Experimental investigation of the interplay between ruminative thinking and working memory capacity: Accounting for modality, affect and metacognition

Experimental investigation of the interplay between ruminative thinking and working memory capacity: Accounting for modality, affect and metacognition | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Experimental investigation of the interplay between ruminative thinking and working memory capacity: Accounting for modality, affect and metacognition Gerly Tamm, Reena Roos, Monika Palu-Laeks, Kristof Hoorelbeke, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6558445/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 Limited cognitive resource theories predict that ruminative thinking will lead to a diminished working memory capacity. While some empirical studies have found proof for the negative effect of rumination on working memory capacity, others have not. Moreover, the arousal-enhancement hypothesis predicts that affective state can enhance memory in the context of high arousal. This study aimed to experimentally induce anger and sadness state-related types of rumination and test their general and specific effects on working memory capacity while considering a set of key moderators: (1) modality, (2) affect, and (3) metacognition. The sample consisted of 65 individuals (average age = 27 years, range 18–50) who were randomly allocated to three experimental groups (sad rumination, angry rumination and controls). All participants completed a set of self-reports, experimental induction of rumination (or a control procedure) and two change detection working memory tasks with visual/abstract and nameable shapes. We found a significant positive effect of state rumination on working memory capacity (p < .033). There were no differences between sad and angry rumination groups. We found that the verbal processing task with nameable shapes resulted in higher k estimates than the visual processing task with abstract shapes (p < .0000001). Modality, affect (incl. depression) and metacognition did not modulate the effect of rumination on working memory capacity. A secondary Bayesian analysis confirmed these results. This study is the first to experimentally test the effects of sad and angry state rumination on working memory capacity estimates. The positive effect of state rumination on k favors the arousal induced enhancement of working memory hypothesis. rumination working memory capacity Cowan’s k negative affect depression modality metacognitive confidence Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Ruminative thinking is a type of repetitive negative thinking that is characterized by dwelling on negative memories while trying to cope with stressful events by trying to understand one’s negative feelings (Nolen-Hoeksema & Morrow, 1991 ; Nolen-Hoeksema et al., 2008 ). It is a transdiagnostic process that significantly predicts clinical depression, anxiety disorders, suicide, and many other negative mental health outcomes (Ehring & Watkins, 2008 ; Rogers & Joiner, 2017 ), and therefore can be considered a significant factor that contributes to the societal burden of adverse mental health outcomes. Its detrimental effects on goal-oriented behavior have been long investigated with a focus on hindered executive functions in affective disorders (Watkins & Brown; 2002 ; Phillipot & Brutoux, 2008; Connolly et al., 2014 ; Du Pont, 2019; Yang et al., 2019). Not only is rumination related to clinical outcomes, its effects extend to goal-oriented cognitive performance in healthy participants: particularly in tasks that require attentional control (e.g., Koster et al., 2013 ; Lissnyder et al., 2012 ; Koster et al., 2011 ; Chuen Yee Lo et al., 2012 ). Attentional control refers to the ability to allocate attentional resources to process information within working memory, a goal-oriented information processing and storage mechanism (Meier & Kane, 2017 ; Cowan et al., 2024 ). Working memory (WM) involves „ ideas that are thought of, or made available to the mind, just when they are needed in order to carry out a mental task or solve a problem “ (Cowan, 1997 ). In this context, ruminative thinking can be considered an attempt to solve problems (goal-oriented) that involves re-occurring thoughts that aim to find a solution to personal problems. However, if these thoughts remain in the focus of attention they will prevent other goal-oriented thoughts to enter WM due to capacity limits. Cognitive resources are limited (Kahneman, 1973 ; Cowan, 1998 ; Baddeley, 1983 ; Oberauer et al., 2016 ; Baumeister et al., 2024 ). Hence, ruminative thoughts will compete for the same cognitive resource as any other type of thoughts. WM change detection experiments have consistently shown that an average human can maintain approximately four chunks of information (i.e., capacity limit, also known as k ) (Cowan et al., 2013 ; Cowan, 2004; Rouder et al., 2011 ) within the focus of attention in WM (Cowan, 2004). When people ruminate, they focus their attention to the self-referential negative thoughts that are actively kept in WM. According to the capacity limit hypothesis, keeping negative thoughts activated in WM consumes WM capacity which should intervene with any WM task performance and lead to lower WM capacity estimates. Indeed, several empirical studied have found that rumination impairs aspects of WM performance (e.g., Curci et al., 2013 ; Bruning et al., 2023 ; Levens et al., 2009 ; Joormann et al., 2011 ). However, none of these studies employed the change detection paradigm to capture the effects of rumination on the k estimate for WM capacity, suggesting a gap in the literature. Moreover, two recent meta-analyses have come to mixed conclusions about which cognitive processes are mostly affected by rumination (Yang et al., 2017 , Zetsche et al., 2018 ). For instance, Yang et al. ( 2017 ) concluded that WM is not affected by rumination. However, WM capacity was not considered as a separate outcome variable. Instead, this meta-analysis merged updating (n-back) and capacity tasks (span-type tasks) into one measure of WM from a set of heterogeneous results that averaged to a null-effect. Zetsche et al. ( 2018 ) concluded that rumination was specifically associated with discarding irrelevant material from WM (i.e., updating). However, effects on WM capacity were not explored, suggesting the need for further research investigating the impact of rumination on WM capacity. Moreover, there are alternative routes through which effects of rumination on WM might unfold. In the next sections, we elaborate on the effects of three potential moderators including (1) WM processing modality, (2) affect and (3) metacognition. First, the shared resource theories agree with the notion that the general cognitive resource is only partly shared between visual and verbal modalities (Nozari & Martin, 2024 ; Cowan et al., 2024 ; Salmela et al., 2014 ; Baddeley, 2007 ; Li & Cowan, 2021 ). Some studies have suggested that depressed individuals prefer visual processing over verbal processing (Lawrence et al., 2018 ). If rumination mostly involves visual imagery, the effect of rumination on WM capacity should be mostly captured by visual WM tasks. Visual and verbal modalities are commonly distinguished based on the processing strategy: e.g., whether people use visual imagery or verbal labels to encode information (Nozari & Martin, 2024 ; Baddeley, 2007 ). This approach is relatively novel considering that there are only a few studies that have studied modality as a potential factor in rumination. For example, Curci et al. ( 2015 ) found some modality specific effects of rumination on WM, however, it focused on the modality of rumination, not WM. Here, we decided to explore the role of modality as a potential modulator of the association between rumination and WM capacity. Second, major theories of rumination (Nolen-Hoeksema, 2003 ; Watkins & Roberts, 2020 ; Matthews & Wells, 2003 ) have stressed the importance of affect in rumination. While the major WM theories do not explicitly account for affect, there is some indication that affect can play a role in which information is preferentially attended and, therefore, activated in WM. The hedonic detector component within the WM system has been proposed (Baddeley, 2007 ). However, empirical proof for this is lacking. Only a few experimental studies have shown that negative affect impairs WM performance mostly in depressed individuals and other clinical samples (Watkins & Brown, 2002 ; Levens et al., 2009 ; Schweitzer et al. 2019). Several studies suggest no detrimental effects of negative affect on WM (e.g., Storbeck & Maswood, 2016 ). The arousal related memory enhancement hypothesis (Cahill & McGaugh, 1995 ; McGaugh, 2018 ) adds that higher state arousal could even temporarily enhance memory performance. This puts emphasis on the need to consider the key dimensions of affect (valence and arousal, Russell, 1980 ; Lang et al., 1998 ) separately because high arousal – negative valence emotions (e.g., sadness) and low arousal – negative emotions (e.g., anger) can potentially lead to different results. Therefore, we aimed to test whether sad (low arousal) and angry (high arousal) rumination (e.g., Peled & Moretti, 2010 ), will lead to differential effects on WM capacity. Third, metacognitive models (Wells & Matthews, 1996 ; Matthews & Wells, 2003 ; Wells, 2019 ) suggest that the level of awareness of one’s own cognitive limits can affect resource allocation in WM. Metacognition is the ability to become aware of and control one’s cognitive and affective processes (Flavell, 1979 ). Being aware of one’s WM should help to allocate more resources to goal (task) oriented processing which will increase accuracy and boost performance. Hence, we expected that self-awareness will modulate the effect of rumination on WM capacity. In sum, considering the inconsistent results in the literature, the (lack of) association between state rumination and WM capacity remains poorly understood. Hence, we propose an experimental study in which we aim to test the effect of rumination on WM capacity while considering the modulatory effects of (1) WM processing modality, (2) affect, and (3) metacognition. The experimental setup aims to manipulate with the processing domains (visual vs verbal), induce anger and sadness related rumination, and capture cognitive confidence ratings about task-performance. Methods Sample A total of 65 participants (20 men and 45 women, average age = 28 years, age range 18–50) were recruited from the local community. All participants were fluent in Estonian language (91% reported Estonian as a mother tongue). Study advertisements were distributed at local public information boards, such as in libraries. The participants responded to a public call for participation by contacting the experimenter. The participants were randomly assigned to the experimental groups (rumination groups and controls), see Table 1 for sample characteristics. Table 1 Sample characteristics (n = 65) Rumination groups Control group Baseline characteristics ANGRY SAD CT N 22 22 21 Sex M:W 6:16 7:15 7:14 Age in years, M [SD] 26 (7.3) 28 (7.6) 26 (8.2) Education (freq.), Primary: Secondary: Higher 1:12:8:1 0:9:13:0 0:8:13:0 Lifetime psychiatric diagnosis (N) 7 3 3 Notes. Lifetime psychiatric diagnosis – number of individuals with self-reported clinical psychiatric diagnosis received throughout life. Power analysis. Prior to data collection, power analysis had been carried for a linear model that would include rumination as the predictor, one interaction term, and up to two additional variables. The potential effect size was estimated as moderate (based on prior correlational studies, e.g., Bernstein et al., 2017 , r = -0.2). The power analysis in R with pwr package defined as pwr.f2.test(u = 5, f2 = 0.2, sig.test = 0.05, power = 0.8) 67 individuals (df = 63.9) to be included into the study in total. Ethics. This study was approved by the local ethics’ committee at the University of Tartu. All participants participated voluntarily and signed the informed consent at the onset of the experiment. All participants were debriefed at the end of the experimental procedure. Information about the resources for psychological help was given to all participants. At the end of the procedure, the participants were offered feedback on their performance in comparison to the average level; students received course credit proportional to their time spent. Questionnaires Montgomery and Åsberg Depression Rating Scale (MADRS) Montgomery and Åsberg Depression Rating Scale (MADRS, Montgomery & Åsberg, 1979) is a well-known instrument for measuring and monitoring symptoms of clinical depression. We used the self-report version of MADRS that consists of 9 items (Svanborg & Åsberg, 1994 ; Kurrikoff jt, 2012). Each item is rated on a six-point scale ranging from 0 to 6 with a minimum total score of 0 and maximum total score of 54. Higher scores indicate the presence of more depressive complaints during the past few days. The MADRS has a high correlation with the Beck Depression Inventory (r = 0.87), well discriminates between the degree of severity of clinical depression with an average of 16 points (SD = 4.4) referring to mild clinical depression, and 22 points (SD = 4.8) referring to severe clinical depression (Svanborg & Åsberg, 1994 ). In this study, the internal consistency of the items was good (Cronbach alpha = .84). Ruminative Response Scale ( Short) (RRS-10) The Ruminative Response Scale (RRS-10) is a commonly used instrument to measure trait depressive rumination (Nolen-Hoeksema & Morrow, 1991 ). The short version of the questionnaire consists of 10 questions that capture brooding and reflection (Treynor et al., 2003 ). It has demonstrated good psychometric properties (Schoofs et al., 2010 ). The items are rated on a 4-point scale ranging from 1 to 4 with a minimum score of 10 and a maximum score of 40. Higher scores reflect higher frequency of depressive rumination. In this study, the internal consistency of the items was good (Cronbach alpha = .85). Anger Rumination Scale (ARS) The Anger Rumination Scale (ARS) is a 19-item measure for anger related iterative thinking that focuses on thoughts about anger inducing past events, and feelings of aggression (Sukhodolsky et al., 2001 ). Its validity and reliability have been considered good (Maxwell et al., 2005 ; Sukhodolsky et al., 2001 ). The items are rated on a 4-point scale ranging from 1 to 4 with a minimum score of 19 and a maximum score of 76. Higher scores reflect higher frequency of anger rumination. In this study, the internal consistency of the items was good (Cronbach alpha = .91). Affect and rumination state reports The affective self-report included six affect related questions, each measuring one aspect of affective processing. The instruction was: “Please use the slider scales below to indicate how you currently feel. Use the mouse to drag the mark to the location that best describes your current state for each of the following aspects.” The items included (1) valence from 0 – negative (e.g., I feel unhappy, depressed, desperate, anxious, irritated) to 100 – positive (e.g., I feel happy, hopeful, satisfied, content), (2) arousal from 0 – low (e.g., I feel sleepy, relaxed, calm) to 100 – high (activated, excited, stimulated), (3) dominance from 0 – low (e.g., I feel influenced, taken care of, respectful, submissive) to 100 – high (e.g., I feel dominant, influential, in control, important), (4) anger from 0 – low (e.g., not angry, not irritated) to 100 – high (e.g., angry, irritated), (5) sadness from 0 – low (e.g., not sad, not depressed, not hopeless/desperate) to 100 – high (e.g., sad, depressed, hopeless/desperate), (6) happiness from 0 – low (e.g., not happy, not satisfied, not cheerful) to 100 – high (e.g., happy, satisfied, cheerful). State rumination was measured as follows: “ Please indicate how much do you currently ruminate? Rumination refers to a repetitive thinking about negative memories about the past. For example, “Why am I always feeling this way?”, “Why do bad things happen to me?”, What if it did not happen to me? ”. The slider scale ranged from 0 – low (no rumination) to 100 – high (severe rumination). As a follow-up to the rumination item, participants were asked to very briefly describe the core content of their ruminative thoughts by inserting a couple of keywords in a text field (these qualitative data are not reported here). Exit questionnaire At the end of the experimental procedure, the participants responded to a short questionnaire about their performance related beliefs and cognitive strategies (not reported here). Change detection task (CDT) Change detection paradigm (Fig. 1 ) was used to capture WM capacity (Rouder et al., 2011 ; Cowan, 2024). E-prime v2.0 was used for programming the CDT. The tasks were presented on Windows OS laptops (12.5’ to 14’ displays). There were two alternative versions of the CDT which were presented in a randomized and counterbalanced sequence. Each of the two used different types of stimuli depending on the processing modality (verbal and visual). Both CDT tasks consisted of 66 trials in total of which 6 were practice trials which were not considered in the analysis. Each trial began with a fixation cross (1000 ms) followed by a presentation of the stimulus set (5 stimuli) for 1300 ms. The set was presented in a circular formation with counterbalanced randomized locations with equal distance between each stimulus. The stimuli were masked with white “clouds” for 2200 ms after which a probe was presented in the middle of the screen until the response (“change”/”different” or “no change”/”same”) was given on a keyboard. The response keys (left arrow – “no change” and right arrow – “change”) were clearly labelled with stickers. The participant was instructed to keep their fingers of their preferred hand close to the keyboard and be ready to respond at each trial as accurately and as quick as possible . The distance between the screen and eyes was approximately 60–70 cm. The participants were instructed to focus on the computer screen throughout the tasks. There was a 1 min break between the two tasks during which the participants were reminded to think about their personal distressful event or the nature video, depending on the experimental group (see rumination induction procedure). The tasks were carried out individually in a dark room in which the main light source was the computer screen. The stimuli were white and presented on a black background. Stimuli Shapes or colors are commonly used features in WM change detection tasks (e.g., Logie et al., 2011 ; Cowan et al., 2013 ). Here, we used shapes in both modality conditions. Importantly, the two types of stimuli (basic vs abstract shapes) had a different verbalization potential as shown by a pilot-test. Here, we conceptualized verbal processing as processing of basic shapes that can be automatically labelled with standard names (e.g., heart shape is commonly labelled as “heart”), and visual processing as processing of abstract shapes that are difficult to label and which do not have standard names. We used a total of 20 unique stimuli (10 in the nameable shapes CDT, and 10 in the visual/abstract CDT). The size of the stimulus pool was similar to other CDT studies (Logie et al., 2011 ). The verbal processing task included the following shapes: heart, square, circle, arrow, diamond, triangle, star, oval, moon, bowtie (Fig. 2 , upper row). The visual processing task included the 10 abstract shapes displayed in Fig. 2 (lower row). Prior to the experiment, we pilot-tested the two types of tasks on a small sample (n = 4). We relied on the qualitative feedback from the participants when dissociating between verbal and visual processing of the shape stimuli: All participants reported that they used verbal labels to memorize the nameable shapes; they could easily recognize and label each shape (e.g., square, triangle etc.). In contrast, the abstract shapes were not easily uniquely labelled; these were encoded in a more abstract way, more “visually”. WM capacity estimation The main outcome measure of the CDT is accuracy, which is considered separately for change trials (in which the probe was not in the array) and no-change trials (in which the probe was present in the array). Correct responses for the correctly detected change trials are called hits, and incorrect indications of change in no-change trials are called false alarms. To estimate the number of items in working memory, we used the recommended formula for k (e.g., Cowan et al., 2013 ; Rouder et al., 2011 ), see the Statistical Analysis section for details. Experimental procedure Setup and questionnaires . All participants completed the tasks in a laboratory. After the participants signed an informed consent form, they were randomly (counterbalanced) assigned to one of three experimental groups (sad/angry/control). They were instructed to complete the tasks individually (alone in the room). The experimenter setup the questionnaires on the computer and left the room. The participants completed the questionnaires, including the baseline affective state report (Fig. 3 ). After this, the experimenter entered the room and started the experimental procedure on the computer. Rumination induction procedure. (1) First, the participants viewed a disturbing video that induced either sadness or anger. For this, two clips were selected and pilot-tested (n = 3) from the Estonian drama movie ”Klass“ (The Class, 2007, directed by Ilmar Raag) about youth violence and bullying (duration 2 min 25 sec / 2 min 42 sec). The control group viewed a video about an Alaskan Denali National Park retrieved from YouTube (duration 2 min 13 sec); (2) Depending on the condition, participants were instructed to recall the saddest or most infuriating experience or event from their lives, imagine it and write a short summary about it on paper (~ 8 min). The control group was instructed to recall the national park video and write a short summary about it; (3) Finally, the participants responded to 14 guided thinking questions about their selected event (selection of 14 questions from the 45-question procedure described by Nolen-Hoeksema and Morrow, 1993 ) (~ 6 min). The control group was instructed to imagine and describe a theater and a train station in their hometown (~ 6 min). The duration of the rumination induction procedure was approximately 16 min in total. All participants completed the affective state report. WM tasks. After the induction procedure the experimenter re-entered the room, instructed the participants and started the next set of tasks; then exited the room. The tasks were presented in two randomized blocks (verbal processing task with nameable shapes and visual/abstract processing task). Between the blocks, a reminder was presented for 1 min which instructed the participants to continue thinking about their selected event, and to add details to their event description. Before each task, the instructions with illustrations were displayed. Each block included practice trials and the task. After the tasks, all participants completed the questionnaire about the affective state report for the third time with follow-up questions. Debriefing. The tasks were followed by a positive emotion induction procedure after which the experimenter had a brief conversation with the participants in which detailed information about the experiment was provided, and qualitative feedback was collected. The experimenter checked the emotional state of the participants. Information about help resources were provided. The full duration of the experimental procedure was 1.5 hours max per participant. Statistical analysis Analytical approach . R Studio was used for the analysis. This study was designed to (1) test the general effect of rumination on WM capacity (rumination conditions vs control), (2) compare anger and sadness related rumination conditions, and (3) explore the effects of modality, depression and metacognition to describe how each of these affected the association between rumination and WM capacity. We used general linear and mixed models (lme4 package, Bates et al., 2024) to test the hypotheses. Paired t-tests were used for manipulation checks to compare affect and rumination indices before vs after induction. P-values were considered significant below .05. Multiple testing was corrected within the models (nested models). Robustness of the results was tested by applying a Bayesian approach (brms package, Bürkner, 2021 ). WM capacity estimation. We estimated the WM capacity with the Cowan’s k formula that is recommended for the single probe type of change detection tasks (Rouder et al. 2011 ). K refers to the estimated WM capacity (the number of information slots available). K is derived from the difference between the hit rate (correct detection of a change) and false alarm rate (incorrect detection of a change) that is multiplied by the set size (N). For the central probe type of CDT (such as is used here), this is divided by the hit rate to account for the change in location: K = N*( hits – FAs )/ hits (Cowan et al., 2013 ). Given that k cannot exceed N, it represents the mean number of items retained from arrays of Set Size N. It is derived by assuming that if the participant knows the probe item then a “no-change” response is given, and otherwise the participant guesses that there has been a change with a guessing rate that is specific to the set size. Here, we estimated k separately for visual and verbal processing CDT tasks, and the overall k in which both tasks were merged into one estimate of k . Invalid responses. Two subjects with missing WM task data were excluded. Task performance was checked for validity. Additionally, two responders with overall k < 0 were considered invalid due to the violation of the qualification rule hits ≤ FAs in both tasks (also see Rouder et al., 2011 ). The two outlier k estimates were well below zero: -0.39 and − 0.47. A negative k suggests that the hit rate was smaller than the false alarm rate. This indicates at chance or below chance performance, which can be considered invalid (similar to Tamm et al., 2017 ). All other responders (n = 61) had hits > FAs and hits ≠ 0. Also, the k values for the two tasks were checked separately and in case at least one task was completed within the valid range (k > = 0), we included that participant’s data into the analysis. Three individuals had negative k values for the visual processing task and positive k values for the task with nameable shapes (verbal processing task). These data were included by rounding the negative k values up to 0 (similar to Cowan et al., 2011 ) Transparency and openness The summarized anonymized dataset and R script will be made available in the OSF upon publication. The design of this study was preplanned and registered as a research project at the Institute of Psychology at the University of Tartu and approved by the Ethics committee at the University of Tartu prior to the data collection. Results Manipulation checks We used self-reported state rumination and affect characteristics as the key indices for the manipulation checks. Before/after scores for each experimental group can be viewed in Table 2 . The rumination induction resulted in significantly higher negative affect and higher arousal in both rumination conditions in comparison to the controls (Table 2 ). There was no specificity to anger vs sad rumination in terms of valence or arousal. Specificity of anger and sad rumination was captured by anger, sadness and happiness reports. Anger and sad rumination conditions induced significantly different levels of sadness, anger and happiness (p < .05): sadness was significantly increased in the sad rumination group but not in the anger rumination and control groups. Anger was significantly increased in the anger rumination group but not in the sad rumination or control groups. Happiness was significantly decreased only in the sad rumination group but not in the anger rumination or control groups. Self-reported rumination was significantly decreased in the control group while there was no change in rumination in the anger and sad rumination groups. Further inspection of the data revealed that there was no bottom or ceiling effect. There were 12 participants who did not respond to the rumination induction as expected (5 in the sad rumination group and 7 in the anger rumination group). When these subjects were excluded, an increase in rumination was significant in anger (t(12) = -4, p < .001) and sad rumination conditions (t(14) = -6.8, p < .000001). Due to the heterogeneity in rumination induction responsiveness, we added a dummy variable (responsiveness to rumination induction: 0–1) to the models as a covariate to take into account the variation in rumination induction efficacy. Table 2 Comparisons of affect and rumination indices between before and after induction Experimental groups T1/Before Mean (SD) T2 /After Mean (SD) Effect p-value State rumination (0 – low … 100 high) Angry rumination 34.70 (30.8) 42.40 (27.27) - ns Sad rumination 30.25 (32.29) 31.5 (26.36) - ns Controls 35.40 (36.2) 20.47 (23.9) ↓less rumination 0.036 Valence (0 – negative … 100 positive) Angry rumination 68.70 (22.9) 55.15 (23.8) ↓more neg 0.032 Sad rumination 73.45 (17.8) 53.50 (24.2) ↓more neg 0.0007 Controls 66.05 (26.87) 74.95 (22.95) - ns Arousal (0 – low … 100 high) Angry rumination 42.45 (18.4) 57.45 (18.6) ↑higher arousal 0.030 Sad rumination 45.35 (26.7) 64.0 (16.3) ↑higher arousal 0.004 Controls 53.09 (21.6) 50.05 (26.6) - ns Dominance (0 – low … 100 high) Angry rumination 53.70 (17.3) 62.80 (16.7) - ns Sad rumination 60.95 (19.5) 48.30 (22.7) (↓less dominant) (0.066) Controls 56.61 (22.8) 58.67 (19.7) - ns Sadness (0 – low … 100 high) Angry rumination 25.15 (21.5) 31.45 (19.3) - ns Sad rumination 14.20 (18.7) 35.9 (22.7) ↑more sad 0.001 Controls 21.57 (27.5) 18.5 (24.7) - ns Anger (0 – low … 100 high) Angry rumination 13.20 (22.4) 30.9 (27.2) ↑more angry 0.024 Sad rumination 10.0 (26.1) 25.45 (26.8) - ns Controls 5.52 (12.9) 7.23 (14.6) - ns Happiness (0 – low … 100 high) Angry rumination 62.35 (25.5) 53.8 (20.7) - ns Sad rumination 72.00 (16.7) 50.5 (25.5) ↓less happy 0.0035 Controls 68.14 (23.6) 72.3 (20.6) - ns Notes. Paired t-tests. ns – not significant (p > > 0.06) Baseline characteristics There were no differences between the experimental groups in baseline measures. Average scores for depression, depressive rumination, anger