Computational brain & behavior最新文献_第9页

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

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

Hierarchical Reinforcement Learning Explains Task Interleaving Behavior 分层强化学习解释任务交错行为

Computational brain & behavior Pub Date : 2020-11-05 DOI: 10.1007/s42113-020-00093-9

Christoph Gebhardt, Antti Oulasvirta, Otmar Hilliges

引用次数: 12

Multidimensionality in Executive Function Profiles in Schizophrenia: a Computational Approach Using the Wisconsin Card Sorting Task 精神分裂症患者执行功能特征的多维性:一种使用威斯康星卡片分类任务的计算方法

Computational brain & behavior Pub Date : 2020-10-21 DOI: 10.1007/s42113-021-00106-1

Darren Haywood, Frank D. Baughman

引用次数: 10

Modeling Strategy Switches in Multi-attribute Decision Making 多属性决策中的策略切换建模

Computational brain & behavior Pub Date : 2020-10-19 DOI: 10.1007/s42113-020-00092-w

M. Lee, K. Gluck

引用次数: 9

Representing and Predicting Everyday Behavior 表示和预测日常行为

Computational brain & behavior Pub Date : 2020-10-07 DOI: 10.31234/osf.io/kb53h

M. Singh, Russell Richie, Sudeep Bhatia

{"title":"Representing and Predicting Everyday Behavior","authors":"M. Singh, Russell Richie, Sudeep Bhatia","doi":"10.31234/osf.io/kb53h","DOIUrl":"https://doi.org/10.31234/osf.io/kb53h","url":null,"abstract":"The prediction of everyday human behavior is a central goal in the behavioral sciences. However, efforts in this direction have been limited, as (1) the behaviors studied in most surveys and experiments represent only a small fraction of all possible behaviors, and (2) it has been difficult to generalize data from existing studies to predict arbitrary behaviors, owing to the difficulty in adequately representing such behaviors. Our paper attempts to address each of these problems. First, by sampling frequent verb phrases in natural language and refining these through human coding, we compile a dataset of nearly 4000 common human behaviors. Second, we use distributed semantic models to obtain vector representations for our behaviors, and combine these with demographic and psychographic data, to build supervised, deep neural network models of behavioral propensities for a representative sample of the US population. Our best models achieve reasonable accuracy rates when predicting propensities for novel (out-of-sample) participants as well as novel behaviors, and offer new insights for modeling psychographic and demographic differences in behavior. This work is a first step towards building predictive theories of everyday behavior, and thus improving the generality and naturalism of research in the behavioral sciences.","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"24 1","pages":"1-21"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85090632","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: 3

The Moderating Role of Feedback on Forgetting in Item Recognition 反馈对遗忘在项目识别中的调节作用

Computational brain & behavior Pub Date : 2020-09-09 DOI: 10.1007/s42113-020-00090-y

Aslı Kılıç, Jessica M. Fontaine, K. Malmberg, A. Criss

引用次数: 1