Computational brain & behavior最新文献

{"title":"Joint Cognitive Models Reveal Sources of Robust Individual Differences in Conflict Processing.","authors":"M Fiona Molloy, Taraz G Lee, John Jonides, Han Zhang, Jacob Sellers, Andrew Heathcote, Chandra Sripada, Alexander S Weigard","doi":"10.1007/s42113-026-00263-1","DOIUrl":"10.1007/s42113-026-00263-1","url":null,"abstract":"<p><p>Experimental manipulations in conflict tasks, e.g., the Stroop, Flanker, and Simon tasks, lead to systematically poorer performance in \"incongruent\" conditions that feature stimuli that contradict task goals. However, substantial recent debate surrounds whether individual differences in conflict task behavior reflect reliable, trait-like mechanistic processes. Much prior work uses difference scores, contrasting performance between incongruent and congruent trials to index conflict suppression ability, but recent work demonstrates these scores exhibit poor psychometric properties. Formal cognitive process models suggest that individual differences in conflict suppression are driven by task-general processes, as opposed to processes specialized for conflict. However, this prior work separately models cognitive process parameters and their covariation, which fails to adequately account for measurement error. Here, we model distinct mechanisms of conflict task performance and their covariance simultaneously using hierarchical Bayesian joint modeling methods for the first time which improves individual estimation and accounts for error. We fit the conflict linear ballistic accumulator model (LBA) to two large datasets containing multiple conflict tasks and test-retest sessions, and an additional large dataset containing a conflict task and simple perceptual decision-making task. First, within conflict tasks, we found moderate test-retest reliability for both conflict-specific processing mechanisms, and, to a larger degree, task-general mechanisms. Second, task-general, but not conflict-specific, mechanisms were correlated across different conflict tasks. Third, these task-general mechanisms were correlated between conflict tasks and a simple decision-making task without conflict suppression demands. Overall, we found robust individual differences in computational mechanisms underlying general decision-making, but not mechanisms specific to conflict processing.</p>","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13004055/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147500944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Finding Closure: A Closer Look at the Gestalt Law of Closure in Convolutional Neural Networks.","authors":"Yuyan Zhang, Derya Soydaner, Lisa Koßmann, Fatemeh Behrad, Johan Wagemans","doi":"10.1007/s42113-025-00251-x","DOIUrl":"https://doi.org/10.1007/s42113-025-00251-x","url":null,"abstract":"<p><p>The human brain has an inherent ability to fill in gaps to perceive figures as complete wholes, even when parts are missing or fragmented. This phenomenon, known as Closure in psychology, is one of the Gestalt laws of perceptual organization. Given the role of Closure in human perception, we investigate whether neural networks exhibit similar functional behavior in object recognition. While the neural substrates of Gestalt principles are thought to involve feedback mechanisms in the brain, convolutional neural networks (CNNs) rely on feedforward architectures. Despite this, we focus on the functional comparison-specifically, object recognition-rather than the underlying mechanisms. We investigate whether CNNs can parallel the human ability to perform Closure. Exploring this crucial visual skill in neural networks can highlight their (dis)similarity to human vision. Recent studies have examined the Closure effect in neural networks, but typically focus on a limited selection of CNNs and yield divergent findings. To address these gaps, we present a systematic framework to investigate Closure. We introduce well-curated datasets designed to test for Closure effects, including both modal and amodal completion. We then conduct experiments on nine CNNs employing different measurements. Our comprehensive analysis reveals that VGG16 and DenseNet-121 exhibit the Closure effect, while other CNNs show variable results. This finding is significant for fields such as AI, Neuroscience, and Psychology, as it bridges understanding across disciplines. By blending insights from psychology and neural network research, we offer a unique perspective that enhances transparency in neural networks.</p>","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"9 1","pages":"104-116"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13035581/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147596474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"When Collaboration Falters, Insensitivity to How Our Actions Affect Others Drives Inflated Self-evaluations.","authors":"Michael Moutoussis, Meera Gosalia, Geert-Jan Will, Giles Story, Tobias U Hauser, Aislinn Bowler, Siobhan Edinboro, Gita Prabhu, Raymond Dolan","doi":"10.1007/s42113-025-00250-y","DOIUrl":"https://doi.org/10.1007/s42113-025-00250-y","url":null,"abstract":"<p><p>During high-stake interactions, people not only evaluate policies or outcomes, but also themselves and others. Such evaluations may be crucial for long-term outcomes, such