rumination, and clinical diagnosis rates and baseline state rumination for experimental group can be found in Table 3 . Correlations between the baseline measures and WM performance can be found in the Supplementary Materials Table S1 . Table 3 Baseline trait characteristics Measures ANGRY (n = 20) SAD (n = 20) CT (n = 21) p-value A MADRS (SD) 12.1 (7.4) 10.3 (6.2) 9.2 (7.4) n.s. RRS (SD) 19.5 (5.5) 18.5 (5.1) 19.5 (5.9) n.s. ARS (SD) 32.8 (6.8) 31.8 (10.5) 33.0 (9.1) n.s. Notes. MADRS – Montgomery-Åsberg Depression Rating Scale, RRS – Ruminative Response Scale, ARS – Anger Rumination Scale. A One-way ANOVA. We observed the distribution of the k estimate (see Figure S1 in Supplementary Materials), Shapiro-Wilk normality test indicated that the distribution did not deviate from the Normal distribution (W = 0.98, p = .54). The effect of rumination on WM capacity estimate k The linear model included two rumination contrasts: a general effect of rumination which contrasted the rumination groups vs controls, and the specific effect which contrasted sad vs anger rumination groups. There was a significant general main effect of rumination on WM capacity estimate k (t(58)= -2.39, p < .020). Rumination conditions (sad and angry merged) were associated with higher capacity than the control condition (Fig. 1 ). There were no significant differences between anger and sad rumination groups. The results remained the same when the dummy variable for rumination induction effectiveness was included into the simple linear regression as a covariate (Table 3 ). Table 3 A linear model for the effect of state rumination on WM capacity estimate (k) Effects Estimate SE t p Intercept 2.39 0.229 10.44 <<.000001 Rumination vs control -0.692 0.289 -2.39 .020 Sad vs angry rumination -0.156 0.238 -0.65 0.515 Induction responsiveness (covariate) -0.035 0.260 -0.14 0.892 Note. p < .05. These are non-standardized estimates. Exploration of the modulatory effects of modality, depression and metacognition The mixed model (Table 4 ) included two rumination contrast variables: (1) the general effect of rumination vs controls and (2) the specific effect of sad vs angry rumination, and three covariates: (3) modality, (4) depression and (5) metacognition. Again, with the inclusion of the covariates, the model (see Table 3 ) detected a significant general effect of rumination. Additionally, there was a main effect of modality on k. Depression and metacognition variables did not have a significant effect on WM capacity (k). Addition of the dummy variable (induction responsiveness) that encoded the rumination induction efficacy did not modify the results. Table 4 Fixed effects from the mixed model predicting WM capacity estimate k from rumination, modality, metacognition, and depression Fixed effects Estimate SE t P Intercept 2.293 0.544 4.213 0.000 Rumination vs control -0.600 0.282 -2.127 0.038* Sad vs angry rumination 0.106 0.235 0.451 0.654 Metacognition 0.147 0.116 1.266 0.211 Modality -1.231 0.140 -8.818 0.000001** Depression 0.009 0.014 0.621 0.538 Induction responsiveness 0.051 0.272 0.188 0.851 Note. *p < .05. **p < 0.00000000001. These are non-standardized estimates. Alternative mixed models were explored by inserting modality, depression and metacognition one by one into an interaction term with the general effect of rumination while keeping the other variables as covariates. This explorative analysis resulted in three additional models, none of which included any significant interaction effects, suggesting that modality, affect, and metacognition did not moderate effects of rumination on WM capacity. Robustness check: Bayesian analysis While the frequentist approach only allows to test the alternative hypothesis, Bayesian models give an opportunity to explore the likelihood for each hypothesis: the null and the alternative hypothesis, and they do not depend as heavily on sample size as the frequentist models do. Here, to further test the sensitivity and robustness of the effect of rumination on WM capacity, we estimated the likelihood of the expected distributions with the Bayesian approach. For this, we used the brms package in R. The family was set to Gaussian. The numeric variables were standardized (z-scores) prior to fitting the model. We defined priors for each fixed effect coefficient in the mixed model as normal distributions with mean = 0 and sd = 1. Normal distribution was selected based on prior knowledge that extremely low and high effects would not be as feasible as effects around zero. The quality of the priors was checked with the Gelman method (as suggested by Gelman et al., 2017 ) and by visual inspection (Figure S3). Overall, the Bayesian model resulted in similar conclusions as the frequentist approach, suggesting evidence for a positive enhancement effect of the rumination induction condition on WM capacity. The model suggested that a positive effect of rumination on WM capacity was 35.04 times more likely than a zero or negative effect of rumination on WM capacity (Supplementary Materials, Table S2A). Importantly, we tested the likelihood of the null-hypothesis (effect = 0) and found that the alternative hypothesis (effect of rumination on WM capacity) was more likely than the null-effect hypothesis (Supplementary Materials, Table S2B). Also, when we tested the specific hypothesis for the negative effect of rumination on WM capacity (Supplementary Materials, Table S2C) we found that the evidence ratio for this effect was very low (0.03), indicating that the negative effect was highly unlikely given the current data. Additionally, the model showed strong evidence for an effect of modality on WM capacity: visual modality was associated with lower capacity than verbal modality. The posterior predictive check with 500 draws showed a good fit. See Supplementary Materials for model specifics (Table S2). The same model was tested with flat priors (uninformative), similar results emerged. Discussion This study aimed to experimentally test the effect of ruminative thinking on working memory capacity estimate k. We induced sad and anger rumination and explored its effects on WM capacity by contrasting anger and sad rumination to one another and to a control group. The manipulation checks indicated that the rumination induction group had more negative affect, higher arousal and more rumination than the control group, however, responsiveness to the induction procedure largely varied between individuals which indicated a need to control for induction responsiveness in the main analysis. In general, we found a significant general effect of rumination on WM capacity estimate k. No differential effects were found for type of rumination (sad vs anger rumination). Rumination was associated with higher capacity estimates, adding proof for the arousal-enhancement hypothesis. Additionally, we found that modality significantly affected WM capacity suggesting that nameable shapes (i.e., verbally processed) stimuli were encoded more efficiently than abstract visual information. In the following sections, we will elaborate on these results and its robustness in the context of major theories of rumination and WM. The effect of rumination on WM capacity The key finding of this study was that state rumination was associated with larger WM capacity, independent of the content of rumination (sad or angry), modality (visual or verbal), level of depression, metacognitive confidence, or rumination induction responsiveness. The apparently contradictory result suggests that WM capacity might not be consumed by state ruminative thinking in the context of shared limited resources (Kahneman, 1973 ; Cowan, 1999 ; Baddeley, 1983 ; Oberauer et al., 2016 ; Baumeister et al., 2024 ). In contrast to the expected impairment of performance, performance was improved by rumination. This result allows for several interpretations. One relates to the arousal hypothesis. The manipulation checks showed that both rumination groups (sad and angry) had significantly higher arousal and more negative affect than the control group. Hence, the positive effect of rumination could have been driven by the negative high arousal affective state. Therefore, we think that the positive effect of rumination could be explained by an increased physiological activation (readiness to respond) and vigilance which was induced by the rumination induction procedure. Here, we did not directly measure physiological activation, however, other studies have shown that self-reported vigilance as well as objective physiological activation have been consistently associated with better cognitive task-engagement and effort (Matthews et al., 2010 ; Howells et al., 2010 ; Pribram & McGuiness, 1975). The induced arousal in the rumination induction conditions might have helped to bring the participants to their peak performance. The Yerkes-Dodson law (Yerkes and Dodson, 1908 and its supporting studies (e.g., Mair et al., 2010; Faller et al., 2019) suggests that cognitive performance is maximum at optimal levels of activation. In the context of our data, post-induction arousal was around 60 on a scale of 0 to 100 and not at an extreme level, which may reflect an optimal level of arousal. Whereas in the control condition, the nature related recreational thoughts might have induced a more mindful state (as indicated by lower levels of rumination). These results provide novel hypotheses and insights into understanding the associations between WM performance and rumination, and may explain why in some clinical cases lower WM capacity has been reported (e.g., in patients with PTSD; Schweizer & Dalgleish, 2011 ). That is, extreme arousal could be induced by more severe memories in clinical cases, e.g., visual intrusions and rumination in PTSD, which can drain WM resources instead of boosting performance. Another explanation comes from the sample viewpoint, considering that our sample included relatively healthy individuals in terms of mental health, their selected negative memories and related ruminative thoughts might not have been “strong” enough to induce a state of pathological rumination in which internally focused attention cannot be easily shifted towards task relevant information (Koster et al., 2011 ). Similarly, Watkins and Brown ( 2002 ) found that state rumination affected counting scores only in depressed individuals but not in healthy controls. Depression has been associated with trait rumination and more severe life-events than is usually seen in the healthy population (e.g., Wiersma et al., 2009 ; Marchetti et al., 2024 ). Thus, the memories and experiences that are activated in state rumination in depression could be qualitatively different from state rumination in healthy individuals. Moreover, the differential effects of state and trait rumination on cognitive performance have been suggested in prior studies (Grant et al., 2021 ). In sum, the current results suggest that in relatively healthy individuals, state rumination can result in higher WM capacity estimates. This is not to say that that rumination can increase WM capacity per se, rather the engagement of WM resources depends on an affective state. The role of modality, depression and metacognition in rumination Prior research has indicated that the effects of ruminative thinking on WM could depend on processing modality (visual vs verbal). For example, Curci et al. ( 2015 ) showed that negative affect was associated with a decrease in the WM performance for the nameable shapes in contrast to neutral and that there were no differences between neutral or negative affect in the visuospatial task. This suggests that negative thinking might mostly affect verbal WM capacity. Here, we considered this by including modality into the model and found no interaction with rumination. However, there was a strong main effect of modality on WM capacity: verbal processing task condition was associated with higher capacity than the visual condition. While there was no interaction with rumination, it can be concluded that the effect of rumination on WM capacity seems to be independent of modality. However, WM capacity estimate depends on the type of stimuli that are used to estimate capacity. Often, colors or shapes have been used to estimate k (Logie et al., 2011 ). Most people can recognize and label “triangle” or “blue” automatically. However, more abstract/visual shapes which cannot be easily labelled seem to require more resources in WM. Thus, this suggests that the way information is stored in WM can have significant impact on performance: verbal processing has an advantage in WM. A similar idea that visual working memory generally requires more resources than verbal WM has been proposed by Gray et al. ( 2017 ) and is further reinforced by studies that have considered the shared and common capacity (Vergauwe et al., 2010 ; Morey & Bieler, 2013 ). However, in contrast to our expectations, processing modality did not moderate effects of rumination on WM capacity. Additionally, we considered two more key covariates: depression and metacognitive awareness. We assumed that depression and metacognition could have a modulatory effect on the association between rumination and WM capacity. The classical metacognitive model (Flavell, 1979 ) suggests that self-awareness (monitoring) should enhance information processing due to resource allocation to the processes that are considered most important. Therefore, awareness of one’s own WM performance should enhance performance. However, the metacognitive models for rumination (Matthews & Wells, 2003 ; Papageorgiou & Wells, 2009 ) specify that this is only the case if ruminative thoughts are considered irrelevant by that person. In case a person has a positive belief that ruminative thinking is appropriate (such as in clinical depression, Watkins & Moulds, 2005 ), then being aware of one’s thoughts will not help to diminish rumination. In contrast, self-awareness can amplify rumination such that WM will become more focused on ruminative thoughts instead of any other task-relevant content. Here, we did not find any modulatory effects of metacognition nor depression on WM capacity, perhaps due to a non-clinical sample and a mix of strategies used: confidence judgements with feedback could have enhanced WM performance in some individuals but impaired in others by reinforcing the need to ruminate about their incorrect responses (those who had strong positive beliefs about rumination as a helpful task-completion strategy). We will elaborate on the need for future studies in the next section. Limitations and future directions The current study explored the association (or lack of association) between rumination and WM capacity, and the role of three confounding variables in this. Surprisingly, the results provided no proof for the modulatory effects of metacognition, modality and depression, contradicting some recent findings. For example, a recent data-driven network model for rumination and its replication (Tamm et al., 2024 , 2025 ) showed that trait rumination was directly linked to effortful/attention control, depression as well as metacognitive abilities. Moreover, a recent study showed that self-awareness can diminish state rumination in case the metacognitive process is guided, such as in guided meditation (e.g., focus on breathing sensation) (Bolzenkötter et al,. 