 2010; DeWitt et al., 2012). This effect has been attributed to a 'Resource-limited' account, whereby more familiar items require fewer encoding resources, allowing spare capacity for visual detail encoding (Popov & Reder, 2020). We expect to replicate this effect, although we are not directly investigating its underlying mechanism. Our second prediction is that people with a higher o will have a better memory for the location in which objects were studied, beyond individual differences in general intelligence, working memory and other factors such as age or gender. Although spatial position is extrinsic to an item's core identity and features, object location and identity are not perceived as strictly independent. For instance, objects shown in the same location are more likely to be reported as having the same identity (Babu et al., 2023; Golomb et al., 2014), and splitting attention across different locations disrupts feature binding (Dowd & Golomb, 2019). One reason that high- o individuals may better retrieve the locations associated with studied objects is that streamlined object processing during encoding preserves capacity for registering context-related details, again consistent with resource-limited accounts (e.g., Popov & Reder, 2020). According to this view, more frequent or familiar items are easier to encode, and as a result are more likely to bind to an episodic context. Individuals who benefit from enhanced perceptual encoding, for instance as a result of perceptual expertise (Curby et al., 2009), show a working memory advantage. Likewise, stronger object representations in those with a higher o may allow for more item-context binding. We acknowledge, however, that this is not the only possible reason o may predict location memory. Another explanation comes from a retrieval-gating account. Episodic memory retrieval occurs when a retrieval cue sufficiently overlaps with a stored memory representation, leading to the reactivation of the encoded information (Tulving & Thomson, 1973). In our task, memory for an object's location depends on, or is 'gated' by, the ability to recover the object representation itself (particularly because each location is associated with multiple different objects). In other words, objects serve as cues: if the object is not remembered at least in part, its associated location cannot be retrieved. In that case, more object locations should be retrieved accurately as a function of o because the objects themselves are better remembered. Thus, both a resource-limited account and a retrieval-gating account make the same prediction, against a common null hypothesis that o will not be related to memory for location since it reflects an ability specific to the invariant processing of shape information. Note, however, that the limitations of our correlational approach will allow us to ask for the first time whether o can predict location memory, not why it does. We have predicted two main effects: better location memory for meaningful objects and better location memory with a higher o . Our third and most critical prediction, however, concerns the role of semantic knowledge in mediating a possible perception-memory relationship. Here, two different hypotheses can be formulated. The first is that perceptual abilities (indexed by o ) can fuel a self-reinforcing cycle that enhances memory (and eventually may shape expertise development in a specific field). We call this the Perceptual-Semantic Synergy account. For example, domain-specific episodic memories (e.g., a birder remembering a warbler’s pose in morning light) could provide foundational material that, through abstraction and integration, builds semantic knowledge and supports expertise (Greenberg & Verfaellie, 2010; Tulving, 1972). Richer semantic knowledge can, in turn, improve episodic recall (e.g., Renoult et al., 2019), as when knowledge of warbler typical behavior supports memory of where someone spotted one. This has important implications for the interaction between object meaning and o : High- o individuals encode objects with precision, automatically, and even when object shape is task-irrelevant (McGugin et al., 2022). By definition, high- o individuals are better at subordinate-level discriminations (Sunday et al., 2021). Therefore, in our study, high- o people should encode both low- and high-meaning objects more precisely, but a precise representation of a high-meaning object is likely to lead to categorization that is more specific (for instance, a chair may be recognized as a “colonial-style antique chair”). This can unlock access to a wider associative network of semantic information (e.g., this style prioritizes practicality, it is often put together with wooden pegs rather than nails…), thereby increasing meaningfulness. In other words, a familiar object encoded with more visual precision gains richer semantic content as well. To the extent that prior meaningfulness has been found to increase episodic memory for objects, including for their location (Gronau et al., 2024; Taevs et al, 2010), this suggests that o would be positively correlated with the magnitude of this effect. In other words, the Perceptual-Semantic Synergy account predicts a stronger positive correlation between o and the accuracy of location judgments for high-meaning relative to low-meaning objects (Figure 1, left). An alternative hypothesis posits an interaction between o and object meaning in the opposite direction: Since nearly all high-meaning objects in our study belong to different basic levels, they can be remembered to a large extent using verbal and semantic information in addition to visual information. Note that the availability of unique verbal labels does not mean that visual information is not encoded, as demonstrated in work where people encode a large number of such objects and can later discriminate them from highly similar objects sharing the same verbal code (Brady et al., 2008). In contrast to high-meaning stimuli, memory for low-meaning objects must primarily rely on visual information because they are less familiar and nameable. We could therefore expect that perceptual skills may be particularly critical for low-meaning objects, resulting in a stronger correlation with o ability than for high-meaning objects. This prediction (Figure 1, right) assumes that perceptual and semantic information function separately but can trade off, such that people rely more on one when the other is absent. We call this the Perceptual-Semantic Trade-off account. Luckily, while both of these hypotheses predict removable interactions (Wagenmakers et al., 2012), each one presents a qualitative contrast with the other. This means that support in favor of one hypothesis provides strong evidence against the other. It is worth noting that, theoretically, a third possibility exists: since o is a perceptual ability in its essence, it might not interact with the semantic meaning of stimuli in predicting location memory. However, we consider this scenario to be rather unlikely, as a sharp perception-memory distinction may reflect historical conventions more than cognitive reality (Firestone & Scholl, 2016). The competing predictions outlined above have never been directly tested. If o predicts memory for where an object was experienced - not just what it looks like- it would significantly advance our understanding of the relationship between perception and memory. How semantic meaning interacts with o in predicting location memory will further constrain our understanding of this relationship. Methods All experimental materials and results are available at the Open Science Framework and can be accessed at https://osf.io/hy763/?view_only=be6bc532f4fa490d98e81e7eb1d8eb86. This study was not preregistered. All experiments were performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethical committee of the Open University of Israel (#3668) and of Vanderbilt Institutional Review Board (#222116). Participants We recruited participants from a prior study in which we had collected measures of object recognition and other cognitive covariates. The original study (Smithson & Gauthier, 2025) was conducted on the Prolific.co platform and recruited participants residing within the US, 18 to 45 years old, reporting fluency in English and normal or corrected-to-normal vision, and having a > 95% approval rating for past studies. That study was 2 sessions long, with 333 participants completing session 1, 298 completing session 2 and 275 people passing exclusion criterion (not performing below chance on more than one test). We invited 255 of these participants (excluding those who were at chance on more than 2 of the 14 tasks in the original studies) for a new task, approximately 1 month after completion of the Smithson et al. (2025) study. Both studies could only be completed on a computer screen, not a mobile or tablet. We were able to collect data from 159 participants, and 154 of them performed above chance (25%) on the 4-alternative forced choice episodic location task. Their mean age was 33.8 (SD=6.6), and they self-reported gender as follows: 83 women, 72 men, 1 prefer not to say. A sensitivity analysis for detecting the incremental contribution of o (after accounting for covariates) indicated that with N = 154, α = .05, and power = .80, the smallest effect size detectable at this level of power is an incremental effect for o of R² = 0.049. Informed consent was obtained from all individual participants included in the study. Procedure Tasks from Smithson et al. (2025) Eight tasks measured participants’ object recognition skills and their proficiency in distinguishing highly similar visual shapes – tasks that when combined form one’s o measure. Four of these tasks were based on increased perceptual demands (e.g., rapid presentation time, noise on the images, degraded silhouettes), while the remaining four relied primarily on memory demands (e.g., encoding a large number of objects, temporal delays, and requiring associations between objects). Four additional tasks measured low level visual ability using oddball decisions with simple visual features, three tasks were included to measure general intelligence ( g ), and 2 tasks were included to measure working memory. We summarize these tasks below and details can be obtained in the original work. Eighteen simple attention checks were embedded throughout the test battery to assess engagement and data quality. These checks typically required straightforward responses to obvious stimuli or explicit instructions (e.g., "click the leftmost option"). Participants were excluded if they made more than 1 attention error. Perceptual o tasks. Four tasks emphasized perceptual discrimination under challenging viewing conditions (Figure 2). Many Objects Oddball required identifying which of three simultaneously presented objects differed in identity across 45 trials. Two objects shared identity but varied in size and orientation, while the third was slightly different. Objects appeared for 750-4000 ms and participants clicked the location where the odd object had appeared. Novel object categories changed each trial, with feedback provided after responses. Silhouette Matching involved matching target objects (YUFOs) to one of three simultaneously presented silhouettes across 40 trials, often requiring viewpoint invariance when silhouettes appeared rotated relative to targets. Ensemble Perception with Transformers tested averaging ability across 50 trials where participants viewed four robot figures (1 s), then selected which of six morphs best represented their average. Performance was measured as absolute error in degrees within the circular morph space. 3AFC Matching with Asymmetrical Greebles required matching target objects after a 500 ms visual mask across 51 trials. Four blocks systematically varied target presentation times (300-1000 ms), viewpoints (same vs. ~30° horizontal rotations), and noise levels. Memory o tasks. Four tasks required encoding and maintaining multiple object representations (Figure 3). Learning Exemplars with Vertical Ziggerins involved memorizing six target objects presented together for 20 s, followed by alternating study-test phases (6, 18, then 24 recognition trials). Participants used "g," "h," and "j" keys to select which of three objects matched a memorized target. Paired Objects required learning five associations between novel 3D objects and abstract 2D shapes. Each pair appeared for 4 s with immediate testing, followed by multiple study-test cycles and 35 total test trials in various formats. New Object began with three novel objects (Quaddles) shown for 3000 ms, then presented 44 test arrays of four objects where participants identified the one that was novel (had never appeared before in the task). Recognition Memory involved studying successive sets of four target novel objects (15 s each) followed by a reading comprehension delay, then 24 recognition trials where targets appeared alongside two distractors from the same object category. Control Measures Working memory (WM) was assessed using Verbal-numerical Binding , where participants remembered word-number pairs (2000 ms each, 1000 ms ISI) across sequences of 2-6 pairs, then identified correct pairings when probed with individual words or numbers (27 total responses). Operation Span required recalling letter sequences (1000 ms each) while performing intervening arithmetic operations (3000 ms or until response), with sequence recall tested after each trial (15 trials total). Low-level visual discrimination (LLV) used the Hanover Early Vision Assessment (HEVA; Kieseler et al., 2022), presenting three images per trial (two identical, one different) across 96 trials. Participants pressed "f" (left), "space" (center), or "j" (right) to identify the odd stimulus. The battery comprised 16 blocks of six trials each, testing four basic visual features: dot distance (discriminating spacing between dot pairs), circle size (detecting size differences in circular stimuli), angle size (identifying angular differences in line configurations), and line length (distinguishing length variations in linear segments). Eight blocks were administered in session one and eight in session two, with each session containing two blocks per feature type. General intelligence ( g ) was measured through Raven's Matrices (selecting which of 8 numbered options completed 3×3 symbol patterns, 18 trials, 10-minute limit), Number Series (choosing the next number from 5 options, 15 trials, 5-minute limit), and a Vocabulary test (selecting the best synonym from 3 options, 30 trials, untimed). Memory for Spatial Location, as a Function of Object Meaningfulness The task consisted of two phases: an encoding phase and a location-memory test phase. During the encoding phase, participants viewed 288 individual object images adapted from Brady et al. (2008), each presented for 2000 ms with an 800 ms inter-stimulus interval (ISI). These objects were positioned at one of 16 locations along the perimeter of an imaginary circle–unbeknownst to participants–with each point spaced 22.5° apart, starting at 11.25° and excluding the canonical axes. Each object spanned approximately 7 degrees, and the circle had a radius of about 11 degrees, assuming a viewing distance of 50 cm. However, as the experiment was conducted online, actual viewing distance may have varied across participants. The objects were selected from a large stimulus pool for which subjective ratings of stimulus familiarity (“How familiar is the stimulus?”) and stimulus knowledge (“Do you know what the stimulus is?”) were collected (both on a scale of 1-7). Since the correlation between the responses to these questions was very high (r=.98, p<.001), the ratings were averaged to form a single ‘meaningfulness' measure, which has previously been shown to predict memorability for both item identity (Shoval et al., 2023) and item spatial location (Gronau et al., 2024). Out of the 288 stimuli, 128 low-meaningful (average meaning score: 2.85, SD =0.31) and 128 high-meaningful (average meaning score: 6.39, SD =0.36) objects served as test items in the subsequent location-memory test phase (Figure 4a). An additional 32 items (16 from each meaningfulness category) were repeated during the encoding phase and used in an item-repetition task (N-back, with repetition-lags of 0,1,2 or 4 items). These items only appeared during the encoding phase, ensuring maintenance of attention while viewing the images. Participants were instructed to memorize the spatial location of each item while simultaneously monitoring for item repetitions, responding by pressing the spacebar each time they detected a repeated item. The encoding phase included 320 trials in total, including item repetitions. The memory-test phase involved a four-alternative forced-choice (4-AFC) test, in which each item appeared in its original location along with three additional locations positioned 90, 180, and 270 degrees from the encoded location. The four items in each trial were presented simultaneously until participants responded by clicking on the original location (Figure 4b). Note that while familiarity and object identification are known to influence memory (e.g., Dall et al., 2021; Reder et al., 2013), they are inherently subjective and likely shaped by multiple dimensions–including conceptual richness, typicality, nameability, and more. Despite this multidimensionality, the selected high-and low-meaning stimuli offered a promising starting point for investigating the relationship between o , semantic interpretation, and episodic location memory. Testing the reliability of the location memory test . Before examining memory results and their correlation with o measures, it was important to assess the reliability of the episodic (location) memory measurements to ensure they were suitable for individual differences analyses. Note that paradigms that are sensitive at the group level do not always produce measurements that are sufficiently reliable for this purpose (Hedge et al., 2018; Ross et al., 2015). There are good reasons to believe that high reliability would be obtained for the measurements relevant to our first question – namely, whether people with a high o would remember object locations better. However, our second question – whether o would interact with the enhancement in location memory for meaningful versus less meaningful objects, is more challenging. Achieving reliability for the meaningfulness effect is likely to be more difficult because it involves comparing measurements obtained for the low and high meaningfulness conditions. In group-analyses, the meaningfulness effect would essentially be handled as a difference score between high- and low-meaningfulness items - but difference scores generally have low reliability (Hedge et al., 2018; Ross et al., 2015). Regressing out the variance for the low meaningful objects is a better option, although if performance on low- and high-meaningfulness items is highly correlated, the reliability of the residuals can still be low (Degutis et al., 2013; Ross et al., 2015). We therefore decided to perform item-level analyses on the memory scores to obtain acceptable reliability, before examining the correlation with o. Results We considered possible self-selection biases for participants, out of those we have invited, who volunteered for this new study (N=159), compared to those we did not hear back from (N=96). There may be several reasons for whether a participant responded: The first study was conducted mid-August and the second mid-September, when some participants may have gone back to college. The two studies were conducted one month apart, with no explicit link between them, so it is unlikely that a participant would consider their experience in the first study (with no feedback on performance) when accepting the second one. The two groups (returning/not returning) did not differ significantly in gender distribution (returning 52% women; not returning, 56% women, χ²(1, 255) = 0.47 value, p = .49). Returning participants were on average 3 years older (33.7 vs. 30.6, t 253 =3.345, p<.001). There were no significant differences for g (t 253 =.943, p=.35), working memory (t 253 =.993, p=.32) or low level vision (t 253 =1.080, p=.28). Returning participants had on average a higher and less variable o (returning: mean = .38, SD = .33; not returning: mean = .27, SD = .42; Welch’s t 164 =2.22, p=.03; Levene’s test for equality of variances, F(1,253) = 6.586, p = .01). One possible reason why individuals with lower visual ability were less likely to participate in the second session is that the task description on Prolific mentioned “memorize the locations of different objects on the screen” whereas that for the first study was more vague: “you will complete a series of short tasks”. In any case, the reduced variability in o would most likely diminish its predictive power, suggesting that any effects we observe could be underestimated. Table 1 includes descriptive statistics for each individual task, as well as for the composites included in our analyses. Op ( o perception) and Om ( o memory) are composite scores derived from each group of four tasks (equally weighted and normalized to z-scores), and O is the equally weighted composite of these two. WM, g and LLV (low-level visual ability) are also normalized, equally weighted composites of the 2, 3 and 4 tasks, respectively, used to estimate them. The reliability of average location judgments was very high (λ2 =.94) but that for the Meaningfulness effect was lower (λ2 =.55). The meaningfulness effect was operationalized as the advantage in location memory performance for high-meaning objects beyond what would be predicted based on performance with low-meaning objects. For each half of the data (odd/even trials from each condition), we regressed the high-meaning location memory performance on low-meaning location memory performance and extracted the residuals. The residuals from each half of the data were then correlated, and this correlation was corrected using the Spearman-Brown formula to estimate the reliability of the full-length meaningfulness effect measure. To improve the reliability of the meaningfulness effect, we used an item analysis to drop the 25 low meaning trials that most correlated with the average of the high meaning condition, and we dropped the 25 high meaning trials with the highest difference between correlation with average of low meaning and correlation with average of high meaning. This operationalizes the belief that both conditions share the same location memory effect, as based on 'pure' perceptual stimulus factors, and that the high meaning condition includes an additional facilitation from meaningfulness. In practice, it is likely that the meaningfulness effect was limited by the fact that meaningfulness estimates were from an independent set of raters, and some of the objects may have corresponded to different or just more variable levels of meaningfulness in this different sample. This trial selection (dropping about 20% of the trials) was entirely blind to any other consideration. Critically for the purpose of individual differences, every participant received a new high and low meaning condition score computed on the same set of trials. We arbitrarily chose 25 trials to drop from each of the meaningfulness conditions (maintaining 103 images in each), without exploring alternatives, as a compromise to keep a large number of trials in the data while dropping a sufficient number to hopefully make an improvement to reliability. As can be seen in Table 1, this process resulted in a meaningfulness effect reliability above .7 (the true reliability, when this selection is applied to a new sample, may be lower because of sampling error). Table 1. Descriptive statistics for individual tasks and composite variables task/variable Mean SD Skewness Kurtosis reliability Op Many Objects Oddball 71.8 % 9.7 -0.08 -0.68 .63 Op Silhouette Matching 58.2 % 13.1 -0.13 -0.53 .71 Op Ensemble Perception 55.8 deg 14.4 0.45 -0.20 .79 Op 3AFC Matching 69.8 % 10.5 -0.87 0.82 .71 Om Learning Exemplars 52.1 % 16.6 -0.26 -0.09 .85 Om Paired Objects 68.9 % 12.7 -0.22 -0.59 .73 Om New Object 77.2 % 10.0 -0.70 0.39 .71 Om Recognition Memory 76.6 % 15.6 -0.29 -0.95 .77 WM Binding 63.1 % 15.5 0.23 -0.57 .70 WM Ospan 73.9 % 16.6 -0.57 0.40 .87 g Ravens 48.9 % 23.5 0.09 -0.89 .81 g Number 70.3 % 22.8 -0.59 -0.54 .81 g Vocabulary 62.6 % 14.6 -0.49 0.10 .75 LLV Circle 76.8 % 12.0 -1.02 1.24 .66 LLV Line 79.2 % 12.0 -1.52 3.31 .68 LLV Angle 77.3 % 12.7 -1.28 1.94 .71 LLV Dot 74.3 % 9.6 -1.97 5.30 .68 Location Memory 43.3 % 11.1 0.76 0.42 .92 Meaning Effect 0 0.11 0.28 0.04 .71 Op 0 0.69 -0.66 0.40 .85 Om 0 0.68 -0.07 -0.62 .88 O 0 0.58 -0.39 0.06 .90 WM 0 0.80 -0.12 -0.05 .83 g 0 0.73 -0.35 -0.15 .87 LLV 0 0.81 -1.74 4.66 .88 Note. Reliability is Guttman lambda2 for simple tasks and Location Memory, Spearman-Brown Corrected for Meaning effect and according to Mosier (1943) for composite variables. Next we report the zero-order correlations between the location memory performance, the meaningfulness effect, age and gender as well as our various composite measures. Table 2. Pearson correlations between study variables Age Gender Op Om o WM g LLV Meaning. effect Gender (W=1) .102 Op .021 -.126 Om -.035 -.152 .413 o -.008 -.165 .842 .839 WM -.006 -.125 .066 .382 .265 g .014 -.079 .579 .476 .628 .255 LLV .002 .101 .539 .334 .520 .170 .503 Meaningfulness Effect .120 .239 .160 .197 .212 .080 .122 .105 Location Memory .199 .031 .373 .437 .482 .188 .382 .283 .506 Note. Uncorrected r-thresholds for N=154 are r≥ .158, p<.05; r≥ .208 p<.01; r≥ .264, p<.001 As shown in Table 2, there was a significant positive correlation between o and location memory ( r = .482, p < .001, 95% CI [.35, .595]), indicating a positive association between a perceptual and an episodic memory index. Furthermore, the correlation between o and location memory for low meaning objects was, r = .434, p < .001, 95% CI [.296; .554] and for high meaning objects was r =.446, p < .001, 95% CI [.309, .564]. These correlations (not presented in the table) were not significantly different from each other (Steigers z =.339, p = .734). The correlation between the Meaningfulness effect and o , however, was significant ( r = .212, p =.008, 95% CI [.056, .359]). To clarify, this analysis relates o to the residualized performance for high meaning objects, i.e., controlling for performance for low meaning objects. The result indicates that o shares unique variance with performance for the location of high meaning objects that is not shared with performance for the location of low meaning objects. However, performance in both conditions was also likely to be partly influenced by factors other than o, including those that relate to location memory such as age, WM, g and LLV (see Table 2). Therefore, we tested our prediction that o is related to location memory, and particularly so for high-meaning objects, using hierarchical multiple regression to control for these other variables. We first predicted location memory performance based on age, gender, g, LLV, WM and added o in a second step. Since o was measured using four visual-perceptual and four memory-recognition tasks, following the approach of Smithson et al. (2025), we also examined whether any observed relationship between o and location memory would survive when o was estimated using only the perceptual tasks that involve no memory demands – that is, the Op composite (Table 3). Hence, in a second model, we predicted location memory performance based on Op, again, while controlling for all other factors. Table 3. Multiple regression models predicting location memory, showing the incremental prediction of o (top), or Op alone (bottom). Criterion: Location Memory (Model including o ) b SE t p Incr. R-squared Age .003 .001 2.746 .007 Gender .022 .016 1.352 .178 g .017 .014 1.235 .219 LLV -.002 .012 -.150 .881 WM .009 .010 .877 .382 o .080 .018 4.340 .000 .090 F = 10.22, p<.000, adj. R-squared =.294 Criterion: Location Memory (Model including Op ) b SE t p Incr. R-squared Age .003 .001 2.547 .012 Gender .016 .017 .950 .344 g .030 .014 2.124 .035 LLV .003 .013 .244 .808 WM .018 .011 1.673 .097 Op .039 .016 2.484 .014 . 032 F = 7.57, p<.000, adj. R-squared =.236 Note: Gender is coded as Women = 1, not-Women = 0 Both models were significant and in both cases, there was a significant incremental contribution of o (9%) or Op alone (3.2%) to the explained variance in location memory performance. In addition, age was a significant predictor in both models, and g was a significant predictor only in the second model (likely because of the slightly higher correlation for o than Op with g ). Next, we tested our prediction that o should be related to the meaningfulness effect with hierarchical multiple regression. In the first step we predicted location memory for high meaning objects based on age, gender, g, LLV, WM, and also controlled for location memory for low meaning objects, while in the second step we added o (or Op in a second model, see Table 4) as a predictor. Both models were significant and in both cases, there was a significant incremental contribution of o (3.2%) or Op (1.5%) to the explained variance in location memory performance. Gender was a significant predictor in both models, with women performing better. Figure 5 illustrates the semi-partial correlations between o (controlling for age, gender, LLV, WM and g) and location memory judgments for each condition. We use semi-partial correlations to show both the main effect of meaningfulness, and its interaction with o . Table 4. Multiple regression models predicting location memory for high-meaning stimuli, showing the incremental prediction of o (top), or Op alone (bottom). Criterion: Location for High Meaning (Model including o ) b SE t p Incr. R-squared Age .002 .001 1.469 .144 Gender .064 .018 3.563 .000 g .000 .016 .003 .998 LLV -.011 .013 -.821 .413 WM .009 .011 .768 .444 Low Meaning .901 .105 8.583 .000 o .066 .021 3.105 .002 .032 F = 22.47, p<.000, adj. R-squared =.519 Criterion: Location for High Meaning (Model including Op ) b SE t p Incr. R-squared Age .002 .001 1.279 .203 Gender .061 .018 3.332 .001 g .008 .016 .474 .636 LLV -.009 .014 -.635 .526 WM .016 .012 1.374 .172 Low Meaning .952 .104 9.121 .000 Op .036 .017 2.072 .040 .015 F = 20.98, p<.000, adj. R-squared =.501 Note: Gender is coded as Women = 1, non-Women = 0 Discussion We aimed to assess the contribution of o , a domain-general skill reflecting the ability to recognize and distinguish visually similar items based on shape, to episodic memory, and specifically to memory for an object's location. Even when manipulated arbitrarily and independently of an item's identity, location memory can be positively influenced by a stimulus’s level of familiarity and/or semantic meaning (DeWitt et al., 2012 ; Gronau et al., 2024 ; Taevs et al, 2010 ). We therefore further examined whether o predicted the accuracy of location judgments and whether o interacted with stimulus meaningfulness to predict this aspect of episodic memory. Our findings provide empirical evidence for both questions. First, individuals with high o exhibited enhanced location memory compared to those with low o . Second, supporting a Perceptual-Semantic Synergy account, the relationship between o and location memory was stronger when participants encoded and retrieved high-meaningfulness items. These findings provide the first direct evidence linking semantic meaning, episodic memory, and visual recognition skills measured at the individual level. O is related to – but clearly distinct from – a range of perceptual abilities (both visual and auditory), as well as higher-level cognitive functions such as working memory and general intelligence (Richler et al., 2019 ; Smithson et al., 2024 ; Sunday et al., 2017 ). Our results extend previous research by suggesting that enhanced visual encoding in individuals with high o contributes to improved memory for instance-specific details, such as object location. Notably, this effect remains robust even after accounting for other cognitive and low-level abilities. Additionally, we emphasize the facilitating role of item meaningfulness in this relationship. Each of these findings is discussed in detail below. High Object Recognition Ability is Linked to Improved Location Memory The ability to extract the invariant properties of an object, whether highly familiar or unfamiliar, is central to the o ability, which underlies robust item identification across varying viewing conditions. At the same time, our findings indicate that individuals with high o scores are also highly sensitive to contextual information that is extrinsic to the object itself, such as the screen location in which it was presented. Although these capabilities may seem contradictory – attending both to invariant features related to object shape and to a feature like location that can clearly vary without any influence on object identity – our results suggest they may nonetheless reflect shared underlying cognitive mechanisms. That is, the ability to encode both stable and context-dependent aspects of an object may represent complementary facets of a common cognitive process. Furthermore, given that spatial location is a well-established component of episodic memory – the "when" and "where" of an event – these findings may further blur the traditional boundaries between perception and memory. Our correlational design does not allow us to distinguish item memory and item-location binding effects, as in paradigms where both features and locations are unique (e.g., Chalfonte & Johnson, 1996 ). In our paradigm, remembering the specific location of an item largely relies on accurate encoding of the item itself, as well as successful item-location binding – both processes that may be more efficient in individuals with high o scores. Our findings are also consistent with models emphasizing the role of attentional and working memory resources, which propose that efficient item encoding frees up cognitive capacity for binding the items to their contextual surroundings (Popov & Reder, 2020 ; Reder et al., 2013 ). Several researchers have suggested that spatial location, along with processes related to location-identity binding, may benefit from a unique attentional and memory status (e.g., Golomb et al., 2014 ; Hollingworth, 2006 ; Kovacs & Harris, 2019 ; Treisman & Gelade, 1980 ). This is reflected in findings showing that not only do objects serve as effective memory cues for their encoded locations, but locations can also cue item identity and the precise visual details of objects. For instance, participants more accurately recognized specific object exemplars and episodic details (such as pose) when items were tested at their original location, compared to when they were presented at a different location in the scene (Hollingworth, 2006 ). However, this enhanced item discrimination due to shared location may come at a cost. When successive pairs of identical or different objects were presented in the same location, participants were biased to report that the identities were the same, even when they were not (Golomb et al., 2014 ; Pertzov & Husain, 2014 ). In other words, shared location increases both correct identifications (hits) and false alarms, suggesting that participants are unable to disregard spatial overlap between stimuli, even when it is irrelevant and detrimental to the task. Although our task involved a long-term memory paradigm, many stimuli appeared in repeated locations, which may have introduced interference in location memory. Nevertheless, high- o participants appear to benefit from enhanced item identification and distinctiveness, which may help mitigate such location-based interference. This pattern suggests that high o supports more precise object-location bindings by enhancing the fidelity of object encoding and reducing confusion between overlapping spatial cues. Our findings echo prior work showing that memorable images aid spatial recall (Trinkl & Wolfe, 2024 ) – in that work, the effect was specific to memory for spatial location, not extending to temporal position in the studied sequence. Interestingly, other work (Smithson et al., 2025 ) found a strong correlation between o and visual spatial ability–that is, the capacity to perceive, process, and mentally manipulate visual material (as measured by mental rotation tasks; Schneider & McGrew, 2018 ). However, although both location memory and mental rotation involve