Investigating the Impact of Personal Preferences on Visual Working Memory Recall

Investigating the Impact of Personal Preferences on Visual Working Memory Recall | 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 Article Investigating the Impact of Personal Preferences on Visual Working Memory Recall Roya Mohammad Sadegh, Ehsan Rezayat This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4724031/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Visual working memory (VWM) plays a vital role in holding visual information in mind, and errors in recall can have significant consequences. Previous research has identified various factors that can influence recall errors in VWM, such as the number of items held in memory and the similarity between items. In this study, we investigated the impact of personal preferences on VWM recall accuracy. Our results showed that recall accuracy was significantly higher for preferential items compared to non-preferential items, indicating that personal preferences can have a significant impact on VWM recall accuracy. Moreover, the distance analysis revealed both attraction and repulsion effects in the VWM content. These findings have important implications for understanding the factors that influence memory recall and may have practical applications in fields such as marketing and design. Overall, this study sheds light on the role of personal preferences in VWM recall accuracy and contributes to a better understanding of the cognitive mechanisms underlying memory recall. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Visual working memory (VWM) stores visual information in different ways and allows us to store features such as color, shape, and location (Diamond, 2013 ) and can store information in a discrete and categorical manner (Zhou et al., 2022 ). VWM is important for tasks that require us to hold visual information in mind, such as remembering the color of a specific object, navigating a familiar environment, or following a set of instructions (Brady & Störmer, 2022 ). However human memory is susceptible to distortion and can give rise to false memories. Many studies have shown that observers' responses in simple visual feature memory tasks deviate in a systematic manner from correct values, and they have conducted numerous studies to understand the nature of these recall errors in VWM (Bae et al., 2015 ; Panichello et al., 2019 ). We knew that errors in working memory arise, and the number of items held simultaneously in working memory can also contribute to increased error rates (Zhang & Luck, 2009 ). These errors may arise partially from noise in the neural representations that underlie memory. Random noise can cause the memory representations to gradually deviate from their original state, leading to behavioral errors (Panichello et al., 2019 ). An example of these errors, is responses drawn from working memory in color memory tasks are significantly biased away from category boundaries and towards category centers. Moreover, different colors can have effects on our activities, and some studies have shown that participants who viewed a red screen performed better on the working memory task compared to those who viewed a neutral screen (Bae et al., 2015 ; Elliot & Maier, 2007 ). Studies have also shown that color working memory may not behave uniformly and may rely on encoding of stimulus categories, along with continuous values, to support comparative stimulus judgments (Bae et al., 2015 ). Based on different researches we know that we have kind of systematic error in VWM. For example, in some kinds of N-back tasks, when the perceptual similarity between a target item and a lure item is high, individuals are more likely to mistakenly identify the lure item as something they have seen before, or In a delayed estimation task, when an observer is instructed to provide their best estimate of a previously presented hue, they are likely to choose the peak of the likelihood distribution, known as the "maximum-likelihood estimate." This can be represented by colored pins in the task. The experimenter can measure the observer's error by calculating the distance between this estimate and the actual hue that was presented (Bays et al., 2024 ), due to delay estimation task’s results during memory recall, there is a possibility that the reporting of features can be influenced by the presence of other items held in working memory, rather than solely focusing on the probed item. This suggests that the memory recall process can be susceptible to interference or contamination from the features of other items being retained concurrently (Ma et al., 2014 ). Recent studies have demonstrated errors (Swap) toward a specific color which was presented in long-term memory due to statistical regularities present during object encoding (Scotti, Hong, Golomb, et al., 2021). These errors refer to the mistaken report of a non-target feature or implicitly biased guessing. Statistical learning refers to the process of acquiring knowledge about patterns in the environment through the detection and internalization of statistical regularities. This acquired knowledge can manifest as changes in behavior (Perruchet & Pacton, 2006 ). The impact of different colors, including individuals' favorite colors, on cognitive processes such as working memory remains uncertain, despite the recognized significance of colors and their categories in cognitive functions (Ester et al., 2020 ; Panichello et al., 2019 ). It is crucial to investigate how favorable stimuli, such as preferred colors, can influence behavior and potentially affect the accuracy or precision of our responses. Understanding these effects can provide valuable insights into the relationship between our preferences and cognitive processes, informing various domains such as psychology, design, and marketing. Therefore, the aim of this study is to further investigate the memory bias towards the favorite stimulus, specifically the favorite color, and its effect on memory errors. Materials and methods Participants All procedures were approved by University of Tehran Ethics Committee (IR.UT.PSYEDU.REC.1403.011) and was in accordance with Helsinki declaration of 1964 and its revisions. Informed consent was obtained from all subjects involved in the study, or their legal guardians in the case of minors, ensuring that their participation was voluntary and that they were fully aware of the study's objectives and potential risks. The experiment included thirty-six participants (7 male,29 female; M = 23.97 years, SD = 7.44) all of them where university students and in exchange they received course credits. One additional participant was excluded based on performing the task unsuccessfully (did not complete all trials). All Participants reported normal or corrected-to-normal acuity and color vision. Stimuli and Procedure The experiment comprised three conditions, a favorit color task (Figure.1A), color-matching task (Figure.1B) and a working memory task (Figure. 