Computational brain & behavior最新文献_第9页

Models of Variability in Probabilistic Causal Judgments. 概率因果判断中的变异性模型。

Computational brain & behavior Pub Date : 2024-10-08 eCollection Date: 2025-03-01 DOI: 10.1007/s42113-024-00223-7

Ivar Kolvoort, Zachary J Davis, Bob Rehder, Leendert van Maanen

{"title":"Models of Variability in Probabilistic Causal Judgments.","authors":"Ivar Kolvoort, Zachary J Davis, Bob Rehder, Leendert van Maanen","doi":"10.1007/s42113-024-00223-7","DOIUrl":"10.1007/s42113-024-00223-7","url":null,"abstract":"Most theories of causal reasoning aim to explain the central tendency of causal judgments. However, experimental studies show that causal judgments are quite variable. In this article, we report the results of an experiment using a novel repeated measures design that demonstrate the existence of meaningful (i.e., not noise-related) within-participant variability. Next, we introduce and assess multiple computational cognitive models that serve as potential accounts of the sources of variability and fit those models to the new empirical data. We find that the Bayesian Mutation Sampler has the best fit to the data and is able to account for a number of unusual features of the response distributions (e.g., bi-modality), supporting the view that the stochastic sampling mechanism it posits reflects the cognitive processes via which people draw causal inferences. Additionally, our findings suggest that incorporating 'non-reasoning' processes, such as rounding and guessing, can improve the ability of models of causal reasoning to account for the observed response distributions. Overall, the study highlights the potential of computational modeling of full response distributions to shed light on the underlying mechanisms of human causal reasoning and identifies promising directions for future research.Supplementary information: The online version contains supplementary material available at 10.1007/s42113-024-00223-7.","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"8 1","pages":"162-188"},"PeriodicalIF":0.0,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13298624/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148457969","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 Effect of Reward Magnitude on Different Types of Exploration in Human Reinforcement Learning. 奖励大小对人类强化学习中不同类型探索的影响。

Computational brain & behavior Pub Date : 2024-10-03 eCollection Date: 2025-03-01 DOI: 10.1007/s42113-024-00224-6

Kanji Shimomura, Kenji Morita

{"title":"The Effect of Reward Magnitude on Different Types of Exploration in Human Reinforcement Learning.","authors":"Kanji Shimomura, Kenji Morita","doi":"10.1007/s42113-024-00224-6","DOIUrl":"10.1007/s42113-024-00224-6","url":null,"abstract":"How humans resolve the explore-exploit dilemma in complex environments is an important open question. Previous studies suggested that environmental richness may affect the degree of exploration in a type-specific manner and reduce random exploration while increasing uncertainty-based exploration. Our study examined this possibility by extending a recently developed two-armed bandit task that can dissociate the uncertainty and novelty of stimuli. To extract the pure effect of environmental richness, we manipulated the reward by its magnitude, not its probability, across blocks because reward probability affects outcome controllability. Participants (N = 198) demonstrated increased optimal choices when the relative reward magnitude was higher. A behavioral analysis with computational modeling revealed that a higher reward magnitude reduced the degree of random exploration but had little effect on the degree of uncertainty- and novelty-based exploration. These results suggest that humans modulate their degree of random exploration depending on the relative level of environmental richness. Combined with findings from previous studies, our findings indicate the possibility that outcome controllability also influences the exploration-exploitation balance in human reinforcement learning.Supplementary information: The online version contains supplementary material available at 10.1007/s42113-024-00224-6.","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"8 1","pages":"147-161"},"PeriodicalIF":0.0,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13298669/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148457933","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

Reclaiming AI as a Theoretical Tool for Cognitive Science. 重新将人工智能作为认知科学的理论工具。

Computational brain & behavior Pub Date : 2024-09-27 eCollection Date: 2024-12-01 DOI: 10.1007/s42113-024-00217-5

Iris van Rooij, Olivia Guest, Federico Adolfi, Ronald de Haan, Antonina Kolokolova, Patricia Rich

{"title":"Reclaiming AI as a Theoretical Tool for Cognitive Science.","authors":"Iris van Rooij, Olivia Guest, Federico Adolfi, Ronald de Haan, Antonina Kolokolova, Patricia Rich","doi":"10.1007/s42113-024-00217-5","DOIUrl":"10.1007/s42113-024-00217-5","url":null,"abstract":"The idea that human cognition is, or can be understood as, a form of computation is a useful conceptual tool for cognitive science. It was a foundational assumption during the birth of cognitive science as a multidisciplinary field, with Artificial Intelligence (AI) as one of its contributing fields. One conception of AI in this context is as a provider of computational tools (frameworks, concepts, formalisms, models, proofs, simulations, etc.) that support theory building in cognitive science. The contemporary field of AI, however, has taken the theoretical possibility of explaining human cognition as a form of computation to imply the practical feasibility of realising human(-like or -level) cognition in factual computational systems, and the field frames this realisation as a short-term inevitability. Yet, as we formally prove herein, creating systems with human(-like or -level) cognition is intrinsically computationally intractable. This means that any factual AI systems created in the short-run are at best decoys. When we think these systems capture something deep about ourselves and our thinking, we induce distorted and impoverished images of ourselves and our cognition. In other words, AI in current practice is deteriorating our theoretical understanding of cognition rather than advancing and enhancing it. The situation could be remediated by releasing the grip of the currently dominant view on AI and by returning to the idea of AI as a theoretical tool for cognitive science. In reclaiming this older idea of AI, however, it is important not to repeat conceptual mistakes of the past (and present) that brought us to where we are today.","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"7 4","pages":"616-636"},"PeriodicalIF":0.0,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13298668/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148457844","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

