Encoding of semantic structure shapes temporal order memory for visual object stimuli

Encoding of semantic structure shapes temporal order memory for visual object stimuli | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Encoding of semantic structure shapes temporal order memory for visual object stimuli Henry David Soldan, Carina Zoellner, Nora Alicia Herweg, Nurten Genc, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6595825/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Jan, 2026 Read the published version in Psychological Research → Version 1 posted 10 You are reading this latest preprint version Abstract Episodic memory does not perfectly reproduce past experiences but combines encoded episode-specific information and semantic knowledge in a constructive way. Previous research has shown that semantic category knowledge can bias location memory for individual items, suggesting that similar mechanisms may affect other key dimensions of episodic memory. Here, we investigated whether immediate temporal order memory is influenced by semantic relatedness between encoded items and whether this effect is modulated by semantic structure at encoding, episodic association strength and semantic typicality. Across two experiments, participants completed a temporal order memory task in which they encoded sequences of object images and subsequently judged the relative temporal proximity between items. Results showed that participants who encoded semantically structured sequences performed significantly better on congruent retrieval trials where the correct choice (the temporally closer item) was semantically related to the cue versus on incongruent trials where the incorrect choice was semantically related to the cue. Participants who did not encode semantically structured sequences did not show the semantic congruence effect and performed worse than those who encoded semantically structured sequences on congruent trials. Across conditions, the temporal distance between items at encoding positively predicted correct temporal proximity judgements. Overall, these findings demonstrate that semantic relatedness between encoded items can facilitate immediate temporal order memory depending on the encoding of semantically structured item sets. We discuss these results regarding the role and potential benefits of semantic and temporal context processing for constructive episodic memory. generative episodic memory memory retrieval prior knowledge scenario construction semantic memory Figures Figure 1 Figure 2 Figure 3 Figure 4 Significance Statement The episodic memory system reconstructs past experiences relying on episode-specific information and semantic knowledge. Across two experiments we show that semantic category knowledge influences memory for the temporal order of events: After encoding sequences of object images which were temporally clustered in categories, participants tended to retrieve semantically related items as having been presented closer in time. This was not the case when semantically related items were encoded in random order, suggesting a key role of semantic processing at encoding. Introduction When recalling events from their personal past, humans rely on episodic memory to retrieve information about what they experienced in a specific spatiotemporal context. While episodic memory serves an important adaptive purpose, it does not always accurately reflect past events but tends to produce systematic errors that reflect its constructive nature (Schacter, 2012 ). The notion that episodic memory is inherently constructive entails that it recombines information from multiple sources to generate coherent scenarios and to enable decisions. Semantic knowledge is a key component of constructive memory processes and has been theorized to shape memory retrieval for a long time (Bartlett, 1932 ). For instance, classic work using the Deese–Roediger–McDermott paradigm (Roediger & McDermott, 1995 ) has demonstrated that semantic associations between items in studied word lists can result in false memories for items that were never part of the learned material. More recent evidence suggests that semantic and episodic memory are closely interconnected in terms of cognitive processes and underlying neural mechanisms (Renoult et al., 2019 ). However, how and under which conditions semantic knowledge systematically shapes episodic memory retrieval is still not completely understood. Thus, we aimed at extending this line of research by investigating how semantic knowledge impacts memory for the temporal order of event sequences. The Scenario Construction Model (SCM, Cheng et al., 2016 ) postulates that episode-specific information is flexibly integrated with semantic knowledge to meet current information processing demands. More specifically, the model posits that episode-specific information is typically stored in the form of a memory trace representing the gist of an episode (i.e., its central aspects and structure). This episodic gist representation is then integrated and completed with consolidated semantic information to form a coherent scenario in a generative memory retrieval process (Cheng et al., 2016 ). Accordingly, memory retrieval should reflect both, episode-specific information and semantic knowledge, in situations where such generative memory processes are evoked. By extension, since most experience involves semantically structured event sequences (such as the temporal and spatial co-occurrence of related “animal concepts” during a visit to the zoo), semantic processing at encoding is likely to influence the generation of event representations and subsequent generative retrieval (Addis, 2018 ). Previous studies have demonstrated episodic-semantic interactions in various memory tasks. For example, participants reliably exhibit robust effects of temporal contiguity and semantic relatedness in free recall tasks (Healey, 2018 ; Howard & Kahana, 2002 ). Pairing word items with semantically related context material at encoding is associated with enhanced retrieval performance for such items, constituting a semantic congruence effect (Bein et al., 2015 ). Notably, this can also be accompanied by increased false recall of semantically related items (Packard et al., 2017 ). Moreover, retrieval of image locations was found to be enhanced when a given item had been encoded near a cluster of semantically related items versus in a random location, while semantic typicality predicted retrieval bias towards category clusters (Tompary & Thompson-Schill, 2021 ). Similar effects of semantic knowledge on memory for episodic features have been shown for a naturalistic episodic navigation task involving encoding of semantically congruent and incongruent object locations (Zöllner et al., 2022 ). In another recent study, the quality of episodic memory for scenes was negatively related to semantic bias in memory retrieval (Ramey et al., 2022 ). The negative effect of semantically incongruent placements on location memory retrieval was absent for scenes which participants were able to fully recollect, suggesting a more pronounced influence of semantic processing on memory retrieval in case of weak episodic representations. Collectively, these findings suggest that a semantically structured encoding context can bias retrieval towards semantic expectations, with this effect potentially being modulated by the strength of episodic memory representations. Arguably, these effects are applicable to the temporal dimension of episodic memory. In addition to physical space, time is a defining context factor in episodic memory organization with specific events being mapped to a specific spatiotemporal context (Tulving, 2002 ). Both space and time are encoded by the hippocampal memory system which enables the organization of discrete events in memory along spatial as well as temporal axes (Ekstrom & Ranganath, 2018 ). Interestingly, semantic relatedness effects appear consistently in conjunction with temporal contiguity effects in free recall experiments, and the organization of memory recall in tasks involving a semantic structure (within the encoding set and/or retrieval cues) suggests an interaction of semantic and temporal context information (Healey et al., 2019 ; Polyn et al., 2011 ). While this work highlights temporal contiguity and semantic relatedness as two key interacting factors in the organization of free recall, it is unclear whether these principles extend to memory for the temporal order of events, an important feature of episodic memory. In a study by Zöllner et al. ( 2022 ), participants exhibited a semantic clustering effect in their recall of the event order suggesting that temporal order memory is influenced by semantic associations between event features. However, whether similar effects of semantic knowledge can be shown in a task specifically designed to probe encoding and retrieval of temporal order information remains to be investigated. In addition, it is unclear whether effects of semantic knowledge on temporal order memory would depend on a semantically structured encoding set, which may evoke semantic processing at encoding. While the SCM does not make specific predictions about encoding processes, the presence of pronounced semantic structure at encoding may shift the memory system towards semantic context processing (Healey et al., 2019 ; Morton & Polyn, 2016 ). This is supported by neuroimaging results showing that neural coding of current and recent semantic category information at encoding predicts recall organization (Chan et al., 2017 ; Morton & Polyn, 2017 ). Conversely, the effect of semantic associations between encoded items on temporal order memory retrieval could be independent of semantic structure within the encoding set and primarily result from weakly encoded episodic memory and/or retrieval-specific processes as emphasized by the SCM. Concerning modulating factors, the SCM would predict a more pronounced influence of semantic knowledge on memory retrieval for weakly encoded episodic memory representations as there should be an increased need for completion of the episodic gist information. Since episodic associations between items are known to be determined by the temporal lag between these items at encoding (e.g., serial positions within an encoded list) and are typically stronger for forward versus backward encoding transitions (Healey et al., 2019 ), these factors may modulate the influence of semantic knowledge on temporal order memory. In addition, increased activation of semantic concepts at retrieval should result in a more pronounced impact of semantic knowledge. Semantic typicality, which refers to the degree to which an element is representative of its associated semantic category (Rosch et al., 1976 ), is a relevant measure in this regard. Semantically typical items are thought to more strongly evoke categorical representations (Collins & Loftus, 1975 ), and location memory for such items has been shown to be influenced by semantic associations to a greater extent (Tompary & Thompson-Schill, 2021 ). Thus, it could be assumed that semantic typicality also modulates the impact of semantic associations on temporal order memory. The current study aimed at investigating the impact of semantic knowledge on memory for the temporal order of event sequences. We conducted two experiments using a temporal order memory task in which participants were presented with alternating encoding and retrieval runs, consisting of encoding sequences of naturalistic visual stimuli and immediate retrieval runs involving forced-choice temporal proximity judgements. In the first experiment, encoding sequences were semantically structured such that images from the same semantic category were clustered in time. This semantic structure was not present in the second experiment where images were presented at random sequence positions. At retrieval, participants were presented with one cue image and two choice images, one of which was semantically related to the cue. Trials in which the semantically related choice was the correct one constituted congruent trials. We predicted that participants would draw on both temporal and semantic associations between encoded items during temporal order memory retrieval, resulting in better performance on congruent retrieval trials. The semantic congruence effect was predicted to be stronger for retrieval trials featuring items which were encoded in backward versus forward encoding direction and for items encoded with greater temporal lag (referred to here as encoding distance), showing the modulatory influence of episodic association strength. Greater semantic typicality of the retrieval cue was expected to be associated with a more pronounced semantic congruence effect, reflecting the modulatory effect of semantic concept activation. Finally, assuming that processing of semantic structure at encoding would increase the likelihood of temporal order memory being influenced by semantic associations, the semantic congruence effect was expected to be more pronounced in the first compared to the second experiment. Nevertheless, based on predictions by the SCM regarding semantic construction at retrieval, we still expected to find the semantic congruence effect in experiment two. Experiment 1 Materials and Methods Participants The required minimum sample size was estimated using G*Power 3.1 (Faul et al., 2009 ) and based on an expected medium effect size for the main effect of the within-subjects factor “congruence” ( d = 0.5, α = 0.05, 1- β = 0.8, two-sided, paired). This resulted in a required sample size of 35 participants. To detect any potential effects of interest in our newly created temporal memory task, we chose to further increase the number of participants beyond this estimated minimum required sample size. Participants were recruited through advertisements on social media networks and at the campus of Ruhr University Bochum. Inclusion criteria comprised: age ranging between 18 and 35 years, no acute neurological and psychiatric illnesses, and normal or corrected-to-normal vision. Dropout due to red-green color vision deficiency ( n = 2) resulted in a final sample size of 54, including 39 women and 15 men aged between 18 and 35 ( M = 22.8, SD = 3.0) years. While six participants completed online sessions (due to the study initially being designed as an online study but switched to lab-based testing when the online experiment hosting service became unavailable), the remaining 48 participants attended lab sessions. The study received approval from the ethics committee of the Faculty of Psychology at Ruhr University Bochum (application number 764), following the guidelines of the Declaration of Helsinki. Participants provided written informed consent and were reimbursed with 10€ or course credits. Stimulus material Stimuli were selected from the THINGS database (Hebart et al., 2019 ) providing images of common object concepts from 27 high-level semantic categories. The complete image pool from which each participant-specific stimulus set was drawn consisted of 8500 images including 588 different living and non-living objects from eleven selected high-level categories: animal, clothing, electronic device, fruit/vegetable (this category was combined from the two separate THINGS categories fruit and vegetable), furniture, musical instrument, office supply, plant, sports equipment, toy, and vehicle. A unique set of images including objects from all eleven high-level categories was drawn from the pool for each participant. Stimulus selection was pseudo-randomized as for each participant, for each block of the temporal memory task, six different categories were drawn and for each of these categories, five different objects were drawn from the complete pool of images. For each of these objects, one image was finally drawn out of the available set. Each participant-specific stimulus set comprised between 321 and 333 ( M = 328) different images corresponding to between 226 and 264 ( M = 246) different objects. Individual color images showed single objects in scenes and were generally presented on a white background screen throughout the experiment. During encoding runs of the temporal memory task, images were displayed at 500 x 500 pixels. During retrieval runs, cue images were also presented at 500 x 500 pixels, while choice images were shown at 300 x 300 pixels. Temporal memory task The temporal memory task was composed of alternating encoding runs during which participants were presented with image sequences and retrieval runs consisting of forced-choice temporal memory retrieval trials (Fig. 1 ). The task was divided into twelve blocks, with each block consisting of one encoding run and one subsequent retrieval run. All participants completed the same experimental task condition. The task was displayed on a computer screen at a resolution of 1920 x 1080 pixels and responses were given using a standard computer mouse. During encoding runs, images were presented individually for two seconds in each trial for a total of 28 trials. After each image presentation, a fixation cross was displayed for 500 milliseconds. For each encoding run, six different categories were randomly drawn from the eleven available categories. From each of these six selected categories, five different objects were randomly chosen and one image was randomly drawn from the available image set for each object, resulting in 30 selected images per encoding run. The image sequences were constructed in a systematic way such that the probability of occurrence of an image from a given category was approximately normally distributed around a “category center” (i.e., category- and run-specific position within the sequence). The images were therefore temporally clustered based on their semantic category, introducing a temporal sequence of individual images and an underlying temporal sequence of image categories for each encoding run. Clusters of images were not clearly separated from each other – images from neighbouring categories could be interspersed. The task version used in experiment 1 is referred to here as “semantic clustering condition”. The first and the last item of each sequence were cut from the encoding set (resulting in 28 instead of 30 images), since otherwise these images would have virtually always belonged to the first and the last categories of the underlying sequence of category clusters, respectively, substantially reducing the variance of semantic categories at these sequence positions. The underlying sequence of categories was never the same for two or more encoding runs within one participant. Importantly, participants were not explicitly made aware of the category-based clustering of images and the consequent sequence of semantic categories underlying each encoding run. Following encoding, immediate memory retrieval occurred after a delay of 20 seconds. During retrieval runs, participants performed 16 trials of forced-choice temporal memory retrieval based on the image sequence from the preceding encoding run. In each trial, participants judged the relative temporal proximity of two choice images to a cue image, with the three images randomly drawn from the preceding encoding sequence. A fixation cross appeared for a duration of 0,5 seconds before the cue image was individually displayed for one second. Two images from the encoding run were then presented immediately afterwards, and participants were prompted to select the one that appeared temporally closer to the cue item in the preceding encoding run. There was no response timeout, the retrieval trial ended as soon as the participant gave a response, after which there was a post-trial gap of one second. Feedback indicating the percentage of correct responses was presented