1C-D). Firstly, participants completed a color-matching task (Brady et al., 2013 ) (Figure.1B). On each of the 40 trials, an image was presented on the left side of the screen with a white background, while a grayscale copy of the image simultaneously appeared on the right side. Participants (head position was not fixed) were instructed to adjust the color of the right item to match the color of the target on the left. This task aimed to assess the accuracy of color perception, as we hypothesized that high fidelity in color perception would be crucial for color memory. This perception task served to detect the fidelity of color perception. All stimuli were generated using the Psychophysics Toolbox (abbreviated as “Psychtoolbox”) (Brainard, 1997 ) for MATLAB. Object stimuli were obtained from the image sets by Brady, Konkle, Alvarez, and Oliva (2008).Posterization was applied to each image, restricting pixel values to white, black, or a single color of interest chosen from 360 RGB color values derived from a one-dimensional selection of the CIE Lab color space provided by MemToolbox (Suchow et al., 2013 ). All objects were real-word objects ( 250 x 250 px ) object was presented in the centre of a white background on a 15-in monitor with a screen resolution 1920 x 1080 Pixels. Following the perception task, a favorit color test was presented (Figure.1A). In this part on each trial, one object – selected randomly without replacement – was presented in grayscale (color was replaced with RGB [128, 128, 128]), and a color wheel was presented around the object Participants were instructed to use their mouse to explore the color wheel and choose their favorite color for the object. They could only determine the color by observing the color of the wheel at the position of their mouse pointer. The purpose of this task was to have participants select their preferred color for the objects on the screen, focusing solely on the colors. Participants were free to choose any available color for the objects. At the end of this task, participants would see all the chosen colors for the objects and confirm their favorite color. Following the favorit color task, participants completed a working memory task (Figure.1D). Here, we used a continuous report method. Each trial began with a black fixation mark presented at the center of the screen. During each trial, 1, 3, or 5 square stimuli (each 110 x 110 pixels) were simultaneously presented for 0.4 seconds in a circle around the fixation mark. The spatial locations of the stimuli were randomly determined relative to the central fixation. Participants were instructed to remember the color of either 1, 3, or 5 square sample stimuli. After a variable memory delay of 1, 3, or 7 seconds, a response screen appeared. The response screen consisted of a square at one of the previous sample locations (the target sample), there was no time limit on the response. Participants used the mouse to drag the circle around the ring, which changed the color of the probe sample. Following a mouse click to confirm their selected color, participants made a confidence range report. This task comprised 120 trials. The favorite color was incorporated into the working memory task by manipulating the colors of the studied stimuli. In all 3-stimulus trials (which accounted for 60% of the trials), one of the squares served as the target sample and was randomly determined from a Short (23–67°), Medium (68–112°), or Long (113–157°) distance in color space from the favourite color (Fig. 1 C). Additionally, one of the distractors was biased toward the favorite color, while the other distractor was colored in exactly the opposite color of the favorite color in the color space, either clockwise or counterclockwise along the color wheel. Participants were not informed about this manipulation of color sampling. These procedures were employed to investigate the influence of favorite color on working memory. Analyses On any given trial, we recorded error and for measuring errors we used a full and modified mixture model(Bays et al., 2009 ; Golomb et al., 2014 ) to account for various sources of error. Participants' responses was converted into an error measurement, we calculated the difference between the reported and correct color values. Then we calculated the absolut value of difference between the response and the favourite,also difference between the response 180-degree opposite of the favourite color If difference between respons and favourite was lower than difference between the response 180-degree opposite of the favourite color, then error sign will be positive and it means that respons was in the direction towards the favourite color, and if it was toward the opposit color the error was signed negative. In simple terms, a mixture model investigates the possibility of both a shift in the average response and the misreporting of distractors, along with the chance of guessing, in statistical analysis (Bays et al., 2009 ). The model comprised distinct distributions, namely the target distribution, swap distribution, and random guessing distribution. The target distribution was a circular Gaussian probability density function (known as von Mises distribution) centered around the initial, accurate color, with adjustable mean and standard deviation. On the other hand, the swap distribution was also a circular Gaussian probability density function (von Mises distribution) centered on the Favorite color. Mathematically the model is described : p(θ) = (1-β-δ-γ) * ϕ (µ, κ) + β * ϕ (FavColor, κ) + δ * ϕ (OppColor, κ) + γ (1/ 2π) where θ is the difference between the reported and correct color values,γ is the probability of random response or guessing,ϕ is a von Mises distribution (the circular analogue of the Gaussian) with µ as its mean and concentration κ,β is the probability of misreporting the Favourite color value and δ is the probability of misreporting the Opposite color of favourite color value. Maximum-likelihood estimates of the parameters µ, κ, γ, β, and δ were obtained separately using the Bays lab toolbox(Bays et al., 2009 ) and MemToolbox (Suchow et al., 2013 ). To model memory response distributions, we classified them into three categories (Short, Medium, and Long) based on the difference in color 9space between the target color and the Favorite color. The reason for doing so is that the relational representation model suggests that the response is likely to be repulsive for shorter distances and attractive for longer distances. Therefore, we analysed each category separately to account for these different types of responses. The mean of the distribution was calculated separately for condition, and submitted to within-subjects’ analyses of variance (ANOVAs) and t tests to make statistical comparisons between conditions. Results For our analysis, the distribution of each participant's memory response was fit using the previously described probabilistic mixture modelling approach. It is noteworthy that in our participant group, consisting of 7 men and 29 women, no particular color preference was observed. The favorite colors chosen were diverse, spanning a wide range of hues, indicating no concentration around any specific color. We examined whether statistical regularities, specifically the propensity of a participant's favorite color to occur frequently among squares in a working memory task, might distort their reports. Figure 2 depicts histograms of memory errors, based on trials where the target square's color was sampled from a Short, Medium, or Long distance in color space from the participant's favorite color. Notably, the tails of the distributions show a trend at long distances: heavier tails on the right indicate a bias toward recall of the preferred color, especially as color distance increases. This indicates the presence a systematic bias in memory. Each histogram illustrates a peak at zero error, indicating instances of accurate color recall. However, as the color distance increases, the spread of errors increases.It is visually apparent that memory errors shifting toward the favorite color, suggesting the occurrence of swap errors. We conducted a repeated-measures ANOVA to explore the impact of the distance from a participant's favorite color on memory accuracy. The analysis revealed a significant main effect of color distance on memory performance ([F(1,1284) = 7.5, p = 0.001]), indicating that memory accuracy decreases as the target color's distance from the favorite color increases. A significant linear contrast further substantiated this relationship ([F(1,642) = 13.10, p < 0.001]), suggesting a consistent increase in memory