Towards a Generalized Bayesian Model of Reconstructive Memory. 重构记忆的广义贝叶斯模型。

Computational brain & behavior Pub Date : 2024-09-27 eCollection Date: 2025-03-01 DOI: 10.1007/s42113-024-00222-8

Zihao Xu, Pernille Hemmer, Qiong Zhang

引用次数: 0

Can a Good Theory Be Built Using Bad Ingredients? 一个好的理论能用坏的材料建立吗？

Computational brain & behavior Pub Date : 2024-09-13 eCollection Date: 2024-12-01 DOI: 10.1007/s42113-024-00220-w

Sarahanne M Field, Leonhard Volz, Artem Kaznatcheev, Noah van Dongen

{"title":"Can a Good Theory Be Built Using Bad Ingredients?","authors":"Sarahanne M Field, Leonhard Volz, Artem Kaznatcheev, Noah van Dongen","doi":"10.1007/s42113-024-00220-w","DOIUrl":"10.1007/s42113-024-00220-w","url":null,"abstract":"The replication crisis threatens to seriously impact theory development in the cognitive, behavioral, and social sciences. We canvas three desiderata of scientific theories (explanation, prediction, and unification) and argue that the extent to which failures of replication prove problematic depends on the primary purpose of a theory. If the aim is to explain how nature works, then accuracy-and thus replicability-of the findings on which the theory is built is essential. If the aim is to predict outcomes, then replicability of findings from which the predictive model or theory is built is only important as far as it affects the reliability and accuracy of the predictions. If the aim is to unify and organize disparate findings, then the replicability of findings plays a non-essential role. The result is that a multifaceted and nuanced perspective is required to assess the value of replicability and the need for replication studies. Specifying a theory's purpose and background commitments should clarify the debate on replication and contribute to better theory development in the cognitive, behavioral, and social sciences.","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"7 4","pages":"608-615"},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13298618/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148457823","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 Workflow for Building Computationally Rational Models of Human Behavior. 构建人类行为计算理性模型的工作流程。

Computational brain & behavior Pub Date : 2024-08-15 eCollection Date: 2024-09-01 DOI: 10.1007/s42113-024-00208-6

Suyog Chandramouli, Danqing Shi, Aini Putkonen, Sebastiaan De Peuter, Shanshan Zhang, Jussi Jokinen, Andrew Howes, Antti Oulasvirta

{"title":"A Workflow for Building Computationally Rational Models of Human Behavior.","authors":"Suyog Chandramouli, Danqing Shi, Aini Putkonen, Sebastiaan De Peuter, Shanshan Zhang, Jussi Jokinen, Andrew Howes, Antti Oulasvirta","doi":"10.1007/s42113-024-00208-6","DOIUrl":"10.1007/s42113-024-00208-6","url":null,"abstract":"Computational rationality explains human behavior as arising due to the maximization of expected utility under the constraints imposed by the environment and limited cognitive resources. This simple assumption, when instantiated via partially observable Markov decision processes (POMDPs), gives rise to a powerful approach for modeling human adaptive behavior, within which a variety of internal models of cognition can be embedded. In particular, such an instantiation enables the use of methods from reinforcement learning (RL) to approximate the optimal policy solution to the sequential decision-making problems posed to the cognitive system in any given setting; this stands in contrast to requiring ad hoc hand-crafted rules for capturing adaptive behavior in more traditional cognitive architectures. However, despite their successes and promise for modeling human adaptive behavior across everyday tasks, computationally rational models that use RL are not easy to build. Being a hybrid of theoretical cognitive models and machine learning (ML) necessitates that model building take into account appropriate practices from both cognitive science and ML. The design of psychological assumptions and machine learning decisions concerning reward specification, policy optimization, parameter inference, and model selection are all tangled processes rife with pitfalls that can hinder the development of valid and effective models. Drawing from a decade of work on this approach, a workflow is outlined for tackling this challenge and is accompanied by a detailed discussion of the pros and cons at key decision points.","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"7 3","pages":"399-419"},"PeriodicalIF":0.0,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13298619/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148457859","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