spatial processing, they may depend on qualitatively different cognitive mechanisms. Notably, the relationship between o and spatial ability was largely accounted for by their shared association with general intelligence (Smithson et al., 2025 ). Here, the contribution of o to location memory remained robust even after statistically controlling for general intelligence and working memory, and was still obtained when using only perceptual o tasks. This strengthens the idea that o facilitates the encoding of spatial information because of mechanisms supporting object recognition via shape, rather than associated abilities. From a neurocognitive perspective, one theory that aligns with our findings and bridges visual perception and episodic memory is the representational-hierarchical model (Bussey et al., 2002 ). According to this model, the difficulty of a perceptual discrimination determines the recruitment of brain systems traditionally associated with memory. Specifically, the ventral visual stream and medial temporal lobe operate in a hierarchy, encoding progressively complex information. At the top of this hierarchy, the perirhinal cortex (PRC) integrates features to form complex visual, semantic, and possibly multimodal representations (Li et al., 2022 ). When the PRC is compromised, deficits arise in both memory and perception, particularly for objects with subtle featural differences. Further, the hippocampus is implicated as the next level in this hierarchy, encoding spatial relations among objects and their broader context (Barense et al., 2007 ; Bussey et al., 2002 ). This framework supports a potential overlap between o and episodic memory, especially in tasks requiring fine perceptual discrimination or contextual detail. While these predictions are primarily based on lesion studies (Li et al., 2022 ), the model has yet to be systematically applied to individual differences in healthy populations. High Object Recognition Ability Predicts the Meaningfulness Effect on Location Memory. One of the central findings of our research is that the semantic meaning of objects moderates the relationship between o and location memory. In prior work, we found that o measured with sets of tasks that use both familiar (real-world) or artificial novel objects is essentially the same at the latent level (Sunday et al., 2021 ). Nevertheless, here o interacted with object's meaningfulness in the context of location memory. Specifically, a stronger positive correlation emerged between o and location memory judgments for high-meaning objects compared to low-meaning ones. As noted earlier, we interpret this effect as reflecting a more efficient use of semantic knowledge embedded within meaningful objects by individuals with high o , consistent with prior evidence that such individuals demonstrate enhanced ability in subordinate-level discriminations (Sunday et al., 2021 ). When an object is processed at a more refined level, it can activate a broader semantic network and carry greater personal relevance. Supporting this interpretation, higher expertise is generally linked to the ability to make finer subordinate-level distinctions, which tend to carry rich semantic content (Johnson & Eilers, 1998 ). Regarding memory performance, studies of visual expertise have shown that experts often demonstrate superior short- and long-term memory within their domain, likely due to the combined effects of visual and conceptual training (e.g., Annis & Palmeri, 2019 ; Curby & Gauthier, 2007 ; Herzmann & Curran, 2011 ). A role for semantic knowledge in long-term memory formation and retrieval has long been established (e.g., Anderson, 1984 ; Brewer & Treyens, 1981 ; Craik & Tulving, 1975 ). However, one line of research has brought this topic back to the forefront, particularly in the context of memory for specific visual details (e.g., Brady et al., 2008 ). For example, Konkle et al. ( 2010 ) found that interference in visual long-term memory is primarily predicted by conceptual distinctiveness rather than perceptual distinctiveness among similar object exemplars. More recently, Kramer et al. ( 2023 ) demonstrated that semantic features have a stronger influence than perceptual features on the memorability of objects and scenes. According to one account, highly memorable images may be more easily recognized because they are more effectively mapped onto semantic features (Deng et al., 2024 ). Additionally, visual working memory capacity is higher for meaningful objects than for meaningless items or simple features, likely due to semantic associations rather than visual complexity alone (Shoval et al., 2023 ; Torres et al., 2024). Semantic meaning can enhance memory for both intrinsic object properties such as color (Chung et al., 2023 ; Gronau & Shachar, 2015 ) and extrinsic details, such as the object’s location or scene background (Gronau et al., 2024 ; Reder et al., 2013 ; Sahar et al., 2024 ). Conceptual meaning has been viewed as a “hook” or “scaffold” that supports the retention of perceptual details in memory (Chung et al., 2023 ; Konkle et al., 2010 ). While such metaphors offer useful heuristics, other researchers have sought to clarify the underlying mechanisms by which highly familiar items benefit memory more than novel or meaningless stimuli. As discussed earlier, resource-limited theories propose that familiar items place lower demands on working memory during encoding, thereby freeing up resources to store visual details and bind items to their contextual surroundings (Popov & Reder, 2020 ; Reder et al., 2013 ). But the impact of semantic meaning extends beyond simple familiarity, reflecting a richer interplay of conceptual knowledge, categorical specificity, and/or verbal encoding processes. In particular, verbal labeling has been proposed as one of several mechanisms underlying the memory advantage for meaningful over abstract or unfamiliar objects, including their enhanced recall in location memory tasks (Choi & L’Hirondelle, 2005 ; Taevs et al., 2010 ). In Choi & L’Hirondelle ( 2005 ), women remembered the location of objects better than men when the objects were concrete rather than abstract. Thus, verbal processing may also help account for the gender difference observed in the present study: women demonstrated greater location memory benefits from object meaningfulness than men. This finding aligns with well-established evidence of female advantages in verbal processing (Bleecker et al., 1988 ; Kramer et al., 2023 ), suggesting that stimulus-naming strategies could have contributed to the observed female advantage in object-location memory tasks. However, it appears unlikely that a useful verbal strategy would involve encoding both the name of the object and a verbal description of its location; the relatively large number of stimuli, as well as the absence of ‘canonical’ locations (e.g., right–left or up–down, positioned at 0/180 or 90/270 degrees), would make this relatively difficult. An alternative is that a better ability to name stimuli during encoding would support stronger semantic processing, which we believe supports richer episodic encoding. This should be explored in future studies. Importantly, we cannot entirely rule out the possibility that residual verbal processing contributed to the observed gender effect, but this would be independent of the relationship we observed between o and location memory, as o tasks do not encourage verbal strategies and o is not related to gender. There are some limitations in this work. We tested participants from a prior study and were therefore constrained both in terms of sample size (by participants who willingly signed up for this follow-up) and in terms of the covariates that were included. We used a coarse measure of meaningfulness, characterized by raters’ understanding of what that concept means, and which likely conflates several dimensions such as familiarity, nameability and complexity. Our meaningful objects were more likely to be living (e.g., animals or food) and likely differed in other ways. Therefore, this work cannot specify which aspects of meaningfulness may be responsible for interacting with o . Because our measures of low-level perception as well as o tasks focus on shape, with color and texture not diagnostic but present in the location memory task, we cannot speak to the role of these other dimensions in visual processing. Furthermore, our study focused on one aspect of episodic memory, namely, spatial location, which may not fully represent other components of episodic memory, such as temporal or other contextual factors. As a result, the generalizability of our findings may be limited. In sum, our results demonstrate that mechanisms that support a better domain-general object recognition ability, o , also play a role in episodic memory by enhancing the binding of meaningful visual objects to spatial context. This challenges traditional cognitive boundaries, showing that perceptual skills contribute to memory encoding processes. Even after controlling for intelligence, working memory, and low-level visual abilities, o remains a significant predictor of performance. The influence of meaningfulness as a moderator highlights the pivotal role of semantic content in memory formation. Our work opens the door to further investigations into the perceptual and semantic underpinnings of episodic recall. These findings call for models that integrate visual and semantic processing to explain how meaning facilitates memory. Declarations The authors have no relevant financial or non-financial interests to disclose. Author Note: This work was supported by the Open University of Israel's Research Fund grant 515549 to NG, and by the David K. Wilson Chair Research Fund from Vanderbilt University and NSF BCS Award 2316474 to IG. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7594990","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":515935325,"identity":"117c0e5c-77e5-4f81-b5de-c9926196a900","order_by":0,"name":"Nurit Gronau","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYJCCAyCCH8FnbDxAUAtIhWQDQksDQS1gawwOoAngBOYSuQ8Pf/h1T874eO/Bx4VtDPL8Dcz4bbGckW5w4GBfsbHZmXPJxjPbGAxnHCDgMIMbaQwHDvYkJG67kWMmzdvGwLiBkF/gWjbPyDH/DdRiT5yWAz8SEjdI5JgxA7UkEtZy5hnDgbMNCcYSQL9I85yTSJ5xmJCW42nMHyr+JMjxt/ce/MxTZmPb397+8AE+LWDA2AYieUCEBAMDM0H1IPAHrmUUjIJRMApGASYAAIuvUVm2OHbeAAAAAElFTkSuQmCC","orcid":"","institution":"Open University of Israel","correspondingAuthor":true,"prefix":"","firstName":"Nurit","middleName":"","lastName":"Gronau","suffix":""},{"id":515935326,"identity":"c96da3dc-df8e-42bf-a6e0-c7dcea3cb981","order_by":1,"name":"Conor J. R. Smithson","email":"","orcid":"","institution":"Vanderbilt University","correspondingAuthor":false,"prefix":"","firstName":"Conor","middleName":"J. R.","lastName":"Smithson","suffix":""},{"id":515935327,"identity":"15c49fcb-2c88-4ebd-8ade-7e0d12825949","order_by":2,"name":"Isabel Gauthier","email":"","orcid":"","institution":"Vanderbilt University","correspondingAuthor":false,"prefix":"","firstName":"Isabel","middleName":"","lastName":"Gauthier","suffix":""}],"badges":[],"createdAt":"2025-09-11 21:38:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7594990/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7594990/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00426-026-02248-y","type":"published","date":"2026-02-07T15:58:32+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":91847407,"identity":"169e23f5-879b-4919-b8b7-cd4a1efdf042","added_by":"auto","created_at":"2025-09-22 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10:22:19","extension":"html","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":210318,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7594990/v1/5b55267c223589bb109f3f1e.html"},{"id":91847399,"identity":"1a162f57-51b1-4941-8134-10073f862b3a","added_by":"auto","created_at":"2025-09-22 10:22:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":44769,"visible":true,"origin":"","legend":"\u003cp\u003eTwo different predictions for how individual differences in \u003cem\u003eo \u003c/em\u003eand object meaningfulness interact to predict location accuracy.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7594990/v1/02ae91b4f753bbb181a63d64.png"},{"id":91847400,"identity":"8cb4112e-f95c-4bbf-ab8f-390ea0a13270","added_by":"auto","created_at":"2025-09-22 10:22:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":74724,"visible":true,"origin":"","legend":"\u003cp\u003eExample trials from (a) the Many Objects Oddball task; (b) the Silhouette Masking task; (c) the Ensemble Perception with Transformers task and d) the 3AFC Matching with Asymmetrical Greebles task.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7594990/v1/9e2f5574a019743b5f44eb48.png"},{"id":91847975,"identity":"5a632614-a4d1-4220-a080-b6a1cc16fa3d","added_by":"auto","created_at":"2025-09-22 10:30:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":65322,"visible":true,"origin":"","legend":"\u003cp\u003eExample trials from (a) the Learning Exemplars with Vertical Ziggerins task; (b) the Pairs Objects task; (c) The New Object task and d) the Recognition Memory task.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7594990/v1/29003217ffd87a5937824ec7.png"},{"id":91847405,"identity":"b0980e3d-3510-4275-adc9-0a52d0c3a726","added_by":"auto","created_at":"2025-09-22 10:22:18","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":137225,"visible":true,"origin":"","legend":"\u003cp\u003eEpisodic Location Memory Task. (a) Example objects with low meaningfulness ratings on top, and high meaningfulness ratings on the bottom; (b) 4AFC display testing for location memory of a specific object. Unbeknownst to participants, stimuli were displayed on an imaginary circle (light gray).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7594990/v1/c9e720c8e003478180157fd1.png"},{"id":91847403,"identity":"3b12b507-f22a-4eb8-a5b3-a0caf4cbbb98","added_by":"auto","created_at":"2025-09-22 10:22:18","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":48134,"visible":true,"origin":"","legend":"\u003cp\u003eSemi-partial correlations between \u003cem\u003eo \u003c/em\u003e(controlling for g, LLV, WM, age and gender) and location memory as a function of meaningfulness (with 95% C.I.). Low meaning: r = .246, high meaning: r = .341. Each participant has one datapoint in each meaningfulness condition.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7594990/v1/559b8151341bd0ca0d0b3353.png"},{"id":102234867,"identity":"a961492c-997e-4b73-ac1e-2dbf4a4cc200","added_by":"auto","created_at":"2026-02-09 16:13:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1500812,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7594990/v1/854213c4-9d14-4469-826c-0aeb2600c677.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Object recognition ability predicts episodic location memory, enhanced by meaningfulness","fulltext":[{"header":"Public Significance","content":"\u003cp\u003ePeople differ in how precisely they tell similar objects apart (\u0026ldquo;object recognition ability\u0026rdquo;). In our experiments, individuals with stronger recognition ability more accurately remembered where objects appeared, especially for meaningful, familiar items, even after accounting for general intelligence, working memory, and basic visual acuity. These findings link perception to memory for specific experiences, suggesting that fine-grained object representations help anchor where things were seen in everyday life.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eDomain-general object recognition ability, or \u003cem\u003eo\u003c/em\u003e, explains shared variance in performance across different object recognition tasks that span multiple domains (Richler et al., 2019; Smithson \u0026amp; Gauthier, 2026; Sunday et al., 2021). At its core, \u003cem\u003eo\u003c/em\u003e reflects the ability to distinguish visually similar objects, such as differentiating between two bird species at the subordinate level (Richler et al., 2019). It is, however, usually measured with novel objects so that semantic knowledge and individual experience with familiar categories do not complicate its interpretation (Richler et al., 2017; Smithson et al., 2024). As such, an important characteristic of \u003cem\u003eo\u003c/em\u003e is its domain-generality: an \u003cem\u003eo\u003c/em\u003e factor measured with several categories of familiar objects (e.g., birds and planes) correlates perfectly at the latent level with an \u003cem\u003eo\u003c/em\u003e factor measured using novel, unfamiliar objects (Sunday et al., 2021). This consistency across familiar and novel stimuli suggests that \u003cem\u003eo\u003c/em\u003e represents a general perceptual ability rather than accumulated expertise. In recent work, we found that \u003cem\u003eo\u003c/em\u003e can be measured using tasks that vary substantially in their cognitive demands - some tasks make substantial recognition-memory demands while others primarily stress perception, yet both types of tasks also tap a common underlying object recognition ability (Smithson \u0026amp; Gauthier, 2025). Thus, even when we measure \u003cem\u003eo\u0026nbsp;\u003c/em\u003ein memory tasks, the memory demands are incidental to the core ability, which involves distinguishing visually similar objects regardless of task format.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHere, we explore for the first time whether \u003cem\u003eo\u0026nbsp;\u003c/em\u003erelates to long-term memory for a property extrinsic to object shape or identity, specifically its location - a key facet of episodic memory (Ranganath, 2010; Tulving, 1972). Although the theoretical definition of \u003cem\u003eo\u0026nbsp;\u003c/em\u003edoes not directly predict whether it should support better episodic memory for non-shape features, addressing this question is important for integrating current knowledge about individual differences in perception and their potential relationship with memory. Moreover, \u003cem\u003eo\u003c/em\u003e is thought to reflect perceptual skills that do not require linkage to semantic knowledge, which is why it can be estimated with novel objects. Yet in the real world, the semantic meaning of objects can often support visual perception through increased familiarity and/or top-down knowledge. Although the processing of both shape and location primarily relies on perceptual abilities, semantic information influences both visual expertise (Gauthier et al., 2003) and episodic memory (Renoult et al., 2019). We will therefore further examine whether, and how, the semantic meaning of real-world objects potentially interacts with the effects of high perceptual ability (\u003cem\u003eo\u003c/em\u003e) on location memory. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSuccess on \u003cem\u003eo\u003c/em\u003e tasks depends on identifying diagnostic features while maintaining invariant representations that abstract away from instance-specific details. This emphasis on invariance would allow someone to recognize a warbler regardless of its pose, lighting conditions or location on a specific branch, focusing on identity-relevant features while ignoring contextual variations. But what of a bird watcher with a high-\u003cem\u003eo?\u0026nbsp;\u003c/em\u003eShould we expect that she would also be better able to remember this contextually-related episodic information? While these skills - visual object recognition and episodic memory - are typically studied separately, both abilities are useful, and may interact in many real world applications. For instance, a crime scene investigator must quickly identify objects despite occlusions but also remember their exact placement. Similarly, a field biologist needs to recognize animal tracks regardless of substrate conditions but also remember the specific timing and location of each sighting. To study interactions between perceptual abilities and meaning for the memory of object location, we asked individuals with varied perceptual abilities (\u003cem\u003eo\u003c/em\u003e) to encode objects low or high in meaningfulness, and later, to report the objects\u0026rsquo; location.\u003c/p\u003e\n\u003cp\u003eOur first prediction is that location memory will be better for objects that are more meaningful. As mentioned above, our study included two kinds of objects, low- and high-meaning objects, as defined by independent ratings (Shoval et al., 2023). Prior work found that both object identity and their arbitrary location are better remembered in long-term memory when items are more meaningful (Gronau et al., 2024; see also Taevs et al., 2010; DeWitt et al., 2012). This effect has been attributed to a \u0026apos;Resource-limited\u0026apos; account, whereby more familiar items require fewer encoding resources, allowing spare capacity for visual detail encoding (Popov \u0026amp; Reder, 2020). We expect to replicate this effect, although we are not directly investigating its underlying mechanism.\u003c/p\u003e\n\u003cp\u003eOur second prediction is that people with a higher \u003cem\u003eo\u0026nbsp;\u003c/em\u003ewill have a better memory for the location in which objects were studied, beyond individual differences in general intelligence, working memory and other factors such as age or gender. Although spatial position is extrinsic to an item\u0026apos;s core identity and features, object location and identity are not perceived as strictly independent. For instance, objects shown in the same location are more likely to be reported as having the same identity (Babu et al., 2023; Golomb et al., 2014), and splitting attention across different locations disrupts feature binding (Dowd \u0026amp; Golomb, 2019).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOne reason that high-\u003cem\u003eo\u003c/em\u003e individuals may better retrieve the locations associated with studied objects is that streamlined object processing during encoding preserves capacity for registering context-related details, again consistent with resource-limited accounts (e.g., Popov \u0026amp; Reder, 2020). According to this view, more frequent or familiar items are easier to encode, and as a result are more likely to bind to an episodic context. Individuals who benefit from enhanced perceptual encoding, for instance as a result of perceptual expertise (Curby et al., 2009), show a working memory advantage. Likewise, stronger object representations in those with a higher \u003cem\u003eo\u0026nbsp;\u003c/em\u003emay allow for more item-context binding. We acknowledge, however, that this is not the only possible reason \u003cem\u003eo\u0026nbsp;\u003c/em\u003emay predict location memory. Another explanation comes from a retrieval-gating account. Episodic memory retrieval occurs when a retrieval cue sufficiently overlaps with a stored memory representation, leading to the reactivation of the encoded information (Tulving \u0026amp; Thomson, 1973). In our task, memory for an object\u0026apos;s location depends on, or is \u0026apos;gated\u0026apos; by, the ability to recover the object representation itself (particularly because each location is associated with multiple different objects). In other words, objects serve as cues: if the object is not remembered at least in part, its associated location cannot be retrieved. In that case, more object locations should be retrieved accurately as a function of \u003cem\u003eo\u003c/em\u003e because the objects themselves are better remembered. Thus, both a resource-limited account and a retrieval-gating account make the same prediction, against a common null hypothesis that \u003cem\u003eo\u0026nbsp;\u003c/em\u003ewill not be related to memory for location since it reflects an ability specific to the invariant processing of shape information. Note, however, that the limitations of our correlational approach will allow us to ask for the first time \u003cem\u003ewhether\u0026nbsp;\u003c/em\u003e\u003cem\u003eo\u003c/em\u003e can predict location memory, not \u003cem\u003ewhy\u003c/em\u003e it does.\u003c/p\u003e\n\u003cp\u003eWe have predicted two main effects: better location memory for meaningful objects and better location memory with a higher \u003cem\u003eo\u003c/em\u003e. Our third and most critical prediction, however, concerns the role of semantic knowledge in mediating a possible perception-memory relationship. Here, two different hypotheses can be formulated. The first is that perceptual abilities (indexed by \u003cem\u003eo\u003c/em\u003e) can fuel a self-reinforcing cycle that enhances memory (and eventually may shape expertise development in a specific field). We call this the Perceptual-Semantic Synergy account. For example, domain-specific episodic memories (e.g., a birder remembering a warbler\u0026rsquo;s pose in morning light) could provide foundational material that, through abstraction and integration, builds semantic knowledge and supports expertise (Greenberg \u0026amp; Verfaellie, 2010; Tulving, 1972). Richer semantic knowledge can, in turn, improve episodic recall (e.g., Renoult et al., 2019), as when knowledge of warbler typical behavior supports memory of \u003cem\u003ewhere\u003c/em\u003e someone spotted one. This has important implications for the interaction between object meaning and \u003cem\u003eo\u003c/em\u003e: High-\u003cem\u003eo\u003c/em\u003e individuals encode objects with precision, automatically, and even when object shape is task-irrelevant (McGugin et al., 2022). By definition, high-\u003cem\u003eo\u003c/em\u003e individuals are better at subordinate-level discriminations (Sunday et al., 2021). Therefore, in our study, high-\u003cem\u003eo\u003c/em\u003e people should encode both low- and high-meaning objects more precisely, but a precise representation of a high-meaning object is likely to lead to categorization that is more specific (for instance, a chair may be recognized as a \u0026ldquo;colonial-style antique chair\u0026rdquo;). This can unlock access to a wider associative network of semantic information (e.g., this style prioritizes practicality, it is often put together with wooden pegs rather than nails\u0026hellip;), thereby increasing meaningfulness. In other words, a familiar object encoded with more visual precision gains richer semantic content as well. To the extent that prior meaningfulness has been found to increase episodic memory for objects, including for their location (Gronau et al., 2024; Taevs et al, 2010), this suggests that \u003cem\u003eo\u0026nbsp;\u003c/em\u003ewould be positively correlated with the magnitude of this effect. In other words, the Perceptual-Semantic Synergy account predicts a stronger positive correlation between \u003cem\u003eo\u003c/em\u003e and the accuracy of location judgments for high-meaning relative to low-meaning objects (Figure 1, left).\u003c/p\u003e\n\u003cp\u003eAn alternative hypothesis posits an interaction between \u003cem\u003eo\u003c/em\u003e and object meaning in the opposite direction: Since nearly all high-meaning objects in our study belong to different basic levels, they can be remembered to a large extent using verbal and semantic information in addition to visual information. Note that the availability of unique verbal labels does not mean that visual information is not encoded, as demonstrated in work where people encode a large number of such objects and can later discriminate them from highly similar objects sharing the same verbal code (Brady et al., 2008). In contrast to high-meaning stimuli, memory for low-meaning objects must primarily rely on visual information because they are less familiar and nameable. We could therefore expect that perceptual skills may be particularly critical for low-meaning objects, resulting in a stronger correlation with \u003cem\u003eo\u003c/em\u003e ability than for high-meaning objects. This prediction (Figure 1, right) assumes that perceptual and semantic information function separately but can trade off, such that people rely more on one when the other is absent. We call this the Perceptual-Semantic Trade-off account. Luckily, while both of these hypotheses predict removable interactions (Wagenmakers et al., 2012), each one presents a qualitative contrast with the other. This means that support in favor of one hypothesis provides strong evidence against the other.\u003c/p\u003e\n\u003cp\u003eIt is worth noting that, theoretically, a third possibility exists: since \u003cem\u003eo\u003c/em\u003e is a perceptual ability in its essence, it might not interact with the semantic meaning of stimuli in predicting location memory. However, we consider this scenario to be rather unlikely, as a sharp perception-memory distinction may reflect historical conventions more than cognitive reality (Firestone \u0026amp; Scholl, 2016).\u003c/p\u003e\n\u003cp\u003eThe competing predictions outlined above have never been directly tested. If \u003cem\u003eo\u003c/em\u003e predicts memory for where an object was experienced - not just what it looks like- it would significantly advance our understanding of the relationship between perception and memory. How semantic meaning interacts with \u003cem\u003eo\u0026nbsp;\u003c/em\u003ein predicting location memory will further constrain our understanding of this relationship.\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eAll experimental materials and results are available at the Open Science Framework and can be accessed at https://osf.io/hy763/?view_only=be6bc532f4fa490d98e81e7eb1d8eb86. This study was not preregistered. All experiments were performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethical committee of the Open University of Israel (#3668) and of Vanderbilt Institutional Review Board (#222116).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe recruited participants from a prior study in which we had collected measures of object recognition and other cognitive covariates. The original study (Smithson \u0026amp; Gauthier, 2025) was conducted on the Prolific.co platform and recruited participants residing within the US, 18 to 45 years old, reporting fluency in English and normal or corrected-to-normal vision, and having a \u0026gt; 95% approval rating for past studies. That study was 2 sessions long, with 333 participants completing session 1, 298 completing session 2 and 275 people passing exclusion criterion (not performing below chance on more than one test). We invited 255 of these participants (excluding those who were at chance on more than 2 of the 14 tasks in the original studies) for a new task, approximately 1 month after completion of the Smithson et al. (2025) study. Both studies could only be completed on a computer screen, not a mobile or tablet. We were able to collect data from 159 participants, and 154 of them performed above chance (25%) on the 4-alternative forced choice episodic location task. Their mean age was 33.8 (SD=6.6), and they self-reported gender as follows: 83 women, 72 men, 1 prefer not to say. A sensitivity analysis for detecting the incremental contribution of \u003cem\u003eo\u003c/em\u003e (after accounting for covariates) indicated that with N = 154, \u0026alpha; = .05, and power = .80, the smallest effect size detectable at this level of power is an incremental effect for \u003cem\u003eo\u003c/em\u003e of R\u0026sup2; = 0.049. Informed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProcedure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTasks from Smithson et al. (2025)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eEight tasks measured participants\u0026rsquo; object recognition skills and their proficiency in distinguishing highly similar visual shapes \u0026ndash; tasks that when combined form one\u0026rsquo;s \u003cem\u003eo\u003c/em\u003e measure. Four of these tasks were based on increased perceptual demands (e.g., rapid presentation time, noise on the images, degraded silhouettes), while the remaining four relied primarily on memory demands (e.g., encoding a large number of objects, temporal delays, and requiring associations between objects). Four additional tasks measured low level visual ability using oddball decisions with simple visual features, three tasks were included to measure general intelligence (\u003cem\u003eg\u003c/em\u003e), and 2 tasks were included to measure working memory. We summarize these tasks below and details can be obtained in the original work. Eighteen simple attention checks were embedded throughout the test battery to assess engagement and data quality. These checks typically required straightforward responses to obvious stimuli or explicit instructions (e.g., \u0026quot;click the leftmost option\u0026quot;). Participants were excluded if they made more than 1 attention error.\u003c/p\u003e\n\u003cp\u003ePerceptual \u003cem\u003eo\u003c/em\u003e tasks. Four tasks emphasized perceptual discrimination under challenging viewing conditions (Figure 2). \u003cem\u003eMany Objects Oddball\u003c/em\u003e required identifying which of three simultaneously presented objects differed in identity across 45 trials. Two objects shared identity but varied in size and orientation, while the third was slightly different. Objects appeared for 750-4000 ms and participants clicked the location where the odd object had appeared. Novel object categories changed each trial, with feedback provided after responses. \u003cem\u003eSilhouette Matching\u003c/em\u003e involved matching target objects (YUFOs) to one of three simultaneously presented silhouettes across 40 trials, often requiring viewpoint invariance when silhouettes appeared rotated relative to targets. \u003cem\u003eEnsemble Perception with Transformers\u003c/em\u003e tested averaging ability across 50 trials where participants viewed four robot figures (1 s), then selected which of six morphs best represented their average. Performance was measured as absolute error in degrees within the circular morph space. \u003cem\u003e3AFC Matching with Asymmetrical Greebles\u003c/em\u003e required matching target objects after a 500 ms visual mask across 51 trials. Four blocks systematically varied target presentation times (300-1000 ms), viewpoints (same vs. ~30\u0026deg; horizontal rotations), and noise levels.\u003c/p\u003e\n\u003cp\u003eMemory \u003cem\u003eo\u003c/em\u003e tasks. Four tasks required encoding and maintaining multiple object representations (Figure 3). \u003cem\u003eLearning Exemplars with Vertical Ziggerins\u003c/em\u003e involved memorizing six target objects presented together for 20 s, followed by alternating study-test phases (6, 18, then 24 recognition trials). Participants used \u0026quot;g,\u0026quot; \u0026quot;h,\u0026quot; and \u0026quot;j\u0026quot; keys to select which of three objects matched a memorized target. \u003cem\u003ePaired Objects\u003c/em\u003e required learning five associations between novel 3D objects and abstract 2D shapes. Each pair appeared for 4 s with immediate testing, followed by multiple study-test cycles and 35 total test trials in various formats. \u003cem\u003eNew Object\u003c/em\u003e began with three novel objects (Quaddles) shown for 3000 ms, then presented 44 test arrays of four objects where participants identified the one that was novel (had never appeared before in the task). \u003cem\u003eRecognition Memory\u003c/em\u003e involved studying successive sets of four target novel objects (15 s each) followed by a reading comprehension delay, then 24 recognition trials where targets appeared alongside two distractors from the same object category.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eControl Measures\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWorking memory (WM) was assessed using \u003cem\u003eVerbal-numerical Binding\u003c/em\u003e, where participants remembered word-number pairs (2000 ms each, 1000 ms ISI) across sequences of 2-6 pairs, then identified correct pairings when probed with individual words or numbers (27 total responses). \u003cem\u003eOperation Span\u003c/em\u003e required recalling letter sequences (1000 ms each) while performing intervening arithmetic operations (3000 ms or until response), with sequence recall tested after each trial (15 trials total).\u003c/p\u003e\n\u003cp\u003eLow-level visual discrimination (LLV) used the Hanover Early Vision Assessment (HEVA; Kieseler et al., 2022), presenting three images per trial (two identical, one different) across 96 trials. Participants pressed \u0026quot;f\u0026quot; (left), \u0026quot;space\u0026quot; (center), or \u0026quot;j\u0026quot; (right) to identify the odd stimulus. The battery comprised 16 blocks of six trials each, testing four basic visual features: dot distance (discriminating spacing between dot pairs), circle size (detecting size differences in circular stimuli), angle size (identifying angular differences in line configurations), and line length (distinguishing length variations in linear segments). Eight blocks were administered in session one and eight in session two, with each session containing two blocks per feature type.