errors with greater color distances (Mauchly’s test of sphericity confirmed that the variances among the experimental conditions were equal (χ²(2) = 3.04, p = 0.21), validating the sphericity assumption of our ANOVA results). In Fig. 3 , the amplitude of various components in our model is displayed, demonstrating that the target consistently exerts a strong influence across all conditions, indicating the highest probability of recalling accurately. Additionally, the figure is demonstrating that guessing (γ denotes the likelihood of a random response, hinting at guessing behaviours.) plays a role across all conditions, surpassing the influence of both 'Favorite' and 'Opposite' components. Figure.4 shows the average error trend in memory recall at different distances from a participant's favorite color classified as short, medium, and long. A value of 0 would indicate no systematic deviation from the correct color. Positive values indicate attraction, suggesting that participants reported a color close to the correct color but slightly shifted either toward their favorite color. On the other hand, negative values indicate repulsion, indicating a deviation away from their favorite color. At short distances, a repulsion effect is evident with errors showing a negative mean, indicating distance from the color of interest. As the distance increases to medium, the repulsive effect decreases slightly. Notably, at long distances, a clear attraction effect appears and the mean errors become positive, indicating a significant bias towards the color of interest. This trend highlights the dynamic effect of color proximity on memory accuracy, changing from repulsion to attraction as distance from the preferred color increases. Other parameters The correlation analysis only revealed significant correlations (p < 0.05) between the errors in long segment and delay. Examining the average confidences across all conditions did not show a significant difference. However, examining the errors of individuals with high (80% and above) and low(40% and lower) confidence in different segments, showed differences only in the short distance segment, the distribution of errors for low confidence trials exhibits a shift in the mean toward favorite color compared to high confidence trials. Exploratory analyses of the target standard deviation and the standard deviation of the standard deviation (Std) of responses toward the favorite color across different distances (Short, Medium, Long) is shown in figure.5. A smaller standard deviation would indicate that the responses are more consistently close to the mean, suggesting less variability in how accurately participants remember the favorite color across different distances. Discussion and Conclusion The results from this experiment can explain the influence of the presence of the favorite color among the available stimuli on their recall .To our knowledge, no previous studies have demonstrated swap and guess errors in working memory due to imposed statistical regularities related to the favorite color .Our findings demonstrate that visual working memory is distorted by implicitly patterns in our environment.This research stems from work in visual working memory that observed attraction and repulsion bias dependent on inter-item similarity to the favorite color (Bae & Luck, 2017 ; Golomb, 2015 ). Here, we shows feature-mixing errors are influenced by target–favorite color similarity,The presence of a noticeable increase in memory errors, specifically swap and geussing errors, concentrated around the favorite color can be visually observed across all color-distance segments. Regardless of the specific segment being considered, there is a clear and evident pattern of higher error rates occurring when participants mistakenly swap the favorite color with other colors in their memory recall tasks especially when the distance is long ,this is in line with the relational representation model (Bae & Luck, 2017 ; Golomb, 2015 ), with repulsion away from the favourite color for the Short and Medium color-distance segments and attraction towards the favourite color for the Long color-distance segment. These findings in visual working memory have conceptual connections to phenomena such as the direction illusion and the tilt illusion, as well as their corresponding visual aftereffects. These perceptual phenomena, studied in previous research (Gibson, 1937 ), demonstrate attraction and repulsion effects based on the distance in feature space between perceptual stimuli. It is important to note, however, that the swap errors observed in working memory cannot be explained by the same perceptual mechanisms as these illusions (Scotti, Hong, Leber, et al., 2021). In addition to considering the distance in color space, we can also explore the similarities between memory and perception in explaining the observed pattern. Notably, a study conducted by Schurgin and colleagues (2018) provided evidence suggesting that both memory and perception do not follow a linear scaling within stimulus space. Instead, they rely on a transformed similarity representation that exhibits a non-linear relationship with stimulus space. This finding challenges a fundamental assumption in existing models of visual working memory. It is important to note that the framework proposed by Schurgin does not pose any issues for our study, as our analyses do not rely on differentiating between guess rate and precision. Therefore, our findings remain valid and unaffected by the concerns raised in that particular framework. The framework presented by Schurgin provides an alternative explanation for the results, further enriching the understanding of the topic (Schurgin et al., 2020 ). In other perevious studies in visual working memory experiments, it has been consistently observed that prior experience has a significant impact on biased recall. The memory system benefits from prior experience because it triggers processes of reinstatement and integration, which enhance memories through associative knowledge (van Kesteren et al., 2016 ). When participants are unable to accurately recall a specific item from memory, the learned regularities can implicitly or explicitly influence their memory reports, favoring previously encountered stimuli. For instance, in a visual working memory task conducted by Pratte (2018), participants were shown an array of colored squares and were later asked to indicate the location of a probed color. Even when the probed color was not originally present in the study array, participants' reports still tended to be centered around the locations of the study items. This suggests that participants were utilizing information about where the memory items could appear to bias their responses or guesses.Our findings align with the study conducted by Lin and colleagues (2021), who investigated the impact of object-based attention on visual working memory. They demonstrated that object-based representations are promoted and suggested the existence of shared attentional mechanisms between perception and memory. In our study, we extended this understanding by highlighting that not only objects but also environmental elements, such as our favorite color, can influence and introduce biases in our memory processes. When it comes to confidence,several studies (Amichetti et al., 2013 ; Bertrand et al., 2017 ) have demonstrated that individuals often exhibit overconfidence and poor metacognitive accuracy when making judgments about their memory performance. Even when participants' judgments show sensitivity to memory-related factors, they still struggle to accurately discriminate between erroneous and accurate responses (Conte et al., 2023 ). This raises questions about the cues that influence metacognitive judgments during working memory tasks and the reasons behind