Computational brain & behavior最新文献_第8页

Exploring the Performance Consequences of Target Prevalence and Ecological Display Designs When Using an Automated Aid 探索目标流行率和生态显示设计在使用自动化辅助时的性能后果

Computational brain & behavior Pub Date : 2021-04-12 DOI: 10.1007/s42113-021-00104-3

Cara M. Kneeland, Joseph W. Houpt, K. Bennett

引用次数: 2

Neural Networks Trained on Natural Scenes Exhibit Gestalt Closure 在自然场景上训练的神经网络表现出完形闭合

Computational brain & behavior Pub Date : 2021-04-09 DOI: 10.1007/s42113-021-00100-7

Been Kim, Emily Reif, M. Wattenberg, Samy Bengio, M. Mozer

引用次数: 35

Alleviating the Cold Start Problem in Adaptive Learning using Data-Driven Difficulty Estimates 用数据驱动的难度估计缓解自适应学习中的冷启动问题

Computational brain & behavior Pub Date : 2021-03-15 DOI: 10.1007/s42113-021-00101-6

Maarten van der Velde, Florian Sense, J. Borst, H. van Rijn

引用次数: 7

Modulation of Dopamine for Adaptive Learning: A Neurocomputational Model. 多巴胺对适应性学习的调节:一个神经计算模型。

Computational brain & behavior Pub Date : 2021-03-01 Epub Date: 2020-06-12 DOI: 10.1007/s42113-020-00083-x

Jeffrey B Inglis, Vivian V Valentin, F Gregory Ashby

{"title":"Modulation of Dopamine for Adaptive Learning: A Neurocomputational Model.","authors":"Jeffrey B Inglis, Vivian V Valentin, F Gregory Ashby","doi":"10.1007/s42113-020-00083-x","DOIUrl":"https://doi.org/10.1007/s42113-020-00083-x","url":null,"abstract":"There have been many proposals that learning rates in the brain are adaptive, in the sense that they increase or decrease depending on environmental conditions. The majority of these models are abstract and make no attempt to describe the neural circuitry that implements the proposed computations. This article describes a biologically detailed computational model that overcomes this shortcoming. Specifically, we propose a neural circuit that implements adaptive learning rates by modulating the gain on the dopamine response to reward prediction errors, and we model activity within this circuit at the level of spiking neurons. The model generates a dopamine signal that depends on the size of the tonically active dopamine neuron population and the phasic spike rate. The model was tested successfully against results from two single-neuron recording studies and a fast-scan cyclic voltammetry study. We conclude by discussing the general applicability of the model to dopamine mediated tasks that transcend the experimental phenomena it was initially designed to address.","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"4 1","pages":"34-52"},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s42113-020-00083-x","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39250531","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: 1

Bayes Factors for Mixed Models 混合模型的贝叶斯因子

Computational brain & behavior Pub Date : 2021-02-22 DOI: 10.1007/s42113-021-00113-2

Johnny van Doorn, F. Aust, J. Haaf, A. Stefan, E. Wagenmakers

引用次数: 22

Brief at the Risk of Being Misunderstood: Consolidating Population- and Individual-Level Tendencies 冒着被误解的风险:巩固群体和个人层面的趋势

Computational brain & behavior Pub Date : 2021-02-02 DOI: 10.1007/s42113-021-00099-x

T. Brochhagen

引用次数: 3

Improving Human Decision-making by Discovering Efficient Strategies for Hierarchical Planning 通过发现层级规划的有效策略来改进人类决策

Computational brain & behavior Pub Date : 2021-01-31 DOI: 10.1007/s42113-022-00128-3

Saksham Consul, Lovis Heindrich, Jugoslav Stojcheski, Falk Lieder

引用次数: 8

A Critical Evaluation of the FBST ev for Bayesian Hypothesis Testing 贝叶斯假设检验中FBST ev的关键评价

Computational brain & behavior Pub Date : 2021-01-04 DOI: 10.1007/s42113-021-00109-y

