Computational brain & behavior最新文献_第10页

Creating Something Different: Similarity, Contrast, and Representativeness in Categorization. 创造不一样的东西：分类中的相似性、对比性和代表性。

Computational brain & behavior Pub Date : 2024-07-11 eCollection Date: 2025-03-01 DOI: 10.1007/s42113-024-00209-5

Joseph L Austerweil, Shi Xian Liew, Nolan Conaway, Kenneth J Kurtz

{"title":"Creating Something Different: Similarity, Contrast, and Representativeness in Categorization.","authors":"Joseph L Austerweil, Shi Xian Liew, Nolan Conaway, Kenneth J Kurtz","doi":"10.1007/s42113-024-00209-5","DOIUrl":"10.1007/s42113-024-00209-5","url":null,"abstract":"The ability to generate new concepts and ideas is among the most fascinating aspects of human cognition, but we do not have a strong understanding of the cognitive processes and representations underlying concept generation. Previous work in this domain has focused on how the statistical structure of known categories generalizes to generated categories, overlooking whether (and if so, how) contrast between the known and generated categories is a factor. In this paper, we explore a different factor: contrast from known categories. We propose two novel approaches to modeling category contrast: one focused on exemplar dissimilarity and another based on the representativeness heuristic. Across three behavioral experiments, we find that people generate new categories that contrast from observed categories and distribute exemplars acoss \"unoccupied\" regions of stimulus space. The model based on the representativeness heuristic captured human category generation better when the known category was well captured by a Gaussian distribution. Conversely, the exemplar-based model captured human-generated categories better when the known category was not Gaussian distributed. Our results suggest contrast is a fundamental principle used in generating exemplars of a new category.Supplementary information: The online version contains supplementary material available at 10.1007/s42113-024-00209-5.","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"8 1","pages":"36-70"},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13298683/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148457916","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}

引用次数: 0

Reliance on Episodic vs. Procedural Systems in Decision-Making Depends on Individual Differences in Their Relative Neural Efficiency. 决策过程中对情景系统和程序系统的依赖取决于其相对神经效率的个体差异。

Computational brain & behavior Pub Date : 2024-05-28 eCollection Date: 2024-09-01 DOI: 10.1007/s42113-024-00203-x

Yuxue C Yang, Catherine Sibert, Andrea Stocco

引用次数: 0

Exploration-Exploitation Mechanisms in Recurrent Neural Networks and Human Learners in Restless Bandit Problems. 循环神经网络的探索-开发机制和人类学习器在不宁土匪问题中的应用。

Computational brain & behavior Pub Date : 2024-05-24 eCollection Date: 2024-09-01 DOI: 10.1007/s42113-024-00202-y

D Tuzsus, A Brands, I Pappas, J Peters

{"title":"Exploration-Exploitation Mechanisms in Recurrent Neural Networks and Human Learners in Restless Bandit Problems.","authors":"D Tuzsus, A Brands, I Pappas, J Peters","doi":"10.1007/s42113-024-00202-y","DOIUrl":"10.1007/s42113-024-00202-y","url":null,"abstract":"A key feature of animal and human decision-making is to balance the exploration of unknown options for information gain (directed exploration) versus selecting known options for immediate reward (exploitation), which is often examined using restless bandit tasks. Recurrent neural network models (RNNs) have recently gained traction in both human and systems neuroscience work on reinforcement learning, due to their ability to show meta-learning of task domains. Here we comprehensively compared the performance of a range of RNN architectures as well as human learners on restless four-armed bandit problems. The best-performing architecture (LSTM network with computation noise) exhibited human-level performance. Computational modeling of behavior first revealed that both human and RNN behavioral data contain signatures of higher-order perseveration, i.e., perseveration beyond the last trial, but this effect was more pronounced in RNNs. In contrast, human learners, but not RNNs, exhibited a positive effect of uncertainty on choice probability (directed exploration). RNN hidden unit dynamics revealed that exploratory choices were associated with a disruption of choice predictive signals during states of low state value, resembling a win-stay-loose-shift strategy, and resonating with previous single unit recording findings in monkey prefrontal cortex. Our results highlight both similarities and differences between exploration behavior as it emerges in meta-learning RNNs, and computational mechanisms identified in cognitive and systems neuroscience work.","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"7 3","pages":"314-356"},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13298653/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148457894","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}

引用次数: 0

Correction: A Sequential Sampling Approach to the Integration of Habits and Goals. 更正：习惯和目标整合的顺序抽样方法。

Computational brain & behavior Pub Date : 2024-03-12 eCollection Date: 2024-09-01 DOI: 10.1007/s42113-024-00201-z

Chao Zhang, Arlette van Wissen, Ron Dotsch, Daniël Lakens, Wijnand A IJsselsteijn