on the screen after each retrieval run. Retrieval trials were defined by the parameters “congruence” (congruent/incongruent), “matching condition” (same/near) and “encoding direction” (forward/backward). If the target (i.e., the correct choice image) belonged to the same category as the cue image or to the category cluster near the category cluster of the cue image, the retrieval trial was defined as “congruent”. In “incongruent” trials, the lure image (i.e., reflecting the incorrect choice) belonged to the same category as the cue image or to the category cluster near the category cluster of the cue image. Moreover, if the target or lure image was from the same category as the cue image, the matching condition of the trial was coded as “same” and if it was from a category whose cluster was near the category cluster of the cue image, it was coded as “near”. In terms of encoding direction, the target and lure images could have appeared either both after (“forward” encoding direction) or both before (“backward” encoding direction) the cue image within the encoding sequence. Analysis of behavioral data Cleaning and preparation of behavioral data from the temporal memory task was performed using custom scripts in Python v3.11 (VanRossum & Drake, 2010 ). There were no missed responses and the complete dataset consisting of 9216 retrieval trials from 48 participants in the lab sessions was included in further analyses. The remaining 1152 trials from six participants who completed online sessions also did not include any missed responses. However, they were initially excluded from the statistical analyses due to concerns about the comparability of the different experimental settings (lab versus online). In a subsequent step, this additional data was incorporated into the analyses to evaluate if this would substantially change the pattern of results. Results from analyses of the complete dataset are reported when no substantial differences were detected. Statistical analyses were performed using R Statistical Software (v4.3.2; R Core Team, 2023 ). In a first step, performance in the temporal memory task was compared against chance-level performance using a two-sided exact binomial test. Next, a random intercept logistic generalized linear mixed-effects model (GLMM) was estimated on a single-trial level to assess the effect of the five independent variables of interest on the binary dependent variable “response” (incorrect/correct) and to consider the nested trial structure in this experiment, where each participant generated 192 retrieval trials. The fixed factors in the model included the binary predictors “congruence” (congruent/incongruent), “matching condition” (same/near) and “encoding direction” (forward/backward) as well as the continuous predictors “encoding distance (cue-lure)” and “cue typicality”. Note that only the encoding distance (that is, number of positions within the encoding sequence) between the cue and lure stimuli of each retrieval trial was entered into the model as this measure strongly correlated with the encoding distance between cue and target stimuli. In addition, as the temporal distance between cue and lure was always larger than that between cue and target, it was assumed to better reflect the relevant episodic association underlying the temporal memory response on a given trial of the task. Continuous predictors were standardized before entering the model. As potential modulatory effects of the different variables on the semantic congruence effect on temporal order memory retrieval were of interest in the present study, the following two-way interactions were included in the model as fixed effects predictors: “congruence x matching condition”, “congruence x encoding direction”, “congruence x encoding distance (cue-lure)” and “congruence x cue typicality”. The random factor “participant” was entered into the model to account for the nested trial structure, and random intercepts were modeled. The GLMM used a logit link function and was fit using maximum likelihood estimation as provided by the “glmer” function of the R package lme4 (Bates et al., 2015 ). Odds ratios ( OR ) and 95% confidence intervals ( CI ) are reported as indicators of statistical significance for each fixed effect predictor. In the case of logistic GLMMs, a confidence interval of OR which does not include the value 1 indicates that a given predictor is statistically significant (Faraway, 2016 ). Results As revealed by the exact binomial test of the ratio of correct trials against a chance level of 0.5, participants performed significantly above chance in the temporal memory task ( P correct response =0.68, p < .001). This was likewise true for the complete dataset including data from the online sessions ( P correct response =0.68, p < .001). The general result pattern of the logistic GLMM analysis did not change after including data from the online sessions, therefore only results from the analysis of the complete dataset will be reported hereafter. The logistic GLMM analysis revealed a significant main effect of congruence on responses in the temporal memory task. Participants were significantly more likely to give a correct response on congruent as compared to incongruent retrieval trials ( OR = 1.44, 95% CI ( OR ) [1.24–1.68], Fig. 2 ). In addition, encoding distance significantly predicted correct responses, with a higher encoding distance being associated with higher probability of correct responses ( OR = 1.27, 95% CI ( OR ) [1.19–1.37], Fig. 2 ). Matching condition was likewise significantly associated with probability of correct responses, with participants being more likely to give a correct response on retrieval trials including same versus near categories ( OR = 1.19, 95% CI ( OR ) [1.05–1.36], Fig. 2 ). Neither encoding direction nor cue typicality were significant individual predictors of correct responses (both 95% CI ( OR ) [1]). In addition, the interaction between congruence and encoding distance was statistically significant ( OR = 1.13, 95% CI ( OR ) [1.02–1.25], Fig. 2 ). Post-hoc comparisons of estimated marginal means indicated that with higher encoding distance, the benefit on congruent versus incongruent trials became larger (at encoding distance = -1: OR = 1.40, 95% CI Tukey ( OR ) [1.23–1.60]; at encoding distance = 1: OR = 1.79, 95% CI Tukey ( OR ) [1.56–2.06]). Moreover, the interaction between congruence and matching condition was statistically significant ( OR = 1.25, 95% CI ( OR ) [1.04–1.51], Fig. 2 ). Post-hoc comparisons of estimated marginal means showed that the effect of congruence significantly differed between levels of matching condition, with a stronger benefit for congruent versus incongruent on same categories ( OR = 1.78, 95% CI Tukey ( OR ) [1.55–2.03]) versus near categories retrieval trials ( OR = 1.42, 95% CI Tukey ( OR ) [1.25–1.61]). Experiment 2 Materials and Methods Participants In experiment 2, participants were recruited in the same manner as in experiment 1, the same inclusion criteria were applied and participants were compensated in the same way. Due to red-green color vision deficiency one participant had to be excluded. The remaining 35 participants (24 women, 10 men, 1 diverse) were between 18 and 35 ( M = 24.9, SD = 4.6) years old. All participants completed the experimental sessions in the lab. Stimulus Material For experiment 2, the stimuli were selected from the same database and in the same manner as in experiment 1. Here, due to the number of blocks in the temporal memory task being reduced to ten, each participant-specific stimulus set comprised 280 different images corresponding to between 214 and 236 ( M = 226) different objects. Temporal memory task In experiment 2, a slightly modified version of the experimental task was used. Participants were asked to complete ten blocks instead of twelve, a modification which was made to adapt and test the experimental task for later use in an fMRI environment. In this version of the temporal memory task (referred to here as “no clustering condition”), images were presented without any clustering based on their semantic category. Instead in any given encoding run, images from all six categories appeared at random sequence positions. As in experiment 1, all participants in this experiment completed the same task condition. During encoding runs, stimuli were presented for two seconds after which a fixation cross was displayed for a randomly chosen duration between 1,5 and 2 seconds (this was again implemented for testing of an fMRI-adapted task version). During retrieval runs, a fixation cross was shown for a randomly chosen duration between 1,5 and 2 seconds before the cue image for a given retrieval trial was individually presented for 2 seconds. This was followed by a fixation cross that was displayed for a randomly chosen duration between 0,5 and 1 second. The two choice images were presented subsequently with a prompt asking the participant to select the one that appeared temporally closer to the cue image in the preceding encoding run. There was a response timeout of 4,5 seconds. The retrieval trial ended as soon as the participant gave a response. The variable “congruence” had three levels in this task version (congruent/incongruent/categories unrelated). In trials labelled as “categories unrelated”, the choice images both belonged to a different category than the cue. Importantly, as there was no underlying sequence of category clusters in encoding runs of experiment 2 due to the omission of category-based clustering, the variable “matching condition” was obsolete here as neighboring categories were not possible. Analysis of behavioral data For experiment 2, the same data cleaning and preparation procedure as in experiment 1 was applied, with the added removal of 48 retrieval trials where participants failed to respond within the time limit. This resulted in a dataset including 5552 retrieval trials from 35 participants. Again, performance in the temporal memory task was first compared against chance-level performance using a two-sided exact binomial test. Moreover, a similar GLMM as in experiment 1 with “response” (incorrect/correct) as dependent variable was estimated, however in this case, the predictor “congruence” had three levels (incongruent/congruent/categories unrelated). The last level applied to retrieval trials in the no clustering condition in which all three stimuli (cue, target, lure) belonged to different categories. The three levels were represented by two binary dummy variables in the model. The remaining fixed effects predictors that were also included in the GLMM analysis for experiment 1 (“encoding direction”, “encoding distance” and “cue typicality”) were all entered into the model, as well as the two-way interactions between each of these predictors and the predictor “congruence”. Again, the random factor “participant” was included to account for the nested trial structure, modelling random intercepts. Results As in experiment 1, participants performed significantly above chance level in the temporal memory task ( P correct response =0.66, p < .001). The logistic GLMM analysis revealed a single significant predictor of task performance in experiment 2: Encoding distance was positively associated with the probability of correct response ( OR = 1.28, 95% CI ( OR ) [1.14–1.43], Fig. 3 ). No further significant main effects or interactions were found (all 95% CI ( OR ) [1]). Joint analysis of temporal memory task data from experiments 1 and 2 In order to directly compare temporal order memory effects between experiments 1 (semantic clustering condition) and experiment 2 (no clustering condition), data from both experiments were analyzed using a single model. To this end, retrieval trials with the congruence level “categories unrelated” were excluded from the dataset of experiment 2, since this type of retrieval trial did not exist in experiment 1. The resulting 13139 retrieval trials from 89 participants were included in the statistical analysis. Fixed effects predictors in the logistic GLMM included “encoding condition” (semantic clustering condition/no clustering condition), “congruence” (incongruent/congruent), “encoding direction”, “encoding distance” and “cue typicality”. Three-way interactions between “encoding condition”, “congruence” and each of the remaining fixed effects predictors were entered into the model as well as the random factor “participant” modeling random intercepts. Results The GLMM which was fit to the complete dataset showed a significant main effect of encoding distance, with higher encoding distance being associated with higher probability of correct response ( OR = 1.28, 95% CI ( OR ) [1.14–1.43], Fig. 4 ). Moreover, the two-way interaction between congruence and encoding condition was statistically significant ( OR = 1.61, 95% CI ( OR ) [1.24–2.09], Fig. 4 ). As per post-hoc comparisons of estimated marginal means, participants were significantly more likely to give a correct response on congruent as compared to incongruent trials in the semantic clustering condition (that is, in retrieval trials of experiment 1; OR = 1.63, 95% CI Tukey ( OR ) [1.49–1.78]; cf. Results of experiment 1), while this contrast was not statistically significant in the no clustering condition (that is, in retrieval trials of experiment 2; OR = 1.01, 95% CI Tukey ( OR ) [0.86–1.19] cf. Results of experiment 2). In addition, participants who completed the semantic clustering condition of the task (experiment 1) showed significantly better performance than participants who completed the no clustering condition (experiment 2) on congruent trials ( OR = 1.37, 95% CI Tukey ( OR ) [1.11–1.70]). The contrast between semantic clustering condition and no clustering condition was not significant for incongruent trials ( OR = 0.85, 95% CI Tukey ( OR ) [0.69–1.05]). Discussion The primary goal of this study was to investigate the influence of semantic knowledge on memory for the temporal order of event sequences. We found that after encoding sequences in which items were clustered based on their semantic categories, there was a benefit of congruent retrieval trials (where cue and target items were semantically related) over incongruent retrieval trials (where cue and lure items were semantically related). This effect was found for trials in which the semantically related items belonged to the same semantic category as well as for trials in which they belonged to two different categories which had neighboring category clusters within the encoding sequence (experiment 1). Importantly, while we expected the semantic congruence effect to be less pronounced in experiment 2 where encoding sequences were not semantically structured by category clustering, we did not find any evidence for the effect in this encoding condition. Comparing datasets from both experiments in a single analysis confirmed these results. In addition, encoding distance between cue and lure items was a positive predictor of correct responses across experiments. Our findings suggest that only when presented with semantically structured image sequences, participants applied this context information to temporal order memory retrieval. Thus, the presence of an implicit semantic structure within the encoding set may have biased participants towards using encoded semantic context information in reconstructing temporal sequences. This is likely to have led to the observed memory benefit on trials where temporal and semantic associations between items aligned (congruent retrieval trials) over trials where temporal and semantic associations did not align (incongruent retrieval trials) in the semantic clustering condition. The current findings are in line with a wide range of previous studies showing that prior knowledge and semantic associations between encoded items influence episodic memory in various tasks, including free recall of word lists (Aka et al., 2021 ), recognition memory (Montefinese et al., 2015 ), spatial (Lu et al., 2024 ; Tompary & Thompson-Schill, 2021 ) and temporal memory tasks (Ishiguro & Saito, 2021 ). Importantly, a common feature of most of these experiments and this study is that participants were never explicitly made aware of the semantic structure underlying the encoded material. Thus, any effects of semantic relatedness of the learned and subsequently retrieved items on memory retrieval would have relied on the activation of pre-existing semantic category knowledge. This general notion is supported by influential models of memory search such as the Context Maintenance and Retrieval (CMR) model, which understands semantic organization of memory recall as a consequence of the activation of pre-established semantic associations feeding into an active context representation that guides retrieval (Polyn et al., 2009 ). While this model was originally introduced as a formal account of memory search in free recall tasks, it appears plausible given previous and our current findings of semantic relatedness effects in episodic memory tasks that similar mechanisms may be active under a wider range of conditions. The observed benefit of congruent over incongruent retrieval trials in our study in experiment 1 extended to near category trials where the semantically related option was an item from a category cluster neighboring the category cluster of the cue within the encoding sequence. This indicates that the observed semantic congruence effect cannot solely be attributed to semantic cueing at retrieval, but that participants used the encoded semantic context information to guide retrieval decisions. Importantly, the semantic congruence effect was not observed in experiment 2 using the no clustering task condition, which further supports the interpretation that encoding semantic context information drives this effect. This is in line with findings from a previous working memory study reporting that semantic relatedness between items systematically influenced serial order errors only when items had been encoded in semantically clustered sets (Kowialiewski et al., 2021 ). Furthermore, the semantic relatedness benefit for the recall of items from working memory is most likely driven by the activation of semantic representations at encoding (Kowialiewski et al., 2022 ). Similarly, differential effects of semantically structured versus unstructured sets of to-be-encoded items have been demonstrated for delayed free recall of word lists (Aka et al., 2021 ). Encoding series of semantically related items versus unrelated items can result in increased generalization but less detailed recognition memory for the learned material (Melega & Sheldon, 2023 ). While these findings highlight the key role of semantically structured encoding sets, the type of memory which was tested does not directly correspond to the present study. However, the underlying mechanisms may extend to memory for the temporal order of events. Interestingly, imagining a spatial context before encoding an event sequence was related to more accurate recency discrimination than encoding without context imagination (Sheldon, 2021 ). Additionally, the spatial encoding context was associated with better performance on difficult recency discrimination trials compared to an abstract conceptual encoding context (Sheldon, 2021 ). These findings reflect a superior interaction between spatial encoding context and temporal order information, enabling accurate reconstruction of temporal context even on difficult trials. Thus, different types of context information might impact the encoding of temporal order information. An important distinction between this and the present study is that in the temporal memory task used here, semantic context information was not merely presented as a cue preceding the encoding