2025), adding proof for the role of metacognition in modulating no only trait but also state ruminative thinking. Therefore, future studies should re-examine the interplay between metacognitive awareness, rumination and WM capacity by considering (1) adding multiple measures for metacognitive awareness, (2) controlling for prior beliefs about rumination, and (3) considering performance related worry as a potential confounding variable. Another important aspect is sample size. Here, the power estimates for the linear model were acceptable, when assuming moderate to strong effects. However, the effect size assumption was not based on prior experimental group comparisons due to lack of such, and we relied on correlations between trait rumination and WM performance from earlier research. Thus, testing the effects in a larger sample could be informative. Finally, replication of the results in multiple samples is important. We conducted a brief exploratory follow-up study and recruited an additional control group with 25 participants to increase it up to the size of the rumination group (sad + angry) to further explore the robustness of the effects. Unfortunately, the two groups had a different average k estimate: the second control group had a significantly higher k than the first control group (t(44) = 2.93, p < 0.005). Also, the additional control group recruitment was carried out by a different experimenter a few years after the initial data had been collected. Hence, the two could not be merged. However, future studies should carefully consider testing the effects against multiple comparable control groups and conditions to explore the sensitivity of the results. Conclusions This study investigated the effect of state rumination on WM capacity estimate k while considering the three potential moderators, including modality, depression and metacognition. We found a positive effect of the rumination groups on WM capacity. The results favor the arousal related enhancement of WM hypothesis. Moreover, arousal related enhancement can indicate better task-engagement and physiological vigilance. The results point out the need to consider affective state-dependent fluctuations when estimating WM capacity. This study provided a novel approach to studying the effects of rumination on WM capacity and pointed out the importance of affect and modality in the interplay between rumination and WM capacity. Declarations Author Contribution This study was proposed and designed by Gerly Tamm and Monika Palu-Laeks. Experimental program was written by Gerly Tamm and pilot-tested by Monika Palu-Laeks. Data was collected by Monika Palu-Laeks and Reena Roos. Literature review was conducted by Gerly Tamm, Monika Palu-Laeks and Reena Roos. Conceptualization of working memory capacity by Nelson Cowan and Gerly Tamm. Conceptualization of rumination by Gerly Tamm, Monika Palu-Laeks and Kristof Hoorelbeke. The data was prepared by Gerly Tamm and Monika Palu-Laeks. Formal analysis was carried out by Gerly Tamm. The first version of the manuscript was written by Gerly Tamm and edited by Kristof Hoorelbeke and Nelson Cowan. All authors contributed to interpretations of the results. 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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-6558445","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":452306962,"identity":"aa5b46f2-e99e-4238-b060-e73a1ea86e76","order_by":0,"name":"Gerly Tamm","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYHACxgMQmhlKSxChB6jUAEixJUA0kKCFx4A4LfLuvQ8O8zD8kdedduab5JeKO3UM0s0H8GoxPHPcAKjFwHDb7dxt0jJnnkkwyBxLwK9lRhoDSAsjWItk22Ggw3IM8GuZ/wysxX7b7Zxn0pL/QFryP+D3iwQbWEsiUAub5McGsC14dTAY8KQxHJxjYJy87XaasTXDscOSbTLH8DtMvv0Y44M3FXK2224nP7z5o+YwP7908wP8thxgYGDigRrLzAMk2PA7C2hLAzDF/IBy4IxRMApGwSgYBcgAAH96Ru6GzRyUAAAAAElFTkSuQmCC","orcid":"","institution":"Ghent University","correspondingAuthor":true,"prefix":"","firstName":"Gerly","middleName":"","lastName":"Tamm","suffix":""},{"id":452306963,"identity":"0c994581-de74-4386-9b07-2d1f58de2648","order_by":1,"name":"Reena Roos","email":"","orcid":"","institution":"University of Tartu","correspondingAuthor":false,"prefix":"","firstName":"Reena","middleName":"","lastName":"Roos","suffix":""},{"id":452306964,"identity":"b7414809-47a4-4479-a7bc-99147da7a021","order_by":2,"name":"Monika Palu-Laeks","email":"","orcid":"","institution":"University of Tartu","correspondingAuthor":false,"prefix":"","firstName":"Monika","middleName":"","lastName":"Palu-Laeks","suffix":""},{"id":452306967,"identity":"b9aeec94-580f-4e71-8915-080e0ba8ef18","order_by":3,"name":"Kristof Hoorelbeke","email":"","orcid":"","institution":"Ghent University","correspondingAuthor":false,"prefix":"","firstName":"Kristof","middleName":"","lastName":"Hoorelbeke","suffix":""},{"id":452306968,"identity":"a7658b93-c48a-4b48-94c7-5beb3e3bad58","order_by":4,"name":"Nelson Cowan","email":"","orcid":"","institution":"University of Missouri","correspondingAuthor":false,"prefix":"","firstName":"Nelson","middleName":"","lastName":"Cowan","suffix":""}],"badges":[],"createdAt":"2025-04-29 16:53:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6558445/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6558445/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82277315,"identity":"1788acad-5d7a-4b52-8fd8-fff8b7c491a5","added_by":"auto","created_at":"2025-05-08 14:50:50","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":45954,"visible":true,"origin":"","legend":"\u003cp\u003eIllustration of the change detection task with nameable shapes . Each trial started with a fixation cross followed by a set of stimuli to be remembered which were presented for 1300 ms . The stimuli were then masked with white “clouds”. Finally, a center probe was presented until response. Change occurred in 50% of the trials. The sequence of the trials was random per each participant. The confidence judgement scale was presented until a response was provided, followed by feedback of the accuracy and a reminder to be ready for the next trial.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6558445/v1/3fc46ecb674d3ba2a1eedacc.jpg"},{"id":82279101,"identity":"c4683492-39dd-498c-ba2f-7981e9b0d847","added_by":"auto","created_at":"2025-05-08 14:58:50","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":29387,"visible":true,"origin":"","legend":"\u003cp\u003eStimuli used in the CDT. The easily nameable stimuli (as a proxy for verbal processing) are displayed in the upper row and the abstract visual stimuli (as a proxy for visual processing) are displayed in the lower row.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6558445/v1/07366b52808534a59d206736.jpg"},{"id":82277317,"identity":"3b2af71c-9b5a-49f6-895c-2fa03d0568f8","added_by":"auto","created_at":"2025-05-08 14:50:50","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":64240,"visible":true,"origin":"","legend":"\u003cp\u003eIllustration of the experimental procedure and approximate duration of each task\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6558445/v1/d11dcb436778951c737c25d1.jpg"},{"id":82279102,"identity":"008daa36-9484-4ddd-95b3-acc48c7cd9d6","added_by":"auto","created_at":"2025-05-08 14:58:50","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":32554,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eFigure 1.\u003c/strong\u003e\u003c/em\u003e \u0026nbsp;The effect of state rumination on WM capacity estimates. Notes. Error bars indicate 95% CI. CT – control group. (A) Overall WM capacity estimate K in three experimental groups. (B) WM capacity estimate for nameable shapes (primarily verbal processing). (C) WM capacity estimate for abstract shapes (primarily visual processing).\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6558445/v1/8705c341c9ed52079f236f33.jpg"},{"id":85877666,"identity":"3b7d2a4f-f302-4dd8-9eeb-6b999d5a9675","added_by":"auto","created_at":"2025-07-02 15:23:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1400458,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6558445/v1/db792da2-a9fc-406d-b84a-2c87ebb8d793.pdf"},{"id":82277318,"identity":"c9eaf87d-a94d-4f96-afde-3836afa0a679","added_by":"auto","created_at":"2025-05-08 14:50:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":309327,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6558445/v1/0710594e8c04d3d0838b06aa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Experimental investigation of the interplay between ruminative thinking and working memory capacity: Accounting for modality, affect and metacognition","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRuminative thinking is a type of repetitive negative thinking that is characterized by dwelling on negative memories while trying to cope with stressful events by trying to understand one\u0026rsquo;s negative feelings (Nolen-Hoeksema \u0026amp; Morrow, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Nolen-Hoeksema et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). It is a transdiagnostic process that significantly predicts clinical depression, anxiety disorders, suicide, and many other negative mental health outcomes (Ehring \u0026amp; Watkins, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Rogers \u0026amp; Joiner, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and therefore can be considered a significant factor that contributes to the societal burden of adverse mental health outcomes. Its detrimental effects on goal-oriented behavior have been long investigated with a focus on hindered executive functions in affective disorders (Watkins \u0026amp; Brown; \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Phillipot \u0026amp; Brutoux, 2008; Connolly et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Du Pont, 2019; Yang et al., 2019).\u003c/p\u003e \u003cp\u003eNot only is rumination related to clinical outcomes, its effects extend to goal-oriented cognitive performance in healthy participants: particularly in tasks that require attentional control (e.g., Koster et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Lissnyder et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Koster et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Chuen Yee Lo et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Attentional control refers to the ability to allocate attentional resources to process information within working memory, a goal-oriented information processing and storage mechanism (Meier \u0026amp; Kane, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Cowan et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Working memory (WM) involves \u0026bdquo;\u003cem\u003eideas that are thought of, or made available to the mind, just when they are needed in order to carry out a mental task or solve a problem\u003c/em\u003e\u0026ldquo; (Cowan, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). In this context, ruminative thinking can be considered an attempt to solve problems (goal-oriented) that involves re-occurring thoughts that aim to find a solution to personal problems. However, if these thoughts remain in the focus of attention they will prevent other goal-oriented thoughts to enter WM due to capacity limits.\u003c/p\u003e \u003cp\u003eCognitive resources are limited (Kahneman, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1973\u003c/span\u003e; Cowan, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Baddeley, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1983\u003c/span\u003e; Oberauer et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Baumeister et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Hence, ruminative thoughts will compete for the same cognitive resource as any other type of thoughts. WM change detection experiments have consistently shown that an average human can maintain approximately four chunks of information (i.e., capacity limit, also known as \u003cem\u003ek\u003c/em\u003e) (Cowan et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Cowan, 2004; Rouder et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) within the focus of attention in WM (Cowan, 2004). When people ruminate, they focus their attention to the self-referential negative thoughts that are actively kept in WM. According to the capacity limit hypothesis, keeping negative thoughts activated in WM consumes WM capacity which should intervene with any WM task performance and lead to lower WM capacity estimates.\u003c/p\u003e \u003cp\u003eIndeed, several empirical studied have found that rumination impairs aspects of WM performance (e.g., Curci et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Bruning et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Levens et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Joormann et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). However, none of these studies employed the change detection paradigm to capture the effects of rumination on the \u003cem\u003ek\u003c/em\u003e estimate for WM capacity, suggesting a gap in the literature. Moreover, two recent meta-analyses have come to mixed conclusions about which cognitive processes are mostly affected by rumination (Yang et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Zetsche et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). For instance, Yang et al. (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) concluded that WM is not affected by rumination. However, WM capacity was not considered as a separate outcome variable. Instead, this meta-analysis merged updating (n-back) and capacity tasks (span-type tasks) into one measure of WM from a set of heterogeneous results that averaged to a null-effect. Zetsche et al. (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) concluded that rumination was specifically associated with discarding irrelevant material from WM (i.e., updating). However, effects on WM capacity were not explored, suggesting the need for further research investigating the impact of rumination on WM capacity. Moreover, there are alternative routes through which effects of rumination on WM might unfold. In the next sections, we elaborate on the effects of three potential moderators including (1) WM processing modality, (2) affect and (3) metacognition.\u003c/p\u003e \u003cp\u003eFirst, the shared resource theories agree with the notion that the general cognitive resource is only partly shared between visual and verbal modalities (Nozari \u0026amp; Martin, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Cowan et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Salmela et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Baddeley, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Li \u0026amp; Cowan, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Some studies have suggested that depressed individuals prefer visual processing over verbal processing (Lawrence et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). If rumination mostly involves visual imagery, the effect of rumination on WM capacity should be mostly captured by visual WM tasks. Visual and verbal modalities are commonly distinguished based on the processing strategy: e.g., whether people use visual imagery or verbal labels to encode information (Nozari \u0026amp; Martin, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Baddeley, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). This approach is relatively novel considering that there are only a few studies that have studied modality as a potential factor in rumination. For example, Curci et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) found some modality specific effects of rumination on WM, however, it focused on the modality of rumination, not WM. Here, we decided to explore the role of modality as a potential modulator of the association between rumination and WM capacity.