as harmonious marriage, confident leadership and indeed mental health. Powerful evaluations occur during interactions, where people can support or let each other down. Thus, we implemented an interactive decision-making game, wherein two real-life participants explicitly evaluated themselves and their play-partner while playing an ecologically framed, probabilistic, iterated prisoner's dilemma. To separate preferences from abilities, participants did not interact with the other directly, but instructed a computer avatar on how to play on their behalf. We tested a range of computational models of participants' person-evaluations. In some, self-evaluation relied on regret or satisfaction regarding one's decisions. However, the winning models relied directly on observed gains and losses. Here, evaluation of the self was proportional to how much one's partner benefited, and vice versa. We found a marked self-positivity bias, which was most prominent in dyads where both partners often defected. Between participants, a self-positivity bias was explained by a reduced weight of one's partner's benefits onto self-evaluation. This suggests that the negative outcomes claimed to attract defensive, external attribution by attribution theorists are one's partner's poor outcomes. Further analysis suggested that a reduced sensitivity to others' outcomes was associated with reduced earnings for the self, hinting at a functional role for person-evaluations in decision-making. Thus, we introduce a novel computational model that provides a concise account of self-serving bias in evaluations, as observed during risky dyadic interactions.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s42113-025-00250-y.</p>","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"9 1","pages":"91-103"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13035595/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147596486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learning to Move and Plan like the Knight: Sequential Decision Making with a Novel Motor Mapping.","authors":"Carlos A Velázquez-Vargas, Jordan A Taylor","doi":"10.1007/s42113-025-00245-9","DOIUrl":"10.1007/s42113-025-00245-9","url":null,"abstract":"<p><p>Many skills that humans acquire throughout their lives, such as playing video games or sports, require substantial motor learning and multi-step planning. While both processes are typically studied separately, they are likely to interact during the acquisition of complex motor skills. In this work, we studied this interaction by assessing human performance in a sequential decision-making task that requires the learning of a non-trivial motor mapping. Participants were tasked to move a cursor from start to target locations in a grid world, using a standard keyboard. Notably, the specific keys were arbitrarily mapped to a movement rule resembling the Knight chess piece. In Experiment 1, we showed the learning of this mapping in the absence of planning, led to significant improvements in the task when presented with sequential decisions at a later stage. Computational modeling analysis revealed that such improvements resulted from an increased learning rate about the state transitions of the motor mapping, which also resulted in more flexible planning from trial to trial (less perseveration or habitual responses). In Experiment 2, we showed that incorporating mapping learning into the planning process, allows us to capture (1) differential task improvements for distinct planning horizons and (2) overall lower performance for longer horizons. Additionally, model analysis suggested that participants may limit their search to three steps ahead. We hypothesize that this limitation in planning horizon arises from capacity constraints in working memory, and may be the reason complex skills are often broken down into individual subroutines or components during learning.</p>","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"8 4","pages":"535-552"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12995368/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147482628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Heightened familiarity drives the negative retrieval bias in depression: Evidence from the PRISM task.","authors":"Andrea M Cataldo, D Merika W Sanders, Steven J Granger, Jeffrey J Starns, Daniel G Dillon","doi":"10.1007/s42113-025-00252-w","DOIUrl":"10.1007/s42113-025-00252-w","url":null,"abstract":"<p><p>Major Depressive Disorder (MDD) is associated with emotional memory deficits, but treatment is limited by a poor understanding of the mechanisms that drive such behavior. Our previous work linked depression to a negative retrieval bias rooted in abnormal evidence accumulation (Cataldo et al., 2023). The Drift Diffusion Model can account for this bias in two ways: increased familiarity, in which depression strengthens evidence for all negative memories-even false ones; or motivated retrieval, in which depression increases the propensity to judge negative items as \"old\"-even if they are weak. Thus, it is unclear whether depression affects the quality of negative memories or the way they are acted upon. The current work distinguishes these accounts via the Parceling Recognition Into Strength and Motivation (PRISM) task, which isolates memory from decision processes