sequence but was closely intertwined with the temporal structure of the encoding sequences. That is, items were not simply associated with a common abstract concept, but semantically related items were clustered in time such that encoded semantic context information could potentially be used to reconstruct the temporal sequence structure. Interestingly, encoding of semantically structured image sequences was associated with benefits, rather than costs, for temporal order memory relative to encoding of unstructured sequences. If temporal proximity judgements at retrieval had been primarily driven by semantic relatedness between the encoded items, one would have expected a substantial cost on incongruent retrieval trials where the semantically related option was the incorrect one. While a facilitative effect of semantic processing on temporal order memory has been found before, results from a recent meta-regression analysis suggest that semantically structured encoding sets are related to increased errors in temporal order memory, especially in tasks that do not require retrieval of individual items, thus mitigating the effect of semantic associations as retrieval cues (Ishiguro & Saito, 2021 ). In the current study, while performance was worse on incongruent retrieval trials compared to congruent ones in the semantic clustering condition, we did not observe a significant difference between performance on incongruent trials in both conditions. This might indicate that participants in fact generated memory representations of temporal context for each sequence which allowed for partly accurate temporal proximity judgements even on incongruent trials. Arguably, participants who completed the semantic clustering encoding condition additionally generated representations of semantic context, which served to increase the probability of a correct response when semantic and temporal context representation indicated the same response (congruent trials) but did not effectively override the temporal context representation on trials where semantic and temporal context did not indicate the same response (incongruent trials). This is in line with recent evidence for benefits of semantic relatedness for temporal order memory when encoding sets featured clustering of semantically related items (Kowialiewski et al., 2024 ). Interestingly, a negative effect of semantic relatedness between encoded items on location memory performance occurred in a task design in which semantic relatedness was established by presenting items which were all associated with a single category (Lu et al., 2024 ). In contrast to the present study however, semantic context did not provide a structure which could have been used to reconstruct category-specific locations but instead provided a grouping mechanism for all items. This suggests that encoding of semantic context information is associated with benefits for the reconstruction of episodic information specifically when semantic context provides a structure that is intertwined and correlates with the spatial or temporal event structure. The SCM (Cheng et al., 2016 ) does not make any explicit assumptions about the role of semantic processes active during encoding of events and primarily emphasizes retrieval mechanisms as the driving factor of semantic effects on episodic reconstruction. Our present findings may extend the SCM by showing that encoding of semantically structured material, potentially through the activation of consolidated semantic associations and the consequent encoding of semantic context, can benefit the reconstruction of temporal-episodic context. Part of the predictions of the current study was that the semantic congruence effect on temporal order memory would be more pronounced for retrieval trials involving items that were encoded with a higher temporal lag (encoding distance) between each other and for retrieval trials where both choice items were encoded before the cue image (backward encoding direction) versus after the cue image (forward encoding direction). We found that encoding distance between cue and lure was a significant positive predictor of correct response across encoding conditions. In experiment 1, this effect was significantly stronger for congruent versus incongruent trials. Thus, against our assumptions, there was a benefit of higher encoding distance between items for temporal order memory. While we predicted that higher encoding distance would be associated with reduced strength of episodic associations (Healey et al., 2019 ) and consequently with a stronger influence of semantic knowledge on temporal order memory, a different mechanism may underlie our observations. Higher encoding distance was shown to be associated with more accurate temporal recency judgements which was attributed to effects of item strength within the acquired memory representation (Sheldon, 2021 ). While the temporal memory task in the present study did not require participants to make recency judgements, a similar mechanism may have yielded the observed result, as relative item strength of the cue, target and/or lure items within the acquired memory representation may have been good indicators of the encoded temporal sequence. In addition, a higher encoding distance between the cue and lure items would have been linked to a higher encoding distance between target and lure in some cases, which may have decreased the interference between these items in reconstructing the temporal context. Moreover, we did not find any significant effect of forward versus backward encoding direction on temporal order memory performance in our task. Applying a similar logic as described above for temporal encoding distance between items, we would have predicted reduced performance specifically on incongruent-backward trials versus incongruent-forward trials. In contrast to that, forward and backward semantic associative links have been shown to be similarly beneficial to immediate serial recall of word lists (Saint-Aubin et al., 2014 ), while another recent study did not report any significant differences in overall accuracy between forward and backward serial recall (Dougherty et al., 2023 ). Encoding direction effects might emerge less reliably than previously assumed, arguably less so in a task where cue items can be used to initiate temporal context reconstruction. The fact that we did not observe the expected interaction effects between semantic congruence and factors linked to episodic association strength may suggest that semantic congruence effects were primarily driven by the encoding of semantic context rather than completion of weak episodic gist representations at retrieval. However, further experiments are needed to clearly establish the relationship between the effects of semantic knowledge and variables linked to the strength of episodic associations in temporal order memory. Furthermore, against our predictions, semantic typicality of items presented at retrieval did not modulate the effect of semantic congruence on temporal order memory. Previous studies reported that effects of semantic knowledge on reconstructing spatial-episodic associations were specifically pronounced for items that had higher typicality for their semantic category (Tompary & Thompson-Schill, 2021 ; Tompary et al., 2023 ). While this suggests that more typical instances of semantic categories evoke more pronounced semantic processing and consequently result in a stronger impact of semantic knowledge on episodic reconstruction when presented as retrieval cues, this mechanism may not have been critical in generating the semantic congruence effect in the present study. Importantly, the task used here featured retrieval trials presenting a set of three images on each iteration. Semantic typicality of individual items is likely to be an important factor modulating semantic processing on retrieval tasks involving responses to single items as was the case in the aforementioned studies. However, its impact may be reduced in situations where participants process a set of items on a given trial due to concurrent activated category representations. In addition, as discussed above, the semantic congruence effect in the current study may have been less a consequence of retrieval-specific effects (such as the spontaneous activation of semantic category representations by highly typical items) than of semantic processing at encoding and consequent expression of encoded semantic context during retrieval. Some limitations apply to the current study. Data collection was split into two separate data collection phases which may limit the validity of direct comparisons between the datasets and analysis of all data using a single model, respectively. In addition, part of the data in experiment 1 was obtained in an online setting while most of the data was collected in testing sessions at the lab. However, analyses that either excluded or included the data obtained through online sessions yielded comparable results. Lastly, the experimental task used in the current study probed memory for the temporal order of events, but the reconstructive character of the memory retrieval process in this task is arguably limited, as recall of temporal order is focused on a small, predefined subset of items on every trial. While this limits the generalizability of our findings towards generative episodic retrieval processes more generally, this paradigm allows for a highly controlled examination of the effects of semantic knowledge on temporal order memory, potentially on the level of single items and trials. This property may be beneficial for future neuroimaging studies investigating the neural mechanisms underlying the effects of semantic knowledge on temporal episodic memory. Conclusion and Future Outlook This study highlights the key role of semantic knowledge in shaping memory for the temporal order of events. Specifically, the presentation of semantically structured image sequences at encoding was linked to a semantic congruence effect on temporal order memory retrieval. Participants who encoded semantically clustered image sequences showed higher retrieval accuracy on congruent trials where the item that had been presented temporally closer to a cue was also semantically related to the cue. Crucially, the effect was limited to the encoding condition in which image sequences were clustered according to semantic categories and was not observed with image sequences in which images were presented at random positions. Additionally, temporal distance between encoded items predicted temporal order memory accuracy across conditions. Collectively, our findings suggest that encoding of semantic context information, via the activation of semantic knowledge by semantically structured encoding sets, benefits temporal order memory when semantic and temporal structure of an event sequence were originally intertwined. These results extend prior research on the effects of semantic knowledge on various memory tasks, specifically by demonstrating the dependence of the semantic congruence effect on the encoding of semantically structured material and by extending this research to memory for the temporal order of naturalistic visual stimuli. Our findings are largely in line with predictions made by influential theoretical frameworks such as the CMR and the SCM. In addition, we provide evidence suggesting that the SCM should specifically incorporate semantic encoding processes as key mechanism explaining how semantic knowledge can influence generative episodic memory. Future research should explore the generalizability of the current findings across different populations and stimulus types, including more complex and naturalistic visual and spatiotemporal materials. Finally, given the potential for semantic knowledge to influence memory retrieval, understanding how different encoding strategies can affect the balance between semantic and episodic memory expression may inform interventions for disorders of memory function and applications in educational settings. Declarations Data availability statement Raw data and analysis scripts for experiment 1, experiment 2 and the joint analysis of both datasets can be retrieved from the Open Science Framework project repository using the following link: https://osf.io/26avd/. Funding and Acknowledgments Funding for this work was supported by the Deutsche Forschungsgemeinschaft (DFG; German Research Foundation) within the FOR 2812: “Constructing scenarios of the past”, grant no. 419039274). The DFG had no further role in study design, collection, analysis and interpretation of data, in the writing of the manuscript and in the decision to submit the paper for publication. We thank Raphael Merz and Marwa Al-Kablawi for help in participant recruitment and data collection. Statements and Declarations None of the authors declare any financial or non-financial interests that would be directly or indirectly related to the submitted work. The submitted work has been approved by the ethics committee of the Faculty of Psychology at Ruhr University Bochum (application number 764), in accordance with the guidelines for research involving human participants of the Declaration of Helsinki. All participants gave their informed consent prior to their participation in the study. Author contributions : Conceptualization: Carina Zoellner, Nora A. Herweg, Oliver T. Wolf Data curation: Carina Zoellner, Henry D. Soldan Formal analysis: Henry D. Soldan Funding acquisition: Nora A. Herweg, Oliver T. Wolf Investigation: Carina Zoellner, Nurten Genc Methodology: Carina Zoellner, Nora A. Herweg Project administration: Carina Zoellner, Henry D. Soldan Resources: Nora A. Herweg, Oliver T. Wolf, Christian J. Merz Software: Nora A. Herweg, Carina Zoellner, Henry D. Soldan Supervision: Christian J. Merz, Oliver T. Wolf Validation: Carina Zoellner, Christian J. Merz, Henry D. Soldan Visualization: Carina Zoellner, Henry D. Soldan Writing – original draft: Henry D. Soldan, Nurten Genc Writing – review & editing: Christian J. Merz, Carina Zoellner, Henry D. Soldan, Oliver T. Wolf References Addis, D. R. (2018). Are episodic memories special? On the sameness of remembered and imagined event simulation. Journal of the Royal Society of New Zealand , 48 (2–3), 64–88. https://doi.org/10.1080/03036758.2018.1439071 Aka, A., Phan, T. D., & Kahana, M. J. (2021). Predicting recall of words and lists. Journal of Experimental Psychology Learning Memory and Cognition , 47 (5), 765–784. https://doi.org/10.1037/xlm0000964 Bartlett, F. C., & Remembering (1932). Cambridge: Cambridge University Press. Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software , 67 (1). https://doi.org/10.18637/jss.v067.i01 Bein, O., Livneh, N., Reggev, N., Gilead, M., Goshen-Gottstein, Y., & Maril, A. (2015). Delineating the effect of semantic congruency on episodic memory: The role of integration and relatedness. PloS One , 10 (2), e0115624. https://doi.org/10.1371/journal.pone.0115624 Chan, S. C. Y., Applegate, M. C., Morton, N. W., Polyn, S. M., & Norman, K. A. (2017). Lingering representations of stimuli influence recall organization. Neuropsychologia , 97 , 72–82. https://doi.org/10.1016/j.neuropsychologia.2017.01.029 Cheng, S., Werning, M., & Suddendorf, T. (2016). Dissociating memory traces and scenario construction in mental time travel. Neuroscience and Biobehavioral Reviews , 60 , 82–89. https://doi.org/10.1016/j.neubiorev.2015.11.011 Collins, A. M., & Loftus, E. F. (1975). A spreading-activation theory of semantic processing. Psychological Review , 82 (6), 407–428. https://doi.org/10.1037/0033-295X.82.6.407 Dougherty, M. R., Halpern, D., & Kahana, M. J. (2023). Forward and backward recall dynamics. Journal of Experimental Psychology Learning Memory and Cognition , 49 (11), 1752–1772. https://doi.org/10.1037/xlm0001254 Ekstrom, A. D., & Ranganath, C. (2018). Space, time, and episodic memory: The hippocampus is all over the cognitive map. Hippocampus , 28 (9), 680–687. https://doi.org/10.1002/hipo.22750 Faraway, J. J. (2016). Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models, Second Edition (Second edition). Chapman & Hall/CRC Texts in Statistical Science . Taylor and Francis, an imprint of Chapman and Hall/CRC. https://doi.org/10.1201/9781315382722 Faul, F., Erdfelder, E., Buchner, A., & Lang, A. G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods , 41 (4), 1149–1160. https://doi.org/10.3758/BRM.41.4.1149 Healey, M. K. (2018). Temporal contiguity in incidentally encoded memories. Journal of Memory and Language , 102 , 28–40. https://doi.org/10.1016/j.jml.2018.04.003 Healey, M. K., Long, N. M., & Kahana, M. J. (2019). Contiguity in episodic memory. Psychonomic Bulletin & Review , 26 (3), 699–720. https://doi.org/10.3758/s13423-018-1537-3 Hebart, M. N., Dickter, A. H., Kidder, A., Kwok, W. Y., Corriveau, A., van Wicklin, C., & Baker, C. I. (2019). Things: A database of 1,854 object concepts and more than 26,000 naturalistic object images. PloS One , 14 (10), e0223792. https://doi.org/10.1371/journal.pone.0223792 Howard, M. W., & Kahana, M. J. (2002). When Does Semantic Similarity Help Episodic Retrieval? Journal of Memory and Language , 46 (1), 85–98. https://doi.org/10.1006/jmla.2001.2798 Ishiguro, S., & Saito, S. (2021). The detrimental effect of semantic similarity in short-term memory tasks: A meta-regression approach. Psychonomic Bulletin & Review , 28 (2), 384–408. https://doi.org/10.3758/s13423-020-01815-7 Kowialiewski, B., Gorin, S., & Majerus, S. (2021). Semantic knowledge constrains the processing of serial order information in working memory. Journal of Experimental Psychology Learning Memory and Cognition , 47 (12), 1958–1970. https://doi.org/10.1037/xlm0001031 Kowialiewski, B., Krasnoff, J., Mizrak, E., & Oberauer, K. (2022). The semantic relatedness effect in serial recall: Deconfounding encoding and recall order. Journal of Memory and Language , 127 , 104377. https://doi.org/10.1016/j.jml.2022.104377 Kowialiewski, B., Majerus, S., & Oberauer, K. (2024). Does semantic similarity affect immediate memory for order? Usually not, but sometimes it does. Journal of Experimental Psychology Learning Memory and Cognition , 50 (1), 68–88. https://doi.org/10.1037/xlm0001279 Lu, X., Zhu, M. J. H., & Risko, E. F. (2024). Semantic relatedness can impair memory for item locations. Psychological Research Psychologische Forschung , 88 (3), 861–879. https://doi.org/10.1007/s00426-023-01889-7 Melega, G., & Sheldon, S. (2023). Conceptual relatedness promotes memory generalization at the cost of detailed recollection. Scientific Reports , 13 (1), 15575. https://doi.org/10.1038/s41598-023-40803-4 Montefinese, M., Zannino, G. D., & Ambrosini, E. (2015). Semantic similarity between old and new items produces false alarms in recognition memory. Psychological Research Psychologische Forschung , 79 (5), 785–794. https://doi.org/10.1007/s00426-014-0615-z Morton, N. W., & Polyn, S. M. (2016). A predictive framework for evaluating models of semantic organization in free recall. Journal of Memory and Language , 86 , 119–140. https://doi.org/10.1016/j.jml.2015.10.002 Morton, N. W., & Polyn, S. M. (2017). Beta-band activity represents the recent past during episodic encoding. Neuroimage , 147 , 692–702. https://doi.org/10.1016/j.neuroimage.2016.12.049 Packard, P. A., Rodríguez-Fornells, A., Bunzeck, N., Nicolás, B., de Diego-Balaguer, R., & Fuentemilla, L. (2017). Semantic Congruence Accelerates the Onset of the Neural Signals of Successful Memory Encoding. Journal of Neuroscience , 37 (2), 291–301. https://doi.org/10.1523/JNEUROSCI.1622-16.2016 Polyn, S. M., Erlikhman, G., & Kahana, M. J. (2011). Semantic cuing and the scale insensitivity of recency and contiguity. Journal of Experimental Psychology Learning Memory and Cognition , 