Alexander Ly, E. Wagenmakers

引用次数: 10

Recovering Reliable Idiographic Biological Parameters from Noisy Behavioral Data: the Case of Basal Ganglia Indices in the Probabilistic Selection Task. 从嘈杂的行为数据中恢复可靠的个体生物学参数:以概率选择任务中的基底神经节指数为例。

Computational brain & behavior Pub Date : 2021-01-01 Epub Date: 2021-03-24 DOI: 10.1007/s42113-021-00102-5

Yinan Xu, Andrea Stocco

{"title":"Recovering Reliable Idiographic Biological Parameters from Noisy Behavioral Data: the Case of Basal Ganglia Indices in the Probabilistic Selection Task.","authors":"Yinan Xu, Andrea Stocco","doi":"10.1007/s42113-021-00102-5","DOIUrl":"https://doi.org/10.1007/s42113-021-00102-5","url":null,"abstract":"Behavioral data, despite being a common index of cognitive activity, is under scrutiny for having poor reliability as a result of noise or lacking replications of reliable effects. Here, we argue that cognitive modeling can be used to enhance the test-retest reliability of the behavioral measures by recovering individual-level parameters from behavioral data. We tested this empirically with the Probabilistic Stimulus Selection (PSS) task, which is used to measure a participant's sensitivity to positive or negative reinforcement. An analysis of 400,000 simulations from an Adaptive Control of Thought-Rational (ACT-R) model of this task showed that the poor reliability of the task is due to the instability of the end-estimates: because of the way the task works, the same participants might sometimes end up having apparently opposite scores. To recover the underlying interpretable parameters and enhance reliability, we used a Bayesian Maximum A Posteriori (MAP) procedure. We were able to obtain reliable parameters across sessions (intraclass correlation coefficient ≈ 0.5). A follow-up study on a modified version of the task also found the same pattern of results, with very poor test-retest reliability in behavior but moderate reliability in recovered parameters (intraclass correlation coefficient ≈ 0.4). Collectively, these results imply that this approach can further be used to provide superior measures in terms of reliability, and bring greater insights into individual differences.","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"4 3","pages":"318-334"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s42113-021-00102-5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25529221","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: 2

Human Belief State-Based Exploration and Exploitation in an Information-Selective Symmetric Reversal Bandit Task. 基于人类信念状态的信息选择对称逆贼任务探索与开发。

Computational brain & behavior Pub Date : 2021-01-01 DOI: 10.1007/s42113-021-00112-3

Lilla Horvath, Stanley Colcombe, Michael Milham, Shruti Ray, Philipp Schwartenbeck, Dirk Ostwald

{"title":"Human Belief State-Based Exploration and Exploitation in an Information-Selective Symmetric Reversal Bandit Task.","authors":"Lilla Horvath, Stanley Colcombe, Michael Milham, Shruti Ray, Philipp Schwartenbeck, Dirk Ostwald","doi":"10.1007/s42113-021-00112-3","DOIUrl":"https://doi.org/10.1007/s42113-021-00112-3","url":null,"abstract":"Humans often face sequential decision-making problems, in which information about the environmental reward structure is detached from rewards for a subset of actions. In the current exploratory study, we introduce an information-selective symmetric reversal bandit task to model such situations and obtained choice data on this task from 24 participants. To arbitrate between different decision-making strategies that participants may use on this task, we developed a set of probabilistic agent-based behavioral models, including exploitative and explorative Bayesian agents, as well as heuristic control agents. Upon validating the model and parameter recovery properties of our model set and summarizing the participants' choice data in a descriptive way, we used a maximum likelihood approach to evaluate the participants' choice data from the perspective of our model set. In brief, we provide quantitative evidence that participants employ a belief state-based hybrid explorative-exploitative strategy on the information-selective symmetric reversal bandit task, lending further support to the finding that humans are guided by their subjective uncertainty when solving exploration-exploitation dilemmas.Supplementary information: The online version contains supplementary material available at 10.1007/s42113-021-00112-3.","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"4 4","pages":"442-462"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s42113-021-00112-3","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10654312","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: 6