\u003c/p\u003e \u003cp\u003eSecond, major theories of rumination (Nolen-Hoeksema, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Watkins \u0026amp; Roberts, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Matthews \u0026amp; Wells, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) have stressed the importance of affect in rumination. While the major WM theories do not explicitly account for affect, there is some indication that affect can play a role in which information is preferentially attended and, therefore, activated in WM. The \u003cem\u003ehedonic detector\u003c/em\u003e component within the WM system has been proposed (Baddeley, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). However, empirical proof for this is lacking. Only a few experimental studies have shown that negative affect impairs WM performance mostly in depressed individuals and other clinical samples (Watkins \u0026amp; Brown, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Levens et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Schweitzer et al. 2019). Several studies suggest no detrimental effects of negative affect on WM (e.g., Storbeck \u0026amp; Maswood, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The arousal related memory enhancement hypothesis (Cahill \u0026amp; McGaugh, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; McGaugh, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) adds that higher state arousal could even temporarily enhance memory performance. This puts emphasis on the need to consider the key dimensions of affect (valence and arousal, Russell, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1980\u003c/span\u003e; Lang et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) separately because high arousal \u0026ndash; negative valence emotions (e.g., sadness) and low arousal \u0026ndash; negative emotions (e.g., anger) can potentially lead to different results. Therefore, we aimed to test whether sad (low arousal) and angry (high arousal) rumination (e.g., Peled \u0026amp; Moretti, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), will lead to differential effects on WM capacity.\u003c/p\u003e \u003cp\u003eThird, metacognitive models (Wells \u0026amp; Matthews, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Matthews \u0026amp; Wells, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Wells, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) suggest that the level of awareness of one\u0026rsquo;s own cognitive limits can affect resource allocation in WM. Metacognition is the ability to become aware of and control one\u0026rsquo;s cognitive and affective processes (Flavell, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1979\u003c/span\u003e). Being aware of one\u0026rsquo;s WM should help to allocate more resources to goal (task) oriented processing which will increase accuracy and boost performance. Hence, we expected that self-awareness will modulate the effect of rumination on WM capacity.\u003c/p\u003e \u003cp\u003eIn sum, considering the inconsistent results in the literature, the (lack of) association between state rumination and WM capacity remains poorly understood. Hence, we propose an experimental study in which we aim to test the effect of rumination on WM capacity while considering the modulatory effects of (1) WM processing modality, (2) affect, and (3) metacognition. The experimental setup aims to manipulate with the processing domains (visual vs verbal), induce anger and sadness related rumination, and capture cognitive confidence ratings about task-performance.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSample\u003c/h2\u003e \u003cp\u003eA total of 65 participants (20 men and 45 women, average age\u0026thinsp;=\u0026thinsp;28 years, age range 18\u0026ndash;50) were recruited from the local community. All participants were fluent in Estonian language (91% reported Estonian as a mother tongue). Study advertisements were distributed at local public information boards, such as in libraries. The participants responded to a public call for participation by contacting the experimenter. The participants were randomly assigned to the experimental groups (rumination groups and controls), see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for sample characteristics.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSample characteristics (n\u0026thinsp;=\u0026thinsp;65)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eRumination groups\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eControl group\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline characteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eANGRY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex M:W\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6:16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7:15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7:14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge in years, M [SD]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (7.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (8.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation (freq.), Primary: Secondary: Higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1:12:8:1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0:9:13:0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0:8:13:0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLifetime psychiatric diagnosis (N)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNotes. Lifetime psychiatric diagnosis \u0026ndash; number of individuals with self-reported clinical psychiatric diagnosis received throughout life.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003ePower analysis.\u003c/b\u003e Prior to data collection, power analysis had been carried for a linear model that would include rumination as the predictor, one interaction term, and up to two additional variables. The potential effect size was estimated as moderate (based on prior correlational studies, e.g., Bernstein et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, r = -0.2). The power analysis in R with \u003cem\u003epwr\u003c/em\u003e package defined as pwr.f2.test(u\u0026thinsp;=\u0026thinsp;5, f2\u0026thinsp;=\u0026thinsp;0.2, sig.test\u0026thinsp;=\u0026thinsp;0.05, power\u0026thinsp;=\u0026thinsp;0.8) 67 individuals (df\u0026thinsp;=\u0026thinsp;63.9) to be included into the study in total.\u003c/p\u003e \u003cp\u003e\u003cb\u003eEthics.\u003c/b\u003e This study was approved by the local ethics\u0026rsquo; committee at the University of Tartu. All participants participated voluntarily and signed the informed consent at the onset of the experiment. All participants were debriefed at the end of the experimental procedure. Information about the resources for psychological help was given to all participants. At the end of the procedure, the participants were offered feedback on their performance in comparison to the average level; students received course credit proportional to their time spent.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eQuestionnaires\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eMontgomery and \u0026Aring;sberg Depression Rating Scale (MADRS)\u003c/h2\u003e \u003cp\u003eMontgomery and \u0026Aring;sberg Depression Rating Scale (MADRS, Montgomery \u0026amp; \u0026Aring;sberg, 1979) is a well-known instrument for measuring and monitoring symptoms of clinical depression. We used the self-report version of MADRS that consists of 9 items (Svanborg \u0026amp; \u0026Aring;sberg, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Kurrikoff jt, 2012). Each item is rated on a six-point scale ranging from 0 to 6 with a minimum total score of 0 and maximum total score of 54. Higher scores indicate the presence of more depressive complaints during the past few days. The MADRS has a high correlation with the Beck Depression Inventory (r\u0026thinsp;=\u0026thinsp;0.87), well discriminates between the degree of severity of clinical depression with an average of 16 points (SD\u0026thinsp;=\u0026thinsp;4.4) referring to mild clinical depression, and 22 points (SD\u0026thinsp;=\u0026thinsp;4.8) referring to severe clinical depression (Svanborg \u0026amp; \u0026Aring;sberg, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). In this study, the internal consistency of the items was good (Cronbach alpha\u0026thinsp;=\u0026thinsp;.84).\u003c/p\u003e \u003cp\u003e \u003cb\u003eRuminative Response Scale\u003c/b\u003e \u003cem\u003e(\u003c/em\u003e\u003cb\u003eShort) (RRS-10)\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe Ruminative Response Scale (RRS-10) is a commonly used instrument to measure trait depressive rumination (Nolen-Hoeksema \u0026amp; Morrow, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). The short version of the questionnaire consists of 10 questions that capture brooding and reflection (Treynor et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). It has demonstrated good psychometric properties (Schoofs et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The items are rated on a 4-point scale ranging from 1 to 4 with a minimum score of 10 and a maximum score of 40. Higher scores reflect higher frequency of depressive rumination. In this study, the internal consistency of the items was good (Cronbach alpha\u0026thinsp;=\u0026thinsp;.85).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAnger Rumination Scale (ARS)\u003c/h3\u003e\n\u003cp\u003eThe Anger Rumination Scale (ARS) is a 19-item measure for anger related iterative thinking that focuses on thoughts about anger inducing past events, and feelings of aggression (Sukhodolsky et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Its validity and reliability have been considered good (Maxwell et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Sukhodolsky et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). The items are rated on a 4-point scale ranging from 1 to 4 with a minimum score of 19 and a maximum score of 76. Higher scores reflect higher frequency of anger rumination. In this study, the internal consistency of the items was good (Cronbach alpha\u0026thinsp;=\u0026thinsp;.91).\u003c/p\u003e\n\u003ch3\u003eAffect and rumination state reports\u003c/h3\u003e\n\u003cp\u003eThe affective self-report included six affect related questions, each measuring one aspect of affective processing. The instruction was: \u003cem\u003e\u0026ldquo;Please use the slider scales below to indicate how you currently feel. Use the mouse to drag the mark to the location that best describes your current state for each of the following aspects.\u0026rdquo;\u003c/em\u003e The items included (1) valence from 0 \u0026ndash; negative (e.g., I feel unhappy, depressed, desperate, anxious, irritated) to 100 \u0026ndash; positive (e.g., I feel happy, hopeful, satisfied, content), (2) arousal from 0 \u0026ndash; low (e.g., I feel sleepy, relaxed, calm) to 100 \u0026ndash; high (activated, excited, stimulated), (3) dominance from 0 \u0026ndash; low (e.g., I feel influenced, taken care of, respectful, submissive) to 100 \u0026ndash; high (e.g., I feel dominant, influential, in control, important), (4) anger from 0 \u0026ndash; low (e.g., not angry, not irritated) to 100 \u0026ndash; high (e.g., angry, irritated), (5) sadness from 0 \u0026ndash; low (e.g., not sad, not depressed, not hopeless/desperate) to 100 \u0026ndash; high (e.g., sad, depressed, hopeless/desperate), (6) happiness from 0 \u0026ndash; low (e.g., not happy, not satisfied, not cheerful) to 100 \u0026ndash; high (e.g., happy, satisfied, cheerful).\u003c/p\u003e \u003cp\u003eState rumination was measured as follows: \u0026ldquo;\u003cem\u003ePlease indicate how much do you currently ruminate? Rumination refers to a repetitive thinking about negative memories about the past. For example, \u0026ldquo;Why am I always feeling this way?\u0026rdquo;, \u0026ldquo;Why do bad things happen to me?\u0026rdquo;, What if it did not happen to me?\u003c/em\u003e\u0026rdquo;. The slider scale ranged from 0 \u0026ndash; low (no rumination) to 100 \u0026ndash; high (severe rumination). As a follow-up to the rumination item, participants were asked to very briefly describe the core content of their ruminative thoughts by inserting a couple of keywords in a text field (these qualitative data are not reported here).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eExit questionnaire\u003c/h2\u003e \u003cp\u003eAt the end of the experimental procedure, the participants responded to a short questionnaire about their performance related beliefs and cognitive strategies (not reported here).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eChange detection task (CDT)\u003c/h3\u003e\n\u003cp\u003eChange detection paradigm (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e) was used to capture WM capacity (Rouder et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Cowan, 2024). E-prime v2.0 was used for programming the CDT. The tasks were presented on Windows OS laptops (12.5\u0026rsquo; to 14\u0026rsquo; displays). There were two alternative versions of the CDT which were presented in a randomized and counterbalanced sequence. Each of the two used different types of stimuli depending on the processing modality (verbal and visual). Both CDT tasks consisted of 66 trials in total of which 6 were practice trials which were not considered in the analysis. Each trial began with a fixation cross (1000 ms) followed by a presentation of the stimulus set (5 stimuli) for 1300 ms. The set was presented in a circular formation with counterbalanced randomized locations with equal distance between each stimulus. The stimuli were masked with white \u0026ldquo;clouds\u0026rdquo; for 2200 ms after which a probe was presented in the middle of the screen until the response (\u0026ldquo;change\u0026rdquo;/\u0026rdquo;different\u0026rdquo; or \u0026ldquo;no change\u0026rdquo;/\u0026rdquo;same\u0026rdquo;) was given on a keyboard. The response keys (left arrow \u0026ndash; \u0026ldquo;no change\u0026rdquo; and right arrow \u0026ndash; \u0026ldquo;change\u0026rdquo;) were clearly labelled with stickers. The participant was instructed to \u003cem\u003ekeep their fingers of their preferred hand close to the keyboard and be ready to respond at each trial as accurately and as quick as possible\u003c/em\u003e. The distance between the screen and eyes was approximately 60\u0026ndash;70 cm. The participants were instructed to focus on the computer screen throughout the tasks. There was a 1 min break between the two tasks during which the participants were reminded to think about their personal distressful event or the nature video, depending on the experimental group (see rumination induction procedure). The tasks were carried out individually in a dark room in which the main light source was the computer screen. The stimuli were white and presented on a black background.