Emotional content effects the precision of visual working memory

Emotional content effects the precision of visual working memory | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (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];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Emotional content effects the precision of visual working memory Zeinab Haghian , Abdol-Hossein Vahabie , Ehsan Rezayat doi: https://doi.org/10.1101/2024.12.01.626217 Zeinab Haghian 1 Department of Cognitive Sciences, Faculty of Psychology and Education , University of Tehran, Tehran, Iran Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abdol-Hossein Vahabie 2 School of Electrical and Computer Engineering , College of Engineering, University of Tehran, Tehran, Iran 1 Department of Cognitive Sciences, Faculty of Psychology and Education , University of Tehran, Tehran, Iran 3 School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM) , Niavaran, Tehran, Iran Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ehsan Rezayat 1 Department of Cognitive Sciences, Faculty of Psychology and Education , University of Tehran, Tehran, Iran 3 School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM) , Niavaran, Tehran, Iran Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: rezayat{at}ut.ac.ir Abstract Full Text Info/History Metrics Preview PDF Abstract Objectives Our working memory (WM) is susceptible to errors influenced by various sources. Recent research has illuminated the intricate relationship between emotional valence and working memory performance. This study aims to comprehensively investigate memory recall biases across different emotional categories. Method To explore this relationship, we designed and implemented a delayed-reproduction facial emotion n-back task. Participants were tasked with recalling the emotional valence of target faces across various n-back conditions, selecting their responses from a continuum of 19 morphed faces ranging from sad to happy. Results Our findings indicate that participants generally exhibit a negativity bias, struggling more with happy faces. Interestingly, they also perceive happy faces as less happy and sad faces as less sad, suggesting both positive and negative reappraisal in their emotional valence perception. This underscores the complex interplay between emotional valence and cognitive performance. Notably, recall of neutral images remained stable and was unaffected by preceding emotional contexts. Conclusion These findings demonstrate that emotional content in working memory significantly impacts errors during WM tasks, with more emotionally charged faces leading to a greater drift toward the lower valence axis. This highlights the need for further exploration of how emotional factors influence cognitive processes in working memory. 1. Introduction Working memory (WM) is essential for goal-directed behaviors, facilitating the temporary storage and manipulation of information necessary for complex cognitive tasks such as reasoning, learning, problem-solving, and comprehension ( Chai et al., 2018 ; Diamond, 2013 ; Malenka et al., 2009 ; Miyake & Shah, 1999 ). As a central component of cognition, WM interacts intricately with other cognitive processes, notably attention, long-term memory, and emotion. Among these, the relationship between WM and emotion has garnered significant interest due to its profound and bidirectional effects. Emotional experiences can enhance or impair WM performance, influenced by factors such as the intensity and valence of the emotion, as well as the cognitive demands of the task ( Mikels & Reuter-Lorenz, 2019 ; Hou et al., 2022). Conversely, WM is crucial for managing and manipulating emotional information, supporting emotional regulation and adaptive decision-making. Thus, the interplay between WM and emotion is complex and context-dependent, reflecting their mutual influence on cognitive and affective functioning. The role of emotion in WM has significant implications for human behavior, particularly in detecting and responding to survival-relevant situations. Emotional stimuli, especially facial expressions, serve as vital social cues and are among the most studied in this context. Research shows that emotional faces are processed more efficiently than neutral ones, highlighting the evolutionary importance of swiftly interpreting emotional signals ( Johansson et al., 2004 ). The emotional significance of a stimulus often enhances its processing efficiency, allowing for quicker detection and evaluation ( Zeelenberg et al., 2006 ). Emotional stimuli can capture cognitive resources more effectively than neutral stimuli, potentially facilitating or disrupting WM performance depending on the context. For instance, high-intensity emotions can demand considerable cognitive resources, possibly impairing WM efficiency (Hou et al., 2022). Despite advancements in understanding these interactions, debates persist regarding the differing impacts of positive and