Journal of Mathematical Psychology最新文献

{"title":"A systematic empirical comparison of active inference and reinforcement learning models in accounting for decision-making under uncertainty","authors":"Ko-Ping Chou,&nbsp;Navid Hakimi,&nbsp;Tzu-Yu Hsu,&nbsp;Ryan Smith","doi":"10.1016/j.jmp.2026.103011","DOIUrl":"10.1016/j.jmp.2026.103011","url":null,"abstract":"<div><div>Reinforcement Learning (RL) and Active Inference (AInf) are related computational frameworks for modeling learning and choice under uncertainty. However, differences in how they account for human behavior on established decision tasks remain unclear. To address this, participants from two samples (Taiwan and US) were asked to complete a three-armed bandit task and several affective measures. In one sample, the task further manipulated gain/loss frame and reward magnitude. Choice data were used to fit several complexity-matched RL and AInf models in order to: 1) perform model comparison; 2) examine relationships between parameters of complexity-matched models and identify the unique choice patterns predicted by each model; and 3) compare external validity of the best-fit models by examining task manipulation effects on parameter estimates and associations with other psychological measures. Bayesian model comparison favored AInf models in both samples. However, RL and AInf models showed similar accuracy in predicting participant choices. Correlations in parameter estimates between models suggested both explained behavior in overlapping but partially distinct ways. Trial-wise analyses suggested RL better explained some patterns of exploitation behavior (stay choices), while AInf appeared to better explain specific patterns of exploratory behavior (switch choices). AInf parameters showed relatively greater sensitivity to task conditions in some cases, while both models showed similar effect size associations with other psychological measures. These results suggest that RL and AInf have similar levels of explanatory power with respect to behavior on this task. Yet, they provide partially distinct explanations and may offer complementary insights. Future research will be necessary to extend this work to other tasks and populations. Toward this end, we provide an open-source toolbox with this article including accessible code for each RL and AInf model variant for future adaptation.</div></div>","PeriodicalId":50140,"journal":{"name":"Journal of Mathematical Psychology","volume":"130 ","pages":"Article 103011"},"PeriodicalIF":1.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148738374","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}

{"title":"There’s nothing out there: Revisiting the influence of non-trivial absence processing on capacity measures for parallel AND processing","authors":"Zachary L. Howard,&nbsp;Nicholas Altieri,&nbsp;Paul M. Garrett,&nbsp;Ami Eidels","doi":"10.1016/j.jmp.2026.103012","DOIUrl":"10.1016/j.jmp.2026.103012","url":null,"abstract":"<div><div>In Systems Factorial Technology, workload capacity analysis assesses a system’s ability to handle an increasing amount of information by comparing observed performance against an unlimited-capacity, independent parallel baseline. In logical AND tasks, however, response times are fundamentally driven by the detection of a single missing target rather than marginal target processing. We previously established that this non-trivial absence processing renders traditional AND-based capacity evaluation mathematically unidentifiable. This article formalizes and extends those arguments through consideration of a recently proposed capacity bound that benchmarks double-target responses against an OR-based absence process. Through formal proof and simulation, we demonstrate that this new bound suffers from the same structural confound when considered through the traditional context-dependent marginals view of workload capacity. We argue that there are fundamental unidentifiabilities between workload capacity and the relative processing speeds of target presence versus target absence in the empirical application of AND tasks, resulting in potentially erroneous attributions of system capacity. Through deconstruction of the AND task we formalize a novel metric that quantifies the capacity of absence processing, by re-framing AND as “OR for absence”. Applying this metric to archival data exposes a pervasive and surprising phenomenon: absence processing is itself severely limited-capacity relative to the UCIP benchmark. We argue that while target capacity in AND environments remains completely obscured by self-terminating absence, quantifying the limits of absence processing represents a vital new direction for understanding multi-channel cognitive architectures.</div></div>","PeriodicalId":50140,"journal":{"name":"Journal of Mathematical Psychology","volume":"130 ","pages":"Article 103012"},"PeriodicalIF":1.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148738373","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}