37 (3), 766–775. https://doi.org/10.1037/a0022475 Polyn, S. M., Norman, K. A., & Kahana, M. J. (2009). A context maintenance and retrieval model of organizational processes in free recall. Psychological Review , 116 (1), 129–156. https://doi.org/10.1037/a0014420 R Core Team (2023). R: A Language and Environment for Statistical Computing . https://www.R-project.org/ Ramey, M. M., Henderson, J. M., & Yonelinas, A. P. (2022). Episodic memory processes modulate how schema knowledge is used in spatial memory decisions. Cognition , 225 , 105111. https://doi.org/10.1016/j.cognition.2022.105111 Renoult, L., Irish, M., Moscovitch, M., & Rugg, M. D. (2019). From Knowing to Remembering: The Semantic-Episodic Distinction. Trends in Cognitive Sciences , 23 (12), 1041–1057. https://doi.org/10.1016/j.tics.2019.09.008 Roediger, H. L., & McDermott, K. B. (1995). Creating false memories: Remembering words not presented in lists. Journal of Experimental Psychology: Learning Memory and Cognition , 21 (4), 803–814. https://doi.org/10.1037//0278-7393.21.4.803 Rosch, E., Simpson, C., & Miller, R. S. (1976). Structural bases of typicality effects. Journal of Experimental Psychology: Human Perception and Performance , 2 (4), 491–502. https://doi.org/10.1037/0096-1523.2.4.491 Saint-Aubin, J., Guérard, K., Chamberland, C., & Malenfant, A. (2014). Delineating the contribution of long-term associations to immediate recall. Memory (Hove England) , 22 (4), 360–373. https://doi.org/10.1080/09658211.2013.794242 Schacter, D. L. (2012). Adaptive constructive processes and the future of memory. The American Psychologist , 67 (8), 603–613. https://doi.org/10.1037/a0029869 Sheldon, S. (2021). The impact of encoding scenarios on different forms of temporal order memory. Psychological Research Psychologische Forschung , 85 (7), 2553–2565. https://doi.org/10.1007/s00426-020-01440-y Tompary, A., & Thompson-Schill, S. L. (2021). Semantic influences on episodic memory distortions. Journal of Experimental Psychology General , 150 (9), 1800–1824. https://doi.org/10.1037/xge0001017 Tompary, A., Xia, A., Coslett, B. H., & Thompson-Schill, S. L. (2023). Disruption of Anterior Temporal Lobe Reduces Distortions in Memory From Category Knowledge. Journal of Cognitive Neuroscience , 35 (12), 1899–1918. https://doi.org/10.1162/jocn_a_02053 Tulving, E. (2002). Episodic memory: From mind to brain. Annual Review of Psychology , 53 (Volume 53, 2002), 1–25. https://doi.org/10.1146/annurev.psych.53.100901.135114 VanRossum, G., & Drake, F. L. (2010). The Python language reference (Release 3.0.1 [repr]. Python documentation manual: / Guido van Rossum; Fred L. Drake [ed.]; Pt. 2 . Python Software Foundation; SoHo Books. Zöllner, C., Klein, N., Cheng, S., Schubotz, R. I., Axmacher, N., & Wolf, O. T. (2022). Where was the toaster? A systematic investigation of semantic construction in a new virtual episodic memory paradigm. Quarterly Journal of Experimental Psychology (2006) , 17470218221116610. https://doi.org/10.1177/17470218221116610 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 13 Jan, 2026 Read the published version in Psychological Research → Version 1 posted Editorial decision: Revision requested 01 Jul, 2025 Reviews received at journal 26 Jun, 2025 Reviews received at journal 28 May, 2025 Reviewers agreed at journal 23 May, 2025 Reviewers agreed at journal 19 May, 2025 Reviewers agreed at journal 19 May, 2025 Reviewers invited by journal 13 May, 2025 Editor assigned by journal 13 May, 2025 Submission checks completed at journal 12 May, 2025 First submitted to journal 05 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6595825","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":456668134,"identity":"88a3f1ff-00d1-4265-922f-045cc17f0b80","order_by":0,"name":"Henry David Soldan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYBACAwhVx8DHkMDA8IEBRCYQpeUwAxtQJeMMErQcAGth5iFGizl777HPPAwH5NjYk589tt1zOI+BPfkAXi2WPeeSZ/Mw1Bmz8TwzN855driYgecZfmsMbuQYA93DnNgmkWAmnXPgcGKDRI4Bfi3338C0pH+TtgBryf9AwBYemJYcM2kGiC14dTAYnMkxZpxjcBjolzdlkj0H0ouBniLgsONnjBneVNTJ8bOnb5P4ccA6j589+QF+a4CAiQfZWDaC6oGA8QcxqkbBKBgFo2DkAgD4V0FOPYg6bgAAAABJRU5ErkJggg==","orcid":"","institution":"Ruhr University Bochum","correspondingAuthor":true,"prefix":"","firstName":"Henry","middleName":"David","lastName":"Soldan","suffix":""},{"id":456668136,"identity":"47706dff-8fbb-42ef-b75d-41136c6235c1","order_by":1,"name":"Carina Zoellner","email":"","orcid":"","institution":"Ruhr University Bochum","correspondingAuthor":false,"prefix":"","firstName":"Carina","middleName":"","lastName":"Zoellner","suffix":""},{"id":456668137,"identity":"0732b1bd-3021-4661-a75d-ded4115c3930","order_by":2,"name":"Nora Alicia Herweg","email":"","orcid":"","institution":"Ruhr University Bochum","correspondingAuthor":false,"prefix":"","firstName":"Nora","middleName":"Alicia","lastName":"Herweg","suffix":""},{"id":456668139,"identity":"3f12e790-6a9c-4f4a-9c55-fc4ab7fcabbb","order_by":3,"name":"Nurten Genc","email":"","orcid":"","institution":"Ruhr University Bochum","correspondingAuthor":false,"prefix":"","firstName":"Nurten","middleName":"","lastName":"Genc","suffix":""},{"id":456668141,"identity":"c38d6e04-8f47-47d1-bc12-ddba844a94e7","order_by":4,"name":"Oliver Tobias Wolf","email":"","orcid":"","institution":"Ruhr University Bochum","correspondingAuthor":false,"prefix":"","firstName":"Oliver","middleName":"Tobias","lastName":"Wolf","suffix":""},{"id":456668142,"identity":"b7b8297f-19d6-4958-adc4-66438cd3ba32","order_by":5,"name":"Christian Josef Merz","email":"","orcid":"","institution":"Ruhr University Bochum","correspondingAuthor":false,"prefix":"","firstName":"Christian","middleName":"Josef","lastName":"Merz","suffix":""}],"badges":[],"createdAt":"2025-05-05 15:38:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6595825/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6595825/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00426-025-02222-0","type":"published","date":"2026-01-13T16:29:36+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83018998,"identity":"6dfdaeab-ab71-4625-8404-82dee9ae40b9","added_by":"auto","created_at":"2025-05-19 07:05:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":738388,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eProcedure and experimental task design\u003c/em\u003e\u003cstrong\u003e a \u003c/strong\u003eThe experimental task consisted of \u003cem\u003en\u003c/em\u003e blocks (\u003cem\u003en\u003c/em\u003e=12 in experiment 1 and \u003cem\u003en\u003c/em\u003e=10 in experiment 2), each including an encoding run and a retrieval run. \u003cstrong\u003eb\u003c/strong\u003e Each encoding run consisted of a sequence of 28 images, belonging to six distinct semantic categories. The images were either temporally clustered around category centers within the sequence (experiment 1, depicted in upper section of\u003cstrong\u003e c\u003c/strong\u003e), or presented in random order (experiment 2, depicted in lower section of \u003cstrong\u003ec\u003c/strong\u003e). Each retrieval run included 16 trials. In each trial, participants were first presented with a cue image. Subsequently, participants were presented with the cue image and two choice images. Participants were asked to indicate which of the two choice images (target = correct choice, lure = incorrect choice) was temporally closer to the cue image during encoding. \u003cstrong\u003ec \u003c/strong\u003eDuring encoding in experiment 1 (semantic clustering condition), the positions of images within the sequences were not random, but clustered around category centers. That is, the probability of an image from a given category to appear was highest close to its associated category center. In contrast, in experiment 2 (no clustering condition), all images were presented in random order. \u003cstrong\u003ed \u003c/strong\u003eDuring retrieval, cue and choice images were chosen based on equally distributed conditions: Trials were either congruent or incongruent; that is, either the target or the lure were semantically related to the cue image. Trials featured items either encoded in forward or in backward direction; that is, the choice images were either presented after or prior to the cue image at encoding. Finally, trials in experiment 1 involved either same or near matching condition; that is, either target or lure belonged to the same category as the cue, or target or lure belonged to a category cluster near the category cluster of the cue. \u003cstrong\u003ee\u003c/strong\u003e Our hypotheses predicted a higher correct response rate for congruent compared to incongruent, for forward compared to backward and for same matching condition compared to near matching condition trials. Furthermore, we predicted a higher encoding distance between cue and lure to be associated with a lower correct response rate and a higher typicality of the cue to be associated with a lower correct response rate. We predicted the previous effects to be stronger for incongruent trials. In analyzing data from experiment 2 (no clustering condition) and comparing effects between experimental encoding conditions, we were interested in whether these predictions could be confirmed nonetheless, in which case semantic congruence effects could be attributed to retrieval-specific processes rather than encoding of semantic structure. The figure was created in https://BioRender.com\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6595825/v1/adfce5f7c472089a17e970fc.png"},{"id":83018682,"identity":"6c8bfad4-de1c-43af-a9cb-4301e6715c7c","added_by":"auto","created_at":"2025-05-19 06:57:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":539506,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eTemporal order memory performance in experiment 1\u003c/em\u003e\u003cstrong\u003e a\u003c/strong\u003e In experiment 1, the probability of an image to appear at a given position within the sequence of images was determined by the proximity to the category center position of the respective semantic category. \u003cstrong\u003eb\u003c/strong\u003e Encoding distance was defined as the standardized temporal distance between cue and lure (the incorrect option) during encoding, that is, the number of positions between both images within the sequence of images. \u003cstrong\u003ec\u003c/strong\u003e Encoding distance between cue and lure significantly predicted the probability of correct responses, with a higher distance being associated with a higher probability. Furthermore, post-hoc analyses revealed that this effect was significantly stronger for congruent compared to incongruent trials. Plotted lines show model estimates and shaded areas depict 95% confidence intervals for estimated trends. \u003cstrong\u003ed \u003c/strong\u003eCongruence was determined by semantic associations between the cue and choice images, whereby on congruent trials, cue and target (the correct option) were semantically related (i.e., they belonged to the same category or to neighboring category clusters with respect to the encoding sequence) and on incongruent trials, cue and lure (the incorrect option) were semantically related. Matching condition was defined by the type of semantic relatedness between cue and target or cue and lure, whereby on same condition trials, the semantically related items belonged to the same category and on near condition trials, the semantically related items belonged to category clusters which had neighboring center positions within the encoding sequence. \u003cstrong\u003ee \u003c/strong\u003eThe probability of correct responses was significantly predicted by congruence and matching condition, and by an interaction between both predictors. Specifically, congruent trials showed a higher probability of correct responses compared to incongruent trials and same condition trials showed a higher probability of correct responses compared to near condition trials. The latter effect was stronger for congruent compared to incongruent trials. Colored bars show model estimates, error bars depict standard errors of estimated means and data points show mean hit rates for individual subjects. *** \u0026lt; 0.001; ** \u0026lt; 0.01. The figure was created in https://BioRender.com\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6595825/v1/05756ec0458f3b0b386a40ab.png"},{"id":83017823,"identity":"9ab4dfcf-b172-4d07-b0ad-33c2343a894d","added_by":"auto","created_at":"2025-05-19 06:49:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":468008,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eTemporal order memory performance in experiment 2\u003c/em\u003e \u003cstrong\u003ea\u003c/strong\u003e In experiment 2, the probability of an image to appear at a given position within the sequence of images was random. \u003cstrong\u003eb\u003c/strong\u003e Encoding distance was defined as the standardized temporal distance between cue and lure (the incorrect option) during encoding, that is, the number of positions between both images within the sequence of images. \u003cstrong\u003ec\u003c/strong\u003e Encoding distance between cue and lure significantly predicted the probability of correct responses, with a higher distance being associated with a higher probability. For reasons of comparability with experiment 1, results are plotted separately for congruent, incongruent and categories unrelated trials. Lines show model estimates and shaded areas depict 95% confidence intervals for estimated trends. \u003cstrong\u003ed \u003c/strong\u003eCongruence was determined by semantic associations between the cue and choice images, whereby on congruent trials, cue and target (the correct option) were semantically related (i.e., they belonged to the same category) and on incongruent trials, cue and lure (the incorrect option) were semantically related. On categories unrelated trials, neither target nor lure was from the same category as the cue. \u003cstrong\u003ee \u003c/strong\u003eThe probability of correct responses was not significantly different among the three levels of congruence. Colored bars show model estimates, error bars depict standard errors of estimated means and data points show mean hit rates for individual subjects. *** \u0026lt; 0.001; n.s. = not significant. The figure was created in https://BioRender.com\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6595825/v1/ecd84e208d0917aaa2561d95.png"},{"id":83018684,"identity":"ed6f0752-62dd-447c-b277-65984b671609","added_by":"auto","created_at":"2025-05-19 06:57:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":545012,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eTemporal order memory task performance across experiments 1 and 2\u003c/em\u003e\u003cstrong\u003e \u003c/strong\u003eThe predicted probability of correct responses for encoding distance between cue and lure (\u003cstrong\u003ea \u003c/strong\u003eand\u003cstrong\u003e b\u003c/strong\u003e) and congruence (\u003cstrong\u003ec\u003c/strong\u003eand \u003cstrong\u003ed\u003c/strong\u003e) was estimated for the semantic clustering condition (\u003cstrong\u003ea \u003c/strong\u003eand \u003cstrong\u003ec\u003c/strong\u003e) and the no clustering condition (\u003cstrong\u003eb \u003c/strong\u003eand \u003cstrong\u003ed\u003c/strong\u003e). In \u003cstrong\u003ea \u003c/strong\u003eand \u003cstrong\u003eb, \u003c/strong\u003elines show model estimates and shaded areas depict 95% confidence intervals for estimated trends. \u003cstrong\u003ec\u003c/strong\u003e and \u003cstrong\u003ed:\u003c/strong\u003e Colored bars show model estimates, error bars depict standard errors of estimated means and data points show mean hit rates for individual participants. We found a main effect of encoding distance between cue and lure across encoding conditions. Furthermore, analyses revealed an interaction effect of encoding condition and congruence. Post-hoc analyses revealed a significantly higher probability of correct responses for congruent compared to incongruent trials in the semantic clustering condition and a significantly higher probability of correct responses for congruent trials in the semantic clustering condition compared to congruent trials in the no clustering condition. *** \u0026lt; 0.001; ** \u0026lt; 0.01; n.s. = not significant. The figure was created in https://BioRender.com\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6595825/v1/be58f19f7a03c0421fb716ae.png"},{"id":100614730,"identity":"5a2f31b3-86c3-4449-9dea-452ec0d91cd8","added_by":"auto","created_at":"2026-01-19 17:23:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3056386,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6595825/v1/fdbe3e19-bf08-4e84-b8e7-09b0c732306b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Encoding of semantic structure shapes temporal order memory for visual object stimuli","fulltext":[{"header":"Significance Statement","content":"\u003cp\u003eThe episodic memory system reconstructs past experiences relying on episode-specific information and semantic knowledge. Across two experiments we show that semantic category knowledge influences memory for the temporal order of events: After encoding sequences of object images which were temporally clustered in categories, participants tended to retrieve semantically related items as having been presented closer in time. This was not the case when semantically related items were encoded in random order, suggesting a key role of semantic processing at encoding.\u0026nbsp;\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eWhen recalling events from their personal past, humans rely on episodic memory to retrieve information about what they experienced in a specific spatiotemporal context. While episodic memory serves an important adaptive purpose, it does not always accurately reflect past events but tends to produce systematic errors that reflect its constructive nature (Schacter, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The notion that episodic memory is inherently constructive entails that it recombines information from multiple sources to generate coherent scenarios and to enable decisions. Semantic knowledge is a key component of constructive memory processes and has been theorized to shape memory retrieval for a long time (Bartlett, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1932\u003c/span\u003e). For instance, classic work using the Deese\u0026ndash;Roediger\u0026ndash;McDermott paradigm (Roediger \u0026amp; McDermott, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1995\u003c/span\u003e) has demonstrated that semantic associations between items in studied word lists can result in false memories for items that were never part of the learned material. More recent evidence suggests that semantic and episodic memory are closely interconnected in terms of cognitive processes and underlying neural mechanisms (Renoult et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, how and under which conditions semantic knowledge systematically shapes episodic memory retrieval is still not completely understood. Thus, we aimed at extending this line of research by investigating how semantic knowledge impacts memory for the temporal order of event sequences.