\u003c/p\u003e \n\u003ch3\u003eStimuli\u003c/h3\u003e\n\u003cp\u003eShapes or colors are commonly used features in WM change detection tasks (e.g., Logie et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Cowan et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Here, we used shapes in both modality conditions. Importantly, the two types of stimuli (basic vs abstract shapes) had a different verbalization potential as shown by a pilot-test. Here, we conceptualized verbal processing as processing of basic shapes that can be automatically labelled with standard names (e.g., heart shape is commonly labelled as \u0026ldquo;heart\u0026rdquo;), and visual processing as processing of abstract shapes that are difficult to label and which do not have standard names. We used a total of 20 unique stimuli (10 in the nameable shapes CDT, and 10 in the visual/abstract CDT). The size of the stimulus pool was similar to other CDT studies (Logie et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The verbal processing task included the following shapes: \u003cem\u003eheart, square, circle, arrow, diamond, triangle, star, oval, moon, bowtie\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e, upper row). The visual processing task included the 10 abstract shapes displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e (lower row).\u003c/p\u003e \u003cp\u003ePrior to the experiment, we pilot-tested the two types of tasks on a small sample (n\u0026thinsp;=\u0026thinsp;4). We relied on the qualitative feedback from the participants when dissociating between verbal and visual processing of the shape stimuli: All participants reported that they used verbal labels to memorize the nameable shapes; they could easily recognize and label each shape (e.g., square, triangle etc.). In contrast, the abstract shapes were not easily uniquely labelled; these were encoded in a more abstract way, more \u0026ldquo;visually\u0026rdquo;.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eWM capacity estimation\u003c/h2\u003e \u003cp\u003eThe main outcome measure of the CDT is accuracy, which is considered separately for change trials (in which the probe was not in the array) and no-change trials (in which the probe was present in the array). Correct responses for the correctly detected change trials are called hits, and incorrect indications of change in no-change trials are called false alarms. To estimate the number of items in working memory, we used the recommended formula for k (e.g., Cowan et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Rouder et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), see the Statistical Analysis section for details.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eExperimental procedure\u003c/h2\u003e \u003cp\u003e\u003cb\u003eSetup and questionnaires\u003c/b\u003e. All participants completed the tasks in a laboratory. After the participants signed an informed consent form, they were randomly (counterbalanced) assigned to one of three experimental groups (sad/angry/control). They were instructed to complete the tasks individually (alone in the room). The experimenter setup the questionnaires on the computer and left the room. The participants completed the questionnaires, including the baseline affective state report (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). After this, the experimenter entered the room and started the experimental procedure on the computer.\u003c/p\u003e \u003cp\u003e \u003cb\u003eRumination induction procedure.\u003c/b\u003e (1) First, the participants viewed a disturbing video that induced either sadness or anger. For this, two clips were selected and pilot-tested (n\u0026thinsp;=\u0026thinsp;3) from the Estonian drama movie \u0026rdquo;Klass\u0026ldquo; (The Class, 2007, directed by Ilmar Raag) about youth violence and bullying (duration 2 min 25 sec / 2 min 42 sec). The control group viewed a video about an Alaskan Denali National Park retrieved from YouTube (duration 2 min 13 sec); (2) Depending on the condition, participants were instructed to recall the saddest or most infuriating experience or event from their lives, imagine it and write a short summary about it on paper (~\u0026thinsp;8 min). The control group was instructed to recall the national park video and write a short summary about it; (3) Finally, the participants responded to 14 guided thinking questions about their selected event (selection of 14 questions from the 45-question procedure described by Nolen-Hoeksema and Morrow, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1993\u003c/span\u003e) (~\u0026thinsp;6 min). The control group was instructed to imagine and describe a theater and a train station in their hometown (~\u0026thinsp;6 min). The duration of the rumination induction procedure was approximately 16 min in total. All participants completed the affective state report.\u003c/p\u003e \u003cp\u003e\u003cb\u003eWM tasks.\u003c/b\u003e After the induction procedure the experimenter re-entered the room, instructed the participants and started the next set of tasks; then exited the room. The tasks were presented in two randomized blocks (verbal processing task with nameable shapes and visual/abstract processing task). Between the blocks, a reminder was presented for 1 min which instructed the participants to continue thinking about their selected event, and to add details to their event description. Before each task, the instructions with illustrations were displayed. Each block included practice trials and the task. After the tasks, all participants completed the questionnaire about the affective state report for the third time with follow-up questions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e\u003cb\u003eDebriefing.\u003c/b\u003e The tasks were followed by a positive emotion induction procedure after which the experimenter had a brief conversation with the participants in which detailed information about the experiment was provided, and qualitative feedback was collected. The experimenter checked the emotional state of the participants. Information about help resources were provided. The full duration of the experimental procedure was 1.5 hours max per participant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003e \u003cb\u003eAnalytical approach\u003c/b\u003e. R Studio was used for the analysis. This study was designed to (1) test the general effect of rumination on WM capacity (rumination conditions vs control), (2) compare anger and sadness related rumination conditions, and (3) explore the effects of modality, depression and metacognition to describe how each of these affected the association between rumination and WM capacity. We used general linear and mixed models (lme4 package, Bates et al., 2024) to test the hypotheses. Paired t-tests were used for manipulation checks to compare affect and rumination indices before vs after induction. P-values were considered significant below .05. Multiple testing was corrected within the models (nested models). Robustness of the results was tested by applying a Bayesian approach (brms package, B\u0026uuml;rkner, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e\u003cb\u003eWM capacity estimation.\u003c/b\u003e We estimated the WM capacity with the Cowan\u0026rsquo;s k formula that is recommended for the single probe type of change detection tasks (Rouder et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). \u003cem\u003eK\u003c/em\u003e refers to the estimated WM capacity (the number of information slots available). \u003cem\u003eK\u003c/em\u003e is derived from the difference between the hit rate (correct detection of a change) and false alarm rate (incorrect detection of a change) that is multiplied by the set size (N). For the central probe type of CDT (such as is used here), this is divided by the hit rate to account for the change in location: \u003cem\u003eK\u003c/em\u003e\u0026thinsp;=\u0026thinsp;N*(\u003cem\u003ehits\u003c/em\u003e \u0026ndash; \u003cem\u003eFAs\u003c/em\u003e)/\u003cem\u003ehits\u003c/em\u003e (Cowan et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Given that \u003cem\u003ek\u003c/em\u003e cannot exceed N, it represents the mean number of items retained from arrays of Set Size N. It is derived by assuming that if the participant knows the probe item then a \u0026ldquo;no-change\u0026rdquo; response is given, and otherwise the participant guesses that there has been a change with a guessing rate that is specific to the set size. Here, we estimated \u003cem\u003ek\u003c/em\u003e separately for visual and verbal processing CDT tasks, and the overall \u003cem\u003ek\u003c/em\u003e in which both tasks were merged into one estimate of \u003cem\u003ek\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e\u003cb\u003eInvalid responses.\u003c/b\u003e Two subjects with missing WM task data were excluded. Task performance was checked for validity. Additionally, two responders with overall \u003cem\u003ek\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0 were considered invalid due to the violation of the qualification rule \u003cem\u003ehits\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;\u003cem\u003eFAs\u003c/em\u003e in both tasks (also see Rouder et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The two outlier k estimates were well below zero: -0.39 and \u0026minus;\u0026thinsp;0.47. A negative k suggests that the hit rate was smaller than the false alarm rate. This indicates at chance or below chance performance, which can be considered invalid (similar to Tamm et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). All other responders (n\u0026thinsp;=\u0026thinsp;61) had \u003cem\u003ehits\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eFAs\u003c/em\u003e and \u003cem\u003ehits\u003c/em\u003e\u0026thinsp;\u0026ne;\u0026thinsp;\u003cem\u003e0.\u003c/em\u003e Also, the k values for the two tasks were checked separately and in case at least one task was completed within the valid range (k\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;0), we included that participant\u0026rsquo;s data into the analysis. Three individuals had negative k values for the visual processing task and positive k values for the task with nameable shapes (verbal processing task). These data were included by rounding the negative k values up to 0 (similar to Cowan et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eTransparency and openness\u003c/h2\u003e \u003cp\u003eThe summarized anonymized dataset and R script will be made available in the OSF upon publication. The design of this study was preplanned and registered as a research project at the Institute of Psychology at the University of Tartu and approved by the Ethics committee at the University of Tartu prior to the data collection.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eManipulation checks\u003c/h2\u003e \u003cp\u003eWe used self-reported state rumination and affect characteristics as the key indices for the manipulation checks. Before/after scores for each experimental group can be viewed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The rumination induction resulted in significantly higher negative affect and higher arousal in both rumination conditions in comparison to the controls (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). There was no specificity to anger vs sad rumination in terms of valence or arousal.\u003c/p\u003e \u003cp\u003eSpecificity of anger and sad rumination was captured by anger, sadness and happiness reports. Anger and sad rumination conditions induced significantly different levels of sadness, anger and happiness (p\u0026thinsp;\u0026lt;\u0026thinsp;.05): sadness was significantly increased in the sad rumination group but not in the anger rumination and control groups. Anger was significantly increased in the anger rumination group but not in the sad rumination or control groups. Happiness was significantly decreased only in the sad rumination group but not in the anger rumination or control groups.\u003c/p\u003e \u003cp\u003eSelf-reported rumination was significantly decreased in the control group while there was no change in rumination in the anger and sad rumination groups. Further inspection of the data revealed that there was no bottom or ceiling effect. There were 12 participants who did not respond to the rumination induction as expected (5 in the sad rumination group and 7 in the anger rumination group). When these subjects were excluded, an increase in rumination was significant in anger (t(12) = -4, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and sad rumination conditions (t(14) = -6.8, p\u0026thinsp;\u0026lt;\u0026thinsp;.000001). Due to the heterogeneity in rumination induction responsiveness, we added a dummy variable (responsiveness to rumination induction: 0\u0026ndash;1) to the models as a covariate to take into account the variation in rumination induction efficacy.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparisons of affect and rumination indices between before and after induction\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExperimental groups\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT1/Before\u003c/p\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT2 /After\u003c/p\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEffect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eState rumination (0 \u0026ndash; low \u0026hellip; 100 high)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAngry rumination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.70 (30.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.40 (27.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSad rumination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.25 (32.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.5 (26.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.40 (36.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.47 (23.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026darr;less rumination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eValence (0 \u0026ndash; negative \u0026hellip; 100 positive)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAngry rumination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.70 (22.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.15 (23.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026darr;more neg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSad rumination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73.45 (17.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.50 (24.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026darr;more neg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66.05 (26.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74.95 (22.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eArousal (0 \u0026ndash; low \u0026hellip; 100 high)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAngry rumination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42.45 (18.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.45 (18.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026uarr;higher arousal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSad rumination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45.35 (26.