{"title":"Lateral inhibition provides a better explanation of double responding than feed-forward inhibition or inhibition-free mechanisms","authors":"Grant J. Taylor ,&nbsp;Nathan J. Evans","doi":"10.1016/j.jmp.2026.102986","DOIUrl":"10.1016/j.jmp.2026.102986","url":null,"abstract":"<div><div>Evidence accumulation models (EAMs) have dominated theoretical accounts of speeded decision-making for decades, with a proliferation of different variants that each provide different theoretical accounts of how the decision-making process operates. However, these variants often show strong mimicry in their predictions about empirical data, hindering the ability to contrast these accounts and make further theoretical progress. Evans et al. (2020a) proposed double responding (DR) – where participants make a second response after their initial response – as an additional constraint to help distinguish between theoretical accounts, and found that models with lateral inhibition provided a superior account of the data to models with feed-forward inhibition or no inhibition. However, the study of Evans et al. (2020a) had two key limitations: (1) the focus on “implicit” double responding behaviour, where participants were not told that they could change their response, and (2) the inability of any model to explain the positively skewed DR response time (DRRT) distributions. Here, we provide a conceptual replication of Evans et al. (2020a), where we explicitly instructed participants that they could change their mind, and included a leaky integrating threshold (LIT) framework to attempt to explain the positively skewed DRRT distributions. As in Evans et al. (2020a), models with lateral inhibition consistently provided a superior account of the data relative to models with feed-forward inhibition and without inhibition, mostly in terms of predictions about DR proportions. However, in contrast to Evans et al. (2020a), we found that the models – even without the LIT framework – were able to predict positively skewed DRRT distributions, with these contrasting findings likely influenced by the shorter DR deadline in Evans et al. (2020a). Our findings provide further evidence that lateral inhibition is crucial to explaining changes of mind in decision-making, and that DRs can serve as an important extra constraint in distinguishing between competing decision-making models.</div></div>","PeriodicalId":50140,"journal":{"name":"Journal of Mathematical Psychology","volume":"129 ","pages":"Article 102986"},"PeriodicalIF":1.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147945041","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}

{"title":"Incorporating the Luce–Krantz threshold model into cultural consensus theory for ordinal categorical data","authors":"Tzu-Yao Lin ,&nbsp;Yung-Fong Hsu","doi":"10.1016/j.jmp.2026.102990","DOIUrl":"10.1016/j.jmp.2026.102990","url":null,"abstract":"<div><div>Cultural consensus theory (CCT), developed by Batchelder and colleagues in the mid-1980s, is a cognitively driven methodology to assess informants’ consensus in which the culturally correct answers are unknown to researchers a priori. The primary goal of CCT is to uncover the cultural knowledge, preferences or beliefs shared by group members. One of the models within CCT, the <em>general Condorcet model</em> (GCM), is designed for dichotomous response data (e.g., true/false) collected from a group of informants who share a common body of cultural knowledge. In many applied settings, however, responses are not limited to binary judgments; instead, informants often convey varying degrees of confidence, which are naturally represented using ordinal response formats such as Likert-type scales. To address this, we propose an extension of the GCM, termed the <em>general Condorcet–Luce–Krantz</em> (GCLK) model, which integrates the GCM with Luce–Krantz threshold theory to accommodate ordinal categorical responses. The model is intended for settings in which the latent cultural answer remains dichotomous while the observed response process is ordinal. In addition to finding out the consensus truth to the items, the GCLK also estimates other response characteristics, including the item-difficulty levels, informants’ competency levels, and guessing biases. We introduce the multicultural version of the GCLK that can help researchers detect the number of cultures for a given dataset. We use the hierarchical Bayesian modeling approach and the Markov chain Monte Carlo sampling method for estimation. A posterior predictive check is established to test the central assumptions of the model. Through a series of simulation studies, we provide a proof of concept by evaluating the applicability of the GCLK and demonstrate that the model achieves good parameter recovery.</div></div>","PeriodicalId":50140,"journal":{"name":"Journal of Mathematical Psychology","volume":"129 ","pages":"Article 102990"},"PeriodicalIF":1.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148178396","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}