\u003c/p\u003e \u003cp\u003eThe Scenario Construction Model (SCM, Cheng et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) postulates that episode-specific information is flexibly integrated with semantic knowledge to meet current information processing demands. More specifically, the model posits that episode-specific information is typically stored in the form of a memory trace representing the gist of an episode (i.e., its central aspects and structure). This episodic gist representation is then integrated and completed with consolidated semantic information to form a coherent scenario in a generative memory retrieval process (Cheng et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Accordingly, memory retrieval should reflect both, episode-specific information and semantic knowledge, in situations where such generative memory processes are evoked. By extension, since most experience involves semantically structured event sequences (such as the temporal and spatial co-occurrence of related \u0026ldquo;animal concepts\u0026rdquo; during a visit to the zoo), semantic processing at encoding is likely to influence the generation of event representations and subsequent generative retrieval (Addis, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrevious studies have demonstrated episodic-semantic interactions in various memory tasks. For example, participants reliably exhibit robust effects of temporal contiguity and semantic relatedness in free recall tasks (Healey, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Howard \u0026amp; Kahana, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Pairing word items with semantically related context material at encoding is associated with enhanced retrieval performance for such items, constituting a semantic congruence effect (Bein et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Notably, this can also be accompanied by increased false recall of semantically related items (Packard et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Moreover, retrieval of image locations was found to be enhanced when a given item had been encoded near a cluster of semantically related items versus in a random location, while semantic typicality predicted retrieval bias towards category clusters (Tompary \u0026amp; Thompson-Schill, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Similar effects of semantic knowledge on memory for episodic features have been shown for a naturalistic episodic navigation task involving encoding of semantically congruent and incongruent object locations (Z\u0026ouml;llner et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In another recent study, the quality of episodic memory for scenes was negatively related to semantic bias in memory retrieval (Ramey et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The negative effect of semantically incongruent placements on location memory retrieval was absent for scenes which participants were able to fully recollect, suggesting a more pronounced influence of semantic processing on memory retrieval in case of weak episodic representations. Collectively, these findings suggest that a semantically structured encoding context can bias retrieval towards semantic expectations, with this effect potentially being modulated by the strength of episodic memory representations.\u003c/p\u003e \u003cp\u003eArguably, these effects are applicable to the temporal dimension of episodic memory. In addition to physical space, time is a defining context factor in episodic memory organization with specific events being mapped to a specific spatiotemporal context (Tulving, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Both space and time are encoded by the hippocampal memory system which enables the organization of discrete events in memory along spatial as well as temporal axes (Ekstrom \u0026amp; Ranganath, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Interestingly, semantic relatedness effects appear consistently in conjunction with temporal contiguity effects in free recall experiments, and the organization of memory recall in tasks involving a semantic structure (within the encoding set and/or retrieval cues) suggests an interaction of semantic and temporal context information (Healey et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Polyn et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). While this work highlights temporal contiguity and semantic relatedness as two key interacting factors in the organization of free recall, it is unclear whether these principles extend to memory for the temporal order of events, an important feature of episodic memory. In a study by Z\u0026ouml;llner et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), participants exhibited a semantic clustering effect in their recall of the event order suggesting that temporal order memory is influenced by semantic associations between event features. However, whether similar effects of semantic knowledge can be shown in a task specifically designed to probe encoding and retrieval of temporal order information remains to be investigated.\u003c/p\u003e \u003cp\u003eIn addition, it is unclear whether effects of semantic knowledge on temporal order memory would depend on a semantically structured encoding set, which may evoke semantic processing at encoding. While the SCM does not make specific predictions about encoding processes, the presence of pronounced semantic structure at encoding may shift the memory system towards semantic context processing (Healey et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Morton \u0026amp; Polyn, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This is supported by neuroimaging results showing that neural coding of current and recent semantic category information at encoding predicts recall organization (Chan et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Morton \u0026amp; Polyn, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Conversely, the effect of semantic associations between encoded items on temporal order memory retrieval could be independent of semantic structure within the encoding set and primarily result from weakly encoded episodic memory and/or retrieval-specific processes as emphasized by the SCM.\u003c/p\u003e \u003cp\u003eConcerning modulating factors, the SCM would predict a more pronounced influence of semantic knowledge on memory retrieval for weakly encoded episodic memory representations as there should be an increased need for completion of the episodic gist information. Since episodic associations between items are known to be determined by the temporal lag between these items at encoding (e.g., serial positions within an encoded list) and are typically stronger for forward versus backward encoding transitions (Healey et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), these factors may modulate the influence of semantic knowledge on temporal order memory. In addition, increased activation of semantic concepts at retrieval should result in a more pronounced impact of semantic knowledge. Semantic typicality, which refers to the degree to which an element is representative of its associated semantic category (Rosch et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1976\u003c/span\u003e), is a relevant measure in this regard. Semantically typical items are thought to more strongly evoke categorical representations (Collins \u0026amp; Loftus, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1975\u003c/span\u003e), and location memory for such items has been shown to be influenced by semantic associations to a greater extent (Tompary \u0026amp; Thompson-Schill, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Thus, it could be assumed that semantic typicality also modulates the impact of semantic associations on temporal order memory.\u003c/p\u003e \u003cp\u003eThe current study aimed at investigating the impact of semantic knowledge on memory for the temporal order of event sequences. We conducted two experiments using a temporal order memory task in which participants were presented with alternating encoding and retrieval runs, consisting of encoding sequences of naturalistic visual stimuli and immediate retrieval runs involving forced-choice temporal proximity judgements. In the first experiment, encoding sequences were semantically structured such that images from the same semantic category were clustered in time. This semantic structure was not present in the second experiment where images were presented at random sequence positions. At retrieval, participants were presented with one cue image and two choice images, one of which was semantically related to the cue. Trials in which the semantically related choice was the correct one constituted congruent trials. We predicted that participants would draw on both temporal and semantic associations between encoded items during temporal order memory retrieval, resulting in better performance on congruent retrieval trials. The semantic congruence effect was predicted to be stronger for retrieval trials featuring items which were encoded in backward versus forward encoding direction and for items encoded with greater temporal lag (referred to here as encoding distance), showing the modulatory influence of episodic association strength. Greater semantic typicality of the retrieval cue was expected to be associated with a more pronounced semantic congruence effect, reflecting the modulatory effect of semantic concept activation. Finally, assuming that processing of semantic structure at encoding would increase the likelihood of temporal order memory being influenced by semantic associations, the semantic congruence effect was expected to be more pronounced in the first compared to the second experiment. Nevertheless, based on predictions by the SCM regarding semantic construction at retrieval, we still expected to find the semantic congruence effect in experiment two.\u003c/p\u003e"},{"header":"Experiment 1","content":"\n\u003ch3\u003eMaterials and Methods \u003c/h3\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eThe required minimum sample size was estimated using G*Power 3.1 (Faul et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) and based on an expected medium effect size for the main effect of the within-subjects factor \u0026ldquo;congruence\u0026rdquo; (\u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.5, \u003cem\u003eα\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05, 1-\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.8, two-sided, paired). This resulted in a required sample size of 35 participants. To detect any potential effects of interest in our newly created temporal memory task, we chose to further increase the number of participants beyond this estimated minimum required sample size.\u003c/p\u003e \u003cp\u003eParticipants were recruited through advertisements on social media networks and at the campus of Ruhr University Bochum. Inclusion criteria comprised: age ranging between 18 and 35 years, no acute neurological and psychiatric illnesses, and normal or corrected-to-normal vision. Dropout due to red-green color vision deficiency (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2) resulted in a final sample size of 54, including 39 women and 15 men aged between 18 and 35 (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;22.8, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.0) years. While six participants completed online sessions (due to the study initially being designed as an online study but switched to lab-based testing when the online experiment hosting service became unavailable), the remaining 48 participants attended lab sessions.\u003c/p\u003e \u003cp\u003e The study received approval from the ethics committee of the Faculty of Psychology at Ruhr University Bochum (application number 764), following the guidelines of the Declaration of Helsinki. Participants provided written informed consent and were reimbursed with 10\u0026euro; or course credits.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStimulus material\u003c/h3\u003e\n\u003cp\u003eStimuli were selected from the THINGS database (Hebart et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) providing images of common object concepts from 27 high-level semantic categories. The complete image pool from which each participant-specific stimulus set was drawn consisted of 8500 images including 588 different living and non-living objects from eleven selected high-level categories: animal, clothing, electronic device, fruit/vegetable (this category was combined from the two separate THINGS categories fruit and vegetable), furniture, musical instrument, office supply, plant, sports equipment, toy, and vehicle. A unique set of images including objects from all eleven high-level categories was drawn from the pool for each participant.\u003c/p\u003e \u003cp\u003eStimulus selection was pseudo-randomized as for each participant, for each block of the temporal memory task, six different categories were drawn and for each of these categories, five different objects were drawn from the complete pool of images. For each of these objects, one image was finally drawn out of the available set. Each participant-specific stimulus set comprised between 321 and 333 (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;328) different images corresponding to between 226 and 264 (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;246) different objects. Individual color images showed single objects in scenes and were generally presented on a white background screen throughout the experiment. During encoding runs of the temporal memory task, images were displayed at 500 x 500 pixels. During retrieval runs, cue images were also presented at 500 x 500 pixels, while choice images were shown at 300 x 300 pixels.\u003c/p\u003e\n\u003ch3\u003eTemporal memory task\u003c/h3\u003e\n\u003cp\u003eThe temporal memory task was composed of alternating encoding runs during which participants were presented with image sequences and retrieval runs consisting of forced-choice temporal memory retrieval trials (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The task was divided into twelve blocks, with each block consisting of one encoding run and one subsequent retrieval run. All participants completed the same experimental task condition. The task was displayed on a computer screen at a resolution of 1920 x 1080 pixels and responses were given using a standard computer mouse.\u003c/p\u003e \u003cp\u003eDuring encoding runs, images were presented individually for two seconds in each trial for a total of 28 trials. After each image presentation, a fixation cross was displayed for 500 milliseconds. For each encoding run, six different categories were randomly drawn from the eleven available categories. From each of these six selected categories, five different objects were randomly chosen and one image was randomly drawn from the available image set for each object, resulting in 30 selected images per encoding run. The image sequences were constructed in a systematic way such that the probability of occurrence of an image from a given category was approximately normally distributed around a \u0026ldquo;category center\u0026rdquo; (i.e., category- and run-specific position within the sequence). The images were therefore temporally clustered based on their semantic category, introducing a temporal sequence of individual images and an underlying temporal sequence of image categories for each encoding run. Clusters of images were not clearly separated from each other \u0026ndash; images from neighbouring categories could be interspersed. The task version used in experiment 1 is referred to here as \u0026ldquo;semantic clustering condition\u0026rdquo;.\u003c/p\u003e \u003cp\u003eThe first and the last item of each sequence were cut from the encoding set (resulting in 28 instead of 30 images), since otherwise these images would have virtually always belonged to the first and the last categories of the underlying sequence of category clusters, respectively, substantially reducing the variance of semantic categories at these sequence positions. The underlying sequence of categories was never the same for two or more encoding runs within one participant. Importantly, participants were not explicitly made aware of the category-based clustering of images and the consequent sequence of semantic categories underlying each encoding run.\u003c/p\u003e \u003cp\u003eFollowing encoding, immediate memory retrieval occurred after a delay of 20 seconds. During retrieval runs, participants performed 16 trials of forced-choice temporal memory retrieval based on the image sequence from the preceding encoding run. In each trial, participants judged the relative temporal proximity of two choice images to a cue image, with the three images randomly drawn from the preceding encoding sequence. A fixation cross appeared for a duration of 0,5 seconds before the cue image was individually displayed for one second. Two images from the encoding run were then presented immediately afterwards, and participants were prompted to select the one that appeared temporally closer to the cue item in the preceding encoding run. There was no response timeout, the retrieval trial ended as soon as the participant gave a response, after which there was a post-trial gap of one second. Feedback indicating the percentage of correct responses was presented on the screen after each retrieval run.\u003c/p\u003e \u003cp\u003eRetrieval trials were defined by the parameters \u0026ldquo;congruence\u0026rdquo; (congruent/incongruent), \u0026ldquo;matching condition\u0026rdquo; (same/near) and \u0026ldquo;encoding direction\u0026rdquo; (forward/backward). If the target (i.e., the correct choice image) belonged to the same category as the cue image or to the category cluster near the category cluster of the cue image, the retrieval trial was defined as \u0026ldquo;congruent\u0026rdquo;. In \u0026ldquo;incongruent\u0026rdquo; trials, the lure image (i.e., reflecting the incorrect choice) belonged to the same category as the cue image or to the category cluster near the category cluster of the cue image. Moreover, if the target or lure image was from the same category as the cue image, the matching condition of the trial was coded as \u0026ldquo;same\u0026rdquo; and if it was from a category whose cluster was near the category cluster of the cue image, it was coded as \u0026ldquo;near\u0026rdquo;. In terms of encoding direction, the target and lure images could have appeared either both after (\u0026ldquo;forward\u0026rdquo; encoding direction) or both before (\u0026ldquo;backward\u0026rdquo; encoding direction) the cue image within the encoding sequence.\u003c/p\u003e\n\u003ch3\u003eAnalysis of behavioral data\u003c/h3\u003e\n\u003cp\u003eCleaning and preparation of behavioral data from the temporal memory task was performed using custom scripts in Python v3.11 (VanRossum \u0026amp; Drake, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). There were no missed responses and the complete dataset consisting of 9216 retrieval trials from 48 participants in the lab sessions was included in further analyses. The remaining 1152 trials from six participants who completed online sessions also did not include any missed responses. However, they were initially excluded from the statistical analyses due to concerns about the comparability of the different experimental settings (lab versus online). In a subsequent step, this additional data was incorporated into the analyses to evaluate if this would substantially change the pattern of results. Results from analyses of the complete dataset are reported when no substantial differences were detected.