by extending single-item recognition to forced choices between targets and lures (Starns et al., 2018). Though motivation to respond \"old\" can bias single-item judgments, it should play little or no role when judging <i>which</i> item is old; thus, familiarity is implicated when valence effects extend across both tasks, and motivation is implicated when they do not. In a sample of 53 adults ranging in depressive severity, we found that the negative retrieval bias extended across single-item and forced-choice recognition, thus supporting false familiarity. A qualitative analysis of participants' self-reported strategies further indicated that increased schema use may be an important mechanism. In sum, we provide critical evidence that the negative retrieval bias in depressed adults results from disrupted memory representations.</p>","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12291096/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144735843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Do Human Reinforcement Learning Models Account for Key Experimental Choice Patterns in the Iowa Gambling Task?","authors":"Sherwin Nedaei Janbesaraei, Amir Hosein Hadian Rasanan, Vahid Nejati, Jamal Amani Rad","doi":"10.1007/s42113-024-00228-2","DOIUrl":"10.1007/s42113-024-00228-2","url":null,"abstract":"<p><p>The Iowa gambling task (IGT) is widely used to study risky decision-making and learning from rewards and punishments. Although numerous cognitive models have been developed using reinforcement learning frameworks to investigate the processes underlying the IGT, no single model has consistently been identified as superior, largely due to the overlooked importance of model flexibility in capturing choice patterns. This study examines whether human reinforcement learning models adequately capture key experimental choice patterns observed in IGT data. Using simulation and parameter space partitioning (PSP) methods, we explored the parameter space of two recently introduced models-Outcome-Representation Learning and Value plus Sequential Exploration-alongside four traditional models. PSP, a global analysis method, investigates what patterns are relevant to the parameters' spaces of a model, thereby providing insights into model flexibility. The PSP study revealed varying potentials among candidate models to generate relevant choice patterns in IGT, suggesting that model selection may be dependent on the specific choice patterns present in a given dataset. We investigated central choice patterns and fitted all models by analyzing a comprehensive data pool (<i>N</i> = 1428) comprising 45 behavioral datasets from both healthy and clinical populations. Applying Akaike and Bayesian information criteria, we found that the Value plus Sequential Exploration model outperformed others due to its balanced potential to generate all experimentally observed choice patterns. These findings suggested that the search for a suitable IGT model may have reached its conclusion, emphasizing the importance of aligning a model's parameter space with experimentally observed choice patterns for achieving high accuracy in cognitive modeling.</p>","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"8 2","pages":"286-320"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12125058/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144200879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Partially Observable Predictor Models for Identifying Cognitive Markers.","authors":"Zita Oravecz, Martin Sliwinski, Sharon H Kim, Lindy Williams, Mindy J Katz, Joachim Vandekerckhove","doi":"10.1007/s42113-025-00238-8","DOIUrl":"10.1007/s42113-025-00238-8","url":null,"abstract":"<p><p>Repeated assessments of cognitive performance yield rich data from which we can extract markers of cognitive performance. Computational cognitive process models are often fit to repeated cognitive assessments to quantify individual differences in terms of substantively meaningful cognitive markers and link them to other person-level variables. Most studies stop at this point and do not test whether these cognitive markers have utility for predicting some meaningful outcomes. Here, we demonstrate a <i>partially observable predictor</i> modeling approach that can fill this gap. Using this approach, we can simultaneously extract cognitive markers from repeated assessment data and use these together with demographic covariates for predictive modeling of a clinically interesting outcome in a Bayesian multilevel modeling framework. We describe this approach by constructing a predictive process model in which features of learning are combined with demographic variables to predict mild cognitive impairment and demonstrate it using data from the Einstein Aging Study.