\u003c/p\u003e\n\u003cp\u003eGeneral intelligence (\u003cem\u003eg\u003c/em\u003e) was measured through \u003cem\u003eRaven\u0026apos;s Matrices\u003c/em\u003e (selecting which of 8 numbered options completed 3\u0026times;3 symbol patterns, 18 trials, 10-minute limit), \u003cem\u003eNumber Series\u003c/em\u003e (choosing the next number from 5 options, 15 trials, 5-minute limit), and a \u003cem\u003eVocabulary\u003c/em\u003e test (selecting the best synonym from 3 options, 30 trials, untimed).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMemory for Spatial Location, as a Function of Object Meaningfulness\u003cbr\u003e\u0026nbsp;\u003c/em\u003eThe task consisted of two phases: an encoding phase and a location-memory test phase. During the encoding phase, participants viewed 288 individual object images adapted from Brady et al. (2008), each presented for 2000 ms with an 800 ms inter-stimulus interval (ISI). These objects were positioned at one of 16 locations along the perimeter of an imaginary circle\u0026ndash;unbeknownst to participants\u0026ndash;with each point spaced 22.5\u0026deg; apart, starting at 11.25\u0026deg; and excluding the canonical axes. Each object spanned approximately 7 degrees, and the circle had a radius of about 11 degrees, assuming a viewing distance of 50 cm. However, as the experiment was conducted online, actual viewing distance may have varied across participants. The objects were selected from a large stimulus pool for which subjective ratings of stimulus familiarity (\u0026ldquo;How familiar is the stimulus?\u0026rdquo;) and stimulus knowledge (\u0026ldquo;Do you know what the stimulus is?\u0026rdquo;) were collected (both on a scale of 1-7). Since the correlation between the responses to these questions was very high (r=.98, p\u0026lt;.001), the ratings were averaged to form a single \u0026lsquo;meaningfulness\u0026apos; measure, which has previously been shown to predict memorability for both item identity (Shoval et al., 2023) and item spatial location (Gronau et al., 2024). Out of the 288 stimuli, 128 low-meaningful (average meaning score: 2.85, SD =0.31) and 128 high-meaningful (average meaning score: 6.39, SD =0.36) objects served as test items in the subsequent location-memory test phase (Figure 4a). An additional 32 items (16 from each meaningfulness category) were repeated during the encoding phase and used in an item-repetition task (N-back, with repetition-lags of 0,1,2 or 4 items). These items only appeared during the encoding phase, ensuring maintenance of attention while viewing the images. Participants were instructed to memorize the spatial location of each item while simultaneously monitoring for item repetitions, responding by pressing the spacebar each time they detected a repeated item. The encoding phase included 320 trials in total, including item repetitions.\u003cbr\u003eThe memory-test phase involved a four-alternative forced-choice (4-AFC) test, in which each item appeared in its original location along with three additional locations positioned 90, 180, and 270 degrees from the encoded location. The four items in each trial were presented simultaneously until participants responded by clicking on the original location (Figure 4b). Note that while familiarity and object identification are known to influence memory (e.g., Dall et al., 2021; Reder et al., 2013), they are inherently subjective and likely shaped by multiple dimensions\u0026ndash;including conceptual richness, typicality, nameability, and more. Despite this multidimensionality, the selected high-and low-meaning stimuli offered a promising starting point for investigating the relationship between \u003cem\u003eo\u003c/em\u003e, semantic interpretation, and episodic location memory.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTesting the reliability of the location memory test\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e Before examining memory results and their correlation with \u003cem\u003eo\u003c/em\u003e measures, it was important to assess the reliability of the episodic (location) memory measurements to ensure they were suitable for individual differences analyses. Note that paradigms that are sensitive at the group level do not always produce measurements that are sufficiently reliable for this purpose (Hedge et al., 2018; Ross et al., 2015).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThere are good reasons to believe that high reliability would be obtained for the measurements relevant to our first question \u0026ndash; namely, whether people with a high \u003cem\u003eo\u0026nbsp;\u003c/em\u003ewould remember object locations better. However, our second question \u0026ndash; whether \u003cem\u003eo\u003c/em\u003e would interact with the enhancement in location memory for meaningful versus less meaningful objects, is more challenging. Achieving reliability for the meaningfulness effect is likely to be more difficult because it involves comparing measurements obtained for the low and high meaningfulness conditions. In group-analyses, the meaningfulness effect would essentially be handled as a difference score between high- and low-meaningfulness items - but difference scores generally have low reliability (Hedge et al., 2018; Ross et al., 2015). Regressing out the variance for the low meaningful objects is a better option, although if performance on low- and high-meaningfulness items is highly correlated, the reliability of the residuals can still be low (Degutis et al., 2013; Ross et al., 2015). We therefore decided to perform item-level analyses on the memory scores to obtain acceptable reliability, before examining the correlation with \u003cem\u003eo.\u003c/em\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eWe considered possible self-selection biases for participants, out of those we have invited, who volunteered for this new study (N=159), compared to those we did not hear back from (N=96). There may be several reasons for whether a participant responded: The first study was conducted mid-August and the second mid-September, when some participants may have gone back to college. The two studies were conducted one month apart, with no explicit link between them, so it is unlikely that a participant would consider their experience in the first study (with no feedback on performance) when accepting the second one. The two groups (returning/not returning) did not differ significantly in gender distribution (returning 52% women; not returning, 56% women, \u0026chi;\u0026sup2;(1, 255) = 0.47 value, p = .49). Returning participants were on average 3 years older (33.7 vs. 30.6, t\u003csub\u003e253\u003c/sub\u003e=3.345, p\u0026lt;.001). There were no significant differences for \u003cem\u003eg\u003c/em\u003e (t\u003csub\u003e253\u003c/sub\u003e=.943, p=.35), working memory (t\u003csub\u003e253\u003c/sub\u003e=.993, p=.32) or low level vision (t\u003csub\u003e253\u003c/sub\u003e=1.080, p=.28). Returning participants had on average a higher and less variable \u003cem\u003eo\u0026nbsp;\u003c/em\u003e(returning: mean = .38, SD = .33; not returning: mean = .27, SD = .42; Welch\u0026rsquo;s t\u003csub\u003e164\u003c/sub\u003e=2.22, p=.03; Levene\u0026rsquo;s test for equality of variances, F(1,253) = 6.586, p = .01). One possible reason why individuals with lower visual ability were less likely to participate in the second session is that the task description on Prolific mentioned \u0026ldquo;memorize the locations of different objects on the screen\u0026rdquo; whereas that for the first study was more vague: \u0026ldquo;you will complete a series of short tasks\u0026rdquo;. In any case, the reduced variability in \u003cem\u003eo\u003c/em\u003e would most likely diminish its predictive power, suggesting that any effects we observe could be underestimated.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1 includes descriptive statistics for each individual task, as well as for the composites included in our analyses. \u003cem\u003eOp\u003c/em\u003e (\u003cem\u003eo\u003c/em\u003e perception) and \u003cem\u003eOm\u003c/em\u003e (\u003cem\u003eo\u003c/em\u003e memory) are composite scores derived from each group of four tasks (equally weighted and normalized to z-scores), and \u003cem\u003eO\u003c/em\u003e is the equally weighted composite of these two. WM, \u003cem\u003eg\u003c/em\u003e and LLV (low-level visual ability) are also normalized, equally weighted composites of the 2, 3 and 4 tasks, respectively, used to estimate them. The reliability of average location judgments was very high (\u0026lambda;2 =.94) but that for the Meaningfulness effect was lower (\u0026lambda;2 =.55). The meaningfulness effect was operationalized as the advantage in location memory performance for high-meaning objects beyond what would be predicted based on performance with low-meaning objects. For each half of the data (odd/even trials from each condition), we regressed the high-meaning location memory performance on low-meaning location memory performance and extracted the residuals. The residuals from each half of the data were then correlated, and this correlation was corrected using the Spearman-Brown formula to estimate the reliability of the full-length meaningfulness effect measure.\u003c/p\u003e\n\u003cp\u003eTo improve the reliability of the meaningfulness effect, we used an item analysis to drop the 25 low meaning trials that most correlated with the average of the high meaning condition, and we dropped the 25 high meaning trials with the highest difference between correlation with average of low meaning and correlation with average of high meaning. This operationalizes the belief that both conditions share the same location memory effect, as based on \u0026apos;pure\u0026apos; perceptual stimulus factors, and that the high meaning condition includes an additional facilitation from meaningfulness. In practice, it is likely that the meaningfulness effect was limited by the fact that meaningfulness estimates were from an independent set of raters, and some of the objects may have corresponded to different or just more variable levels of meaningfulness in this different sample. This trial selection (dropping about 20% of the trials) was entirely blind to any other consideration. Critically for the purpose of individual differences, every participant received a new high and low meaning condition score computed on the same set of trials. We arbitrarily chose 25 trials to drop from each of the meaningfulness conditions (maintaining 103 images in each), without exploring alternatives, as a compromise to keep a large number of trials in the data while dropping a sufficient number to hopefully make an improvement to reliability. As can be seen in Table 1, this process resulted in a meaningfulness effect reliability above .7 (the true reliability, when this selection is applied to a new sample, may be lower because of sampling error).\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"600\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"bottom\" style=\"width: 491px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1. Descriptive statistics for individual tasks and composite variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003e\n \u003cp\u003e\u003cstrong\u003etask/variable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSkewness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKurtosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ereliability\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003e\n \u003cp\u003eOp Many Objects Oddball\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e71.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e9.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e-0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003e\n \u003cp\u003eOp Silhouette Matching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e58.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e13.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e-0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003e\n \u003cp\u003eOp Ensemble Perception\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e55.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e\n \u003cp\u003edeg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e14.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e-0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003e\n \u003cp\u003eOp 3AFC Matching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e69.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e10.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003e\n \u003cp\u003eOm Learning Exemplars\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e52.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e16.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003e\n \u003cp\u003eOm Paired Objects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e68.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e12.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e-0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003e\n \u003cp\u003eOm New Object\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e77.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e10.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003e\n \u003cp\u003eOm Recognition Memory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e76.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e15.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e-0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003e\n \u003cp\u003eWM Binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e63.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e15.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e-0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003e\n \u003cp\u003eWM Ospan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e73.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e16.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003e\n \u003cp\u003eg Ravens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e48.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e23.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e-0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003e\n \u003cp\u003eg Number\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e70.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e22.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e-0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003e\n \u003cp\u003eg Vocabulary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e62.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e14.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003e\n \u003cp\u003eLLV Circle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e76.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e12.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003e\n \u003cp\u003eLLV Line\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e79.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e12.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e3.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003e\n \u003cp\u003eLLV Angle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e77.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e12.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003e\n \u003cp\u003eLLV Dot\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e74.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e9.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-1.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e5.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003e\n \u003cp\u003eLocation Memory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e43.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e11.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003e\n \u003cp\u003eMeaning Effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003e\n \u003cp\u003eOp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003e\n \u003cp\u003eOm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e-0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003e\n \u003cp\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003e\n \u003cp\u003eWM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003e\n \u003cp\u003eg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 188px;\"\u003e\n \u003cp\u003eLLV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-1.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e4.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 564px;\"\u003e\n \u003cp\u003e\u003cem\u003eNote.\u0026nbsp;\u003c/em\u003eReliability is Guttman lambda2 for simple tasks and Location Memory, Spearman-Brown Corrected for Meaning effect and according to Mosier (1943) for composite variables.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNext we report the zero-order correlations between the location memory performance, the meaningfulness effect, age and gender as well as our various composite measures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Pearson correlations between study variables\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"676\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.3174%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.6409%;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.356%;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.2374%;\"\u003e\n \u003cp\u003e\u003cem\u003eOp\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.1365%;\"\u003e\n \u003cp\u003e\u003cem\u003eOm\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.2374%;\"\u003e\n \u003cp\u003e\u003cem\u003eo\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.1365%;\"\u003e\n \u003cp\u003eWM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.632%;\"\u003e\n \u003cp\u003eg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.1365%;\"\u003e\n \u003cp\u003eLLV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8694%;\"\u003e\n \u003cp\u003eMeaning.