{"title":"Hierarchical modeling of the relationships between internalizing psychiatric symptom factors and attitudes to risk and ambiguity using a Laplace-based variational approximation","authors":"Johan Vegelius,&nbsp;Andreas Frick","doi":"10.1016/j.jmp.2026.102991","DOIUrl":"10.1016/j.jmp.2026.102991","url":null,"abstract":"<div><div>Common hypotheses in psychiatric research regard relationships between latent variables such as risk-taking and ambiguity attitudes, alongside psychiatric symptoms. The prevailing methodological approach is to estimate the latent variables in a first step, and in a second step, use these estimates in the statistical analysis. Using estimates instead of their true values can lead to bias and reduced power. This study aims to develop a new hierarchical method, based on a Laplace-based variational approximation, to mitigate these issues. The developed method outperforms the prevailing method in terms of power, accuracy and out-of-sample prediction. An empirical example demonstrates the usefulness of the method. The developed approach is computationally efficient and suitable to apply to many data types.</div></div>","PeriodicalId":50140,"journal":{"name":"Journal of Mathematical Psychology","volume":"129 ","pages":"Article 102991"},"PeriodicalIF":1.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148178556","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}

{"title":"Systems factorial technology: Unconditional interaction contrasts for correct and incorrect responses","authors":"Matthias Gondan,&nbsp;Lukas Legner","doi":"10.1016/j.jmp.2026.102983","DOIUrl":"10.1016/j.jmp.2026.102983","url":null,"abstract":"<div><div>Response time is probably the most important metric in cognitive psychology. Many studies focus on average response time, but even more information can be obtained considering the shape of the response time distributions. In their seminal work, Townsend and Nozawa (1995) investigated the interaction contrast of response time distributions in two-factorial experiments for different cognitive architectures, including serial and parallel processing, with exhaustive and self-terminating stopping rules. They derived distinct, nonparametric predictions for the interaction contrast under fairly weak assumptions: selective influence of the factorial manipulations, and stochastic ordering of the processing times for the different factor levels. Their original theory is limited to tasks with ceiling accuracy. Extensions to more difficult tasks either conditioned the response time distributions on accuracy, which interferes with the assumption of stochastic ordering. Other approaches focused on a special case of the paradigm (i.e., redundant signals tasks). Here we show that with a slight extension of the stochastic dominance assumption, Townsend and Nozawa’s theorems can be generalized to difficult tasks that entail non-negligible error rates. We demonstrate statistical tests and illustrate methods to assess their power. We investigate additional generalizations of the theory to higher-order experimental manipulations and larger networks of serial and parallel processes. Interesting special cases such as redundant signals tasks and parametric experimental variations, as well as applications of the theory in areas outside cognitive psychology are proposed and discussed. More than 150 years after Donders, we can finally design and analyze interesting reaction time experiments with non-negligible error rates.</div></div>","PeriodicalId":50140,"journal":{"name":"Journal of Mathematical Psychology","volume":"129 ","pages":"Article 102983"},"PeriodicalIF":1.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147798360","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}

{"title":"Sampling in constrained space: Efficient estimation of model evidence under equality and inequality constraints","authors":"Lukas L. Lengersdorff ,&nbsp;Maarten Marsman","doi":"10.1016/j.jmp.2026.102989","DOIUrl":"10.1016/j.jmp.2026.102989","url":null,"abstract":"<div><div>Many scientific theories imply equality and inequality constraints on the statistical model underlying the observed data. Incorporating such constraints into hypothesis testing increases the efficiency and precision of inference. However, existing tools of testing such hypotheses are computationally expensive and do not scale well to complex sets of constraints. Here, we introduce Sampling in Constrained Space (SICS), a general approach for performing Bayesian hypothesis testing on constrained models. Combining Hamiltonian Monte Carlo algorithms with reparameterization, SICS allows the efficient sampling of the posterior distribution from those sections of the parameter space that fulfill all constraints. This, in turn, enables estimating the marginal likelihood under the constrained model, through established algorithms such as bridge sampling. We demonstrate the versatility of SICS by analyzing a learning task dataset using constrained reinforcement learning models, from both a single-level and hierarchical perspective. Our results show that SICS outperforms previous approaches in terms of precision and efficiency. By providing guidelines on its application, we hope to make SICS an integral part of scientists’ inferential toolkit.</div></div>","PeriodicalId":50140,"journal":{"name":"Journal of Mathematical Psychology","volume":"129 ","pages":"Article 102989"},"PeriodicalIF":1.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147798361","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}