the lack of metacognitive accuracy in working memory.In our study, we provided evidence that errors and confidence distributions in memory performance vary across different segments and distances from our favorite color. These findings suggest that factors such as the emotional significance of stimuli and their relation to our favorite color, personal preferences, and attentional biases towards certain features or colors can influence metacognitive judgments in working memory tasks.Further research can delve into the specific cues and mechanisms that contribute to metacognitive judgments during ongoing WM tasks. By identifying these cues, we can develop interventions or training programs aimed at enhancing individuals' metacognitive accuracy and improving their ability to evaluate the reliability of their memory performance. In real-world scenarios where the reliability of memories is crucial, it becomes essential to investigate how prior experiences and our favorite environment influence memory retrieval. Understanding the role of prior experiences and personal preferences in memory processes can provide valuable insights into the factors that shape our ability to retrieve accurate and reliable memories. By examining how our personal preferences, such as our favorite environment, impact memory recall in all other types of memory, we can gain a deeper understanding of the complex interplay between cognition, emotions, and memory. Declarations Author Contribution R.M.S. and E.R. wrote the main manuscript text and R.M.S. do experiment and analysis . All authors reviewed the manuscript. Data availability The datasets used during the current study available from the corresponding author on reasonable request References Amichetti, N. M., Stanley, R. S., White, A. G., & Wingfield, A. (2013). Monitoring the capacity of working memory: Executive control and effects of listening effort. Memory & Cognition , 41 (6), 839–849. https://doi.org/10.3758/s13421-013-0302-0 Bae, G.-Y., & Luck, S. J. 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Psychophysical scaling reveals a unified theory of visual memory strength. Nature Human Behaviour , 4 (11), 1156–1172. https://doi.org/10.1038/s41562-020-00938-0 Scotti, P. S., Hong, Y., Golomb, J. D., & Leber, A. B. (2021). Statistical learning as a reference point for memory distortions: Swap and shift errors. Attention, Perception, & Psychophysics , 83 (4), 1652–1672. https://doi.org/10.3758/s13414-020-02236-3 Scotti, P. S., Hong, Y., Leber, A. B., & Golomb, J. D. (2021). Visual working memory items drift apart due to active, not passive, maintenance. Journal of Experimental Psychology: General , 150 (12), 2506–2524. https://doi.org/10.1037/xge0000890 Suchow, J. W., Brady, T. F., Fougnie, D., & Alvarez, G. A. (2013). Modeling visual working memory with the MemToolbox. Journal of Vision , 13 (10), 9–9. https://doi.org/10.1167/13.10.9 van Kesteren, M. T. R., Brown, T. I., & Wagner, A. D. (2016). Interactions between Memory and New Learning: Insights from fMRI Multivoxel Pattern Analysis. Frontiers in Systems Neuroscience , 10 . https://doi.org/10.3389/fnsys.2016.00046 Zhang, W., & Luck, S. J. (2009). Sudden Death and Gradual Decay in Visual Working Memory. Psychological Science , 20 (4), 423–428. https://doi.org/10.1111/j.1467-9280.2009.02322.x Zhou, C., Lorist, M. M., & Mathôt, S. (2022). Is Categorization in Visual Working Memory a Way to Reduce Mental Effort? A Pupillometry Study. Cognitive Science , 46 (9). https://doi.org/10.1111/cogs.13194 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4724031","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":334798746,"identity":"c2c61b3f-d59f-447b-a113-7526b64b0c0d","order_by":0,"name":"Roya Mohammad Sadegh","email":"","orcid":"","institution":"University of Tehran","correspondingAuthor":false,"prefix":"","firstName":"Roya","middleName":"Mohammad","lastName":"Sadegh","suffix":""},{"id":334798747,"identity":"371ef7a7-44d0-4e58-9d16-a011174abda8","order_by":1,"name":"Ehsan Rezayat","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuUlEQVRIiWNgGAWjYBADOSgtQbQOA2MGNlK1JDawEavWvL394Weemj/pG+43MH74wWCRT1CLzJkDydI8xwxyNxxjYJbsYZCwbCCkRUIi4YA0DxtYC4M0kG9A0BYJ+YfNv3n+GaQbAG35TZwWCWY2ad42gwSgFjYibeFJY7Oc22dsOPNYYptljwExWtiPP77x5pucPN/hw4dv/KioI6wFBJh4wBRjAzB+iNIAVPuDSIWjYBSMglEwQgEA8sgw0v/NhGYAAAAASUVORK5CYII=","orcid":"","institution":"University of Tehran","correspondingAuthor":true,"prefix":"","firstName":"Ehsan","middleName":"","lastName":"Rezayat","suffix":""}],"badges":[],"createdAt":"2024-07-11 12:12:51","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4724031/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4724031/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61853728,"identity":"aee7cd6f-1d35-42bc-a071-7fe98741cbd1","added_by":"auto","created_at":"2024-08-06 09:29:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":116078,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTask Illustrations for Memory and Color Preference Tests.\u003c/strong\u003e \u003cstrong\u003e(A) Favorite Color Selection\u003c/strong\u003e: Participants are shown an object, such as a backpack, and asked to select their preferred color for it using a color wheel. This task assesses individual color preferences and their influence on memory. \u003cstrong\u003e(B) Color Matching Task\u003c/strong\u003e: This panel illustrates a color adjustment task where participants match the color of one square to another, testing accuracy in color perception\u003cstrong\u003e. (C)\u003c/strong\u003e \u003cstrong\u003ecolor wheel: \u003c/strong\u003eShows a color wheel marked with an example of participant's favorite color (FC) and variable distances from it the same color wheel used for the working memory task. \u003cstrong\u003e(D) Example of working memory trial sequences.\u003c/strong\u003e In which participants reported the color of a sample after a variable delay. They made their report by adjusting the hue of the response probe by rotating a response wheel (black circle) using a mouse.\u003c/p\u003e","description":"","filename":"figure1finaler.png","url":"https://assets-eu.researchsquare.com/files/rs-4724031/v1/0678bd01f6194896dc82f879.png"},{"id":61852988,"identity":"28f8d8a6-4923-43c5-8a25-114f24924a1b","added_by":"auto","created_at":"2024-08-06 09:21:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":58561,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResponse Histograms of Memory Errors. \u003c/strong\u003eHistograms represents the distribution of errors in the memory task, where errors are calculated as the difference between the reported and originally presented colors. Errors are calculated by converting each response into an error measurement, quantifying the deviation from the correct color value. Errors were signed positive if the memory response was biased towards the Rich color and signed negative if biased away.\u003c/p\u003e","description":"","filename":"Figure2Hist.Horizentalaiergray.png","url":"https://assets-eu.researchsquare.com/files/rs-4724031/v1/e47cdf5a45e9324b45a58ee9.png"},{"id":61852991,"identity":"f0c3f2ae-16dc-4b44-960f-6c0e7aab6bcb","added_by":"auto","created_at":"2024-08-06 09:21:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":20313,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBar plots the amplitude of various model components across three conditions, identified by the color legend (Short, Medium, and Long distances). \u003c/strong\u003eThe components measured include 'Target', 'Favorite', 'Opposite', and 'Guess'. 'Target' indicating the base accuracy, 'Favorite' showing influence by personal preference, and 'Opposite' representing an opposite color of favorite color. Error bars represent the standard error of the mean (SEMs).