Computational brain & behavior最新文献_第8页

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

Perceptual Decision-Making in Children: Age-Related Differences and EEG Correlates. 儿童的感知决策：与年龄有关的差异和脑电图相关性。

Computational brain & behavior Pub Date : 2021-01-01 Epub Date: 2020-06-19 DOI: 10.1007/s42113-020-00087-7

Catherine Manning, Eric-Jan Wagenmakers, Anthony M Norcia, Gaia Scerif, Udo Boehm

{"title":"Perceptual Decision-Making in Children: Age-Related Differences and EEG Correlates.","authors":"Catherine Manning, Eric-Jan Wagenmakers, Anthony M Norcia, Gaia Scerif, Udo Boehm","doi":"10.1007/s42113-020-00087-7","DOIUrl":"10.1007/s42113-020-00087-7","url":null,"abstract":"Children make faster and more accurate decisions about perceptual information as they get older, but it is unclear how different aspects of the decision-making process change with age. Here, we used hierarchical Bayesian diffusion models to decompose performance in a perceptual task into separate processing components, testing age-related differences in model parameters and links to neural data. We collected behavioural and EEG data from 96 6- to 12-year-old children and 20 adults completing a motion discrimination task. We used a component decomposition technique to identify two response-locked EEG components with ramping activity preceding the response in children and adults: one with activity that was maximal over centro-parietal electrodes and one that was maximal over occipital electrodes. Younger children had lower drift rates (reduced sensitivity), wider boundary separation (increased response caution) and longer non-decision times than older children and adults. Yet, model comparisons suggested that the best model of children's data included age effects only on drift rate and boundary separation (not non-decision time). Next, we extracted the slope of ramping activity in our EEG components and covaried these with drift rate. The slopes of both EEG components related positively to drift rate, but the best model with EEG covariates included only the centro-parietal component. By decomposing performance into distinct components and relating them to neural markers, diffusion models have the potential to identify the reasons why children with developmental conditions perform differently to typically developing children and to uncover processing differences inapparent in the response time and accuracy data alone.","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"4 1","pages":"53-69"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7870772/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25388133","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

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

The Costs and Benefits of Goal-Directed Attention in Deep Convolutional Neural Networks. 深度卷积神经网络中目标导向注意力的成本与收益。

Computational brain & behavior Pub Date : 2021-01-01 Epub Date: 2021-02-12 DOI: 10.1007/s42113-021-00098-y

Xiaoliang Luo, Brett D Roads, Bradley C Love

{"title":"The Costs and Benefits of Goal-Directed Attention in Deep Convolutional Neural Networks.","authors":"Xiaoliang Luo, Brett D Roads, Bradley C Love","doi":"10.1007/s42113-021-00098-y","DOIUrl":"https://doi.org/10.1007/s42113-021-00098-y","url":null,"abstract":"People deploy top-down, goal-directed attention to accomplish tasks, such as finding lost keys. By tuning the visual system to relevant information sources, object recognition can become more efficient (a benefit) and more biased toward the target (a potential cost). Motivated by selective attention in categorisation models, we developed a goal-directed attention mechanism that can process naturalistic (photographic) stimuli. Our attention mechanism can be incorporated into any existing deep convolutional neural networks (DCNNs). The processing stages in DCNNs have been related to ventral visual stream. In that light, our attentional mechanism incorporates top-down influences from prefrontal cortex (PFC) to support goal-directed behaviour. Akin to how attention weights in categorisation models warp representational spaces, we introduce a layer of attention weights to the mid-level of a DCNN that amplify or attenuate activity to further a goal. We evaluated the attentional mechanism using photographic stimuli, varying the attentional target. We found that increasing goal-directed attention has benefits (increasing hit rates) and costs (increasing false alarm rates). At a moderate level, attention improves sensitivity (i.e. increases <math> <msup><mrow><mi>d</mi></mrow> <mrow><mi>'</mi></mrow> </msup> </math> ) at only a moderate increase in bias for tasks involving standard images, blended images and natural adversarial images chosen to fool DCNNs. These results suggest that goal-directed attention can reconfigure general-purpose DCNNs to better suit the current task goal, much like PFC modulates activity along the ventral stream. In addition to being more parsimonious and brain consistent, the mid-level attention approach performed better than a standard machine learning approach for transfer learning, namely retraining the final network layer to accommodate the new task.","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"4 2","pages":"213-230"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s42113-021-00098-y","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39669144","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: 13

Hidden Markov Models of Evidence Accumulation in Speeded Decision Tasks 快速决策任务中证据积累的隐马尔可夫模型

Computational brain & behavior Pub Date : 2020-12-16 DOI: 10.1007/s42113-021-00115-0

Š. Kucharský, Nd Tran, Karel Veldkamp, M. Raijmakers, I. Visser

引用次数: 3

Simultaneous Hierarchical Bayesian Parameter Estimation for Reinforcement Learning and Drift Diffusion Models: a Tutorial and Links to Neural Data. 同时层次贝叶斯参数估计强化学习和漂移扩散模型:教程和链接到神经数据。

Computational brain & behavior Pub Date : 2020-12-01 Epub Date: 2020-05-26 DOI: 10.1007/s42113-020-00084-w

Mads L Pedersen, Michael J Frank

{"title":"Simultaneous Hierarchical Bayesian Parameter Estimation for Reinforcement Learning and Drift Diffusion Models: a Tutorial and Links to Neural Data.","authors":"Mads L Pedersen, Michael J Frank","doi":"10.1007/s42113-020-00084-w","DOIUrl":"https://doi.org/10.1007/s42113-020-00084-w","url":null,"abstract":"Cognitive models have been instrumental for generating insights into the brain processes underlying learning and decision making. In reinforcement learning it has recently been shown that not only choice proportions but also their latency distributions can be well captured when the choice function is replaced with a sequential sampling model such as the drift diffusion model. Hierarchical Bayesian parameter estimation further enhances the identifiability of distinct learning and choice parameters. One caveat is that these models can be time-consuming to build, sample from, and validate, especially when models include links between neural activations and model parameters. Here we describe a novel extension to the widely used hierarchical drift diffusion model (HDDM) toolbox, which facilitates flexible construction, estimation, and evaluation of the reinforcement learning drift diffusion model (RLDDM) using hierarchical Bayesian methods. We describe the types of experiments most applicable to the model and provide a tutorial to illustrate how to perform quantitative data analysis and model evaluation. Parameter recovery confirmed that the method can reliably estimate parameters with varying numbers of synthetic subjects and trials. We also show that the simultaneous estimation of learning and choice parameters can improve the sensitivity to detect brain-behavioral relationships, including the impact of learned values and fronto-basal ganglia activity patterns on dynamic decision parameters.","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"3 4","pages":"458-471"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s42113-020-00084-w","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39593178","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: 26

Breaking Deadlocks: Reward Probability and Spontaneous Preference Shape Voluntary Decisions and Electrophysiological Signals in Humans 打破僵局:奖励概率和自发偏好形成人类自愿决策和电生理信号

Computational brain & behavior Pub Date : 2020-11-30 DOI: 10.1007/s42113-020-00096-6

Wojciech Zajkowski, D. Krzemiński, Jacopo Barone, L. Evans, Jiaxiang Zhang

引用次数: 2