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.0 (16.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026uarr;higher arousal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53.09 (21.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.05 (26.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDominance (0 \u0026ndash; low \u0026hellip; 100 high)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAngry rumination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53.70 (17.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.80 (16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSad rumination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.95 (19.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.30 (22.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(\u0026darr;less dominant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.066)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.61 (22.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.67 (19.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSadness (0 \u0026ndash; low \u0026hellip; 100 high)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAngry rumination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.15 (21.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.45 (19.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSad rumination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.20 (18.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.9 (22.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026uarr;more sad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.57 (27.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.5 (24.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAnger (0 \u0026ndash; low \u0026hellip; 100 high)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAngry rumination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.20 (22.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.9 (27.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026uarr;more angry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSad rumination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.0 (26.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.45 (26.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.52 (12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.23 (14.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHappiness (0 \u0026ndash; low \u0026hellip; 100 high)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAngry rumination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.35 (25.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53.8 (20.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSad rumination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.00 (16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.5 (25.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026darr;less happy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.14 (23.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72.3 (20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNotes. Paired t-tests. ns \u0026ndash; not significant (p\u0026thinsp;\u0026gt;\u0026thinsp;\u0026gt;\u0026thinsp;0.06)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics\u003c/h2\u003e \u003cp\u003eThere were no differences between the experimental groups in baseline measures. Average scores for depression, depressive rumination, anger rumination, and clinical diagnosis rates and baseline state rumination for experimental group can be found in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Correlations between the baseline measures and WM performance can be found in the Supplementary Materials Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline trait characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeasures\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eANGRY (n\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSAD (n\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCT (n\u0026thinsp;=\u0026thinsp;21)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMADRS (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.1 (7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.3 (6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.2 (7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003en.s.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRRS (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.5 (5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.5 (5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.5 (5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003en.s.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eARS (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.8 (6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.8 (10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.0 (9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003en.s.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNotes. MADRS \u0026ndash; Montgomery-\u0026Aring;sberg Depression Rating Scale, RRS \u0026ndash; Ruminative Response Scale, ARS \u0026ndash; Anger Rumination Scale. \u003csup\u003eA\u003c/sup\u003eOne-way ANOVA.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe observed the distribution of the k estimate (see Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e in Supplementary Materials), Shapiro-Wilk normality test indicated that the distribution did not deviate from the Normal distribution (W\u0026thinsp;=\u0026thinsp;0.98, p\u0026thinsp;=\u0026thinsp;.54).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eThe effect of rumination on WM capacity estimate k\u003c/h2\u003e \u003cp\u003eThe linear model included two rumination contrasts: a general effect of rumination which contrasted the rumination groups vs controls, and the specific effect which contrasted sad vs anger rumination groups. There was a significant general main effect of rumination on WM capacity estimate \u003cem\u003ek\u003c/em\u003e (t(58)= -2.39, p\u0026thinsp;\u0026lt;\u0026thinsp;.020). Rumination conditions (sad and angry merged) were associated with higher capacity than the control condition (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e). There were no significant differences between anger and sad rumination groups. The results remained the same when the dummy variable for rumination induction effectiveness was included into the simple linear regression as a covariate (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eA linear model for the effect of state rumination on WM capacity estimate (k)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEffects\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026lt;.000001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRumination vs control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSad vs angry rumination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.515\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInduction responsiveness (covariate)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.892\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote. p\u0026thinsp;\u0026lt;\u0026thinsp;.05. These are non-standardized estimates.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eExploration of the modulatory effects of modality, depression and metacognition\u003c/h2\u003e \u003cp\u003eThe mixed model (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e) included two rumination contrast variables: (1) the general effect of rumination vs controls and (2) the specific effect of sad vs angry rumination, and three covariates: (3) modality, (4) depression and (5) metacognition. Again, with the inclusion of the covariates, the model (see Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e) detected a significant general effect of rumination. Additionally, there was a main effect of modality on k. Depression and metacognition variables did not have a significant effect on WM capacity (k). Addition of the dummy variable (induction responsiveness) that encoded the rumination induction efficacy did not modify the results.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFixed effects from the mixed model predicting WM capacity estimate k from rumination, modality, metacognition, and depression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed effects\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRumination vs control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.038*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSad vs angry rumination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.654\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetacognition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-8.818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.538\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInduction responsiveness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.851\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote. *p\u0026thinsp;\u0026lt;\u0026thinsp;.05. **p\u0026thinsp;\u0026lt;\u0026thinsp;0.00000000001. These are non-standardized estimates.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAlternative mixed models were explored by inserting modality, depression and metacognition one by one into an interaction term with the general effect of rumination while keeping the other variables as covariates. This explorative analysis resulted in three additional models, none of which included any significant interaction effects, suggesting that modality, affect, and metacognition did not moderate effects of rumination on WM capacity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eRobustness check: Bayesian analysis\u003c/h2\u003e \u003cp\u003eWhile the frequentist approach only allows to test the alternative hypothesis, Bayesian models give an opportunity to explore the likelihood for each hypothesis: the null and the alternative hypothesis, and they do not depend as heavily on sample size as the frequentist models do. Here, to further test the sensitivity and robustness of the effect of rumination on WM capacity, we estimated the likelihood of the expected distributions with the Bayesian approach. For this, we used the \u003cem\u003ebrms\u003c/em\u003e package in R. The family was set to Gaussian. The numeric variables were standardized (z-scores) prior to fitting the model. We defined priors for each fixed effect coefficient in the mixed model as normal distributions with mean\u0026thinsp;=\u0026thinsp;0 and sd\u0026thinsp;=\u0026thinsp;1. Normal distribution was selected based on prior knowledge that extremely low and high effects would not be as feasible as effects around zero. The quality of the priors was checked with the Gelman method (as suggested by Gelman et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and by visual inspection (Figure S3).\u003c/p\u003e \u003cp\u003eOverall, the Bayesian model resulted in similar conclusions as the frequentist approach, suggesting evidence for a positive enhancement effect of the rumination induction condition on WM capacity. The model suggested that a positive effect of rumination on WM capacity was 35.04 times more likely than a zero or negative effect of rumination on WM capacity (Supplementary Materials, Table S2A). Importantly, we tested the likelihood of the null-hypothesis (effect\u0026thinsp;=\u0026thinsp;0) and found that the alternative hypothesis (effect of rumination on WM capacity) was more likely than the null-effect hypothesis (Supplementary Materials, Table S2B). Also, when we tested the specific hypothesis for the negative effect of rumination on WM capacity (Supplementary Materials, Table S2C) we found that the evidence ratio for this effect was very low (0.03), indicating that the negative effect was highly unlikely given the current data.\u003c/p\u003e \u003cp\u003e Additionally, the model showed strong evidence for an effect of modality on WM capacity: visual modality was associated with lower capacity than verbal modality. The posterior predictive check with 500 draws showed a good fit. See Supplementary Materials for model specifics (Table S2). The same model was tested with flat priors (uninformative), similar results emerged.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to experimentally test the effect of ruminative thinking on working memory capacity estimate k. We induced sad and anger rumination and explored its effects on WM capacity by contrasting anger and sad rumination to one another and to a control group. The manipulation checks indicated that the rumination induction group had more negative affect, higher arousal and more rumination than the control group, however, responsiveness to the induction procedure largely varied between individuals which indicated a need to control for induction responsiveness in the main analysis. In general, we found a significant general effect of rumination on WM capacity estimate k. No differential effects were found for type of rumination (sad vs anger rumination). Rumination was associated with higher capacity estimates, adding proof for the arousal-enhancement hypothesis. Additionally, we found that modality significantly affected WM capacity suggesting that nameable shapes (i.e., verbally processed) stimuli were encoded more efficiently than abstract visual information. In the following sections, we will elaborate on these results and its robustness in the context of major theories of rumination and WM.\u003c/p\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eThe effect of rumination on WM capacity\u003c/h2\u003e \u003cp\u003eThe key finding of this study was that state rumination was associated with larger WM capacity, independent of the content of rumination (sad or angry), modality (visual or verbal), level of depression, metacognitive confidence, or rumination induction responsiveness. The apparently contradictory result suggests that WM capacity might not be consumed by state ruminative thinking in the context of shared limited resources (Kahneman, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1973\u003c/span\u003e; Cowan, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Baddeley, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1983\u003c/span\u003e; Oberauer et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Baumeister et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In contrast to the expected impairment of performance, performance was improved by rumination. This result allows for several interpretations. One relates to the arousal hypothesis. The manipulation checks showed that both rumination groups (sad and angry) had significantly higher arousal and more negative affect than the control group. Hence, the positive effect of rumination could have been driven by the negative high arousal affective state. Therefore, we think that the positive effect of rumination could be explained by an increased physiological activation (readiness to respond) and vigilance which was induced by the rumination induction procedure. Here, we did not directly measure physiological activation, however, other studies have shown that self-reported vigilance as well as objective physiological activation have been consistently associated with better cognitive task-engagement and effort (Matthews et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Howells et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Pribram \u0026amp; McGuiness, 1975).