\u003c/p\u003e","description":"","filename":"Figure3ModelFiter.png","url":"https://assets-eu.researchsquare.com/files/rs-4724031/v1/49317964c20be278dc52ced2.png"},{"id":61853730,"identity":"70ce5bd2-2427-4e98-be42-e1b2066d42a7","added_by":"auto","created_at":"2024-08-06 09:29:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":22146,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMean in Memory Recall by Color Distance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis graph illustrates the mean error in memory recall for colors at different distances from a participant's favorite color, categorized as Short, Medium, and Long. Negative values indicate a repulsion effect, where the recalled color is biased away from the favorite color, observed in the Short and Medium distances. Positive values, seen in the Long distance category, indicate an attraction effect, where the recalled color shifts towards the favorite color. Error bars are \u003cem\u003eSEM\u003c/em\u003e .\u003c/p\u003e","description":"","filename":"Figure4MeanandSDerfinal.png","url":"https://assets-eu.researchsquare.com/files/rs-4724031/v1/c9a1364f40b0487a9038914b.png"},{"id":61854397,"identity":"58e7d909-eee6-4b6b-8600-ac849cc43a4d","added_by":"auto","created_at":"2024-08-06 09:37:28","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":23497,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBar plots depict estimates for standard deviation distribution\u003c/strong\u003e. \u0026nbsp;\u003cem\u003e(\u003c/em\u003eA) shows the standard deviation for the target distribution, indicating the variability in recalling the actual target color across Short, Medium, and Long distances. (B) Swap error standard deviation (Sd).\u003c/p\u003e","description":"","filename":"Figure5MeanandSDer.png","url":"https://assets-eu.researchsquare.com/files/rs-4724031/v1/88374382d631af52f90cfb30.png"},{"id":63035475,"identity":"7c7ef31d-b397-4fba-b6a0-7806104ee2b1","added_by":"auto","created_at":"2024-08-22 10:20:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":661691,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4724031/v1/8ad7593d-5be9-4a82-ba72-f622d870c879.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Investigating the Impact of Personal Preferences on Visual Working Memory Recall","fulltext":[{"header":"Introduction","content":"\u003cp\u003eVisual working memory (VWM) stores visual information in different ways and allows us to store features such as color, shape, and location (Diamond, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and can store information in a discrete and categorical manner (Zhou et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). VWM is important for tasks that require us to hold visual information in mind, such as remembering the color of a specific object, navigating a familiar environment, or following a set of instructions (Brady \u0026amp; St\u0026ouml;rmer, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However human memory is susceptible to distortion and can give rise to false memories. Many studies have shown that observers' responses in simple visual feature memory tasks deviate in a systematic manner from correct values, and they have conducted numerous studies to understand the nature of these recall errors in VWM (Bae et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Panichello et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). We knew that errors in working memory arise, and the number of items held simultaneously in working memory can also contribute to increased error rates (Zhang \u0026amp; Luck, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). These errors may arise partially from noise in the neural representations that underlie memory. Random noise can cause the memory representations to gradually deviate from their original state, leading to behavioral errors (Panichello et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). An example of these errors, is responses drawn from working memory in color memory tasks are significantly biased away from category boundaries and towards category centers. Moreover, different colors can have effects on our activities, and some studies have shown that participants who viewed a red screen performed better on the working memory task compared to those who viewed a neutral screen (Bae et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Elliot \u0026amp; Maier, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStudies have also shown that color working memory may not behave uniformly and may rely on encoding of stimulus categories, along with continuous values, to support comparative stimulus judgments (Bae et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Based on different researches we know that we have kind of systematic error in VWM. For example, in some kinds of N-back tasks, when the perceptual similarity between a target item and a lure item is high, individuals are more likely to mistakenly identify the lure item as something they have seen before, or\u003c/p\u003e \u003cp\u003eIn a delayed estimation task, when an observer is instructed to provide their best estimate of a previously presented hue, they are likely to choose the peak of the likelihood distribution, known as the \"maximum-likelihood estimate.\" This can be represented by colored pins in the task. The experimenter can measure the observer's error by calculating the distance between this estimate and the actual hue that was presented (Bays et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), due to delay estimation task\u0026rsquo;s results during memory recall, there is a possibility that the reporting of features can be influenced by the presence of other items held in working memory, rather than solely focusing on the probed item. This suggests that the memory recall process can be susceptible to interference or contamination from the features of other items being retained concurrently (Ma et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecent studies have demonstrated errors (Swap) toward a specific color which was presented in long-term memory due to statistical regularities present during object encoding (Scotti, Hong, Golomb, et al., 2021). These errors refer to the mistaken report of a non-target feature or implicitly biased guessing. Statistical learning refers to the process of acquiring knowledge about patterns in the environment through the detection and internalization of statistical regularities. This acquired knowledge can manifest as changes in behavior (Perruchet \u0026amp; Pacton, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The impact of different colors, including individuals' favorite colors, on cognitive processes such as working memory remains uncertain, despite the recognized significance of colors and their categories in cognitive functions (Ester et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Panichello et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). It is crucial to investigate how favorable stimuli, such as preferred colors, can influence behavior and potentially affect the accuracy or precision of our responses. Understanding these effects can provide valuable insights into the relationship between our preferences and cognitive processes, informing various domains such as psychology, design, and marketing. Therefore, the aim of this study is to further investigate the memory bias towards the favorite stimulus, specifically the favorite color, and its effect on memory errors.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003e All procedures were approved by University of Tehran Ethics Committee (IR.UT.PSYEDU.REC.1403.011) and was in accordance with Helsinki declaration of 1964 and its revisions. Informed consent was obtained from all subjects involved in the study, or their legal guardians in the case of minors, ensuring that their participation was voluntary and that they were fully aware of the study's objectives and potential risks.