negative emotions on WM performance. Some studies suggest that positive emotions broaden cognitive scope, aiding WM ( Fredrickson, 2001 ), while others argue that negative emotions, like sadness or anxiety, may narrow attentional focus, enhancing task-specific performance ( Gasper & Clore, 2002 ; Shen et al., 2023 ; Kuehne et al., 2021 ). A key factor in emotional processing is valence, which denotes the intrinsic positivity or negativity of a stimulus. Researchers have explored whether stimuli at the extremes of the valence continuum—positive (e.g., happy) or negative (e.g., sad or angry)—receive preferential processing. This has led to the identification of emotional processing biases, such as positive bias (favoring positive stimuli) and negative bias (favoring negative stimuli) ( Dolcos et al., 2021 ; Xu et al., 2021 ). While evidence supports both biases, their nature and mechanisms remain actively investigated. Negative biases are often linked to the evolutionary significance of threat detection, while positive biases may arise from the frequent occurrence of positive stimuli in daily life ( Esteves & Öhman, 1993 ; Johnston et al., 2001 ). To study WM, researchers often use paradigms that require participants to determine if a presented object matches a previously seen one, yielding binary outcomes (correct or incorrect) ( Bays et al., 2009 ). However, these methods can limit the assessment of memory retention quality. Alternative paradigms, such as “delayed-reproduction” tasks, offer continuous measures of WM performance, allowing for a more nuanced understanding of how items are retained and manipulated ( Gorgoraptis et al., 2011 ). Our study employed a delayed-reproduction facial emotion N-Back task using morphed emotional faces to investigate the interaction between emotional content and WM performance. This design enabled precise measurement of perceptual errors and reaction times, facilitating the exploration of positive and negative biases in recalling emotional stimuli. By examining how emotional valence and intensity influence cognitive processes, our research contributes to understanding emotion-cognition interactions, with implications for emotional regulation, adaptive behavior, and everyday functioning. 2. Materials and Methods 2.1 Participants A total of 31 healthy undergraduates and graduate students, (17 female, age : [19, 38], mean ± SD : 24.3 ± 3.05) volunteered to attend this experiment after providing written informed consent online to procedures approved by the local ethics committee. All participants had normal or corrected-to-normal visual acuity. All procedures were approved by University of Tehran Science Committee ( Approval ID IR.UT.PSYEDU.REC.1403.012 ) and was in accordance with Helsinki declaration of 1964 and its revisions. 2.2 Stimuli and Apparatus We created a neutral face (emotion and sex-neutral) using FaceGen Modeller 3.5 (Singular Inversions, ON, Canada). This face was then morphed into two categories, sad and happy, at 10% intervals using InterFace software ( Kramer et al., 2017 ). This process resulted in 19 morphed face stimuli, ranging from 90% happy to 90% sad ( Figure 1A ). All morphed faces were then cropped into oval shapes, using Adobe Photoshop software and presented on a screen with a gray background. Luminance was controlled for in the emotional faces using the SHINE toolbox in MATLAB ( Willenbockel et al., 2010 ). We considered a series of images, with Image 1 depicting 90% sadness and Image 19 depicting 90% happiness, while the intermediate images were arranged sequentially. In our study, images 1 through 7 were categorized as sad, images 8 through 12 were categorized as neutral, and images 13 through 19 were categorized as happy. Face stimuli (3.33°× 4.76°) were presented on the center of a 23-inch TFT monitor with a resolution of 1920 × 1080 pixels and a refresh rate of 60 Hz. The experiment was programmed using the PsychToolbox 3 extension ( Brainard, 1997 ) running in MATLAB (2019b) (MathWorks, Natick, MA, United States). Download figure Open in new tab FIGURE 1 Experiment procedure and task. (A) Continuum of Morphed Facial Stimuli. A series of facial stimuli created through a morphing process. (B) The schematic description of the trial sequence for behavioral task. Each trial began with presenting a fixation dot for 1 second, followed by three ‘sample face’ presentation (1 second for each one) and a delay (1.5 or 3 seconds). A number were appeared on screen to show which image would be remembered. Then A ‘test face’ was presented and the subject had to change the stimulus to match it to the remembered ‘sample face’. 