Computational brain & behavior最新文献

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

Probabilistic Choice Induced by Strength of Preference 偏好强度诱导的概率选择

Computational brain & behavior Pub Date : 2023-09-26 DOI: 10.1007/s42113-023-00176-3

Daniel R. Cavagnaro, Michel Regenwetter

引用次数: 0

An Extension and Clinical Application of the SIMPLE Model to the Free Recall of Repeated and Semantically Related Items 简单模型在重复和语义相关项目自由回忆中的推广及临床应用

Computational brain & behavior Pub Date : 2023-09-25 DOI: 10.1007/s42113-023-00182-5

Holly A. Westfall, Michael D. Lee

引用次数: 0

Online Simulator-Based Experimental Design for Cognitive Model Selection 基于在线模拟器的认知模型选择实验设计

Computational brain & behavior Pub Date : 2023-09-21 DOI: 10.1007/s42113-023-00180-7

Alexander Aushev, Aini Putkonen, Grégoire Clarté, Suyog Chandramouli, Luigi Acerbi, Samuel Kaski, Andrew Howes

{"title":"Online Simulator-Based Experimental Design for Cognitive Model Selection","authors":"Alexander Aushev, Aini Putkonen, Grégoire Clarté, Suyog Chandramouli, Luigi Acerbi, Samuel Kaski, Andrew Howes","doi":"10.1007/s42113-023-00180-7","DOIUrl":"https://doi.org/10.1007/s42113-023-00180-7","url":null,"abstract":"Abstract The problem of model selection with a limited number of experimental trials has received considerable attention in cognitive science, where the role of experiments is to discriminate between theories expressed as computational models. Research on this subject has mostly been restricted to optimal experiment design with analytically tractable models. However, cognitive models of increasing complexity with intractable likelihoods are becoming more commonplace. In this paper, we propose BOSMOS, an approach to experimental design that can select between computational models without tractable likelihoods. It does so in a data-efficient manner by sequentially and adaptively generating informative experiments. In contrast to previous approaches, we introduce a novel simulator-based utility objective for design selection and a new approximation of the model likelihood for model selection. In simulated experiments, we demonstrate that the proposed BOSMOS technique can accurately select models in up to two orders of magnitude less time than existing LFI alternatives for three cognitive science tasks: memory retention, sequential signal detection, and risky choice.","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136136212","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: 1

Feature Attention as a Control Mechanism for the Balance of Speed and Accuracy in Visual Search 特征注意是视觉搜索中速度与准确性平衡的控制机制

Computational brain & behavior Pub Date : 2023-06-13 DOI: 10.1007/s42113-023-00171-8

Thom Griffith, Florence J Townend, Sophie Baker, N. Lepora

引用次数: 0

There Is no Theory-Free Measure of "Swaps" in Visual Working Memory Experiments. 在视觉工作记忆实验中，没有“交换”的无理论测量。

Computational brain & behavior Pub Date : 2023-06-01 DOI: 10.1007/s42113-022-00150-5

Jamal R Williams, Maria M Robinson, Timothy F Brady

{"title":"There Is no Theory-Free Measure of \"Swaps\" in Visual Working Memory Experiments.","authors":"Jamal R Williams, Maria M Robinson, Timothy F Brady","doi":"10.1007/s42113-022-00150-5","DOIUrl":"https://doi.org/10.1007/s42113-022-00150-5","url":null,"abstract":"<p><p>Visual working memory is highly limited, and its capacity is tied to many indices of cognitive function. For this reason, there is much interest in understanding its architecture and the sources of its limited capacity. As part of this research effort, researchers often attempt to decompose visual working memory errors into different kinds of errors, with different origins. One of the most common kinds of memory error is referred to as a \"swap,\" where people report a value that closely resembles an item that was not probed (e.g., an incorrect, non-target item). This is typically assumed to reflect confusions, like location binding errors, which result in the wrong item being reported. Capturing swap rates reliably and validly is of great importance because it permits researchers to accurately decompose different sources of memory errors and elucidate the processes that give rise to them. Here, we ask whether different visual working memory models yield robust and consistent estimates of swap rates. This is a major gap in the literature because in both empirical and modeling work, researchers measure swaps without motivating their choice of swap model. Therefore, we use extensive parameter recovery simulations with three mainstream swap models to demonstrate how the choice of measurement model can result in very large differences in estimated swap rates. We find that these choices can have major implications for how swap rates are estimated to change across conditions. In particular, each of the three models we consider can lead to differential quantitative and qualitative interpretations of the data. Our work serves as a cautionary note to researchers as well as a guide for model-based measurement of visual working memory processes.</p>","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"6 2","pages":"159-171"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10270377/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10132505","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: 5

A General Integrative Neurocognitive Modeling Framework to Jointly Describe EEG and Decision-making on Single Trials 单次试验联合描述脑电与决策的综合神经认知建模框架

Computational brain & behavior Pub Date : 2023-04-05 DOI: 10.1007/s42113-023-00167-4

A. Ghaderi-Kangavari, J. Rad, Michael D. Nunez

引用次数: 5

Reinforcement Learning Under Uncertainty: Expected Versus Unexpected Uncertainty and State Versus Reward Uncertainty 不确定性下的强化学习:期望不确定性与意外不确定性、状态不确定性与奖励不确定性

Computational brain & behavior Pub Date : 2023-03-20 DOI: 10.1007/s42113-022-00165-y

Adnane Ez-zizi, S. Farrell, David S. Leslie, Gaurav Malhotra, Casimir J. H. Ludwig

引用次数: 0