\u003c/p\u003e \u003cp\u003eThe experiment included thirty-six participants (7 male,29 female; M\u0026thinsp;=\u0026thinsp;23.97 years, SD\u0026thinsp;=\u0026thinsp;7.44) all of them where university students and in exchange they received course credits. One additional participant was excluded based on performing the task unsuccessfully (did not complete all trials). All Participants reported normal or corrected-to-normal acuity and color vision.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStimuli and Procedure\u003c/h2\u003e \u003cp\u003eThe experiment comprised three conditions, a favorit color task (Figure.1A), color-matching task (Figure.1B) and a working memory task (Figure. 1C-D). Firstly, participants completed a color-matching task (Brady et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) (Figure.1B). On each of the 40 trials, an image was presented on the left side of the screen with a white background, while a grayscale copy of the image simultaneously appeared on the right side. Participants (head position was not fixed) were instructed to adjust the color of the right item to match the color of the target on the left. This task aimed to assess the accuracy of color perception, as we hypothesized that high fidelity in color perception would be crucial for color memory. This perception task served to detect the fidelity of color perception.\u003c/p\u003e \u003cp\u003eAll stimuli were generated using the Psychophysics Toolbox (abbreviated as \u0026ldquo;Psychtoolbox\u0026rdquo;) (Brainard, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) for MATLAB. Object stimuli were obtained from the image sets by Brady, Konkle, Alvarez, and Oliva (2008).Posterization was applied to each image, restricting pixel values to white, black, or a single color of interest chosen from 360 RGB color values derived from a one-dimensional selection of the CIE Lab color space provided by MemToolbox (Suchow et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAll objects were real-word objects ( 250 x 250 px ) object was presented in the centre of a white background on a 15-in monitor with a screen resolution 1920 x 1080 Pixels.\u003c/p\u003e \u003cp\u003eFollowing the perception task, a favorit color test was presented (Figure.1A). In this part on each trial, one object \u0026ndash; selected randomly without replacement \u0026ndash; was presented in grayscale (color was replaced with RGB [128, 128, 128]), and a color wheel was presented around the object Participants were instructed to use their mouse to explore the color wheel and choose their favorite color for the object. They could only determine the color by observing the color of the wheel at the position of their mouse pointer. The purpose of this task was to have participants select their preferred color for the objects on the screen, focusing solely on the colors. Participants were free to choose any available color for the objects. At the end of this task, participants would see all the chosen colors for the objects and confirm their favorite color.\u003c/p\u003e \u003cp\u003eFollowing the favorit color task, participants completed a working memory task (Figure.1D). Here, we used a continuous report method. Each trial began with a black fixation mark presented at the center of the screen. During each trial, 1, 3, or 5 square stimuli (each 110 x 110 pixels) were simultaneously presented for 0.4 seconds in a circle around the fixation mark. The spatial locations of the stimuli were randomly determined relative to the central fixation. Participants were instructed to remember the color of either 1, 3, or 5 square sample stimuli. After a variable memory delay of 1, 3, or 7 seconds, a response screen appeared. The response screen consisted of a square at one of the previous sample locations (the target sample), there was no time limit on the response. Participants used the mouse to drag the circle around the ring, which changed the color of the probe sample. Following a mouse click to confirm their selected color, participants made a confidence range report. This task comprised 120 trials.\u003c/p\u003e \u003cp\u003eThe favorite color was incorporated into the working memory task by manipulating the colors of the studied stimuli. In all 3-stimulus trials (which accounted for 60% of the trials), one of the squares served as the target sample and was randomly determined from a Short (23\u0026ndash;67\u0026deg;), Medium (68\u0026ndash;112\u0026deg;), or Long (113\u0026ndash;157\u0026deg;) distance in color space from the favourite color (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Additionally, one of the distractors was biased toward the favorite color, while the other distractor was colored in exactly the opposite color of the favorite color in the color space, either clockwise or counterclockwise along the color wheel. Participants were not informed about this manipulation of color sampling. These procedures were employed to investigate the influence of favorite color on working memory.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eAnalyses\u003c/h2\u003e \u003cp\u003eOn any given trial, we recorded error and for measuring errors we used a full and modified mixture model(Bays et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Golomb et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) to account for various sources of error. Participants' responses was converted into an error measurement, we calculated the difference between the reported and correct color values. Then we calculated the absolut value of difference between the response and the favourite,also difference between the response 180-degree opposite of the favourite color If difference between respons and favourite was lower than difference between the response 180-degree opposite of the favourite color, then error sign will be positive and it means that respons was in the direction towards the favourite color, and if it was toward the opposit color the error was signed negative.\u003c/p\u003e \u003cp\u003eIn simple terms, a mixture model investigates the possibility of both a shift in the average response and the misreporting of distractors, along with the chance of guessing, in statistical analysis (Bays et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The model comprised distinct distributions, namely the target distribution, swap distribution, and random guessing distribution. The target distribution was a circular Gaussian probability density function (known as von Mises distribution) centered around the initial, accurate color, with adjustable mean and standard deviation. On the other hand, the swap distribution was also a circular Gaussian probability density function (von Mises distribution) centered on the Favorite color. Mathematically the model is described :\u003c/p\u003e \u003cp\u003ep(θ) = (1-β-δ-γ) * ϕ\u003csub\u003e(\u0026micro;, κ)\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β * ϕ\u003csub\u003e(FavColor, κ)\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;δ * ϕ\u003csub\u003e(OppColor, κ)\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;γ (1/ 2π)\u003c/p\u003e \u003cp\u003ewhere θ is the difference between the reported and correct color values,γ is the probability of random response or guessing,ϕ is a von Mises distribution (the circular analogue of the Gaussian) with \u0026micro; as its mean and concentration κ,β is the probability of misreporting the Favourite color value and δ is the probability of misreporting the Opposite color of favourite color value. Maximum-likelihood estimates of the parameters \u0026micro;, κ, γ, β, and δ were obtained separately using the Bays lab toolbox(Bays et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) and MemToolbox (Suchow et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo model memory response distributions, we classified them into three categories (Short, Medium, and Long) based on the difference in color 9space between the target color and the Favorite color. The reason for doing so is that the relational representation model suggests that the response is likely to be repulsive for shorter distances and attractive for longer distances. Therefore, we analysed each category separately to account for these different types of responses.