2.3 Procedure Participants sat comfortably on a chair at a fixed distance of ∼60 cm from the screen in a dimly lit room, with their heads resting on a chinrest. Before the main task, instructions were displayed on the screen, and the examiner explained the procedure to each participant. Participants completed five practice trials to familiarize themselves with the task. Each trial began with a fixation spot (black, 0.3° diameter, Figure 1B ), on which participants were instructed to maintain their gaze. After 1 second, three face stimuli (“ sample faces ”) appeared on the screen simultaneously, each for one second, with numbers 1, 2, or 3 displayed above the respective faces. These faces were selected pseudorandomly from 0%, 20%, 40%, 60%, or 80% morphed faces, with one of the three faces serving as the target face. Following the presentation of the sample faces, a fixation cross (black, 0.2° length of each line) appeared at the center of the screen for a delay period of either 1.5 or 3 seconds, chosen pseudorandomly for each trial. Participants were instructed to maintain their gaze on the fixation cross during this delay. After the delay, a number (1, 2, or 3) corresponding to one of the sample faces was displayed on the screen for one second. Subsequently, a random face (“ response face ”), which could be any of the 19 morphed faces, was presented. Participants were instructed to use the “Up” and “Down” arrow keys on the keyboard to adjust the “response face” through the morphs to match it to the “target face” corresponding to the displayed number (1, 2, or 3). They pressed the “ENTER” key to submit their responses. Each block consisted of 54 trials (2 delays × 9 sample faces × 3 repetitions). Each participant completed three blocks, with the option to take a 3–5 minutes break between blocks. We recorded participants’ choices and reaction times for each trial. No feedback was provided regarding their responses or reaction times. 2.4 Data analysis To quantify participants’ perception accuracy, we defined the error on each trial as the difference between the target face and the participant’s selected face, calculated as (E rror = Response - Target ). This error ranged from -17 to +17, where positive errors indicated that participants perceived the image as more pleasant in valence than it was, and negative errors indicated that they perceived it as more unpleasant in valence. For instance, if the target face was Image 16 (60% happy) and the participant selected Image 13 (30% happy), the error would be -3, reflecting a perception of less happiness than it actually was. This method allowed us to systematically assess the accuracy of emotional recognition in our participants. We applied the generalized linear mixed-effects model (GLMM) for this study, which is specified to analyze the effects of reaction time (RT), intensity, and cognitive load (N-Back) on error rates across trials and participants for each emotion (Sad, Neutral, Happy). The model is expressed as: where Error ij represents the error for participant i on trial j, β 0 is the fixed-effect intercept, and β 1 , β 2 , and β 3 are fixed-effect coefficients quantifying the influence of RT, intensity, and N-Back, respectively. The term captures participant-specific variability as a random intercept, while ϵ ij ∼ N (0, σ 2 ) represents residual trial-level error. This GLMM structure enables the simultaneous assessment of both fixed effects, which are consistent across participants, and random effects, which account for individual differences. By incorporating hierarchical variance components, the model provides a robust framework for examining the predictors of error in perceptual accuracy while controlling for within-subject dependencies ( Bolker et al., 2009 ; Bates et al., 2015 ). 3. Results Our findings show that participants recalling happy images indeed tended to have lower precision (higher mean absolute errors) across all n-back conditions compared to those recalling sad or neutral images ( Figure 2A ). Participants also have less precision (made significantly more errors) in the happy condition compared to the sad condition (p = 0.0201). This finding suggests that the emotional state of happiness may impair cognitive performance, leading to a higher error rate. However, there were no significant differences in errors between the sad and neutral conditions (p = 0.7124), nor between the neutral and happy conditions (p = 0.1531). A one-way ANOVA was conducted to examine the effect of emotional categories (Sad, Neutral, Happy) on response errors. The results indicated a significant effect of emotion on errors at the (p < 0.05) level for the three conditions [F(2, 5019) = 324.48, p < 0.001] ( Figure 3 ). Post hoc comparisons using the Bonferroni correction revealed significant differences between the groups: Sad vs. Neutral (mean difference = 1.3483, (p < 0.0001)), Sad vs. Happy (mean difference = 2.6661, (p < 0.0001)), and Neutral vs. Happy (mean difference = 1.3178, (p < 0.0001)). Moreover, the reaction times also have similar results ( Figure 2B ), participants had significantly longer RTs in the neutral condition compared to the sad condition (p = 0.0413). This suggests that the neutral emotional state may slow down cognitive processing speed compared to the sad state. Sadness might heighten focus