Journal of Mathematical Psychology最新文献_第8页

Sense of agency in operations with delays: A free-energy model and application to interface design 延迟操作中的代入感：自由能模型及在界面设计中的应用

IF 1.8 4区心理学

Journal of Mathematical Psychology Pub Date : 2024-06-01 Epub Date: 2024-05-15 DOI: 10.1016/j.jmp.2024.102859

Masaki Isono, Hideyoshi Yanagisawa

引用次数: 0

The generalized Robbins–Monro process and its application to psychophysical experiments for threshold estimation 广义罗宾斯-蒙罗过程及其在阈值估计心理物理实验中的应用

IF 1.8 4区心理学

Journal of Mathematical Psychology Pub Date : 2024-06-01 Epub Date: 2024-04-11 DOI: 10.1016/j.jmp.2024.102855

Hau-Hung Yang, Yung-Fong Hsu

{"title":"The generalized Robbins–Monro process and its application to psychophysical experiments for threshold estimation","authors":"Hau-Hung Yang, Yung-Fong Hsu","doi":"10.1016/j.jmp.2024.102855","DOIUrl":"https://doi.org/10.1016/j.jmp.2024.102855","url":null,"abstract":"<div><p>In classical psychophysics, the study of threshold and underlying representations is of theoretical interest, and the relevant issue of finding the stimulus intensity corresponding to a certain threshold level is an important topic. In the literature, researchers have developed various adaptive (also known as ‘up-down’) methods, including the fixed step-size and variable step-size methods, for the estimation of threshold. A common feature of this family of methods is that the stimulus to be assigned to the current trial depends upon the participant’s response in the previous trial(s), and very often a binary response format is adopted. A well-known earlier work of the variable step-size adaptive methods is the Robbins–Monro process (and its accelerated version). However, previous studies have paid little attention to other facets of response variables (in addition to the binary response variable) that could be jointly embedded into the process. This article concerns a generalization of the Robbins–Monro process by incorporating an additional response variable, such as the response time or the response confidence, into the process. We first prove the consistency of the estimator from the generalized method. We then conduct a Monte Carlo simulation study to explore some finite-sample properties of the estimator from the generalized method with either the response time or the response confidence as the variable of interest, and compare its performance with the original method. The results show that the two methods (and their accelerated version) are comparable. The issue of relative efficiency is also discussed.</p></div>","PeriodicalId":50140,"journal":{"name":"Journal of Mathematical Psychology","volume":"120 ","pages":"Article 102855"},"PeriodicalIF":1.8,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140543297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The mathematical psychology of Peter Fishburn 彼得-菲什伯恩的数学心理学

IF 1.8 4区心理学

Journal of Mathematical Psychology Pub Date : 2024-06-01 Epub Date: 2024-05-14 DOI: 10.1016/j.jmp.2024.102845

Fred S. Roberts , Clintin P. Davis-Stober , Michel Regenwetter

引用次数: 0

On the (non-) reliance on algorithms—A decision-theoretic account 关于（不）依赖算法--决策理论的解释

IF 1.8 4区心理学

Journal of Mathematical Psychology Pub Date : 2024-04-01 Epub Date: 2024-03-26 DOI: 10.1016/j.jmp.2024.102844

Bernard Sinclair-Desgagné

引用次数: 0

Exploring well-gradedness in polytomous knowledge structures 探索多项式知识结构中的良好分级性

IF 1.8 4区心理学

Journal of Mathematical Psychology Pub Date : 2024-04-01 Epub Date: 2024-01-16 DOI: 10.1016/j.jmp.2024.102840

Bo Wang , Jinjin Li

引用次数: 0

An accidental image feature that appears but not disappears 意外出现但未消失的图像特征

IF 1.8 4区心理学

Journal of Mathematical Psychology Pub Date : 2024-04-01 Epub Date: 2024-02-14 DOI: 10.1016/j.jmp.2024.102841

Tadamasa Sawada , Denis Volk

引用次数: 0

A tutorial on Bayesian inference for dynamical modeling of eye-movement control during reading 阅读过程中眼动控制动态建模的贝叶斯推理教程

IF 1.8 4区心理学

Journal of Mathematical Psychology Pub Date : 2024-04-01 Epub Date: 2024-03-10 DOI: 10.1016/j.jmp.2024.102843

Ralf Engbert , Maximilian M. Rabe

{"title":"A tutorial on Bayesian inference for dynamical modeling of eye-movement control during reading","authors":"Ralf Engbert , Maximilian M. Rabe","doi":"10.1016/j.jmp.2024.102843","DOIUrl":"https://doi.org/10.1016/j.jmp.2024.102843","url":null,"abstract":"<div><p>Dynamical models are crucial for developing process-oriented, quantitative theories in cognition and behavior. Due to the impressive progress in cognitive theory, domain-specific dynamical models are complex, which typically creates challenges in statistical inference. Mathematical models of eye-movement control might be looked upon as a representative case study. In this tutorial, we introduce and analyze the SWIFT model (Engbert et al., 2002; Engbert et al., 2005), a dynamical modeling framework for eye-movement control in reading that was developed to explain all types of saccades observed in experiments from an activation-based approach. We provide an introduction to dynamical modeling, which explains the basic concepts of SWIFT and its statistical inference. We discuss the likelihood function of a simplified version of the SWIFT model as a key foundation for Bayesian parameter estimation (Rabe et al., 2021; Seelig et al., 2019). In posterior predictive checks, we demonstrate that the simplified model can reproduce interindividual differences via parameter variation. All computations in this tutorial are implemented in the <span>R</span>-Language for Statistical Computing and are made publicly available. We expect that the tutorial might be helpful for advancing dynamical models in other areas of cognitive science.</p></div>","PeriodicalId":50140,"journal":{"name":"Journal of Mathematical Psychology","volume":"119 ","pages":"Article 102843"},"PeriodicalIF":1.8,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140069384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic noise estimation: A generalized method for modeling noise fluctuations in decision-making 动态噪音估计：决策中噪声波动建模的通用方法