\u003c/p\u003e \u003cp\u003eThe mean of the distribution was calculated separately for condition, and submitted to within-subjects\u0026rsquo; analyses of variance (ANOVAs) and t tests to make statistical comparisons between conditions.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eFor our analysis, the distribution of each participant's memory response was fit using the previously described probabilistic mixture modelling approach. It is noteworthy that in our participant group, consisting of 7 men and 29 women, no particular color preference was observed. The favorite colors chosen were diverse, spanning a wide range of hues, indicating no concentration around any specific color.\u003c/p\u003e \u003cp\u003eWe examined whether statistical regularities, specifically the propensity of a participant's favorite color to occur frequently among squares in a working memory task, might distort their reports. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e depicts histograms of memory errors, based on trials where the target square's color was sampled from a Short, Medium, or Long distance in color space from the participant's favorite color. Notably, the tails of the distributions show a trend at long distances: heavier tails on the right indicate a bias toward recall of the preferred color, especially as color distance increases. This indicates the presence a systematic bias in memory.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eEach histogram illustrates a peak at zero error, indicating instances of accurate color recall. However, as the color distance increases, the spread of errors increases.It is visually apparent that memory errors shifting toward the favorite color, suggesting the occurrence of swap errors.\u003c/p\u003e \u003cp\u003eWe conducted a repeated-measures ANOVA to explore the impact of the distance from a participant's favorite color on memory accuracy. The analysis revealed a significant main effect of color distance on memory performance ([F(1,1284)\u0026thinsp;=\u0026thinsp;7.5, p\u0026thinsp;=\u0026thinsp;0.001]), indicating that memory accuracy decreases as the target color's distance from the favorite color increases. A significant linear contrast further substantiated this relationship ([F(1,642)\u0026thinsp;=\u0026thinsp;13.10, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001]), suggesting a consistent increase in memory errors with greater color distances (Mauchly\u0026rsquo;s test of sphericity confirmed that the variances among the experimental conditions were equal (χ\u0026sup2;(2)\u0026thinsp;=\u0026thinsp;3.04, p\u0026thinsp;=\u0026thinsp;0.21), validating the sphericity assumption of our ANOVA results).\u003c/p\u003e \u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the amplitude of various components in our model is displayed, demonstrating that the target consistently exerts a strong influence across all conditions, indicating the highest probability of recalling accurately. Additionally, the figure is demonstrating that guessing (γ denotes the likelihood of a random response, hinting at guessing behaviours.) plays a role across all conditions, surpassing the influence of both 'Favorite' and 'Opposite' components.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure.4 shows the average error trend in memory recall at different distances from a participant's favorite color classified as short, medium, and long. A value of 0 would indicate no systematic deviation from the correct color. Positive values indicate attraction, suggesting that participants reported a color close to the correct color but slightly shifted either toward their favorite color. On the other hand, negative values indicate repulsion, indicating a deviation away from their favorite color.\u003c/p\u003e \u003cp\u003eAt short distances, a repulsion effect is evident with errors showing a negative mean, indicating distance from the color of interest. As the distance increases to medium, the repulsive effect decreases slightly. Notably, at long distances, a clear attraction effect appears and the mean errors become positive, indicating a significant bias towards the color of interest. This trend highlights the dynamic effect of color proximity on memory accuracy, changing from repulsion to attraction as distance from the preferred color increases.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eOther parameters\u003c/h2\u003e \u003cp\u003eThe correlation analysis only revealed significant correlations (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between the errors in long segment and delay. Examining the average confidences across all conditions did not show a significant difference. However, examining the errors of individuals with high (80% and above) and low(40% and lower) confidence in different segments, showed differences only in the short distance segment, the distribution of errors for low confidence trials exhibits a shift in the mean toward favorite color compared to high confidence trials.\u003c/p\u003e \u003cp\u003eExploratory analyses of the target standard deviation and the standard deviation of the standard deviation (Std) of responses toward the favorite color across different distances (Short, Medium, Long) is shown in figure.5. A smaller standard deviation would indicate that the responses are more consistently close to the mean, suggesting less variability in how accurately participants remember the favorite color across different distances.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion and Conclusion","content":"\u003cp\u003eThe results from this experiment can explain the influence of the presence of the favorite color among the available stimuli on their recall .To our knowledge, no previous studies have demonstrated swap and guess errors in working memory due to imposed statistical regularities related to the favorite color .Our findings demonstrate that visual working memory is distorted by implicitly patterns in our environment.This research stems from work in visual working memory that observed attraction and repulsion bias dependent on inter-item similarity to the favorite color (Bae \u0026amp; Luck, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Golomb, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Here, we shows feature-mixing errors are influenced by target–favorite color similarity,The presence of a noticeable increase in memory errors, specifically swap and geussing errors, concentrated around the favorite color can be visually observed across all color-distance segments. Regardless of the specific segment being considered, there is a clear and evident pattern of higher error rates occurring when participants mistakenly swap the favorite color with other colors in their memory recall tasks especially when the distance is long ,this is in line with the relational representation model (Bae \u0026amp; Luck, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Golomb, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), with repulsion away from the favourite color for the Short and Medium color-distance segments and attraction towards the favourite color for the Long color-distance segment. These findings in visual working memory have conceptual connections to phenomena such as the direction illusion and the tilt illusion, as well as their corresponding visual