and vigilance, leading to faster reaction times, whereas a neutral state might lack the emotional arousal needed to enhance cognitive performance. Additionally, participants had significantly longer RTs in the happy condition compared to the sad condition (p = 0.0008), indicating that happiness may impair cognitive performance by slowing down reaction times. There were no significant differences in RTs between the neutral and happy conditions (p = 0.1770), suggesting that both emotional states had a similar impact on cognitive processing speed. Despite these observed patterns, the ANOVA results showed no significant differences in errors or reaction times (RTs) across the different n-back conditions (1-back, 2-back, 3-back). This suggests that the cognitive load imposed by the varying n-back tasks did not significantly affect participants’ performance in terms of errors or RTs. Participants appeared equally adept at handling the cognitive demands of each n-back condition, indicating that the differences in cognitive load were not substantial enough to produce measurable effects in this context. Download figure Open in new tab Figure 2 Happy content have more Mean Errors and Reaction Times for Emotional Recognition Across N-back Conditions. (A) Mean absolute errors for each emotion (sad, neutral, happy) across three n-back conditions (1-back, 2-back, 3-back). (B) Mean reaction times (RTs) for the same emotional categories and n-back conditions error bar is standard error across subjects. Download figure Open in new tab FIGURE 3 Error Distributions for Emotional Recognition were shifted in opposite direction of valance axis. The x-axis quantifies the error value, reflecting the deviation of the participant’s response from the target image. The y-axis enumerates the frequency of occurrences for each error value, corresponding to the number of images selected by participants. (A) represents the distribution for happy emotions, (B) for neutral emotions, and (C) for sad emotions. D-F, Quantile-Quantile Distributions for Emotional Recognition Errors. (D) represents the distribution for happy emotions, (E) for neutral emotions, and (F) for sad emotions. We also found that participants recalled happy images as more unpleasant valence and sad images as more pleasant valence than they were initially presented. The analysis of skewness for the error distributions across different emotional categories reveals significant insights into the asymmetry of the data ( Figure 3 ). The error distribution for the Happy category exhibits a negative skewness (-1.7198), indicating a higher frequency of extreme negative errors. This suggests that when participants attempt to recall happy images, they tend to make larger negative errors, meaning they chose images with a more unpleasant valence compared to the response image. In contrast, the Neutral category shows a slight negative skewness (-0.7085), reflecting a relatively balanced distribution of errors with a minor tendency towards larger negative errors. This indicates a slight preference for sadder images over happier ones in valence when recalling neutral images. The Sad category, however, displays a positive skewness (1.5368), indicating a higher frequency of extreme positive errors. This implies that when participants attempt to recall sad images, they are more prone to making larger positive errors, meaning they chose images with a more pleasant valence compared to the response image. Using GLMM for the emotions of Sad, Neutral, and Happy, we also investigate the relationship between the predictor variables (Reaction Time (RT), Intensity, and N-Back) and the dependent variable (Error). The results of the regression analyses for the sad, neutral, and happy emotional categories reveal distinct relationships between error rates and predictors, including response time (RT), intensity, and cognitive load (N-Back). Across all three emotional categories, RT and intensity emerged as significant predictors of error rate, although the direction and magnitude of these relationships varied. The N-Back predictor, representing cognitive load, generally did not reach significance, suggesting it has a limited direct impact on error rates when controlling for RT and intensity. View this table: View inline View popup Download powerpoint Table 1 Generalized Linear Mixed Model Results Table 2, shows the Generalized Linear Mixed Model Results, which indicate the explanatory power of the fixed effects and the full model, respectively. For Sad and Happy emotions, the models show higher explanatory power, with marginal R 2 values. This demonstrates that both the fixed predictors (RT, Intensity, and N-Back) and random effects (subject variability) contribute meaningfully to explaining Error. In these emotions, Intensity emerges as a significant predictor, with higher intensities reducing Error. Conversely, the Neutral emotion model exhibits relatively weak explanatory power, suggesting that other unmeasured