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;effect\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.3174%;\"\u003e\n \u003cp\u003eGender (W=1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4.6409%;\"\u003e\n \u003cp\u003e.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.356%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4.2374%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4.1365%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4.2374%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4.1365%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.632%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4.1365%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.8694%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.3174%;\"\u003e\n \u003cp\u003e\u003cem\u003eOp\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4.6409%;\"\u003e\n \u003cp\u003e.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.356%;\"\u003e\n \u003cp\u003e-.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4.2374%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4.1365%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4.2374%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4.1365%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.632%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4.1365%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.8694%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.3174%;\"\u003e\n \u003cp\u003e\u003cem\u003eOm\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4.6409%;\"\u003e\n \u003cp\u003e-.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.356%;\"\u003e\n \u003cp\u003e-.152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4.2374%;\"\u003e\n \u003cp\u003e.413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4.1365%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4.2374%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4.1365%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.632%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4.1365%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.8694%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.3174%;\"\u003e\n \u003cp\u003e\u003cem\u003eo\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4.6409%;\"\u003e\n \u003cp\u003e-.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.356%;\"\u003e\n \u003cp\u003e-.165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4.2374%;\"\u003e\n \u003cp\u003e.842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4.1365%;\"\u003e\n \u003cp\u003e.839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4.2374%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4.1365%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.632%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4.1365%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.8694%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.3174%;\"\u003e\n \u003cp\u003eWM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4.6409%;\"\u003e\n \u003cp\u003e-.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.356%;\"\u003e\n \u003cp\u003e-.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4.2374%;\"\u003e\n \u003cp\u003e.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4.1365%;\"\u003e\n \u003cp\u003e.382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4.2374%;\"\u003e\n \u003cp\u003e.265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4.1365%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.632%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4.1365%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.8694%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.3174%;\"\u003e\n \u003cp\u003eg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4.6409%;\"\u003e\n \u003cp\u003e.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.356%;\"\u003e\n \u003cp\u003e-.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4.2374%;\"\u003e\n \u003cp\u003e.579\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4.1365%;\"\u003e\n \u003cp\u003e.476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4.2374%;\"\u003e\n \u003cp\u003e.628\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4.1365%;\"\u003e\n \u003cp\u003e.255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.632%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4.1365%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.8694%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.3174%;\"\u003e\n \u003cp\u003eLLV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4.6409%;\"\u003e\n \u003cp\u003e.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.356%;\"\u003e\n \u003cp\u003e.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4.2374%;\"\u003e\n \u003cp\u003e.539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4.1365%;\"\u003e\n \u003cp\u003e.334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4.2374%;\"\u003e\n \u003cp\u003e.520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4.1365%;\"\u003e\n \u003cp\u003e.170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.632%;\"\u003e\n \u003cp\u003e.503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4.1365%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.8694%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.3174%;\"\u003e\n \u003cp\u003eMeaningfulness Effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4.6409%;\"\u003e\n \u003cp\u003e.120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.356%;\"\u003e\n \u003cp\u003e.239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4.2374%;\"\u003e\n \u003cp\u003e.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4.1365%;\"\u003e\n \u003cp\u003e.197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4.2374%;\"\u003e\n \u003cp\u003e.212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4.1365%;\"\u003e\n \u003cp\u003e.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.632%;\"\u003e\n \u003cp\u003e.122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4.1365%;\"\u003e\n \u003cp\u003e.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.8694%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.3174%;\"\u003e\n \u003cp\u003eLocation Memory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4.6409%;\"\u003e\n \u003cp\u003e.199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6.356%;\"\u003e\n \u003cp\u003e.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4.2374%;\"\u003e\n \u003cp\u003e.373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4.1365%;\"\u003e\n \u003cp\u003e.437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4.2374%;\"\u003e\n \u003cp\u003e.482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4.1365%;\"\u003e\n \u003cp\u003e.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3.632%;\"\u003e\n \u003cp\u003e.382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4.1365%;\"\u003e\n \u003cp\u003e.283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.8694%;\"\u003e\n \u003cp\u003e.506\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" valign=\"bottom\" style=\"width: 48.7297%;\"\u003e\n \u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e Uncorrected r-thresholds for N=154 are r\u0026ge; .158, \u0026nbsp;p\u0026lt;.05; r\u0026ge; .208 p\u0026lt;.01; r\u0026ge; .264, p\u0026lt;.001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAs shown in Table 2, there was a significant positive correlation between \u003cem\u003eo\u003c/em\u003e and location memory (\u003cem\u003er\u003c/em\u003e = .482, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, 95% CI [.35, .595]), indicating a positive association between a perceptual and an episodic memory index. Furthermore, the correlation between \u003cem\u003eo\u0026nbsp;\u003c/em\u003eand location memory for low meaning objects was, \u003cem\u003er\u003c/em\u003e = .434, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, 95% CI [.296; .554] and for high meaning objects was \u003cem\u003er\u003c/em\u003e =.446, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, 95% CI [.309, .564]. These correlations (not presented in the table) were not significantly different from each other (Steigers z =.339, \u003cem\u003ep\u003c/em\u003e = .734). The correlation between the Meaningfulness effect and \u003cem\u003eo\u003c/em\u003e, however, was significant (\u003cem\u003er\u003c/em\u003e = .212, \u003cem\u003ep\u003c/em\u003e =.008, 95% CI [.056, .359]). To clarify, this analysis relates \u003cem\u003eo\u003c/em\u003e to the residualized performance for high meaning objects, i.e., controlling for performance for low meaning objects. The result indicates that \u003cem\u003eo\u003c/em\u003e shares unique variance with performance for the location of high meaning objects that is not shared with performance for the location of low meaning objects. However, performance in both conditions was also likely to be partly influenced by factors other than \u003cem\u003eo,\u0026nbsp;\u003c/em\u003eincluding those that relate to location memory such as age, WM, g and LLV (see Table 2).\u003c/p\u003e\n\u003cp\u003eTherefore, we tested our prediction that \u003cem\u003eo\u003c/em\u003e is related to location memory, and particularly so for high-meaning objects, using hierarchical multiple regression to control for these other variables. We first predicted location memory performance based on age, gender, g, LLV, WM and added \u003cem\u003eo\u0026nbsp;\u003c/em\u003ein a second step. Since \u003cem\u003eo\u003c/em\u003e was measured using four visual-perceptual and four memory-recognition tasks, following the approach of Smithson et al. (2025), we also examined whether any observed relationship between \u003cem\u003eo\u003c/em\u003e and location memory would survive when \u003cem\u003eo\u003c/em\u003e was estimated using only the perceptual tasks that involve no memory demands \u0026ndash; \u0026nbsp;that is, the \u003cem\u003eOp\u003c/em\u003e composite (Table 3). Hence, in a second model, we predicted location memory performance based on \u003cem\u003eOp,\u0026nbsp;\u003c/em\u003eagain,\u003cem\u003e\u0026nbsp;\u003c/em\u003ewhile controlling for all other factors.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"606\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 62.327%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3. Multiple regression models predicting location memory, showing the incremental prediction of \u003cem\u003eo\u003c/em\u003e (top), or \u003cem\u003eOp\u003c/em\u003e alone (bottom).\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 33.9262%;\"\u003e\n \u003cp\u003eCriterion: Location Memory (Model including \u003cem\u003eo\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.6197%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.5092%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.4929%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.7248%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.5092%;\"\u003e\n \u003cp\u003eb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.6923%;\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.6197%;\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.5092%;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.4929%;\"\u003e\n \u003cp\u003eIncr. R-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.7248%;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.5092%;\"\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.6923%;\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.6197%;\"\u003e\n \u003cp\u003e2.746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.5092%;\"\u003e\n \u003cp\u003e.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.4929%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.7248%;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.5092%;\"\u003e\n \u003cp\u003e.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.6923%;\"\u003e\n \u003cp\u003e.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.6197%;\"\u003e\n \u003cp\u003e1.352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.5092%;\"\u003e\n \u003cp\u003e.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.4929%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.7248%;\"\u003e\n \u003cp\u003eg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.5092%;\"\u003e\n \u003cp\u003e.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.6923%;\"\u003e\n \u003cp\u003e.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.6197%;\"\u003e\n \u003cp\u003e1.235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.5092%;\"\u003e\n \u003cp\u003e.219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.4929%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.7248%;\"\u003e\n \u003cp\u003eLLV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.5092%;\"\u003e\n \u003cp\u003e-.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.6923%;\"\u003e\n \u003cp\u003e.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.6197%;\"\u003e\n \u003cp\u003e-.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.5092%;\"\u003e\n \u003cp\u003e.881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.4929%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.7248%;\"\u003e\n \u003cp\u003eWM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.5092%;\"\u003e\n \u003cp\u003e.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.6923%;\"\u003e\n \u003cp\u003e.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.6197%;\"\u003e\n \u003cp\u003e.877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.5092%;\"\u003e\n \u003cp\u003e.382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.4929%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.7248%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eo\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.5092%;\"\u003e\n \u003cp\u003e.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.6923%;\"\u003e\n \u003cp\u003e.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.6197%;\"\u003e\n \u003cp\u003e4.340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.5092%;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.4929%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.090\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 33.9262%;\"\u003e\n \u003cp\u003eF = 10.22, p\u0026lt;.000, adj. R-squared =.294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.6197%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.5092%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.4929%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.7248%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.5092%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.6923%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.6197%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.5092%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.4929%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 33.9262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eCriterion: Location Memory (Model including \u003cem\u003eOp\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.6197%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.5092%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.4929%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.7248%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.5092%;\"\u003e\n \u003cp\u003eb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.6923%;\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.6197%;\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.5092%;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.4929%;\"\u003e\n \u003cp\u003eIncr. R-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.7248%;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.5092%;\"\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.6923%;\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.6197%;\"\u003e\n \u003cp\u003e2.547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.5092%;\"\u003e\n \u003cp\u003e.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.4929%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.7248%;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.5092%;\"\u003e\n \u003cp\u003e.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.6923%;\"\u003e\n \u003cp\u003e.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.6197%;\"\u003e\n \u003cp\u003e.950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.5092%;\"\u003e\n \u003cp\u003e.344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.4929%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.7248%;\"\u003e\n \u003cp\u003eg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.5092%;\"\u003e\n \u003cp\u003e.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.6923%;\"\u003e\n \u003cp\u003e.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.6197%;\"\u003e\n \u003cp\u003e2.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.5092%;\"\u003e\n \u003cp\u003e.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.4929%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.7248%;\"\u003e\n \u003cp\u003eLLV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.5092%;\"\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.6923%;\"\u003e\n \u003cp\u003e.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.6197%;\"\u003e\n \u003cp\u003e.244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.5092%;\"\u003e\n \u003cp\u003e.808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.4929%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.7248%;\"\u003e\n \u003cp\u003eWM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.5092%;\"\u003e\n \u003cp\u003e.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.6923%;\"\u003e\n \u003cp\u003e.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.6197%;\"\u003e\n \u003cp\u003e1.673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.5092%;\"\u003e\n \u003cp\u003e.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.4929%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.7248%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eOp\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.5092%;\"\u003e\n \u003cp\u003e.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.6923%;\"\u003e\n \u003cp\u003e.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.6197%;\"\u003e\n \u003cp\u003e2.484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.5092%;\"\u003e\n \u003cp\u003e.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.4929%;\"\u003e\n \u003cp\u003e.