Mnemonic vs. Executive Contributions to the N400: A Connectionist Approach to False Memories. 助记与执行对N400的贡献：错误记忆的连接主义方法。

Computational brain & behavior Pub Date : 2024-08-02 eCollection Date: 2024-09-01 DOI: 10.1007/s42113-024-00210-y

Leo Sokolovič, Markus J Hofmann

{"title":"Mnemonic vs. Executive Contributions to the N400: A Connectionist Approach to False Memories.","authors":"Leo Sokolovič, Markus J Hofmann","doi":"10.1007/s42113-024-00210-y","DOIUrl":"10.1007/s42113-024-00210-y","url":null,"abstract":"The associative-read-out model (AROM) is an interactive activation model (IAM) of semantic and episodic memory, which has already predicted behavioral and neural data during recognition memory. The core AROM is a deterministic model containing the original IAM and a semantic layer, where long-term associations between words are defined by word co-occurrence statistics. Episodic traces on the other hand are reflected by the resting level activations of semantic word units. However, the AROM lacks an explicit decision layer. Here, we extend the AROM with a layer encompassing linear leaky competing accumulators, where evidence accumulation is driven by the activation from the episodic-semantic layer. We fit the resulting AROM + with individual episodic memory traces and decision parameters and show that it can account for accuracies and response times in a recognition memory task. Further it mechanistically shows that false and veridical memories are driven by the combination of the semantic and episodic memory signals. When exploring decision-related vs. mnemonic explanations of the N400, we find that the amplitude of the N400 is driven by episodic-semantic activation at frontal, central and posterior locations. This amplitude modulation is strongest over frontal electrodes supporting the view that the frontal FN400 is also driven by memory signals. In addition, the amplitude of the FN400 was influenced by the response criteria. In sum, the AROM + provides a convenient tool for disentangling episodic, semantic, and executive processes in recognition memory.Supplementary information: The online version contains supplementary material available at 10.1007/s42113-024-00210-y.","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"7 3","pages":"357-377"},"PeriodicalIF":0.0,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13298628/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148457841","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

Reconciling Signal-Detection Models of Criterion Learning with the Generalized Matching Law. 标准学习的信号检测模型与广义匹配律的调和。

Computational brain & behavior Pub Date : 2024-08-01 eCollection Date: 2025-03-01 DOI: 10.1007/s42113-024-00212-w

Christina Koß, Luis de la Cuesta-Ferrer, Maik C Stüttgen, Frank Jäkel

{"title":"Reconciling Signal-Detection Models of Criterion Learning with the Generalized Matching Law.","authors":"Christina Koß, Luis de la Cuesta-Ferrer, Maik C Stüttgen, Frank Jäkel","doi":"10.1007/s42113-024-00212-w","DOIUrl":"10.1007/s42113-024-00212-w","url":null,"abstract":"To make decisions that lead to favorable outcomes, animals have to consider both their perceptual uncertainty as well as uncertainty about the outcomes of their actions, such as reinforcements. There is a long tradition of research investigating how the reinforcement structure of a task controls animals' response behavior. The relation between reinforcement and response rates has been described by the matching law and its generalizations for tasks with and without perceptual uncertainty. The influence of perceptual uncertainty on decision behavior is traditionally modeled with signal detection theory, which posits that a decision criterion is placed on an internal evidence axis. Where this criterion is placed and how it is updated based on reinforcements are open questions within signal detection theory. Various criterion learning models have been proposed; however, their steady-state behavior across different experimental conditions is not consistent with the aforementioned empirical matching laws. Here, we integrate models of criterion learning from signal detection theory with matching laws from animal learning theory to gain a better understanding of the mechanisms by which reinforcements and perceptual uncertainty jointly shape behavior. To do so, we first derive the criterion position that leads to behavior aligned with those laws. We then develop a model that updates the decision criterion trial by trial to learn this criterion position. Our model fits data from a previous experiment well and generates behavior in simulations that is in line with matching laws for perceptual tasks and the subjects' behavior in the experiment.","PeriodicalId":72660,"journal":{"name":"Computational brain & behavior","volume":"8 1","pages":"111-133"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13298690/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148457980","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

Correction to: Memory Retrieval in Online Sentence Parsing: Empirical Evidence, Computational Modelling, and Simulations. 修正：在线句子解析中的记忆检索：经验证据，计算模型和模拟。

Computational brain & behavior Pub Date : 2024-08-01 eCollection Date: 2024-09-01 DOI: 10.1007/s42113-024-00215-7

Hiroki Fujita

引用次数: 0

The Whys and Hows of Theory-Comparing Cognitive Science and Economics. 理论的原因与方法——比较认知科学与经济学。

Computational brain & behavior Pub Date : 2024-07-29 eCollection Date: 2024-12-01 DOI: 10.1007/s42113-024-00213-9

Patricia Rich

引用次数: 0