factors may play a larger role in determining Error in this condition. Across all models, RT consistently predicts Error, with faster responses associated with reduced precision, while N-Back has no significant impact. Figure 4 , demonstrates the varying influence of image number on Error across different emotional states. The significant relationship for Happy and Sad emotions highlights the importance of Intensity as a predictor of Error, while the weak relationship for Neutral emotion suggests that other factors may play a more substantial role. Overall, these models highlight the varying influence of RT, Intensity, and N-Back on Error across different emotional states, with Intensity and RT being more consistently significant predictors. Download figure Open in new tab FIGURE 4 Linear Regression Fitted Errors by Emotion across valence axis. Scatter points, represent individual participant error at each intensity level. Fitted lines, show the predicted trend of error as number of image increases (image 1 = 90% sad or – 90%, image 19 = 90% happy or + 90%), based on linear regression. Shaded areas are confidence intervals, indicate the 95% confidence range around the fitted lines, reflecting the uncertainty in the regression predictions. 4. Discussion Our study investigated how emotional valence and intensity, as well as cognitive load, influence perceptual accuracy and reaction times (RTs) in a delayed-reproduction task involving emotional morphed faces. The findings underscore the nuanced interplay between emotion and WM, contributing to our understanding of emotional biases and their implications for cognitive processing. Our results revealed that emotional valence significantly influences perceptual accuracy. Participants exhibited higher error rates when recalling happy images compared to sad or neutral images. This finding aligns with evidence suggesting that happiness, despite being associated with positive emotional states, can reduce cognitive performance by broadening attention and increasing distractibility ( Fredrickson, 2001 ; Rowe et al., 2007 ). Conversely, sadness, often linked to negative emotional states, appears to enhance focus, potentially reducing error rates by narrowing attention and fostering more detailed processing ( Gasper & Clore, 2002 ). These findings support the notion that negative emotions such as sadness may aid cognitive performance in specific contexts, consistent with theories of negativity bias. Negative stimuli, including sad or angry faces, are often processed more efficiently due to their evolutionary relevance in threat detection and survival ( Vaish et al., 2008 ; Öhman et al., 2001 ). However, the higher errors observed for happy faces suggest a “positivity penalty,” in which the lighter cognitive demands associated with positive stimuli may lead to less precise memory encoding or retrieval. One intriguing finding was the valence reversal observed in participants’ recall of emotional faces. Happy images were recalled as less pleasant and sad images as less unpleasant than their initial presentation. This phenomenon may reflect the role of cognitive reappraisal, a strategy in which individuals reinterpret emotional stimuli to modify their emotional impact ( Gross, 2015 ). Positive reappraisal, such as downplaying sadness, and negative reappraisal, such as emphasizing the drawbacks of happiness, could explain these shifts in perceived valence ( Flores-Torres et al., 2022 ; Tsai et al., 2024 ). Valence reversals challenge conventional expectations of positive recall for positive stimuli and negative recall for negative stimuli. They underscore the complex influence of emotional context and cognitive processes on memory encoding and retrieval, suggesting that emotions are not static but dynamically reinterpreted based on situational factors ( Wang & Yin, 2023 ). Our findings provide further evidence for both negativity and positivity biases. The negativity bias, observed in faster and more accurate responses to sad faces, reflects the evolutionary importance of negative stimuli in threat detection and adaptive behavior ( Fox et al., 2000 ; Vaish et al., 2008 ). Conversely, the positivity bias, supported by the happy-face advantage, highlights the preferential processing of positive stimuli due to their higher frequency and social relevance ( Feyereisen et al., 1986 ; Öhman et al., 2001 ). These biases are not mutually exclusive and may operate simultaneously, influenced by task demands, stimulus properties, and individual differences. For instance, the negativity bias may dominate in tasks requiring rapid threat detection, while the positivity bias may emerge in contexts emphasizing social interactions or long-term memory ( Adolphs, 2002 ; Mikels & Reuter-Lorenz, 2019 ). As previously discussed, sad targets tend to be perceived as more pleasant, whereas happy targets are perceived as less pleasant as they are. This pattern is reflected in a shift in error patterns. This trend is