Computational brain & behavior最新文献_第3页

Traveling Salesperson Problem with Simple Obstacles: The Role of Multidimensional Scaling and the Role of Clustering 具有简单障碍的旅行推销员问题:多维尺度的作用和聚类的作用

Computational brain & behavior Pub Date : 2022-09-21 DOI: 10.1007/s42113-022-00155-0

Jacob VanDrunen, Kevin Nam, Mark Beers, Z. Pizlo

引用次数: 1

When Fixed and Random Effects Mismatch: Another Case of Inflation of Evidence in Non-Maximal Models 当固定效应和随机效应不匹配时:非最大值模型中证据膨胀的另一个例子

Computational brain & behavior Pub Date : 2022-09-15 DOI: 10.1007/s42113-022-00152-3

J. Veríssimo

引用次数: 3

Relating a Spiking Neural Network Model and the Diffusion Model of Decision-Making. 将尖峰神经网络模型与决策扩散模型联系起来。

Computational brain & behavior Pub Date : 2022-09-01 Epub Date: 2022-06-13 DOI: 10.1007/s42113-022-00143-4

Akash Umakantha, Braden A Purcell, Thomas J Palmeri

{"title":"Relating a Spiking Neural Network Model and the Diffusion Model of Decision-Making.","authors":"Akash Umakantha, Braden A Purcell, Thomas J Palmeri","doi":"10.1007/s42113-022-00143-4","DOIUrl":"10.1007/s42113-022-00143-4","url":null,"abstract":"Many models of decision making assume accumulation of evidence to threshold as a core mechanism to predict response probabilities and response times. A spiking neural network model (Wang, 2002) instantiates these mechanisms at the level of biophysically-plausible pools of neurons with excitatory and inhibitory connections, and has numerous model parameters tuned by physiological measures. The diffusion model (Ratcliff, 1978) is a cognitive model that can be fitted to a range of behaviors and conditions. We investigated how parameters of the cognitive-level diffusion model relate to the parameters of a neural-level spiking model. In each simulated \"experiment\", we generated \"data\" from the spiking neural network by factorially combining a manipulation of choice difficulty (via the input to the spiking model) and a manipulation of one of the core parameters of the spiking model. We then fitted the diffusion model to these simulated data to observe how manipulation of each core spiking model parameter mapped on to fitted drift rate, response threshold, and non-decision time. Manipulations of parameters in the spiking model related to input sensitivity, threshold, and stimulus processing time mapped on to their conceptual analogues in the diffusion model, namely drift rate, threshold, and non-decision time. Manipulations of parameters in the spiking model with no direct analogue to the diffusion model, non-stimulus-specific background input, strength of recurrent excitation, and receptor conductances, mapped on to threshold in the diffusion model. We discuss implications of these results for interpretations of fits of the diffusion model to behavioral data.","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"5 3","pages":"279-301"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9673774/pdf/nihms-1830711.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40480290","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

A Multinomial Processing Tree Model of the 2-back Working Memory Task. 双背工作记忆任务的多项式处理树模型。

Computational brain & behavior Pub Date : 2022-09-01 Epub Date: 2022-06-07 DOI: 10.1007/s42113-022-00138-1

Michael D Lee, Percy K Mistry, Vinod Menon

{"title":"A Multinomial Processing Tree Model of the 2-back Working Memory Task.","authors":"Michael D Lee, Percy K Mistry, Vinod Menon","doi":"10.1007/s42113-022-00138-1","DOIUrl":"10.1007/s42113-022-00138-1","url":null,"abstract":"The <math><mi>n</mi></math>-back task is a widely used behavioral task for measuring working memory and the ability to inhibit interfering information. We develop a novel model of the commonly used 2-back task using the cognitive psychometric framework provided by Multinomial Processing Trees. Our model involves three parameters: a memory parameter, corresponding to how well an individual encodes and updates sequence information about presented stimuli; a decision parameter corresponding to how well participants execute choices based on information stored in memory; and a base-rate parameter corresponding to bias for responding \"yes\" or \"no\". We test the parameter recovery properties of the model using existing 2-back experimental designs, and demonstrate the application of the model to two previous data sets: one from social psychology involving faces corresponding to different races (Stelter and Degner, British Journal of Psychology 109:777-798, 2018), and one from cognitive neuroscience involving more than 1000 participants from the Human Connectome Project (Van Essen et al., Neuroimage 80:62-79, 2013). We demonstrate that the model can be used to infer interpretable individual-level parameters. We develop a hierarchical extension of the model to test differences between stimulus conditions, comparing faces of different races, and comparing face to non-face stimuli. We also develop a multivariate regression extension to examine the relationship between the model parameters and individual performance on standardized cognitive measures including the List Sorting and Flanker tasks. We conclude by discussing how our model can be used to dissociate underlying cognitive processes such as encoding failures, inhibition failures, and binding failures.","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"5 3","pages":"261-278"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10593202/pdf/nihms-1890058.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49694796","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

Generalization in Distant Regions of a Rule-Described Category Space: a Mixed Exemplar and Logical-Rule-Based Account 规则描述的范畴空间的远域泛化:一个混合范例和基于逻辑规则的解释

Computational brain & behavior Pub Date : 2022-08-16 DOI: 10.1007/s42113-022-00151-4

Robert M. Nosofsky, Mingjia Hu

引用次数: 0

Diffusion Decision Modeling of Retrieval Following the Temporal Selection of Behaviorally Relevant Moments 基于行为相关时刻时间选择的检索扩散决策建模

Computational brain & behavior Pub Date : 2022-08-10 DOI: 10.1007/s42113-022-00148-z

Hamid B. Turker, K. Swallow

引用次数: 2

Choice Rules Can Affect the Informativeness of Model Comparisons 选择规则会影响模型比较的信息量

Computational brain & behavior Pub Date : 2022-07-21 DOI: 10.1007/s42113-022-00142-5

Veronika Zilker

引用次数: 0

Disentangling Different Aspects of Between-Item Similarity Unveils Evidence Against the Ensemble Model of Lineup Memory 解开项目间相似性的不同方面揭示了反对阵容记忆集合模型的证据

Computational brain & behavior Pub Date : 2022-07-18 DOI: 10.1007/s42113-022-00135-4

Constantin G. Meyer-Grant, K. C. Klauer

引用次数: 1

Influences of Reinforcement and Choice Histories on Choice Behavior in Actor-Critic Learning 强化和选择历史对行动者-批评学习中选择行为的影响

Computational brain & behavior Pub Date : 2022-07-11 DOI: 10.1007/s42113-022-00145-2

K. Katahira, K. Kimura

引用次数: 0

Over-precise Predictions Cannot Identify Good Choice Models 过于精确的预测无法识别出好的选择模型

Computational brain & behavior Pub Date : 2022-07-07 DOI: 10.1007/s42113-022-00146-1

A. Sifar, Nisheeth Srivastava

引用次数: 0