引用次数: 0

Externally Provided Rewards Increase Internal Preference, but Not as Much as Preferred Ones Without Extrinsic Rewards. 外部奖励会增加内部偏好，但不如没有外部奖励的偏好多。

Computational brain & behavior Pub Date : 2024-02-21 eCollection Date: 2025-03-01 DOI: 10.1007/s42113-024-00198-5

Jianhong Zhu, Kentaro Katahira, Makoto Hirakawa, Takashi Nakao

{"title":"Externally Provided Rewards Increase Internal Preference, but Not as Much as Preferred Ones Without Extrinsic Rewards.","authors":"Jianhong Zhu, Kentaro Katahira, Makoto Hirakawa, Takashi Nakao","doi":"10.1007/s42113-024-00198-5","DOIUrl":"10.1007/s42113-024-00198-5","url":null,"abstract":"It is well known that preferences are formed through choices, known as choice-induced preference change (CIPC). However, whether value learned through externally provided rewards influences the preferences formed through CIPC remains unclear. To address this issue, we used tasks for decision-making guided by reward provided by the external environment (externally guided decision-making; EDM) and for decision-making guided by one's internal preference (internally guided decision-making; IDM). In the IDM task, we presented stimuli with learned value in the EDM and novel stimuli to examine whether the value in the EDM affects preferences. Stimuli reinforced by rewards given in the EDM were reflected in the IDM's initial preference and further increased through CIPC in the IDM. However, such stimuli were not as strongly preferred as the most preferred novel stimulus in the IDM (superiority of intrinsically learned values; SIV), suggesting that the values learned by the EDM and IDM differ. The underlying process of this phenomenon is discussed in terms of the fundamental self-hypothesis.Supplementary information: The online version contains supplementary material available at 10.1007/s42113-024-00198-5.","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"8 1","pages":"71-91"},"PeriodicalIF":0.0,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13298676/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148457930","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}

引用次数: 0

Actively Learning to Learn Causal Relationships. 积极学习学习因果关系。

Computational brain & behavior Pub Date : 2024-01-05 eCollection Date: 2024-03-01 DOI: 10.1007/s42113-023-00195-0

Chentian Jiang, Christopher G Lucas

{"title":"Actively Learning to Learn Causal Relationships.","authors":"Chentian Jiang, Christopher G Lucas","doi":"10.1007/s42113-023-00195-0","DOIUrl":"10.1007/s42113-023-00195-0","url":null,"abstract":"How do people actively learn to learn? That is, how and when do people choose actions that facilitate long-term learning and choosing future actions that are more informative? We explore these questions in the domain of active causal learning. We propose a hierarchical Bayesian model that goes beyond past models by predicting that people pursue information not only about the causal relationship at hand but also about causal overhypotheses-abstract beliefs about causal relationships that span multiple situations and constrain how we learn the specifics in each situation. In two active \"blicket detector\" experiments with 14 between-subjects manipulations, our model was supported by both qualitative patterns in participant behavior and an individual differences-based model comparison. Our results suggest when there are abstract similarities across active causal learning problems, people readily learn and transfer overhypotheses reflecting these similarities. Moreover, people exploit these overhypotheses to facilitate long-term active learning.Supplementary information: The online version contains supplementary material available at 10.1007/s42113-023-00195-0.","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"7 1","pages":"80-105"},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13292816/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148457846","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}

引用次数: 0

Lessons for Theory from Scientific Domains Where Evidence is Sparse or Indirect. 证据稀少或间接的科学领域给理论的教训。

Computational brain & behavior Pub Date : 2024-01-01 Epub Date: 2024-12-03 DOI: 10.1007/s42113-024-00214-8

Marieke Woensdregt, Riccardo Fusaroli, Patricia Rich, Martin Modrák, Antonina Kolokolova, Cory Wright, Anne S Warlaumont

{"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":"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.","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}

引用次数: 0

Quantifying Individual Variability in Neural Control Circuit Regulation Using Single-Subject fMRI 使用单受试者fMRI量化神经控制回路调节的个体变异性

Computational brain & behavior Pub Date : 2023-11-09 DOI: 10.1007/s42113-023-00185-2

Rajat Kumar, Helmut H. Strey, Lilianne R. Mujica-Parodi

{"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}

引用次数: 0

Towards Dependent Race Models for the Stop-Signal Paradigm 停止-信号范式的依赖竞赛模型

Computational brain & behavior Pub Date : 2023-11-06 DOI: 10.1007/s42113-023-00184-3

Hans Colonius, Paria Jahansa, Harry Joe, Adele Diederich

引用次数: 0

Modeling Time Cell Neuron-Level Dynamics 时间细胞神经元水平动力学建模

Computational brain & behavior Pub Date : 2023-10-26 DOI: 10.1007/s42113-023-00183-4

Mustafa Zeki, Fuat Balci

引用次数: 0