IF 1.8 4区心理学

Journal of Mathematical Psychology Pub Date : 2024-04-01 Epub Date: 2024-02-28 DOI: 10.1016/j.jmp.2024.102842

Jing-Jing Li , Chengchun Shi , Lexin Li , Anne G.E. Collins

{"title":"Dynamic noise estimation: A generalized method for modeling noise fluctuations in decision-making","authors":"Jing-Jing Li , Chengchun Shi , Lexin Li , Anne G.E. Collins","doi":"10.1016/j.jmp.2024.102842","DOIUrl":"https://doi.org/10.1016/j.jmp.2024.102842","url":null,"abstract":"<div><p>Computational cognitive modeling is an important tool for understanding the processes supporting human and animal decision-making. Choice data in decision-making tasks are inherently noisy, and separating noise from signal can improve the quality of computational modeling. Common approaches to model decision noise often assume constant levels of noise or exploration throughout learning (e.g., the <span><math><mi>ϵ</mi></math></span>-softmax policy). However, this assumption is not guaranteed to hold – for example, a subject might disengage and lapse into an inattentive phase for a series of trials in the middle of otherwise low-noise performance. Here, we introduce a new, computationally inexpensive method to dynamically estimate the levels of noise fluctuations in choice behavior, under a model assumption that the agent can transition between two discrete latent states (e.g., fully engaged and random). Using simulations, we show that modeling noise levels dynamically instead of statically can substantially improve model fit and parameter estimation, especially in the presence of long periods of noisy behavior, such as prolonged lapses of attention. We further demonstrate the empirical benefits of dynamic noise estimation at the individual and group levels by validating it on four published datasets featuring diverse populations, tasks, and models. Based on the theoretical and empirical evaluation of the method reported in the current work, we expect that dynamic noise estimation will improve modeling in many decision-making paradigms over the static noise estimation method currently used in the modeling literature, while keeping additional model complexity and assumptions minimal.</p></div>","PeriodicalId":50140,"journal":{"name":"Journal of Mathematical Psychology","volume":"119 ","pages":"Article 102842"},"PeriodicalIF":1.8,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139985604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A contextual range-dependent model for choice under risk 风险下选择的上下文范围依赖模型

IF 1.8 4区心理学

Journal of Mathematical Psychology Pub Date : 2024-02-01 Epub Date: 2023-11-30 DOI: 10.1016/j.jmp.2023.102821

Manel Baucells , Michał Lewandowski , Krzysztof Kontek

引用次数: 0

Expressions for Bayesian confidence of drift diffusion observers in fluctuating stimuli tasks 波动刺激任务中漂移扩散观测器的贝叶斯置信度表达式

IF 1.8 4区心理学

Journal of Mathematical Psychology Pub Date : 2023-12-01 Epub Date: 2023-10-04 DOI: 10.1016/j.jmp.2023.102815

Joshua Calder-Travis , Rafal Bogacz , Nick Yeung

{"title":"Expressions for Bayesian confidence of drift diffusion observers in fluctuating stimuli tasks","authors":"Joshua Calder-Travis , Rafal Bogacz , Nick Yeung","doi":"10.1016/j.jmp.2023.102815","DOIUrl":"https://doi.org/10.1016/j.jmp.2023.102815","url":null,"abstract":"<div><p>We introduce a new approach to modelling decision confidence, with the aim of enabling computationally cheap predictions while taking into account, and thereby exploiting, trial-by-trial variability in stochastically fluctuating stimuli. Using the framework of the drift diffusion model of decision making, along with time-dependent thresholds and the idea of a Bayesian confidence readout, we derive expressions for the probability distribution over confidence reports. In line with current models of confidence, the derivations allow for the accumulation of “pipeline” evidence that has been received but not processed by the time of response, the effect of drift rate variability, and metacognitive noise. The expressions are valid for stimuli that change over the course of a trial with normally-distributed fluctuations in the evidence they provide. A number of approximations are made to arrive at the final expressions, and we test all approximations via simulation. The derived expressions contain only a small number of standard functions, and require evaluating only once per trial, making trial-by-trial modelling of confidence data in stochastically fluctuating stimuli tasks more feasible. We conclude by using the expressions to gain insight into the confidence of optimal observers, and empirically observed patterns.</p></div>","PeriodicalId":50140,"journal":{"name":"Journal of Mathematical Psychology","volume":"117 ","pages":"Article 102815"},"PeriodicalIF":1.8,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50189497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0