aftereffects. These perceptual phenomena, studied in previous research (Gibson, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1937\u003c/span\u003e), demonstrate attraction and repulsion effects based on the distance in feature space between perceptual stimuli. It is important to note, however, that the swap errors observed in working memory cannot be explained by the same perceptual mechanisms as these illusions (Scotti, Hong, Leber, et al., 2021). In addition to considering the distance in color space, we can also explore the similarities between memory and perception in explaining the observed pattern. Notably, a study conducted by Schurgin and colleagues (2018) provided evidence suggesting that both memory and perception do not follow a linear scaling within stimulus space. Instead, they rely on a transformed similarity representation that exhibits a non-linear relationship with stimulus space. This finding challenges a fundamental assumption in existing models of visual working memory. It is important to note that the framework proposed by Schurgin does not pose any issues for our study, as our analyses do not rely on differentiating between guess rate and precision. Therefore, our findings remain valid and unaffected by the concerns raised in that particular framework. The framework presented by Schurgin provides an alternative explanation for the results, further enriching the understanding of the topic (Schurgin et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn other perevious studies in visual working memory experiments, it has been consistently observed that prior experience has a significant impact on biased recall. The memory system benefits from prior experience because it triggers processes of reinstatement and integration, which enhance memories through associative knowledge (van Kesteren et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). When participants are unable to accurately recall a specific item from memory, the learned regularities can implicitly or explicitly influence their memory reports, favoring previously encountered stimuli. For instance, in a visual working memory task conducted by Pratte (2018), participants were shown an array of colored squares and were later asked to indicate the location of a probed color. Even when the probed color was not originally present in the study array, participants' reports still tended to be centered around the locations of the study items. This suggests that participants were utilizing information about where the memory items could appear to bias their responses or guesses.Our findings align with the study conducted by Lin and colleagues (2021), who investigated the impact of object-based attention on visual working memory. They demonstrated that object-based representations are promoted and suggested the existence of shared attentional mechanisms between perception and memory. In our study, we extended this understanding by highlighting that not only objects but also environmental elements, such as our favorite color, can influence and introduce biases in our memory processes.\u003c/p\u003e \u003cp\u003eWhen it comes to confidence,several studies (Amichetti et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Bertrand et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) have demonstrated that individuals often exhibit overconfidence and poor metacognitive accuracy when making judgments about their memory performance. Even when participants' judgments show sensitivity to memory-related factors, they still struggle to accurately discriminate between erroneous and accurate responses (Conte et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This raises questions about the cues that influence metacognitive judgments during working memory tasks and the reasons behind the lack of metacognitive accuracy in working memory.In our study, we provided evidence that errors and confidence distributions in memory performance vary across different segments and distances from our favorite color. These findings suggest that factors such as the emotional significance of stimuli and their relation to our favorite color, personal preferences, and attentional biases towards certain features or colors can influence metacognitive judgments in working memory tasks.Further research can delve into the specific cues and mechanisms that contribute to metacognitive judgments during ongoing WM tasks. By identifying these cues, we can develop interventions or training programs aimed at enhancing individuals' metacognitive accuracy and improving their ability to evaluate the reliability of their memory performance.\u003c/p\u003e \u003cp\u003eIn real-world scenarios where the reliability of memories is crucial, it becomes essential to investigate how prior experiences and our favorite environment influence memory retrieval. Understanding the role of prior experiences and personal preferences in memory processes can provide valuable insights into the factors that shape our ability to retrieve accurate and reliable memories. By examining how our personal preferences, such as our favorite environment, impact memory recall in all other types of memory, we can gain a deeper understanding of the complex interplay between cognition, emotions, and memory.\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eR.M.S. and E.R. wrote the main manuscript text and R.M.S. do experiment and analysis . All authors reviewed the manuscript.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used during the current study available from the corresponding author on reasonable request\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAmichetti, N. M., Stanley, R. S., White, A. G., \u0026amp; Wingfield, A. (2013). 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A Pupillometry Study. \u003cem\u003eCognitive Science\u003c/em\u003e, \u003cem\u003e46\u003c/em\u003e(9). https://doi.org/10.1111/cogs.13194\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4724031/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4724031/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eVisual working memory (VWM) plays a vital role in holding visual information in mind, and errors in recall can have significant consequences. Previous research has identified various factors that can influence recall errors in VWM, such as the number of items held in memory and the similarity between items. In this study, we investigated the impact of personal preferences on VWM recall accuracy. Our results showed that recall accuracy was significantly higher for preferential items compared to non-preferential items, indicating that personal preferences can have a significant impact on VWM recall accuracy. Moreover, the distance analysis revealed both attraction and repulsion effects in the VWM content. These findings have important implications for understanding the factors that influence memory recall and may have practical applications in fields such as marketing and design. Overall, this study sheds light on the role of personal preferences in VWM recall accuracy and contributes to a better understanding of the cognitive mechanisms underlying memory recall.\u003c/p\u003e","manuscriptTitle":"Investigating the Impact of Personal Preferences on Visual Working Memory Recall","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-06 09:21:23","doi":"10.21203/rs.3.rs-4724031/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b7e18392-6e2b-46a1-94f9-bcbb2eb0a76c","owner":[],"postedDate":"August 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-22T10:12:04+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-06 09:21:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4724031","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4724031","identity":"rs-4724031","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}