\u003cstrong\u003e032\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 33.9262%;\"\u003e\n \u003cp\u003eF = 7.57, p\u0026lt;.000, adj. R-squared =.236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.6197%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.5092%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.4929%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"bottom\" style=\"width: 50.9446%;\"\u003e\n \u003cp\u003e\u003cem\u003eNote:\u003c/em\u003e Gender is coded as Women = 1, not-Women = 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.4929%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eBoth models were significant and in both cases, there was a significant incremental contribution of \u003cem\u003eo\u0026nbsp;\u003c/em\u003e(9%)\u003cem\u003e\u0026nbsp;\u003c/em\u003eor\u003cem\u003e\u0026nbsp;Op\u0026nbsp;\u003c/em\u003ealone\u003cem\u003e\u0026nbsp;\u003c/em\u003e(3.2%) to the explained variance in location memory performance. In addition, age was a significant predictor in both models, and \u003cem\u003eg\u003c/em\u003e was a significant predictor only in the second model (likely because of the slightly higher correlation for \u003cem\u003eo\u003c/em\u003e than \u003cem\u003eOp\u003c/em\u003e with \u003cem\u003eg\u003c/em\u003e).\u003c/p\u003e\n\u003cp\u003eNext, we tested our prediction that \u003cem\u003eo\u003c/em\u003e should be related to the meaningfulness effect with hierarchical multiple regression. In the first step we predicted location memory for high meaning objects based on age, gender, g, LLV, WM, and also controlled for location memory for low meaning objects, while in the second step we added \u0026nbsp;\u003cem\u003eo\u003c/em\u003e (or \u003cem\u003eOp\u0026nbsp;\u003c/em\u003ein a second model, see Table 4) as a predictor. Both models were significant and in both cases, there was a significant incremental contribution of \u003cem\u003eo\u0026nbsp;\u003c/em\u003e(3.2%)\u003cem\u003e\u0026nbsp;\u003c/em\u003eor\u003cem\u003e\u0026nbsp;Op\u0026nbsp;\u003c/em\u003e(1.5%) to the explained variance in location memory performance. Gender was a significant predictor in both models, with women performing better. Figure 5 illustrates the semi-partial correlations between \u003cem\u003eo\u003c/em\u003e (controlling for age, gender, LLV, WM and g) and location memory judgments for each condition. We use semi-partial correlations to show both the main effect of meaningfulness, and its interaction with \u003cem\u003eo\u003c/em\u003e.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"640\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" valign=\"top\" style=\"width: 41.9097%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 4. Multiple regression models predicting location memory for high-meaning stimuli, showing the incremental prediction of \u003cem\u003eo\u003c/em\u003e (top), or \u003cem\u003eOp\u003c/em\u003e alone (bottom).\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 31.0736%;\"\u003e\n \u003cp\u003eCriterion: Location for High Meaning (Model including \u003cem\u003eo\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.7168%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 12.3288%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.6674%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.4687%;\"\u003e\n \u003cp\u003eb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.9375%;\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.7168%;\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 12.3288%;\"\u003e\n \u003cp\u003eIncr. R-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.6674%;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.4687%;\"\u003e\n \u003cp\u003e.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.9375%;\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.7168%;\"\u003e\n \u003cp\u003e1.469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e.144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 12.3288%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.6674%;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.4687%;\"\u003e\n \u003cp\u003e.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.9375%;\"\u003e\n \u003cp\u003e.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.7168%;\"\u003e\n \u003cp\u003e3.563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 12.3288%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.6674%;\"\u003e\n \u003cp\u003eg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.4687%;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.9375%;\"\u003e\n \u003cp\u003e.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.7168%;\"\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 12.3288%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.6674%;\"\u003e\n \u003cp\u003eLLV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.4687%;\"\u003e\n \u003cp\u003e-.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.9375%;\"\u003e\n \u003cp\u003e.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.7168%;\"\u003e\n \u003cp\u003e-.821\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e.413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 12.3288%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.6674%;\"\u003e\n \u003cp\u003eWM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.4687%;\"\u003e\n \u003cp\u003e.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.9375%;\"\u003e\n \u003cp\u003e.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.7168%;\"\u003e\n \u003cp\u003e.768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e.444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 12.3288%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.6674%;\"\u003e\n \u003cp\u003eLow Meaning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.4687%;\"\u003e\n \u003cp\u003e.901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.9375%;\"\u003e\n \u003cp\u003e.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.7168%;\"\u003e\n \u003cp\u003e8.583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 12.3288%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.6674%;\"\u003e\n \u003cp\u003e\u003cem\u003eo\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.4687%;\"\u003e\n \u003cp\u003e.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.9375%;\"\u003e\n \u003cp\u003e.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.7168%;\"\u003e\n \u003cp\u003e3.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 12.3288%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.032\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 31.0736%;\"\u003e\n \u003cp\u003eF = 22.47, p\u0026lt;.000, adj. R-squared =.519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.7168%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 12.3288%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.6674%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.4687%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.9375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.7168%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 12.3288%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 31.0736%;\"\u003e\n \u003cp\u003eCriterion: Location for High Meaning (Model including \u003cem\u003eOp\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.7168%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 12.3288%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.6674%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.4687%;\"\u003e\n \u003cp\u003eb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.9375%;\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.7168%;\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 12.3288%;\"\u003e\n \u003cp\u003eIncr. R-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.6674%;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.4687%;\"\u003e\n \u003cp\u003e.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.9375%;\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.7168%;\"\u003e\n \u003cp\u003e1.279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e.203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 12.3288%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.6674%;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.4687%;\"\u003e\n \u003cp\u003e.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.9375%;\"\u003e\n \u003cp\u003e.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.7168%;\"\u003e\n \u003cp\u003e3.332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 12.3288%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.6674%;\"\u003e\n \u003cp\u003eg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.4687%;\"\u003e\n \u003cp\u003e.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.9375%;\"\u003e\n \u003cp\u003e.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.7168%;\"\u003e\n \u003cp\u003e.474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e.636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 12.3288%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.6674%;\"\u003e\n \u003cp\u003eLLV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.4687%;\"\u003e\n \u003cp\u003e-.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.9375%;\"\u003e\n \u003cp\u003e.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.7168%;\"\u003e\n \u003cp\u003e-.635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e.526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 12.3288%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.6674%;\"\u003e\n \u003cp\u003eWM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.4687%;\"\u003e\n \u003cp\u003e.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.9375%;\"\u003e\n \u003cp\u003e.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.7168%;\"\u003e\n \u003cp\u003e1.374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e.172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 12.3288%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.6674%;\"\u003e\n \u003cp\u003eLow Meaning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.4687%;\"\u003e\n \u003cp\u003e.952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.9375%;\"\u003e\n \u003cp\u003e.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.7168%;\"\u003e\n \u003cp\u003e9.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 12.3288%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.6674%;\"\u003e\n \u003cp\u003e\u003cem\u003eOp\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7.4687%;\"\u003e\n \u003cp\u003e.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14.9375%;\"\u003e\n \u003cp\u003e.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.7168%;\"\u003e\n \u003cp\u003e2.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 12.3288%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.015\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 31.0736%;\"\u003e\n \u003cp\u003eF = 20.98, p\u0026lt;.000, adj. R-squared =.501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.7168%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 11.4726%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 12.3288%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" valign=\"bottom\" style=\"width: 40.6631%;\"\u003e\n \u003cp\u003eNote: Gender is coded as Women = 1, non-Women = 0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe aimed to assess the contribution of \u003cem\u003eo\u003c/em\u003e, a domain-general skill reflecting the ability to recognize and distinguish visually similar items based on shape, to episodic memory, and specifically to memory for an object's location. Even when manipulated arbitrarily and independently of an item's identity, location memory can be positively influenced by a stimulus\u0026rsquo;s level of familiarity and/or semantic meaning (DeWitt et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Gronau et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Taevs et al, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). We therefore further examined whether \u003cem\u003eo\u003c/em\u003e predicted the accuracy of location judgments and whether \u003cem\u003eo\u003c/em\u003e interacted with stimulus meaningfulness to predict this aspect of episodic memory. Our findings provide empirical evidence for both questions. First, individuals with high \u003cem\u003eo\u003c/em\u003e exhibited enhanced location memory compared to those with low \u003cem\u003eo\u003c/em\u003e. Second, supporting a Perceptual-Semantic Synergy account, the relationship between \u003cem\u003eo\u003c/em\u003e and location memory was stronger when participants encoded and retrieved high-meaningfulness items.\u003c/p\u003e\u003cp\u003eThese findings provide the first direct evidence linking semantic meaning, episodic memory, and visual recognition skills measured at the individual level. \u003cem\u003eO\u003c/em\u003e is related to\u003cb\u003e\u0026ndash;\u003c/b\u003ebut clearly distinct from\u003cb\u003e\u0026ndash;\u003c/b\u003ea range of perceptual abilities (both visual and auditory), as well as higher-level cognitive functions such as working memory and general intelligence (Richler et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Smithson et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sunday et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Our results extend previous research by suggesting that enhanced visual encoding in individuals with high \u003cem\u003eo\u003c/em\u003e contributes to improved memory for instance-specific details, such as object location. Notably, this effect remains robust even after accounting for other cognitive and low-level abilities. Additionally, we emphasize the facilitating role of item meaningfulness in this relationship. Each of these findings is discussed in detail below.\u003c/p\u003e\n\u003ch3\u003eHigh Object Recognition Ability is Linked to Improved Location Memory\u003c/h3\u003e\n\u003cp\u003eThe ability to extract the invariant properties of an object, whether highly familiar or unfamiliar, is central to the \u003cem\u003eo\u003c/em\u003e ability, which underlies robust item identification across varying viewing conditions. At the same time, our findings indicate that individuals with high \u003cem\u003eo\u003c/em\u003e scores are also highly sensitive to contextual information that is extrinsic to the object itself, such as the screen location in which it was presented. Although these capabilities may seem contradictory \u003cb\u003e\u0026ndash;\u003c/b\u003e attending both to invariant features related to object shape and to a feature like location that can clearly vary without any influence on object identity \u003cb\u003e\u0026ndash;\u003c/b\u003e our results suggest they may nonetheless reflect shared underlying cognitive mechanisms. That is, the ability to encode both stable and context-dependent aspects of an object may represent complementary facets of a common cognitive process. Furthermore, given that spatial location is a well-established component of episodic memory \u003cb\u003e\u0026ndash;\u003c/b\u003e the \"when\" and \"where\" of an event \u003cb\u003e\u0026ndash;\u003c/b\u003e these findings may further blur the traditional boundaries between perception and memory.\u003c/p\u003e\u003cp\u003eOur correlational design does not allow us to distinguish item memory and item-location binding effects, as in paradigms where both features and locations are unique (e.g., Chalfonte \u0026amp; Johnson, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). In our paradigm, remembering the specific location of an item largely relies on accurate encoding of the item itself, as well as successful item-location binding \u003cb\u003e\u0026ndash;\u003c/b\u003e both processes that may be more efficient in individuals with high \u003cem\u003eo\u003c/em\u003e scores. Our findings are also consistent with models emphasizing the role of attentional and working memory resources, which propose that efficient item encoding frees up cognitive capacity for binding the items to their contextual surroundings (Popov \u0026amp; Reder, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Reder et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Several researchers have suggested that spatial location, along with processes related to location-identity binding, may benefit from a unique attentional and memory status (e.g., Golomb et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Hollingworth, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Kovacs \u0026amp; Harris, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Treisman \u0026amp; Gelade, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1980\u003c/span\u003e). This is reflected in findings showing that not only do objects serve as effective memory cues for their encoded locations, but locations can also cue item identity and the precise visual details of objects. For instance, participants more accurately recognized specific object exemplars and episodic details (such as pose) when items were tested at their original location, compared to when they were presented at a different location in the scene (Hollingworth, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). However, this enhanced item discrimination due to shared location may come at a cost. When successive pairs of identical or different objects were presented in the same location, participants were biased to report that the identities were the same, even when they were not (Golomb et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Pertzov \u0026amp; Husain, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In other words, shared location increases both correct identifications (hits) and false alarms, suggesting that participants are unable to disregard spatial overlap between stimuli, even when it is irrelevant and detrimental to the task. Although our task involved a long-term memory paradigm, many stimuli appeared in repeated locations, which may have introduced interference in location memory. Nevertheless, high-\u003cem\u003eo\u003c/em\u003e participants appear to benefit from enhanced item identification and distinctiveness, which may help mitigate such location-based interference.