\u003c/p\u003e \u003cp\u003eStatistical analyses were performed using R Statistical Software (v4.3.2; R Core Team, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In a first step, performance in the temporal memory task was compared against chance-level performance using a two-sided exact binomial test. Next, a random intercept logistic generalized linear mixed-effects model (GLMM) was estimated on a single-trial level to assess the effect of the five independent variables of interest on the binary dependent variable \u0026ldquo;response\u0026rdquo; (incorrect/correct) and to consider the nested trial structure in this experiment, where each participant generated 192 retrieval trials. The fixed factors in the model included the binary predictors \u0026ldquo;congruence\u0026rdquo; (congruent/incongruent), \u0026ldquo;matching condition\u0026rdquo; (same/near) and \u0026ldquo;encoding direction\u0026rdquo; (forward/backward) as well as the continuous predictors \u0026ldquo;encoding distance (cue-lure)\u0026rdquo; and \u0026ldquo;cue typicality\u0026rdquo;. Note that only the encoding distance (that is, number of positions within the encoding sequence) between the cue and lure stimuli of each retrieval trial was entered into the model as this measure strongly correlated with the encoding distance between cue and target stimuli. In addition, as the temporal distance between cue and lure was always larger than that between cue and target, it was assumed to better reflect the relevant episodic association underlying the temporal memory response on a given trial of the task. Continuous predictors were standardized before entering the model.\u003c/p\u003e \u003cp\u003eAs potential modulatory effects of the different variables on the semantic congruence effect on temporal order memory retrieval were of interest in the present study, the following two-way interactions were included in the model as fixed effects predictors: \u0026ldquo;congruence x matching condition\u0026rdquo;, \u0026ldquo;congruence x encoding direction\u0026rdquo;, \u0026ldquo;congruence x encoding distance (cue-lure)\u0026rdquo; and \u0026ldquo;congruence x cue typicality\u0026rdquo;. The random factor \u0026ldquo;participant\u0026rdquo; was entered into the model to account for the nested trial structure, and random intercepts were modeled. The GLMM used a logit link function and was fit using maximum likelihood estimation as provided by the \u0026ldquo;glmer\u0026rdquo; function of the R package lme4 (Bates et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Odds ratios (\u003cem\u003eOR\u003c/em\u003e) and 95% confidence intervals (\u003cem\u003eCI\u003c/em\u003e) are reported as indicators of statistical significance for each fixed effect predictor. In the case of logistic GLMMs, a confidence interval of OR which does not include the value 1 indicates that a given predictor is statistically significant (Faraway, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eResults\u003c/h3\u003e\n\u003cp\u003eAs revealed by the exact binomial test of the ratio of correct trials against a chance level of 0.5, participants performed significantly above chance in the temporal memory task (\u003cem\u003eP\u003c/em\u003e\u003csub\u003ecorrect response\u003c/sub\u003e=0.68, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). This was likewise true for the complete dataset including data from the online sessions (\u003cem\u003eP\u003c/em\u003e\u003csub\u003ecorrect response\u003c/sub\u003e=0.68, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). The general result pattern of the logistic GLMM analysis did not change after including data from the online sessions, therefore only results from the analysis of the complete dataset will be reported hereafter. The logistic GLMM analysis revealed a significant main effect of congruence on responses in the temporal memory task. Participants were significantly more likely to give a correct response on congruent as compared to incongruent retrieval trials (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.44, 95% \u003cem\u003eCI\u003c/em\u003e (\u003cem\u003eOR\u003c/em\u003e) [1.24\u0026ndash;1.68], Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In addition, encoding distance significantly predicted correct responses, with a higher encoding distance being associated with higher probability of correct responses (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.27, 95% \u003cem\u003eCI\u003c/em\u003e (\u003cem\u003eOR\u003c/em\u003e) [1.19\u0026ndash;1.37], Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Matching condition was likewise significantly associated with probability of correct responses, with participants being more likely to give a correct response on retrieval trials including same versus near categories (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.19, 95% \u003cem\u003eCI\u003c/em\u003e (\u003cem\u003eOR\u003c/em\u003e) [1.05\u0026ndash;1.36], Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Neither encoding direction nor cue typicality were significant individual predictors of correct responses (both 95% \u003cem\u003eCI\u003c/em\u003e (\u003cem\u003eOR\u003c/em\u003e) [\u0026lt;\u0026thinsp;1,\u0026gt;1]). In addition, the interaction between congruence and encoding distance was statistically significant (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.13, 95% \u003cem\u003eCI\u003c/em\u003e (\u003cem\u003eOR\u003c/em\u003e) [1.02\u0026ndash;1.25], Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Post-hoc comparisons of estimated marginal means indicated that with higher encoding distance, the benefit on congruent versus incongruent trials became larger (at encoding distance = -1: \u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.40, 95% \u003cem\u003eCI\u003c/em\u003e\u003csub\u003e\u003cem\u003eTukey\u003c/em\u003e\u003c/sub\u003e (\u003cem\u003eOR\u003c/em\u003e) [1.23\u0026ndash;1.60]; at encoding distance\u0026thinsp;=\u0026thinsp;1: \u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.79, 95% \u003cem\u003eCI\u003c/em\u003e\u003csub\u003e\u003cem\u003eTukey\u003c/em\u003e\u003c/sub\u003e (\u003cem\u003eOR\u003c/em\u003e) [1.56\u0026ndash;2.06]). Moreover, the interaction between congruence and matching condition was statistically significant (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.25, 95% \u003cem\u003eCI\u003c/em\u003e (\u003cem\u003eOR\u003c/em\u003e) [1.04\u0026ndash;1.51], Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Post-hoc comparisons of estimated marginal means showed that the effect of congruence significantly differed between levels of matching condition, with a stronger benefit for congruent versus incongruent on same categories (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.78, 95% \u003cem\u003eCI\u003c/em\u003e\u003csub\u003e\u003cem\u003eTukey\u003c/em\u003e\u003c/sub\u003e (\u003cem\u003eOR\u003c/em\u003e) [1.55\u0026ndash;2.03]) versus near categories retrieval trials (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.42, 95% \u003cem\u003eCI\u003c/em\u003e\u003csub\u003e\u003cem\u003eTukey\u003c/em\u003e\u003c/sub\u003e (\u003cem\u003eOR\u003c/em\u003e) [1.25\u0026ndash;1.61]).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Experiment 2","content":"\n\u003ch3\u003eMaterials and Methods\u003c/h3\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eIn experiment 2, participants were recruited in the same manner as in experiment 1, the same inclusion criteria were applied and participants were compensated in the same way. Due to red-green color vision deficiency one participant had to be excluded. The remaining 35 participants (24 women, 10 men, 1 diverse) were between 18 and 35 (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;24.9, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.6) years old. All participants completed the experimental sessions in the lab.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStimulus Material\u003c/h2\u003e \u003cp\u003eFor experiment 2, the stimuli were selected from the same database and in the same manner as in experiment 1. Here, due to the number of blocks in the temporal memory task being reduced to ten, each participant-specific stimulus set comprised 280 different images corresponding to between 214 and 236 (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;226) different objects.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eTemporal memory task\u003c/h2\u003e \u003cp\u003eIn experiment 2, a slightly modified version of the experimental task was used. Participants were asked to complete ten blocks instead of twelve, a modification which was made to adapt and test the experimental task for later use in an fMRI environment. In this version of the temporal memory task (referred to here as \u0026ldquo;no clustering condition\u0026rdquo;), images were presented without any clustering based on their semantic category. Instead in any given encoding run, images from all six categories appeared at random sequence positions. As in experiment 1, all participants in this experiment completed the same task condition.\u003c/p\u003e \u003cp\u003eDuring encoding runs, stimuli were presented for two seconds after which a fixation cross was displayed for a randomly chosen duration between 1,5 and 2 seconds (this was again implemented for testing of an fMRI-adapted task version). During retrieval runs, a fixation cross was shown for a randomly chosen duration between 1,5 and 2 seconds before the cue image for a given retrieval trial was individually presented for 2 seconds. This was followed by a fixation cross that was displayed for a randomly chosen duration between 0,5 and 1 second. The two choice images were presented subsequently with a prompt asking the participant to select the one that appeared temporally closer to the cue image in the preceding encoding run. There was a response timeout of 4,5 seconds. The retrieval trial ended as soon as the participant gave a response.\u003c/p\u003e \u003cp\u003eThe variable \u0026ldquo;congruence\u0026rdquo; had three levels in this task version (congruent/incongruent/categories unrelated). In trials labelled as \u0026ldquo;categories unrelated\u0026rdquo;, the choice images both belonged to a different category than the cue. Importantly, as there was no underlying sequence of category clusters in encoding runs of experiment 2 due to the omission of category-based clustering, the variable \u0026ldquo;matching condition\u0026rdquo; was obsolete here as neighboring categories were not possible.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of behavioral data\u003c/h2\u003e \u003cp\u003eFor experiment 2, the same data cleaning and preparation procedure as in experiment 1 was applied, with the added removal of 48 retrieval trials where participants failed to respond within the time limit. This resulted in a dataset including 5552 retrieval trials from 35 participants. Again, performance in the temporal memory task was first compared against chance-level performance using a two-sided exact binomial test. Moreover, a similar GLMM as in experiment 1 with \u0026ldquo;response\u0026rdquo; (incorrect/correct) as dependent variable was estimated, however in this case, the predictor \u0026ldquo;congruence\u0026rdquo; had three levels (incongruent/congruent/categories unrelated). The last level applied to retrieval trials in the no clustering condition in which all three stimuli (cue, target, lure) belonged to different categories. The three levels were represented by two binary dummy variables in the model. The remaining fixed effects predictors that were also included in the GLMM analysis for experiment 1 (\u0026ldquo;encoding direction\u0026rdquo;, \u0026ldquo;encoding distance\u0026rdquo; and \u0026ldquo;cue typicality\u0026rdquo;) were all entered into the model, as well as the two-way interactions between each of these predictors and the predictor \u0026ldquo;congruence\u0026rdquo;. Again, the random factor \u0026ldquo;participant\u0026rdquo; was included to account for the nested trial structure, modelling random intercepts.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eResults\u003c/h3\u003e\n\u003cp\u003eAs in experiment 1, participants performed significantly above chance level in the temporal memory task (\u003cem\u003eP\u003c/em\u003e\u003csub\u003ecorrect response\u003c/sub\u003e=0.66, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). The logistic GLMM analysis revealed a single significant predictor of task performance in experiment 2: Encoding distance was positively associated with the probability of correct response (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.28, 95% \u003cem\u003eCI\u003c/em\u003e (\u003cem\u003eOR\u003c/em\u003e) [1.14\u0026ndash;1.43], Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). No further significant main effects or interactions were found (all 95% \u003cem\u003eCI\u003c/em\u003e (\u003cem\u003eOR\u003c/em\u003e) [\u0026lt;\u0026thinsp;1,\u0026gt;1]).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eJoint analysis of temporal memory task data from experiments 1 and 2\u003c/h2\u003e \u003cp\u003eIn order to directly compare temporal order memory effects between experiments 1 (semantic clustering condition) and experiment 2 (no clustering condition), data from both experiments were analyzed using a single model. To this end, retrieval trials with the congruence level \u0026ldquo;categories unrelated\u0026rdquo; were excluded from the dataset of experiment 2, since this type of retrieval trial did not exist in experiment 1. The resulting 13139 retrieval trials from 89 participants were included in the statistical analysis. Fixed effects predictors in the logistic GLMM included \u0026ldquo;encoding condition\u0026rdquo; (semantic clustering condition/no clustering condition), \u0026ldquo;congruence\u0026rdquo; (incongruent/congruent), \u0026ldquo;encoding direction\u0026rdquo;, \u0026ldquo;encoding distance\u0026rdquo; and \u0026ldquo;cue typicality\u0026rdquo;. Three-way interactions between \u0026ldquo;encoding condition\u0026rdquo;, \u0026ldquo;congruence\u0026rdquo; and each of the remaining fixed effects predictors were entered into the model as well as the random factor \u0026ldquo;participant\u0026rdquo; modeling random intercepts.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eResults\u003c/h3\u003e\n\u003cp\u003eThe GLMM which was fit to the complete dataset showed a significant main effect of encoding distance, with higher encoding distance being associated with higher probability of correct response (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.28, 95% \u003cem\u003eCI\u003c/em\u003e (\u003cem\u003eOR\u003c/em\u003e) [1.14\u0026ndash;1.43], Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Moreover, the two-way interaction between congruence and encoding condition was statistically significant (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.61, 95% \u003cem\u003eCI\u003c/em\u003e (\u003cem\u003eOR\u003c/em\u003e) [1.24\u0026ndash;2.09], Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). As per post-hoc comparisons of estimated marginal means, participants were significantly more likely to give a correct response on congruent as compared to incongruent trials in the semantic clustering condition (that is, in retrieval trials of experiment 1; \u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.63, 95% \u003cem\u003eCI\u003c/em\u003e\u003csub\u003e\u003cem\u003eTukey\u003c/em\u003e\u003c/sub\u003e (\u003cem\u003eOR\u003c/em\u003e) [1.49\u0026ndash;1.78]; cf. Results of experiment 1), while this contrast was not statistically significant in the no clustering condition (that is, in retrieval trials of experiment 2; \u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.01, 95% \u003cem\u003eCI\u003c/em\u003e\u003csub\u003e\u003cem\u003eTukey\u003c/em\u003e\u003c/sub\u003e (\u003cem\u003eOR\u003c/em\u003e) [0.86\u0026ndash;1.19] cf. Results of experiment 2). In addition, participants who completed the semantic clustering condition of the task (experiment 1) showed significantly better performance than participants who completed the no clustering condition (experiment 2) on congruent trials (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.37, 95% \u003cem\u003eCI\u003c/em\u003e\u003csub\u003e\u003cem\u003eTukey\u003c/em\u003e\u003c/sub\u003e (\u003cem\u003eOR\u003c/em\u003e) [1.11\u0026ndash;1.70]). The contrast between semantic clustering condition and no clustering condition was not significant for incongruent trials (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.85, 95% \u003cem\u003eCI\u003c/em\u003e\u003csub\u003e\u003cem\u003eTukey\u003c/em\u003e\u003c/sub\u003e (\u003cem\u003eOR\u003c/em\u003e) [0.69\u0026ndash;1.05]).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe primary goal of this study was to investigate the influence of semantic knowledge on memory for the temporal order of event sequences. We found that after encoding sequences in which items were clustered based on their semantic categories, there was a benefit of congruent retrieval trials (where cue and target items were semantically related) over incongruent retrieval trials (where cue and lure items were semantically related). This effect was found for trials in which the semantically related items belonged to the same semantic category as well as for trials in which they belonged to two different categories which had neighboring category clusters within the encoding sequence (experiment 1). Importantly, while we expected the semantic congruence effect to be less pronounced in experiment 2 where encoding sequences were not semantically structured by category clustering, we did not find any evidence for the effect in this encoding condition. Comparing datasets from both experiments in a single analysis confirmed these results. In addition, encoding distance between cue and lure items was a positive predictor of correct responses across experiments.\u003c/p\u003e \u003cp\u003eOur findings suggest that only when presented with semantically structured image sequences, participants applied this context information to temporal order memory retrieval. Thus, the presence of an implicit semantic structure within the encoding set may have biased participants towards using encoded semantic context information in reconstructing temporal sequences. This is likely to have led to the observed memory benefit on trials where temporal and semantic associations between items aligned (congruent retrieval trials) over trials where temporal and semantic associations did not align (incongruent retrieval trials) in the semantic clustering condition.\u003c/p\u003e \u003cp\u003eThe current findings are in line with a wide range of previous studies showing that prior knowledge and semantic associations between encoded items influence episodic memory in various tasks, including free recall of word lists (Aka et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), recognition memory (Montefinese et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), spatial (Lu et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Tompary \u0026amp; Thompson-Schill, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and temporal memory tasks (Ishiguro \u0026amp; Saito, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Importantly, a common feature of most of these experiments and this study is that participants were never explicitly made aware of the semantic structure underlying the encoded material. Thus, any effects of semantic relatedness of the learned and subsequently retrieved items on memory retrieval would have relied on the activation of pre-existing semantic category knowledge. This general notion is supported by influential models of memory search such as the Context Maintenance and Retrieval (CMR) model, which understands semantic organization of memory recall as a consequence of the activation of pre-established semantic associations feeding into an active context representation that guides retrieval (Polyn et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). While this model was originally introduced as a formal account of memory search in free recall tasks, it appears plausible given previous and our current findings of semantic relatedness effects in episodic memory tasks that similar mechanisms may be active under a wider range of conditions.