</p>","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"8 3","pages":"410-420"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12423142/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145066579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"What's Surprising About Surprisal.","authors":"Sophie Slaats, Andrea E Martin","doi":"10.1007/s42113-025-00237-9","DOIUrl":"10.1007/s42113-025-00237-9","url":null,"abstract":"<p><p>In the computational and experimental psycholinguistic literature, the mechanisms behind syntactic structure building (e.g., combining words into phrases and sentences) are the subject of considerable debate. Much experimental work has shown that surprisal is a good predictor of human behavioral and neural data. These findings have led some authors to model language comprehension in a purely probabilistic way. In this paper, we use simulation to exemplify why surprisal works so well to model human data and to illustrate why exclusive reliance on it can be problematic for the development of mechanistic theories of language comprehension, particularly those with emphasis on meaning composition. Rather than arguing for the importance of structural or probabilistic information to the exclusion or exhaustion of the other, we argue more emphasis should be placed on understanding how the brain leverages both types of information (viz., statistical and structured). We propose that probabilistic information is an important <i>cue</i> to the structure in the message, but is not a substitute for the structure itself-neither computationally, formally, nor conceptually. Surprisal and other probabilistic metrics must play a key role as theoretical objects in any explanatory mechanistic theory of language processing, but that role remains in the service of the brain's goal of constructing structured meaning from sensory input.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s42113-025-00237-9.</p>","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"8 2","pages":"233-248"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12125142/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144200923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lessons for Theory from Scientific Domains Where Evidence is Sparse or Indirect.","authors":"Marieke Woensdregt, Riccardo Fusaroli, Patricia Rich, Martin Modrák, Antonina Kolokolova, Cory Wright, Anne S Warlaumont","doi":"10.1007/s42113-024-00214-8","DOIUrl":"10.1007/s42113-024-00214-8","url":null,"abstract":"<p><p>In many scientific fields, sparseness and indirectness of empirical evidence pose fundamental challenges to theory development. Theories of the evolution of human cognition provide a guiding example, where the targets of study are evolutionary processes that occurred in the ancestors of present-day humans. In many cases, the evidence is both very sparse and very indirect (e.g., archaeological findings regarding anatomical changes that might be related to the evolution of language capabilities); in other cases, the evidence is less sparse but still very indirect (e.g., data on cultural transmission in groups of contemporary humans and non-human primates). From examples of theoretical and empirical work in this domain, we distill five virtuous practices that scientists could aim to satisfy when evidence is sparse or indirect: (i) making assumptions explicit, (ii) making alternative theories explicit, (iii) pursuing computational and formal modelling, (iv) seeking external consistency with theories of related phenomena, and (v) triangulating across different forms and sources of evidence. Thus, rather than inhibiting theory development, sparseness or indirectness of evidence can catalyze it. To the extent that there are continua of sparseness and indirectness that vary across domains and that the principles identified here always apply to some degree, the solutions and advantages proposed here may generalise to other scientific domains.</p>","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"7 4","pages":"588-607"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11666647/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142900782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantifying Individual Variability in Neural Control Circuit Regulation Using Single-Subject fMRI","authors":"Rajat Kumar, Helmut H. Strey, Lilianne R. Mujica-Parodi","doi":"10.1007/s42113-023-00185-2","DOIUrl":"https://doi.org/10.1007/s42113-023-00185-2","url":null,"abstract":"Abstract As a field, control systems engineering has developed quantitative methods to characterize the regulation of systems or processes, whose functioning is ubiquitous within synthetic systems. In this context, a control circuit is objectively “well regulated” when discrepancy between desired and achieved output trajectories is minimized and “robust” to the degree that it can regulate well in response to a wide range of stimuli. Most psychiatric disorders are assumed to reflect dysregulation of brain circuits. Yet, probing circuit regulation requires fundamentally different analytic strategies than the correlations relied upon for analyses of connectivity and their resultant networks. Here, we demonstrate how well-established methods for system identification in control systems engineering may be applied to functional magnetic resonance imaging (fMRI) data to extract generative computational models of human brain circuits. As required for clinical neurodiagnostics, we show these models to be extractable even at the level of the single subject. Control parameters provide two quantitative measures of direct relevance for psychiatric disorders: a circuit’s sensitivity to external perturbation and its dysregulation.","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":" 26","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135241605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}