{"title":"A unified framework for conjunctive and competence-based attribute functions","authors":"Bo Wang ,&nbsp;Jinjin Li ,&nbsp;Huihua Shi ,&nbsp;Zhuoheng Chen","doi":"10.1016/j.jmp.2026.102992","DOIUrl":"10.1016/j.jmp.2026.102992","url":null,"abstract":"<div><div>Competence-based extensions of polytomous knowledge space theory have recently seen significant development, primarily through specialized conjunctive or disjunctive models. The conjunctive attribute map proposed by Stefanutti et al. (2023) assumes that a single, conjunctive set of attributes is required for each item-response, while other models, such as that of Wang et al. (2025), have explored purely disjunctive mechanisms. However, these frameworks do not readily account for scenarios where a given response can be justified by one of several distinct sets of attributes—a structure analogous to the competency model in dichotomous theory. This paper introduces a generalized attribute function which maps each item-response pair to a collection of attribute sets. This framework naturally subsumes the conjunctive model as the special case where this collection is a singleton. We establish a rigorous axiomatic foundation for this function, specified by several general conditions. We then derive the corresponding item-response function and the maximal polytomous assessment structure. We show that while our framework generalizes the conjunctive model, its disjunctive specialization is structurally distinct from other disjunctive approaches. The utility of this generalized model for capturing complex reasoning paths is demonstrated with a detailed example.</div></div>","PeriodicalId":50140,"journal":{"name":"Journal of Mathematical Psychology","volume":"129 ","pages":"Article 102992"},"PeriodicalIF":1.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147991412","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}

{"title":"Toward a unified perspective on assessment models, Part III: Polytomous and continuous latent variables","authors":"Stefano Noventa ,&nbsp;Jürgen Heller ,&nbsp;Tim Fabian Schaffland ,&nbsp;Augustin Kelava","doi":"10.1016/j.jmp.2026.102993","DOIUrl":"10.1016/j.jmp.2026.102993","url":null,"abstract":"<div><div>In the past years, several theories for assessment have been developed within the overlapping fields of Psychometrics and Mathematical Psychology. The most notable are Item Response Theory (IRT), Cognitive Diagnostic Assessment (CDA), and Knowledge Structure Theory (KST). In spite of their common goals, these theories are largely independent and focus on different aspects. In Part I of this work, a general framework was introduced that, by means of two primitives (structure and process) and two operations (factorization and reparametrization), allows to derive the models of these theories. A two-processes approach provided a taxonomy encompassing IRT, CDA, and KST models. In Part II, the framework was used to derive KST and CDA models based on dichotomous latent variables. In this third contribution, IRT, CDA, and KST models based on polytomous and continuous latent variables are derived. Implications for IRT models are discussed. A special focus is on the implicit assumptions required to move from the discrete case to the continuous one.</div></div>","PeriodicalId":50140,"journal":{"name":"Journal of Mathematical Psychology","volume":"129 ","pages":"Article 102993"},"PeriodicalIF":1.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148178395","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}

{"title":"Representing choice probabilities by ranking probabilities via entropy maximization","authors":"Karim Kilani ,&nbsp;Hans Colonius","doi":"10.1016/j.jmp.2026.102970","DOIUrl":"10.1016/j.jmp.2026.102970","url":null,"abstract":"<div><div>Falmagne’s representation problem is revisited by maximizing Shannon entropy applied to ranking probabilities, under the linear constraints imposed by choice probabilities. Unlike Falmagne’s recursive construction, our method leads directly to an explicit solution, obtained after transforming the initial system into an equivalent one via alternating sums, in the spirit of Block–Marschak polynomials. We compute this solution for the Luce model and the generalized extreme value model, and show that, as soon as there are at least four alternatives, the construction based on Shannon entropy is only one among infinitely many possible representations. Other solutions could be obtained by maximizing alternative entropy functions, further highlighting the potential role of information theory in enriching the analysis of stochastic choice.</div></div>","PeriodicalId":50140,"journal":{"name":"Journal of Mathematical Psychology","volume":"128 ","pages":"Article 102970"},"PeriodicalIF":1.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077915","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}