further elucidated by the findings from the GLMM analysis. For higher intensities of sadness, such as 80% sad, the error rate increases compared to other intensity levels. This suggests that as the intensity of sadness increases, it is perceived as progressively more pleasant, potentially contributing to greater categorization errors. Conversely, for happy images, higher intensities, such as 80% happy, are associated with fewer errors, indicating that such stimuli are perceived as less pleasant than lower-intensity happy images. These findings suggest a perceptual asymmetry, where participants are less likely to select responses representing extreme levels of sadness or happiness, potentially due to the cognitive and affective demands of processing high-intensity emotions. Interestingly, the effect of intensity was less pronounced for neutral faces, which may reflect their lack of emotional salience. This aligns with prior research indicating that neutral faces are processed more variably, often influenced by the emotional context of preceding stimuli ( Kauschke et al., 2019 ; Liberman, Manassi, & Whitney, 2018 ). Reaction times (RTs) to emotional faces can vary based on the task and emotional context, reflecting differences in attentional focus and processing priorities. In our study, participants responded faster to sad faces compared to happy faces, which aligns with research suggesting that negative emotions like sadness may heighten vigilance, narrow attentional focus and facilitate processing speed, potentially reflecting an adaptive mechanism for managing negative emotions ( Tipples, 2019 ). Conversely, the “happy-face advantage” is often observed in studies focusing on emotion recognition tasks, where happy faces are processed more quickly due to their visual salience and association with positive social cues ( Esteves & Öhman, 1993 ; Srinivasan & Gupta, 2011 ). These conflicting patterns highlight how task-specific demands, such as attention to detail or broader contextual processing, influence emotional face perception. Contrary to expectations, cognitive load, as manipulated through the N-Back conditions, did not significantly influence error rates or RTs. Although errors in the 2-back condition were descriptively higher across all emotional categories, these differences were not statistically significant, as confirmed by ANOVA. This aligns with findings by Holmer et al. (2024) , who reported that mimicry interference primarily affects tasks with lower cognitive demands (e.g., 1-back). The trend observed in the 2-back condition may be attributable to primacy and recency effects, where items at the beginning or end of a sequence are more easily recalled ( Sternberg & Sternberg, 2017 ; Glanzer & Cunitz, 1966 ). The lack of significant differences across N-Back levels may also reflect the relatively small sample size or the specific design of the WM task. Future research should explore whether increasing task difficulty or incorporating additional WM measures, such as complex span tasks, could better capture the interaction between cognitive load and emotional processing. Author contribution All authors contributed to the article and approved the submitted version. Acknowledgments We would like to gratitude to Ali Asgari and Mohsen Honar for their feedback on task design. References ↵ Adolphs , R. ( 2002 ). Recognizing emotion from facial expressions: Psychological and neurological mechanisms . Behavioral and Cognitive Neuroscience Reviews , 1 ( 1 ), 21 – 62 . doi: 10.1177/1534582302001001003 OpenUrl CrossRef PubMed ↵ Bates , D. , Mächler , M. , Bolker , B. , & Walker , S. ( 2015 ). Fitting Linear Mixed-Effects Models Using lme4 . Journal of Statistical Software , 67 ( 1 ), 1 – 48 . doi: 10.18637/jss.v067.i01 OpenUrl CrossRef PubMed ↵ Bays , P. M. , Catalao , R. F. G. , & Husain , M. ( 2009 ). The precision of visual working memory is set by allocation of a shared resource . Journal of Vision , 9 ( 10 ), 7 .1-11. doi: 10.1167/9.10.7 OpenUrl Abstract / FREE Full Text ↵ Bolker , B. M. , Brooks , M. E. , Clark , C. J. , Geange , S. W. , Poulsen , J. R. , Stevens , M. H. H. , & White , J.-S. S. ( 2009 ). Generalized linear mixed models: A practical guide for ecology and evolution . Trends in Ecology & Evolution , 24 ( 3 ), 127 – 135 . doi: 10.1016/j.tree.2008.10.008 OpenUrl CrossRef PubMed Web of Science ↵ Brainard , D. H. ( 1997 ). The psychophysics toolbox . Spatial vision , 10 , 433 – 436 . OpenUrl CrossRef PubMed Web of Science ↵ Chai , W. J. , Abd Hamid , A. I. , & Abdullah , J. M. ( 2018 ). Working memory from the psychological and neurosciences perspectives: A review . Frontiers in Psychology , 9 , Article 401 . doi: 10.3389/fpsyg.2018.00401 OpenUrl CrossRef PubMed ↵ Diamond , A. ( 2013 ). Executive functions . Annual Review of Psychology , 64 , 135 – 168 . doi: 10.1146/annurev-psych-113011-143750 OpenUrl CrossRef PubMed Web of Science ↵ Dolcos , S. , Hu , Y. , Williams , C. , Bogdan , P. C. , Hohl , K. , Berenbaum , H. , & Dolcos , F. ( 2021 ). Cultivating affective resilience: Proof-of-principle evidence of translational benefits from a novel cognitive-emotional training intervention . Frontiers in Psychology , 12 , 585536 . doi: 10.3389/fpsyg.2021.585536 OpenUrl CrossRef PubMed ↵ Esteves , F. , & Öhman , A. ( 1993 ). Masking the face: Recognition of emotional facial expressions as a function of the parameters of backward masking . Scandinavian Journal of Psychology , 34 ( 1 ), 1 – 18 . doi: 10.1111/j.1467-9450.1993.tb01096.x OpenUrl CrossRef PubMed Web of Science ↵ H.D. Ellis , M. A. Jeeves , F. Newcombe , & A. Young Feyereisen , P. , Malet , C. , & Martin , Y. ( 1986 ). Is the faster processing of expressions of happiness modality-specific? In H.D. Ellis , M. A. Jeeves , F. Newcombe , & A. Young (Eds.), Aspects of face processing (Vol. 28 ). Springer , Dordrecht . doi: 10.1007/978-94-009-4420-6_37 OpenUrl CrossRef ↵ Flores-Torres , J. , Gómez-Pérez , L. , & McRae , K. ( 2022 ). Positive cognitive reappraisal is beneficial for women’s but not for men’s IGT decision-making . Motivation and Emotion , 46 , 350 – 365 . doi: 10.1007/s11031-022-09927-4 OpenUrl CrossRef ↵ Fox , E. , Lester , V. , Russo , R. , Bowles , R. J. , Pichler , A. , & Dutton , K. ( 2000 ). Facial expressions of emotion: Are angry faces detected more efficiently? Cognition and Emotion , 14 ( 1 ), 61 – 92 . doi: 10.1080/026999300378996 OpenUrl CrossRef PubMed Web of Science ↵ Fredrickson , B. L. ( 2001 ). The role of positive emotions in positive psychology: The broaden-and-build theory of positive emotions . American Psychologist , 56 ( 3 ), 218 – 226 . doi: 10.1037//0003-066x.56.3.218 OpenUrl CrossRef PubMed Web of Science ↵ Gasper , K. , & Clore , G. ( 2002 ). Attending to the big picture: Mood and global versus local processing of visual information . Psychological Science , 13 ( 1 ), 34 – 40 . doi: 10.1111/1467-9280.00406 OpenUrl CrossRef PubMed Web of Science ↵ Glanzer , M. , & Cunitz , A. R. ( 1966 ). Two storage mechanisms in free recall . Journal of Verbal Learning and Verbal Behavior , 5 ( 4 ), 351 – 360 . doi: 10.1016/S0022-5371(66)80044-0 OpenUrl CrossRef Web of Science ↵ Gorgoraptis , N. , Catalao , R. F. G. , Bays , P. M. , & Husain , M. ( 2011 ). Dynamic updating of working memory resources for visual objects . Journal of Neuroscience , 31 ( 23 ), 8502 – 8511 . doi: 10.1523/JNEUROSCI.0208-11.2011 OpenUrl Abstract / FREE Full Text ↵ Gross , J. J. ( 2015 ). Emotion regulation: Current status and future prospects . Psychological Inquiry , 26 ( 1 ), 1 – 26 . doi: 10.1080/1047840X.2015.965321 OpenUrl CrossRef ↵ Holmer , E. , Rönnberg , J. , Asutay , E. , Tirado , C. , & Ekberg , M. ( 2024 ). Facial mimicry interference reduces working memory accuracy for facial emotion expressions . PLOS ONE , 19 ( 6 ), e0306113 . doi: 10.1371/journal.pone.0306113 OpenUrl CrossRef PubMed Hou , T.-Y. , & Cai , W.-P. ( 2022 ). What emotion dimensions can affect working memory performance in healthy adults? A review . World Journal of Clinical Cases , 10 ( 2 ), 401 – 411 . doi: 10.12998/wjcc.v10.i2.401 OpenUrl CrossRef PubMed ↵ Johansson , M. , Mecklinger , A. , & Treese , A.-C. ( 2004 ). Recognition memory for emotional and neutral faces: An event-related potential study . Journal of Cognitive Neuroscience , 16 ( 10 ), 1840 – 1853 . doi: 10.1162/0898929042947883 OpenUrl CrossRef PubMed ↵ Johnston , P. J. , Katsikitis , M. , & Carr , V. J. ( 2001 ). A generalised deficit can account for problems in facial emotion recognition in schizophrenia . Biological Psychology , 58 ( 3 ), 203 – 227 . doi: 10.1016/S0301-0511(01)00114-4 OpenUrl CrossRef PubMed Web of Science ↵ Kauschke , C. , Bahn , D. , Vesker , M. , & Schwarzer , G. ( 2019 ). The role of emotional valence for the processing of facial and verbal stimuli—positivity or negativity bias? Frontiers in Psychology , 10 , Article 1654 . doi: 10.3389/fpsyg.2019.01654 OpenUrl CrossRef Kim , S. A. , & Kim , S. H. ( 2021 ). Frontiers in Behavioral Neuroscience , 15 , Article 683833 . doi: 10.3389/fnbeh.2021.683833 OpenUrl CrossRef PubMed ↵ Kramer , R. S. S. , Jenkins , R. , & Burton , A. M. ( 2017 ). InterFace: A software package for face image warping, averaging, and principal components analysis . Behavior Research Methods , 49 ( 6 ), 2002 – 2011 . doi: 10.3758/s13428-016-0837-7 OpenUrl CrossRef PubMed ↵ Kuehne , M. , Zaehle , T. , & Lobmaier , J. S. ( 2021 ). Effects of posed smiling on memory for happy and sad facial expressions . Scientific Reports , 11 , 89828 . doi: 10.1038/s41598-021-89828-7 OpenUrl CrossRef ↵ Liberman , A. , Manassi , M. , & Whitney , D. ( 2018 ). Serial dependence promotes the stability of perceived emotional expression depending on face similarity . Attention, Perception, & Psychophysics , 80 ( 6 ), 1461 – 1473 . doi: 10.3758/s13414-018-1533-8 OpenUrl CrossRef PubMed ↵ A. Sydor & R. Y. Brown Malenka , R. C. , Nestler , E. J. , & Hyman , S. E. ( 2009 ). Higher cognitive function and behavioral control . In A. Sydor & R. Y. Brown (Eds.), Molecular Neuropharmacology: A Foundation for Clinical Neuroscience (2nd ed., pp. 313 – 321 ). New York : McGraw-Hill Medical . ↵ Mikels , J. A. , & Reuter-Lorenz , P. A. ( 2019 ). Affective working memory: An integrative psychological construct . Perspectives on Psychological Science , 14 ( 4 ), 543 – 559 . doi: 10.1177/1745691619837597 OpenUrl CrossRef PubMed ↵ Miyake , A. , & Shah , P. ( 1999 ). Models of working memory: Mechanisms of active maintenance and executive control . Cambridge : Cambridge University Press . ↵ Öhman , A. , Lundqvist , D. , & Esteves , F. ( 2001 ). The face in the crowd revisited: A threat advantage with schematic stimuli . Journal of Personality and Social Psychology , 80 ( 3 ), 381 – 396 . doi: 10.1037/0022-3514.80.3.381 OpenUrl CrossRef PubMed Web of Science ↵ Rowe , G. , Hirsh , J. B. , & Anderson , A. K. ( 2007 ). Positive affect increases the breadth of attentional selection . Proceedings of the National Academy of Sciences of the United States of America , 104 ( 1 ), 383 – 388 . doi: 10.1073/pnas.0605198104 OpenUrl Abstract / FREE Full Text ↵ Shen , Y. , Li , Z. , Shao , M. , Liu , Y. , & Zhang , Y. ( 2023 ). Effects of emotional faces on working memory in adolescents with learning anxiety . Current Psychology , 42 , 11575 – 11585 . doi: 10.1007/s12144-021-02469-x OpenUrl CrossRef ↵ Srinivasan , N. , & Gupta , R. ( 2011 ). Rapid communication: Global-local processing affects recognition of distractor emotional faces . Quarterly Journal of Experimental Psychology (Hove) , 64 ( 3 ), 425 – 433 . doi: 10.1080/17470218.2011.552981 OpenUrl CrossRef Web of Science ↵ Sternberg , R. J. , & Sternberg , K. ( 2017 ). Cognitive psychology (7th ed .). Cengage Learning . ↵ Tipples , J. ( 2019 ). Recognising and reacting to angry and happy facial expressions: A diffusion model analysis . Psychological Research , 83 ( 1 ), 37 – 47 . doi: 10.1007/s00426-018-1092-6 OpenUrl CrossRef PubMed ↵ Tsai , N. , Hawkesworth , J. , Dieffenbach , J. , Hua , D. , Eneva , E. , & Gabrieli , J. ( 2024 ). Social good reappraisal as a novel and effective emotion regulation strategy . PLOS ONE . doi: 10.1371/journal.pone.0305756 OpenUrl CrossRef ↵ Vaish , A. , Grossmann , T. , & Woodward , A. ( 2008 ). Not all emotions are created equal: The negativity bias in social-emotional development . Psychological Bulletin , 134 ( 3 ), 383 – 403 . doi: 10.1037/0033-2909.134.3.383 OpenUrl CrossRef PubMed Web of Science ↵ Wang , Y.-X. , & Yin , B. ( 2023 ). A new understanding of the cognitive reappraisal technique: An extension based on the schema theory . Frontiers in Behavioral Neuroscience . doi: 10.3389/fnbeh.2023.1174585 OpenUrl CrossRef ↵ Willenbockel , V. , Sadr , J. , Fiset , D. , Horne , G. O. , Gosselin , F. , & Tanaka , J. W. ( 2010 ). Controlling low-level image properties: The SHINE toolbox . Behavior Research Methods , 42 ( 3 ), 671 – 684 . doi: 10.3758/BRM.42.3.671 OpenUrl CrossRef PubMed Web of Science ↵ Xu , Q. , Ye , C. , Gu , S. , Hu , Z. , Lei , Y. , Li , X. , Huang , L. , & Liu , Q. ( 2021 ). Negative and positive bias for emotional faces: Evidence from the attention and working memory paradigms . Neural Plasticity , 2021 , 8851066 . doi: 10.1155/2021/8851066 OpenUrl CrossRef PubMed ↵ Zeelenberg , R. , Wagenmakers , E.-J. , & Rotteveel , M. ( 2006 ). The impact of emotion on perception: Bias or enhanced processing? Psychological Science , 17 ( 4 ), 287 – 291 . doi: 10.1111/j.1467-9280.2006.01700.x OpenUrl CrossRef PubMed Web of Science View the discussion thread. Back to top Previous Next Posted December 04, 2024. Download PDF Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Emotional content effects the precision of visual working memory Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. 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Share Emotional content effects the precision of visual working memory Zeinab Haghian , Abdol-Hossein Vahabie , Ehsan Rezayat bioRxiv 2024.12.01.626217; doi: https://doi.org/10.1101/2024.12.01.626217 Share This Article: Copy Citation Tools Emotional content effects the precision of visual working memory Zeinab Haghian , Abdol-Hossein Vahabie , Ehsan Rezayat bioRxiv 2024.12.01.626217; doi: https://doi.org/10.1101/2024.12.01.626217 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Neuroscience Subject Areas All Articles Animal Behavior and Cognition (7646) Biochemistry (17728) Bioengineering (13917) Bioinformatics (42038) Biophysics (21489) Cancer Biology (18637) Cell Biology (25554) Clinical Trials (138) Developmental Biology (13403) Ecology (19941) Epidemiology (2067) Evolutionary Biology (24368) Genetics (15624) Genomics (22547) Immunology (17764) Microbiology (40475) Molecular Biology (17208) Neuroscience (88756) Paleontology (667) Pathology (2842) Pharmacology and Toxicology (4834) Physiology (7659) Plant Biology (15175) Scientific Communication and Education (2047) Synthetic Biology (4304) Systems Biology (9835) Zoology (2272)