\u003c/p\u003e \u003cp\u003eThe observed benefit of congruent over incongruent retrieval trials in our study in experiment 1 extended to near category trials where the semantically related option was an item from a category cluster neighboring the category cluster of the cue within the encoding sequence. This indicates that the observed semantic congruence effect cannot solely be attributed to semantic cueing at retrieval, but that participants used the encoded semantic context information to guide retrieval decisions. Importantly, the semantic congruence effect was not observed in experiment 2 using the no clustering task condition, which further supports the interpretation that encoding semantic context information drives this effect. This is in line with findings from a previous working memory study reporting that semantic relatedness between items systematically influenced serial order errors only when items had been encoded in semantically clustered sets (Kowialiewski et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Furthermore, the semantic relatedness benefit for the recall of items from working memory is most likely driven by the activation of semantic representations at encoding (Kowialiewski et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Similarly, differential effects of semantically structured versus unstructured sets of to-be-encoded items have been demonstrated for delayed free recall of word lists (Aka et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Encoding series of semantically related items versus unrelated items can result in increased generalization but less detailed recognition memory for the learned material (Melega \u0026amp; Sheldon, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). While these findings highlight the key role of semantically structured encoding sets, the type of memory which was tested does not directly correspond to the present study. However, the underlying mechanisms may extend to memory for the temporal order of events. Interestingly, imagining a spatial context before encoding an event sequence was related to more accurate recency discrimination than encoding without context imagination (Sheldon, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Additionally, the spatial encoding context was associated with better performance on difficult recency discrimination trials compared to an abstract conceptual encoding context (Sheldon, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These findings reflect a superior interaction between spatial encoding context and temporal order information, enabling accurate reconstruction of temporal context even on difficult trials. Thus, different types of context information might impact the encoding of temporal order information. An important distinction between this and the present study is that in the temporal memory task used here, semantic context information was not merely presented as a cue preceding the encoding sequence but was closely intertwined with the temporal structure of the encoding sequences. That is, items were not simply associated with a common abstract concept, but semantically related items were clustered in time such that encoded semantic context information could potentially be used to reconstruct the temporal sequence structure.\u003c/p\u003e \u003cp\u003eInterestingly, encoding of semantically structured image sequences was associated with benefits, rather than costs, for temporal order memory relative to encoding of unstructured sequences. If temporal proximity judgements at retrieval had been primarily driven by semantic relatedness between the encoded items, one would have expected a substantial cost on incongruent retrieval trials where the semantically related option was the incorrect one. While a facilitative effect of semantic processing on temporal order memory has been found before, results from a recent meta-regression analysis suggest that semantically structured encoding sets are related to increased errors in temporal order memory, especially in tasks that do not require retrieval of individual items, thus mitigating the effect of semantic associations as retrieval cues (Ishiguro \u0026amp; Saito, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In the current study, while performance was worse on incongruent retrieval trials compared to congruent ones in the semantic clustering condition, we did not observe a significant difference between performance on incongruent trials in both conditions. This might indicate that participants in fact generated memory representations of temporal context for each sequence which allowed for partly accurate temporal proximity judgements even on incongruent trials. Arguably, participants who completed the semantic clustering encoding condition additionally generated representations of semantic context, which served to increase the probability of a correct response when semantic and temporal context representation indicated the same response (congruent trials) but did not effectively override the temporal context representation on trials where semantic and temporal context did not indicate the same response (incongruent trials). This is in line with recent evidence for benefits of semantic relatedness for temporal order memory when encoding sets featured clustering of semantically related items (Kowialiewski et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Interestingly, a negative effect of semantic relatedness between encoded items on location memory performance occurred in a task design in which semantic relatedness was established by presenting items which were all associated with a single category (Lu et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In contrast to the present study however, semantic context did not provide a structure which could have been used to reconstruct category-specific locations but instead provided a grouping mechanism for all items. This suggests that encoding of semantic context information is associated with benefits for the reconstruction of episodic information specifically when semantic context provides a structure that is intertwined and correlates with the spatial or temporal event structure. The SCM (Cheng et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) does not make any explicit assumptions about the role of semantic processes active during encoding of events and primarily emphasizes retrieval mechanisms as the driving factor of semantic effects on episodic reconstruction. Our present findings may extend the SCM by showing that encoding of semantically structured material, potentially through the activation of consolidated semantic associations and the consequent encoding of semantic context, can benefit the reconstruction of temporal-episodic context.\u003c/p\u003e \u003cp\u003ePart of the predictions of the current study was that the semantic congruence effect on temporal order memory would be more pronounced for retrieval trials involving items that were encoded with a higher temporal lag (encoding distance) between each other and for retrieval trials where both choice items were encoded before the cue image (backward encoding direction) versus after the cue image (forward encoding direction). We found that encoding distance between cue and lure was a significant positive predictor of correct response across encoding conditions. In experiment 1, this effect was significantly stronger for congruent versus incongruent trials. Thus, against our assumptions, there was a benefit of higher encoding distance between items for temporal order memory. While we predicted that higher encoding distance would be associated with reduced strength of episodic associations (Healey et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and consequently with a stronger influence of semantic knowledge on temporal order memory, a different mechanism may underlie our observations. Higher encoding distance was shown to be associated with more accurate temporal recency judgements which was attributed to effects of item strength within the acquired memory representation (Sheldon, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). While the temporal memory task in the present study did not require participants to make recency judgements, a similar mechanism may have yielded the observed result, as relative item strength of the cue, target and/or lure items within the acquired memory representation may have been good indicators of the encoded temporal sequence. In addition, a higher encoding distance between the cue and lure items would have been linked to a higher encoding distance between target and lure in some cases, which may have decreased the interference between these items in reconstructing the temporal context.\u003c/p\u003e \u003cp\u003eMoreover, we did not find any significant effect of forward versus backward encoding direction on temporal order memory performance in our task. Applying a similar logic as described above for temporal encoding distance between items, we would have predicted reduced performance specifically on incongruent-backward trials versus incongruent-forward trials. In contrast to that, forward and backward semantic associative links have been shown to be similarly beneficial to immediate serial recall of word lists (Saint-Aubin et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), while another recent study did not report any significant differences in overall accuracy between forward and backward serial recall (Dougherty et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Encoding direction effects might emerge less reliably than previously assumed, arguably less so in a task where cue items can be used to initiate temporal context reconstruction. The fact that we did not observe the expected interaction effects between semantic congruence and factors linked to episodic association strength may suggest that semantic congruence effects were primarily driven by the encoding of semantic context rather than completion of weak episodic gist representations at retrieval. However, further experiments are needed to clearly establish the relationship between the effects of semantic knowledge and variables linked to the strength of episodic associations in temporal order memory.\u003c/p\u003e \u003cp\u003eFurthermore, against our predictions, semantic typicality of items presented at retrieval did not modulate the effect of semantic congruence on temporal order memory. Previous studies reported that effects of semantic knowledge on reconstructing spatial-episodic associations were specifically pronounced for items that had higher typicality for their semantic category (Tompary \u0026amp; Thompson-Schill, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Tompary et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). While this suggests that more typical instances of semantic categories evoke more pronounced semantic processing and consequently result in a stronger impact of semantic knowledge on episodic reconstruction when presented as retrieval cues, this mechanism may not have been critical in generating the semantic congruence effect in the present study. Importantly, the task used here featured retrieval trials presenting a set of three images on each iteration. Semantic typicality of individual items is likely to be an important factor modulating semantic processing on retrieval tasks involving responses to single items as was the case in the aforementioned studies. However, its impact may be reduced in situations where participants process a set of items on a given trial due to concurrent activated category representations. In addition, as discussed above, the semantic congruence effect in the current study may have been less a consequence of retrieval-specific effects (such as the spontaneous activation of semantic category representations by highly typical items) than of semantic processing at encoding and consequent expression of encoded semantic context during retrieval.\u003c/p\u003e \u003cp\u003eSome limitations apply to the current study. Data collection was split into two separate data collection phases which may limit the validity of direct comparisons between the datasets and analysis of all data using a single model, respectively. In addition, part of the data in experiment 1 was obtained in an online setting while most of the data was collected in testing sessions at the lab. However, analyses that either excluded or included the data obtained through online sessions yielded comparable results. Lastly, the experimental task used in the current study probed memory for the temporal order of events, but the reconstructive character of the memory retrieval process in this task is arguably limited, as recall of temporal order is focused on a small, predefined subset of items on every trial. While this limits the generalizability of our findings towards generative episodic retrieval processes more generally, this paradigm allows for a highly controlled examination of the effects of semantic knowledge on temporal order memory, potentially on the level of single items and trials. This property may be beneficial for future neuroimaging studies investigating the neural mechanisms underlying the effects of semantic knowledge on temporal episodic memory.\u003c/p\u003e "},{"header":"Conclusion and Future Outlook","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003cp\u003eThis study highlights the key role of semantic knowledge in shaping memory for the temporal order of events. Specifically, the presentation of semantically structured image sequences at encoding was linked to a semantic congruence effect on temporal order memory retrieval. Participants who encoded semantically clustered image sequences showed higher retrieval accuracy on congruent trials where the item that had been presented temporally closer to a cue was also semantically related to the cue. Crucially, the effect was limited to the encoding condition in which image sequences were clustered according to semantic categories and was not observed with image sequences in which images were presented at random positions. Additionally, temporal distance between encoded items predicted temporal order memory accuracy across conditions. Collectively, our findings suggest that encoding of semantic context information, via the activation of semantic knowledge by semantically structured encoding sets, benefits temporal order memory when semantic and temporal structure of an event sequence were originally intertwined. These results extend prior research on the effects of semantic knowledge on various memory tasks, specifically by demonstrating the dependence of the semantic congruence effect on the encoding of semantically structured material and by extending this research to memory for the temporal order of naturalistic visual stimuli. Our findings are largely in line with predictions made by influential theoretical frameworks such as the CMR and the SCM. In addition, we provide evidence suggesting that the SCM should specifically incorporate semantic encoding processes as key mechanism explaining how semantic knowledge can influence generative episodic memory. Future research should explore the generalizability of the current findings across different populations and stimulus types, including more complex and naturalistic visual and spatiotemporal materials. Finally, given the potential for semantic knowledge to influence memory retrieval, understanding how different encoding strategies can affect the balance between semantic and episodic memory expression may inform interventions for disorders of memory function and applications in educational settings.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRaw data and analysis scripts for experiment 1, experiment 2 and the joint analysis of both datasets can be retrieved from the Open Science Framework project repository using the following link: https://osf.io/26avd/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding and Acknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunding for this work was supported by the Deutsche Forschungsgemeinschaft (DFG; German Research Foundation) within the FOR 2812: \u0026ldquo;Constructing scenarios of the past\u0026rdquo;, grant no. 419039274). The DFG had no further role in study design, collection, analysis and interpretation of data, in the writing of the manuscript and in the decision to submit the paper for publication. We thank Raphael Merz and Marwa Al-Kablawi for help in participant recruitment and data collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatements and Declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone of the authors declare any financial or non-financial interests that would be directly or indirectly related to the submitted work. The submitted work has been approved by the ethics committee of the Faculty of Psychology at Ruhr University Bochum (application number 764), in accordance with the guidelines for research involving human participants of the Declaration of Helsinki. All participants gave their informed consent prior to their participation in the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: Carina Zoellner, Nora A. Herweg, Oliver T. Wolf\u003c/p\u003e\n\u003cp\u003eData curation: Carina Zoellner, Henry D. Soldan\u003c/p\u003e\n\u003cp\u003eFormal analysis: Henry D. Soldan\u003c/p\u003e\n\u003cp\u003eFunding acquisition: Nora A. Herweg, Oliver T. Wolf\u003c/p\u003e\n\u003cp\u003eInvestigation: Carina Zoellner, Nurten Genc\u003c/p\u003e\n\u003cp\u003eMethodology: Carina Zoellner, Nora A. Herweg\u003c/p\u003e\n\u003cp\u003eProject administration: Carina Zoellner, Henry D. Soldan\u003c/p\u003e\n\u003cp\u003eResources: Nora A. Herweg, Oliver T. Wolf, Christian J. Merz\u003c/p\u003e\n\u003cp\u003eSoftware: Nora A. Herweg, Carina Zoellner, Henry D. Soldan\u003c/p\u003e\n\u003cp\u003eSupervision: Christian J. Merz, Oliver T. Wolf\u003c/p\u003e\n\u003cp\u003eValidation: Carina Zoellner, Christian J. Merz, Henry D. Soldan\u003c/p\u003e\n\u003cp\u003eVisualization: Carina Zoellner, Henry D. Soldan\u003c/p\u003e\n\u003cp\u003eWriting \u0026ndash; original draft: Henry D. Soldan, Nurten Genc\u003c/p\u003e\n\u003cp\u003eWriting \u0026ndash; review \u0026amp; editing: Christian J. Merz, Carina Zoellner, Henry D. Soldan, Oliver T. Wolf\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAddis, D. R. (2018). Are episodic memories special? On the sameness of remembered and imagined event simulation. \u003cem\u003eJournal of the Royal Society of New Zealand\u003c/em\u003e, \u003cem\u003e48\u003c/em\u003e(2\u0026ndash;3), 64\u0026ndash;88. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/03036758.2018.1439071\u003c/span\u003e\u003cspan address=\"10.1080/03036758.2018.1439071\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAka, A., Phan, T. D., \u0026amp; Kahana, M. J. (2021). Predicting recall of words and lists. \u003cem\u003eJournal of Experimental Psychology Learning Memory and Cognition\u003c/em\u003e, \u003cem\u003e47\u003c/em\u003e(5), 765\u0026ndash;784. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/xlm0000964\u003c/span\u003e\u003cspan address=\"10.1037/xlm0000964\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBartlett, F. C., \u0026amp; Remembering (1932). Cambridge: Cambridge University Press.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBates, D., M\u0026auml;chler, M., Bolker, B., \u0026amp; Walker, S. (2015). Fitting Linear Mixed-Effects Models Using lme4. \u003cem\u003eJournal of Statistical Software\u003c/em\u003e, \u003cem\u003e67\u003c/em\u003e(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.18637/jss.v067.i01\u003c/span\u003e\u003cspan address=\"10.18637/jss.v067.i01\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBein, O., Livneh, N., Reggev, N., Gilead, M., Goshen-Gottstein, Y., \u0026amp; Maril, A. (2015). Delineating the effect of semantic congruency on episodic memory: The role of integration and relatedness. \u003cem\u003ePloS One\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(2), e0115624. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0115624\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0115624\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChan, S. C. Y., Applegate, M. C., Morton, N. W., Polyn, S. M., \u0026amp; Norman, K. A. (2017). Lingering representations of stimuli influence recall organization. \u003cem\u003eNeuropsychologia\u003c/em\u003e, \u003cem\u003e97\u003c/em\u003e, 72\u0026ndash;82. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neuropsychologia.2017.01.029\u003c/span\u003e\u003cspan address=\"10.1016/j.neuropsychologia.2017.01.029\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng, S., Werning, M., \u0026amp; Suddendorf, T. (2016). Dissociating memory traces and scenario construction in mental time travel. \u003cem\u003eNeuroscience and Biobehavioral Reviews\u003c/em\u003e, \u003cem\u003e60\u003c/em\u003e, 82\u0026ndash;89. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neubiorev.2015.11.011\u003c/span\u003e\u003cspan address=\"10.1016/j.neubiorev.2015.11.011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCollins, A. M., \u0026amp; Loftus, E. F. (1975). A spreading-activation theory of semantic processing. \u003cem\u003ePsychological Review\u003c/em\u003e, \u003cem\u003e82\u003c/em\u003e(6), 407\u0026ndash;428. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/0033-295X.82.6.407\u003c/span\u003e\u003cspan address=\"10.1037/0033-295X.82.6.407\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDougherty, M. R., Halpern, D., \u0026amp; Kahana, M. J. (2023). Forward and backward recall dynamics. \u003cem\u003eJournal of Experimental Psychology Learning Memory and Cognition\u003c/em\u003e, \u003cem\u003e49\u003c/em\u003e(11), 1752\u0026ndash;1772. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/xlm0001254\u003c/span\u003e\u003cspan address=\"10.1037/xlm0001254\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEkstrom, A. D., \u0026amp; Ranganath, C. (2018). Space, time, and episodic memory: The hippocampus is all over the cognitive map. \u003cem\u003eHippocampus\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(9), 680\u0026ndash;687. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/hipo.22750\u003c/span\u003e\u003cspan address=\"10.1002/hipo.22750\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFaraway, J. J. (2016). \u003cem\u003eExtending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models, Second Edition\u003c/em\u003e (Second edition). \u003cem\u003eChapman \u0026amp; Hall/CRC Texts in Statistical Science\u003c/em\u003e. Taylor and Francis, an imprint of Chapman and Hall/CRC. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1201/9781315382722\u003c/span\u003e\u003cspan address=\"10.1201/9781315382722\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFaul, F., Erdfelder, E., Buchner, A., \u0026amp; Lang, A. G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. \u003cem\u003eBehavior Research Methods\u003c/em\u003e, \u003cem\u003e41\u003c/em\u003e(4), 1149\u0026ndash;1160. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3758/BRM.41.4.1149\u003c/span\u003e\u003cspan address=\"10.3758/BRM.41.4.1149\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHealey, M. K. (2018). Temporal contiguity in incidentally encoded memories. \u003cem\u003eJournal of Memory and Language\u003c/em\u003e, \u003cem\u003e102\u003c/em\u003e, 28\u0026ndash;40. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jml.2018.04.003\u003c/span\u003e\u003cspan address=\"10.1016/j.jml.2018.04.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHealey, M. K., Long, N. M., \u0026amp; Kahana, M. J. (2019). Contiguity in episodic memory. \u003cem\u003ePsychonomic Bulletin \u0026amp; Review\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e(3), 699\u0026ndash;720. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3758/s13423-018-1537-3\u003c/span\u003e\u003cspan address=\"10.3758/s13423-018-1537-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHebart, M. N., Dickter, A. H., Kidder, A., Kwok, W. Y., Corriveau, A., van Wicklin, C., \u0026amp; Baker, C. I. (2019). Things: A database of 1,854 object concepts and more than 26,000 naturalistic object images. \u003cem\u003ePloS One\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(10), e0223792. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0223792\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0223792\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoward, M. W., \u0026amp; Kahana, M. J. (2002). When Does Semantic Similarity Help Episodic Retrieval? \u003cem\u003eJournal of Memory and Language\u003c/em\u003e, \u003cem\u003e46\u003c/em\u003e(1), 85\u0026ndash;98. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1006/jmla.2001.2798\u003c/span\u003e\u003cspan address=\"10.1006/jmla.2001.2798\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIshiguro, S., \u0026amp; Saito, S. (2021). The detrimental effect of semantic similarity in short-term memory tasks: A meta-regression approach. \u003cem\u003ePsychonomic Bulletin \u0026amp; Review\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(2), 384\u0026ndash;408. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3758/s13423-020-01815-7\u003c/span\u003e\u003cspan address=\"10.3758/s13423-020-01815-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKowialiewski, B., Gorin, S., \u0026amp; Majerus, S. (2021). Semantic knowledge constrains the processing of serial order information in working memory. \u003cem\u003eJournal of Experimental Psychology Learning Memory and Cognition\u003c/em\u003e, \u003cem\u003e47\u003c/em\u003e(12), 1958\u0026ndash;1970. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/xlm0001031\u003c/span\u003e\u003cspan address=\"10.1037/xlm0001031\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKowialiewski, B., Krasnoff, J., Mizrak, E., \u0026amp; Oberauer, K. (2022). The semantic relatedness effect in serial recall: Deconfounding encoding and recall order. \u003cem\u003eJournal of Memory and Language\u003c/em\u003e, \u003cem\u003e127\u003c/em\u003e, 104377. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jml.2022.104377\u003c/span\u003e\u003cspan address=\"10.1016/j.jml.2022.104377\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKowialiewski, B., Majerus, S., \u0026amp; Oberauer, K. (2024). Does semantic similarity affect immediate memory for order? Usually not, but sometimes it does. \u003cem\u003eJournal of Experimental Psychology Learning Memory and Cognition\u003c/em\u003e, \u003cem\u003e50\u003c/em\u003e(1), 68\u0026ndash;88. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/xlm0001279\u003c/span\u003e\u003cspan address=\"10.1037/xlm0001279\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu, X., Zhu, M. J. H., \u0026amp; Risko, E. F. (2024). Semantic relatedness can impair memory for item locations. \u003cem\u003ePsychological Research Psychologische Forschung\u003c/em\u003e, \u003cem\u003e88\u003c/em\u003e(3), 861\u0026ndash;879. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00426-023-01889-7\u003c/span\u003e\u003cspan address=\"10.1007/s00426-023-01889-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMelega, G., \u0026amp; Sheldon, S. (2023). Conceptual relatedness promotes memory generalization at the cost of detailed recollection. \u003cem\u003eScientific Reports\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(1), 15575. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-023-40803-4\u003c/span\u003e\u003cspan address=\"10.1038/s41598-023-40803-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMontefinese, M., Zannino, G. D., \u0026amp; Ambrosini, E. (2015). Semantic similarity between old and new items produces false alarms in recognition memory. \u003cem\u003ePsychological Research Psychologische Forschung\u003c/em\u003e, \u003cem\u003e79\u003c/em\u003e(5), 785\u0026ndash;794. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00426-014-0615-z\u003c/span\u003e\u003cspan address=\"10.1007/s00426-014-0615-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorton, N. W., \u0026amp; Polyn, S. M. (2016). A predictive framework for evaluating models of semantic organization in free recall. \u003cem\u003eJournal of Memory and Language\u003c/em\u003e, \u003cem\u003e86\u003c/em\u003e, 119\u0026ndash;140. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jml.2015.10.002\u003c/span\u003e\u003cspan address=\"10.1016/j.jml.2015.10.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorton, N. W., \u0026amp; Polyn, S. M. (2017). Beta-band activity represents the recent past during episodic encoding. \u003cem\u003eNeuroimage\u003c/em\u003e, \u003cem\u003e147\u003c/em\u003e, 692\u0026ndash;702. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neuroimage.2016.12.049\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2016.12.049\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePackard, P. A., Rodr\u0026iacute;guez-Fornells, A., Bunzeck, N., Nicol\u0026aacute;s, B., de Diego-Balaguer, R., \u0026amp; Fuentemilla, L. (2017). Semantic Congruence Accelerates the Onset of the Neural Signals of Successful Memory Encoding. \u003cem\u003eJournal of Neuroscience\u003c/em\u003e, \u003cem\u003e37\u003c/em\u003e(2), 291\u0026ndash;301. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1523/JNEUROSCI.1622-16.2016\u003c/span\u003e\u003cspan address=\"10.1523/JNEUROSCI.1622-16.2016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePolyn, S. M., Erlikhman, G., \u0026amp; Kahana, M. J. (2011). Semantic cuing and the scale insensitivity of recency and contiguity. \u003cem\u003eJournal of Experimental Psychology Learning Memory and Cognition\u003c/em\u003e, \u003cem\u003e37\u003c/em\u003e(3), 766\u0026ndash;775. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/a0022475\u003c/span\u003e\u003cspan address=\"10.1037/a0022475\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePolyn, S. M., Norman, K. A., \u0026amp; Kahana, M. J. (2009). A context maintenance and retrieval model of organizational processes in free recall. \u003cem\u003ePsychological Review\u003c/em\u003e, \u003cem\u003e116\u003c/em\u003e(1), 129\u0026ndash;156. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/a0014420\u003c/span\u003e\u003cspan address=\"10.1037/a0014420\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR Core Team (2023). \u003cem\u003eR: A Language and Environment for Statistical Computing\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.R-project.org/\u003c/span\u003e\u003cspan address=\"https://www.R-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRamey, M. M., Henderson, J. M., \u0026amp; Yonelinas, A. P. (2022). Episodic memory processes modulate how schema knowledge is used in spatial memory decisions. \u003cem\u003eCognition\u003c/em\u003e, \u003cem\u003e225\u003c/em\u003e, 105111. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cognition.2022.105111\u003c/span\u003e\u003cspan address=\"10.1016/j.cognition.2022.105111\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRenoult, L., Irish, M., Moscovitch, M., \u0026amp; Rugg, M. D. (2019). From Knowing to Remembering: The Semantic-Episodic Distinction. \u003cem\u003eTrends in Cognitive Sciences\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(12), 1041\u0026ndash;1057. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.tics.2019.09.008\u003c/span\u003e\u003cspan address=\"10.1016/j.tics.2019.09.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoediger, H. L., \u0026amp; McDermott, K. B. (1995). Creating false memories: Remembering words not presented in lists. \u003cem\u003eJournal of Experimental Psychology: Learning Memory and Cognition\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e(4), 803\u0026ndash;814. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037//0278-7393.21.4.803\u003c/span\u003e\u003cspan address=\"10.1037//0278-7393.21.4.803\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRosch, E., Simpson, C., \u0026amp; Miller, R. S. (1976). Structural bases of typicality effects. \u003cem\u003eJournal of Experimental Psychology: Human Perception and Performance\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(4), 491\u0026ndash;502. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/0096-1523.2.4.491\u003c/span\u003e\u003cspan address=\"10.1037/0096-1523.2.4.491\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaint-Aubin, J., Gu\u0026eacute;rard, K., Chamberland, C., \u0026amp; Malenfant, A. (2014). Delineating the contribution of long-term associations to immediate recall. \u003cem\u003eMemory (Hove England)\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(4), 360\u0026ndash;373. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/09658211.2013.794242\u003c/span\u003e\u003cspan address=\"10.1080/09658211.2013.794242\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchacter, D. L. (2012). Adaptive constructive processes and the future of memory. \u003cem\u003eThe American Psychologist\u003c/em\u003e, \u003cem\u003e67\u003c/em\u003e(8), 603\u0026ndash;613. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/a0029869\u003c/span\u003e\u003cspan address=\"10.1037/a0029869\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSheldon, S. (2021). The impact of encoding scenarios on different forms of temporal order memory. \u003cem\u003ePsychological Research Psychologische Forschung\u003c/em\u003e, \u003cem\u003e85\u003c/em\u003e(7), 2553\u0026ndash;2565. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00426-020-01440-y\u003c/span\u003e\u003cspan address=\"10.1007/s00426-020-01440-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTompary, A., \u0026amp; Thompson-Schill, S. L. (2021). Semantic influences on episodic memory distortions. \u003cem\u003eJournal of Experimental Psychology General\u003c/em\u003e, \u003cem\u003e150\u003c/em\u003e(9), 1800\u0026ndash;1824. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/xge0001017\u003c/span\u003e\u003cspan address=\"10.1037/xge0001017\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTompary, A., Xia, A., Coslett, B. H., \u0026amp; Thompson-Schill, S. L. (2023). Disruption of Anterior Temporal Lobe Reduces Distortions in Memory From Category Knowledge. \u003cem\u003eJournal of Cognitive Neuroscience\u003c/em\u003e, \u003cem\u003e35\u003c/em\u003e(12), 1899\u0026ndash;1918. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1162/jocn_a_02053\u003c/span\u003e\u003cspan address=\"10.1162/jocn_a_02053\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTulving, E. (2002). Episodic memory: From mind to brain. \u003cem\u003eAnnual Review of Psychology\u003c/em\u003e, \u003cem\u003e53\u003c/em\u003e(Volume 53, 2002), 1\u0026ndash;25. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1146/annurev.psych.53.100901.135114\u003c/span\u003e\u003cspan address=\"10.1146/annurev.psych.53.100901.135114\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVanRossum, G., \u0026amp; Drake, F. L. (2010). \u003cem\u003eThe Python language reference\u003c/em\u003e (Release 3.0.1 [repr]. \u003cem\u003ePython documentation manual: / Guido van Rossum; Fred L. Drake [ed.]; Pt. 2\u003c/em\u003e. Python Software Foundation; SoHo Books.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZ\u0026ouml;llner, C., Klein, N., Cheng, S., Schubotz, R. I., Axmacher, N., \u0026amp; Wolf, O. T. (2022). Where was the toaster? A systematic investigation of semantic construction in a new virtual episodic memory paradigm. \u003cem\u003eQuarterly Journal of Experimental Psychology (2006)\u003c/em\u003e, 17470218221116610. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/17470218221116610\u003c/span\u003e\u003cspan address=\"10.1177/17470218221116610\" 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":"generative episodic memory, memory retrieval, prior knowledge, scenario construction, semantic memory","lastPublishedDoi":"10.21203/rs.3.rs-6595825/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6595825/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEpisodic memory does not perfectly reproduce past experiences but combines encoded episode-specific information and semantic knowledge in a constructive way. Previous research has shown that semantic category knowledge can bias location memory for individual items, suggesting that similar mechanisms may affect other key dimensions of episodic memory. Here, we investigated whether immediate temporal order memory is influenced by semantic relatedness between encoded items and whether this effect is modulated by semantic structure at encoding, episodic association strength and semantic typicality. Across two experiments, participants completed a temporal order memory task in which they encoded sequences of object images and subsequently judged the relative temporal proximity between items. Results showed that participants who encoded semantically structured sequences performed significantly better on congruent retrieval trials where the correct choice (the temporally closer item) was semantically related to the cue versus on incongruent trials where the incorrect choice was semantically related to the cue. Participants who did not encode semantically structured sequences did not show the semantic congruence effect and performed worse than those who encoded semantically structured sequences on congruent trials. Across conditions, the temporal distance between items at encoding positively predicted correct temporal proximity judgements. Overall, these findings demonstrate that semantic relatedness between encoded items can facilitate immediate temporal order memory depending on the encoding of semantically structured item sets. We discuss these results regarding the role and potential benefits of semantic and temporal context processing for constructive episodic memory.\u003c/p\u003e","manuscriptTitle":"Encoding of semantic structure shapes temporal order memory for visual object stimuli","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-19 06:49:10","doi":"10.21203/rs.3.rs-6595825/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-01T09:35:03+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-26T16:45:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-28T19:26:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"53143673683519142740814654406852655360","date":"2025-05-23T17:23:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"291967130535324958886690185934044775605","date":"2025-05-19T19:49:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"270255568105327224945117845362338283872","date":"2025-05-19T09:09:45+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-13T16:57:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-13T15:35:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-12T09:10:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"Psychological Research","date":"2025-05-05T15:36:49+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":"962207e3-6945-4032-849e-30b1b4cb6afd","owner":[],"postedDate":"May 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-19T16:47:25+00:00","versionOfRecord":{"articleIdentity":"rs-6595825","link":"https://doi.org/10.1007/s00426-025-02222-0","journal":{"identity":"psychological-research","isVorOnly":false,"title":"Psychological Research"},"publishedOn":"2026-01-13 16:29:36","publishedOnDateReadable":"January 13th, 2026"},"versionCreatedAt":"2025-05-19 06:49:10","video":"","vorDoi":"10.1007/s00426-025-02222-0","vorDoiUrl":"https://doi.org/10.1007/s00426-025-02222-0","workflowStages":[]},"version":"v1","identity":"rs-6595825","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6595825","identity":"rs-6595825","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}