\u003c/p\u003e \u003cp\u003e The induced arousal in the rumination induction conditions might have helped to bring the participants to their peak performance. The Yerkes-Dodson law (Yerkes and Dodson, 1908 and its supporting studies (e.g., Mair et al., 2010; Faller et al., 2019) suggests that cognitive performance is maximum at optimal levels of activation. In the context of our data, post-induction arousal was around 60 on a scale of 0 to 100 and not at an extreme level, which may reflect an optimal level of arousal. Whereas in the control condition, the nature related recreational thoughts might have induced a more mindful state (as indicated by lower levels of rumination). These results provide novel hypotheses and insights into understanding the associations between WM performance and rumination, and may explain why in some clinical cases lower WM capacity has been reported (e.g., in patients with PTSD; Schweizer \u0026amp; Dalgleish, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). That is, extreme arousal could be induced by more severe memories in clinical cases, e.g., visual intrusions and rumination in PTSD, which can drain WM resources instead of boosting performance.\u003c/p\u003e \u003cp\u003eAnother explanation comes from the sample viewpoint, considering that our sample included relatively healthy individuals in terms of mental health, their selected negative memories and related ruminative thoughts might not have been \u0026ldquo;strong\u0026rdquo; enough to induce a state of pathological rumination in which internally focused attention cannot be easily shifted towards task relevant information (Koster et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Similarly, Watkins and Brown (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) found that state rumination affected counting scores only in depressed individuals but not in healthy controls. Depression has been associated with trait rumination and more severe life-events than is usually seen in the healthy population (e.g., Wiersma et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Marchetti et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Thus, the memories and experiences that are activated in state rumination in depression could be qualitatively different from state rumination in healthy individuals. Moreover, the differential effects of state and trait rumination on cognitive performance have been suggested in prior studies (Grant et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn sum, the current results suggest that in relatively healthy individuals, state rumination can result in higher WM capacity estimates. This is not to say that that rumination can increase WM capacity per se, rather the engagement of WM resources depends on an affective state.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eThe role of modality, depression and metacognition in rumination\u003c/h2\u003e \u003cp\u003e Prior research has indicated that the effects of ruminative thinking on WM could depend on processing modality (visual vs verbal). For example, Curci et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) showed that negative affect was associated with a decrease in the WM performance for the nameable shapes in contrast to neutral and that there were no differences between neutral or negative affect in the visuospatial task. This suggests that negative thinking might mostly affect verbal WM capacity. Here, we considered this by including modality into the model and found no interaction with rumination. However, there was a strong main effect of modality on WM capacity: verbal processing task condition was associated with higher capacity than the visual condition. While there was no interaction with rumination, it can be concluded that the effect of rumination on WM capacity seems to be independent of modality. However, WM capacity estimate depends on the type of stimuli that are used to estimate capacity. Often, colors or shapes have been used to estimate k (Logie et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Most people can recognize and label \u0026ldquo;triangle\u0026rdquo; or \u0026ldquo;blue\u0026rdquo; automatically. However, more abstract/visual shapes which cannot be easily labelled seem to require more resources in WM. Thus, this suggests that the way information is stored in WM can have significant impact on performance: verbal processing has an advantage in WM. A similar idea that visual working memory generally requires more resources than verbal WM has been proposed by Gray et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and is further reinforced by studies that have considered the shared and common capacity (Vergauwe et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Morey \u0026amp; Bieler, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). However, in contrast to our expectations, processing modality did not moderate effects of rumination on WM capacity.\u003c/p\u003e \u003cp\u003eAdditionally, we considered two more key covariates: depression and metacognitive awareness. We assumed that depression and metacognition could have a modulatory effect on the association between rumination and WM capacity. The classical metacognitive model (Flavell, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1979\u003c/span\u003e) suggests that self-awareness (monitoring) should enhance information processing due to resource allocation to the processes that are considered most important. Therefore, awareness of one\u0026rsquo;s own WM performance should enhance performance. However, the metacognitive models for rumination (Matthews \u0026amp; Wells, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Papageorgiou \u0026amp; Wells, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) specify that this is only the case if ruminative thoughts are considered irrelevant by that person. In case a person has a positive belief that ruminative thinking is appropriate (such as in clinical depression, Watkins \u0026amp; Moulds, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), then being aware of one\u0026rsquo;s thoughts will not help to diminish rumination. In contrast, self-awareness can amplify rumination such that WM will become more focused on ruminative thoughts instead of any other task-relevant content. Here, we did not find any modulatory effects of metacognition nor depression on WM capacity, perhaps due to a non-clinical sample and a mix of strategies used: confidence judgements with feedback could have enhanced WM performance in some individuals but impaired in others by reinforcing the need to ruminate about their incorrect responses (those who had strong positive beliefs about rumination as a helpful task-completion strategy). We will elaborate on the need for future studies in the next section.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and future directions\u003c/h2\u003e \u003cp\u003eThe current study explored the association (or lack of association) between rumination and WM capacity, and the role of three confounding variables in this. Surprisingly, the results provided no proof for the modulatory effects of metacognition, modality and depression, contradicting some recent findings. For example, a recent data-driven network model for rumination and its replication (Tamm et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) showed that trait rumination was directly linked to effortful/attention control, depression as well as metacognitive abilities. Moreover, a recent study showed that self-awareness can diminish state rumination in case the metacognitive process is guided, such as in guided meditation (e.g., focus on breathing sensation) (Bolzenk\u0026ouml;tter et al,. 2025), adding proof for the role of metacognition in modulating no only trait but also state ruminative thinking. Therefore, future studies should re-examine the interplay between metacognitive awareness, rumination and WM capacity by considering (1) adding multiple measures for metacognitive awareness, (2) controlling for prior beliefs about rumination, and (3) considering performance related worry as a potential confounding variable.\u003c/p\u003e \u003cp\u003eAnother important aspect is sample size. Here, the power estimates for the linear model were acceptable, when assuming moderate to strong effects. However, the effect size assumption was not based on prior experimental group comparisons due to lack of such, and we relied on correlations between trait rumination and WM performance from earlier research. Thus, testing the effects in a larger sample could be informative.\u003c/p\u003e \u003cp\u003eFinally, replication of the results in multiple samples is important. We conducted a brief exploratory follow-up study and recruited an additional control group with 25 participants to increase it up to the size of the rumination group (sad\u0026thinsp;+\u0026thinsp;angry) to further explore the robustness of the effects. Unfortunately, the two groups had a different average k estimate: the second control group had a significantly higher k than the first control group (t(44)\u0026thinsp;=\u0026thinsp;2.93, p\u0026thinsp;\u0026lt;\u0026thinsp;0.005). Also, the additional control group recruitment was carried out by a different experimenter a few years after the initial data had been collected. Hence, the two could not be merged. However, future studies should carefully consider testing the effects against multiple comparable control groups and conditions to explore the sensitivity of the results.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study investigated the effect of state rumination on WM capacity estimate k while considering the three potential moderators, including modality, depression and metacognition. We found a positive effect of the rumination groups on WM capacity. The results favor the arousal related enhancement of WM hypothesis. Moreover, arousal related enhancement can indicate better task-engagement and physiological vigilance. The results point out the need to consider affective state-dependent fluctuations when estimating WM capacity. This study provided a novel approach to studying the effects of rumination on WM capacity and pointed out the importance of affect and modality in the interplay between rumination and WM capacity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThis study was proposed and designed by Gerly Tamm and Monika Palu-Laeks. Experimental program was written by Gerly Tamm and pilot-tested by Monika Palu-Laeks. Data was collected by Monika Palu-Laeks and Reena Roos. Literature review was conducted by Gerly Tamm, Monika Palu-Laeks and Reena Roos. Conceptualization of working memory capacity by Nelson Cowan and Gerly Tamm. Conceptualization of rumination by Gerly Tamm, Monika Palu-Laeks and Kristof Hoorelbeke. The data was prepared by Gerly Tamm and Monika Palu-Laeks. Formal analysis was carried out by Gerly Tamm. The first version of the manuscript was written by Gerly Tamm and edited by Kristof Hoorelbeke and Nelson Cowan. All authors contributed to interpretations of the results. Funding was acquired by Gerly Tamm.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe Authors are grateful to Prof. Ernst Koster from Ghent University for his insightful comments on the manuscript and to data scientist Dr. Dries Debeer from Ghent University for his advice on Bayesian modelling and to Prof. Scott Saults from the University of Missouri for his advice on using E-Prime.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eSummarized data is provided in the supplementary information files that can be accessed here: https://osf.io/4mnqr/files/osfstorage\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBaddeley, A. D. (1983). Working memory. \u003cem\u003ePhilosophical Transactions of the Royal Society of London. B, Biological Sciences\u003c/em\u003e, \u003cem\u003e302\u003c/em\u003e(1110), 311-324.\u003c/li\u003e\n\u003cli\u003eBaddeley, A. D. 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Shedding light on the association between repetitive negative thinking and deficits in cognitive control\u0026ndash;A meta-analysis. \u003cem\u003eClinical Psychology Review\u003c/em\u003e, \u003cem\u003e63\u003c/em\u003e, 56-65.\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":"rumination, working memory capacity, Cowan’s k, negative affect, depression, modality, metacognitive confidence","lastPublishedDoi":"10.21203/rs.3.rs-6558445/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6558445/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLimited cognitive resource theories predict that ruminative thinking will lead to a diminished working memory capacity. While some empirical studies have found proof for the negative effect of rumination on working memory capacity, others have not. Moreover, the arousal-enhancement hypothesis predicts that affective state can enhance memory in the context of high arousal. This study aimed to experimentally induce anger and sadness state-related types of rumination and test their general and specific effects on working memory capacity while considering a set of key moderators: (1) modality, (2) affect, and (3) metacognition. The sample consisted of 65 individuals (average age\u0026thinsp;=\u0026thinsp;27 years, range 18\u0026ndash;50) who were randomly allocated to three experimental groups (sad rumination, angry rumination and controls). All participants completed a set of self-reports, experimental induction of rumination (or a control procedure) and two change detection working memory tasks with visual/abstract and nameable shapes. We found a significant positive effect of state rumination on working memory capacity (p\u0026thinsp;\u0026lt;\u0026thinsp;.033). There were no differences between sad and angry rumination groups. We found that the verbal processing task with nameable shapes resulted in higher k estimates than the visual processing task with abstract shapes (p\u0026thinsp;\u0026lt;\u0026thinsp;.0000001). Modality, affect (incl. depression) and metacognition did not modulate the effect of rumination on working memory capacity. A secondary Bayesian analysis confirmed these results. This study is the first to experimentally test the effects of sad and angry state rumination on working memory capacity estimates. The positive effect of state rumination on k favors the arousal induced enhancement of working memory hypothesis.\u003c/p\u003e","manuscriptTitle":"Experimental investigation of the interplay between ruminative thinking and working memory capacity: Accounting for modality, affect and metacognition","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-08 14:50:45","doi":"10.21203/rs.3.rs-6558445/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":"d82b02dd-bb2b-4179-af24-11e53518dd26","owner":[],"postedDate":"May 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-02T15:23:31+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-08 14:50:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6558445","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6558445","identity":"rs-6558445","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}