\u003c/p\u003e\u003cp\u003eThis pattern suggests that high \u003cem\u003eo\u003c/em\u003e supports more precise object-location bindings by enhancing the fidelity of object encoding and reducing confusion between overlapping spatial cues. Our findings echo prior work showing that memorable images aid spatial recall (Trinkl \u0026amp; Wolfe, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) \u0026ndash; in that work, the effect was specific to memory for spatial location, not extending to temporal position in the studied sequence. Interestingly, other work (Smithson et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) found a strong correlation between \u003cem\u003eo\u003c/em\u003e and visual spatial ability\u0026ndash;that is, the capacity to perceive, process, and mentally manipulate visual material (as measured by mental rotation tasks; Schneider \u0026amp; McGrew, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, although both location memory and mental rotation involve spatial processing, they may depend on qualitatively different cognitive mechanisms. Notably, the relationship between \u003cem\u003eo\u003c/em\u003e and spatial ability was largely accounted for by their shared association with general intelligence (Smithson et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Here, the contribution of \u003cem\u003eo\u003c/em\u003e to location memory remained robust even after statistically controlling for general intelligence and working memory, and was still obtained when using only perceptual \u003cem\u003eo\u003c/em\u003e tasks. This strengthens the idea that \u003cem\u003eo\u003c/em\u003e facilitates the encoding of spatial information because of mechanisms supporting object recognition via shape, rather than associated abilities.\u003c/p\u003e\u003cp\u003eFrom a neurocognitive perspective, one theory that aligns with our findings and bridges visual perception and episodic memory is the \u003cem\u003erepresentational-hierarchical model\u003c/em\u003e (Bussey et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). According to this model, the difficulty of a perceptual discrimination determines the recruitment of brain systems traditionally associated with memory. Specifically, the ventral visual stream and medial temporal lobe operate in a hierarchy, encoding progressively complex information. At the top of this hierarchy, the perirhinal cortex (PRC) integrates features to form complex visual, semantic, and possibly multimodal representations (Li et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). When the PRC is compromised, deficits arise in both memory and perception, particularly for objects with subtle featural differences. Further, the hippocampus is implicated as the next level in this hierarchy, encoding spatial relations among objects and their broader context (Barense et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Bussey et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). This framework supports a potential overlap between \u003cem\u003eo\u003c/em\u003e and episodic memory, especially in tasks requiring fine perceptual discrimination or contextual detail. While these predictions are primarily based on lesion studies (Li et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), the model has yet to be systematically applied to individual differences in healthy populations.\u003c/p\u003e\u003cp\u003e\u003cem\u003eHigh Object Recognition Ability Predicts the Meaningfulness Effect on Location Memory.\u003c/em\u003e\u003c/p\u003e\u003cp\u003eOne of the central findings of our research is that the semantic meaning of objects moderates the relationship between \u003cem\u003eo\u003c/em\u003e and location memory. In prior work, we found that \u003cem\u003eo\u003c/em\u003e measured with sets of tasks that use both familiar (real-world) or artificial novel objects is essentially the same at the latent level (Sunday et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Nevertheless, here \u003cem\u003eo\u003c/em\u003e interacted with object's meaningfulness in the context of location memory. Specifically, a stronger positive correlation emerged between \u003cem\u003eo\u003c/em\u003e and location memory judgments for high-meaning objects compared to low-meaning ones. As noted earlier, we interpret this effect as reflecting a more efficient use of semantic knowledge embedded within meaningful objects by individuals with high \u003cem\u003eo\u003c/em\u003e, consistent with prior evidence that such individuals demonstrate enhanced ability in subordinate-level discriminations (Sunday et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). When an object is processed at a more refined level, it can activate a broader semantic network and carry greater personal relevance. Supporting this interpretation, higher expertise is generally linked to the ability to make finer subordinate-level distinctions, which tend to carry rich semantic content (Johnson \u0026amp; Eilers, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Regarding memory performance, studies of visual expertise have shown that experts often demonstrate superior short- and long-term memory within their domain, likely due to the combined effects of visual and conceptual training (e.g., Annis \u0026amp; Palmeri, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Curby \u0026amp; Gauthier, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Herzmann \u0026amp; Curran, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eA role for semantic knowledge in long-term memory formation and retrieval has long been established (e.g., Anderson, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1984\u003c/span\u003e; Brewer \u0026amp; Treyens, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1981\u003c/span\u003e; Craik \u0026amp; Tulving, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1975\u003c/span\u003e). However, one line of research has brought this topic back to the forefront, particularly in the context of memory for specific visual details (e.g., Brady et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). For example, Konkle et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) found that interference in visual long-term memory is primarily predicted by conceptual distinctiveness rather than perceptual distinctiveness among similar object exemplars. More recently, Kramer et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) demonstrated that semantic features have a stronger influence than perceptual features on the memorability of objects and scenes. According to one account, highly memorable images may be more easily recognized \u003cem\u003ebecause\u003c/em\u003e they are more effectively mapped onto semantic features (Deng et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Additionally, visual working memory capacity is higher for meaningful objects than for meaningless items or simple features, likely due to semantic associations rather than visual complexity alone (Shoval et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Torres et al., 2024).\u003c/p\u003e\u003cp\u003eSemantic meaning can enhance memory for both intrinsic object properties such as color (Chung et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Gronau \u0026amp; Shachar, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and extrinsic details, such as the object\u0026rsquo;s location or scene background (Gronau et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Reder et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Sahar et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Conceptual meaning has been viewed as a \u0026ldquo;hook\u0026rdquo; or \u0026ldquo;scaffold\u0026rdquo; that supports the retention of perceptual details in memory (Chung et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Konkle et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). While such metaphors offer useful heuristics, other researchers have sought to clarify the underlying mechanisms by which highly familiar items benefit memory more than novel or meaningless stimuli. As discussed earlier, resource-limited theories propose that familiar items place lower demands on working memory during encoding, thereby freeing up resources to store visual details and bind items to their contextual surroundings (Popov \u0026amp; Reder, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Reder et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). But the impact of semantic meaning extends beyond simple familiarity, reflecting a richer interplay of conceptual knowledge, categorical specificity, and/or verbal encoding processes. In particular, verbal labeling has been proposed as one of several mechanisms underlying the memory advantage for meaningful over abstract or unfamiliar objects, including their enhanced recall in location memory tasks (Choi \u0026amp; L\u0026rsquo;Hirondelle, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Taevs et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In Choi \u0026amp; L\u0026rsquo;Hirondelle (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), women remembered the location of objects better than men when the objects were concrete rather than abstract. Thus, verbal processing may also help account for the gender difference observed in the present study: women demonstrated greater location memory benefits from object meaningfulness than men. This finding aligns with well-established evidence of female advantages in verbal processing (Bleecker et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Kramer et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), suggesting that stimulus-naming strategies could have contributed to the observed female advantage in object-location memory tasks. However, it appears unlikely that a useful verbal strategy would involve encoding both the name of the object and a verbal description of its location; the relatively large number of stimuli, as well as the absence of \u0026lsquo;canonical\u0026rsquo; locations (e.g., right\u0026ndash;left or up\u0026ndash;down, positioned at 0/180 or 90/270 degrees), would make this relatively difficult. An alternative is that a better ability to name stimuli during encoding would support stronger semantic processing, which we believe supports richer episodic encoding. This should be explored in future studies. Importantly, we cannot entirely rule out the possibility that residual verbal processing contributed to the observed gender effect, but this would be independent of the relationship we observed between \u003cem\u003eo\u003c/em\u003e and location memory, as \u003cem\u003eo\u003c/em\u003e tasks do not encourage verbal strategies and \u003cem\u003eo\u003c/em\u003e is not related to gender.\u003c/p\u003e\u003cp\u003eThere are some limitations in this work. We tested participants from a prior study and were therefore constrained both in terms of sample size (by participants who willingly signed up for this follow-up) and in terms of the covariates that were included. We used a coarse measure of meaningfulness, characterized by raters\u0026rsquo; understanding of what that concept means, and which likely conflates several dimensions such as familiarity, nameability and complexity. Our meaningful objects were more likely to be living (e.g., animals or food) and likely differed in other ways. Therefore, this work cannot specify which aspects of meaningfulness may be responsible for interacting with \u003cem\u003eo\u003c/em\u003e. Because our measures of low-level perception as well as \u003cem\u003eo\u003c/em\u003e tasks focus on shape, with color and texture not diagnostic but present in the location memory task, we cannot speak to the role of these other dimensions in visual processing. Furthermore, our study focused on one aspect of episodic memory, namely, spatial location, which may not fully represent other components of episodic memory, such as temporal or other contextual factors. As a result, the generalizability of our findings may be limited.\u003c/p\u003e\u003cp\u003eIn sum, our results demonstrate that mechanisms that support a better domain-general object recognition ability, \u003cem\u003eo\u003c/em\u003e, also play a role in episodic memory by enhancing the binding of meaningful visual objects to spatial context. This challenges traditional cognitive boundaries, showing that perceptual skills contribute to memory encoding processes. Even after controlling for intelligence, working memory, and low-level visual abilities, \u003cem\u003eo\u003c/em\u003e remains a significant predictor of performance. The influence of meaningfulness as a moderator highlights the pivotal role of semantic content in memory formation. Our work opens the door to further investigations into the perceptual and semantic underpinnings of episodic recall. These findings call for models that integrate visual and semantic processing to explain how meaning facilitates memory.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Note:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Open University of Israel\u0026apos;s Research Fund grant 515549 to NG, and by the David K. Wilson Chair Research Fund from Vanderbilt University and NSF BCS Award 2316474 to IG. Correspondence concerning this article should be addressed to Nurit Gronau, Education and Psychology Department, The Open University of Israel, 1 University Rd., Raanana, Israel, 4353701; Email: [email protected]; ORCID 0000-0002-2468-5302.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank Gaya Aran and Kobi Lindson for assisting in data collection.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eN.G. and I.G. wrote the main manuscript text; C.J.S. analyzed the data, advised and provided major insights. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll experimental materials and results are available at the Open Science Framework and can beaccessed at [https://osf.io/hy763/?view\\_only=be6bc532f4fa490d98e81e7eb1d8eb86](https:/osf.io/hy763/?view_only=be6bc532f4fa490d98e81e7eb1d8eb86) . 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On the interpretation of removable interactions: A survey of the field 33 years after Loftus. \u003cem\u003eMemory \u0026amp; Cognition\u003c/em\u003e, \u003cem\u003e40\u003c/em\u003e(2), 145\u0026ndash;160. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3758/s13421-011-0158-0\u003c/span\u003e\u003cspan address=\"10.3758/s13421-011-0158-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"psychological-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prpf","sideBox":"Learn more about [Psychological Research](http://link.springer.com/journal/426)","snPcode":"426","submissionUrl":"https://submission.nature.com/new-submission/426/3","title":"Psychological Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7594990/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7594990/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePeople differ in their ability to distinguish visually similar items, a domain-general ability known as \u003cem\u003eo\u003c/em\u003e (Richler et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). While \u003cem\u003eo\u003c/em\u003e typically involves extracting invariant object properties, we investigated whether it also relates to long-term memory for episodic information extrinsic to object identity, specifically, object location. We further examined whether this relationship is influenced by stimulus meaningfulness, a factor known to enhance long-term memory, by using both high- and low-meaning stimuli. Participants completed a location memory test, a series of visual object-recognition tasks assessing \u003cem\u003eo\u003c/em\u003e, and other cognitive covariate measures. Results showed a positive correlation between \u003cem\u003eo\u003c/em\u003e and location memory, which was stronger for high-meaning than for low-meaning stimuli. This suggests that semantic content may enhance the link between object recognition and episodic location memory. Importantly, these effects remained after controlling for age, gender, low-level visual perception, working memory, and general intelligence. Our findings indicate that domain-general object recognition ability contributes to episodic memory by supporting the binding of meaningful objects to their spatial context. This challenges traditional cognitive boundaries by integrating current knowledge about individual differences in perception and memory, with semantic meaning acting as a significant moderator.\u003c/p\u003e","manuscriptTitle":"Object recognition ability predicts episodic location memory, enhanced by meaningfulness","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-22 10:22:14","doi":"10.21203/rs.3.rs-7594990/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-10-21T21:45:11+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-29T01:14:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"65378853853324801680536125598122449516","date":"2025-09-15T01:30:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"289739550230451255097679361146799768658","date":"2025-09-13T05:25:15+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-12T09:47:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-12T09:04:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-12T02:38:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Psychological Research","date":"2025-09-11T21:28:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"psychological-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prpf","sideBox":"Learn more about [Psychological Research](http://link.springer.com/journal/426)","snPcode":"426","submissionUrl":"https://submission.nature.com/new-submission/426/3","title":"Psychological Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d06f215c-7621-4e63-92a0-e4936e564334","owner":[],"postedDate":"September 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-09T16:09:32+00:00","versionOfRecord":{"articleIdentity":"rs-7594990","link":"https://doi.org/10.1007/s00426-026-02248-y","journal":{"identity":"psychological-research","isVorOnly":false,"title":"Psychological Research"},"publishedOn":"2026-02-07 15:58:32","publishedOnDateReadable":"February 7th, 2026"},"versionCreatedAt":"2025-09-22 10:22:14","video":"","vorDoi":"10.1007/s00426-026-02248-y","vorDoiUrl":"https://doi.org/10.1007/s00426-026-02248-y","workflowStages":[]},"version":"v1","identity":"rs-7594990","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7594990","identity":"rs-7594990","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}