Publications on Erfan Jaripour

Trial-by-Trial Behavioral Adaptation in a Restless Bandit Task: A Mixed-Effects Modeling Approach

Mon, 17 Aug 2026 00:00:00 +0000

Citation

Jaripour, E. (2026). Trial-by-Trial Behavioral Adaptation in a Restless Bandit Task: A Mixed-Effects Modeling Approach. Research Square Preprint. DOI: 10.21203/rs.3.rs-10740751/v1

Abstract

Adaptive decision-making requires individuals to modify behavior as reward environments change over time. Restless bandit tasks provide a framework for studying behavioral adaptation under dynamic conditions, although observable changes do not necessarily reveal latent cognitive mechanisms. The present study examined trial-by-trial behavioral adaptation in a four-arm restless bandit task using a publicly available dataset of 965 participants and 139,816 analyzed trials. Rather than estimating latent reinforcement-learning parameters, the study examined observable outcomes, trial-related change, differences across payoff environments, and individual differences in baseline behavior and adaptation. Mixed-effects models were fitted to payoff-maximizing choice, obtained reward, log-transformed reaction time, and choice switching. The primary analysis used a quadratic trial trajectory with payoff-environment interactions and participant-specific random intercepts, linear slopes, and quadratic slopes. The quadratic model provided substantially better AIC fit than the corresponding linear interaction model. Payoff-maximizing choice showed distinct linear and quadratic trajectories across payoff environments, while participants varied in baseline performance and trial-related change. Secondary analyses showed different reward trajectories across payoff environments, decreasing reaction times across trials without clear payoff-group differences, and decreasing choice switching, with evidence that this trajectory differed for Group 3 but not Group 4 relative to the reference environment. These findings indicate nonlinear changes in observable decision behavior during repeated decisions in a dynamic reward environment. Because latent values, beliefs, and learning parameters were not estimated, the findings do not identify the mechanisms underlying these changes. Instead, they provide a quantitative characterization of observable behavioral adaptation and a basis for future computational investigations of decision-making in dynamic environments.

A Hierarchical Bayesian Analysis of Memory Load Effects on Visual Working Memory Precision

Thu, 23 Jul 2026 00:00:00 +0000

Citation

Jaripour, E. (2026). A Hierarchical Bayesian Analysis of Memory Load Effects on Visual Working Memory Precision. Research Square Preprint. DOI: 10.21203/rs.3.rs-10469232/v1

Abstract

Visual working memory is limited in both capacity and precision, and computational models increasingly aim to characterize how memory representations vary with increasing load and across individuals. The present study applied hierarchical Bayesian modeling to a publicly available continuous-report visual working-memory dataset to examine the relationship between memory load and precision while comparing alternative statistical representations of performance. Trial-level angular errors from three experiments were analyzed using Bayesian models with von Mises likelihoods. The evaluated models included a null hierarchical model with no memory-load effect, a hierarchical model estimating a population-level effect of set size with participant-specific variation, and a nonlinear hierarchical model allowing independent set-size effects through a sum-to-zero parameterization. Model performance was evaluated using posterior predictive checks, convergence diagnostics, and approximate leave-one-out cross-validation. Results showed that memory precision decreased systematically as memory load increased and that participants exhibited substantial differences in baseline precision. The nonlinear hierarchical model achieved the strongest predictive performance among the evaluated models, indicating that the relationship between memory load and precision was not fully captured by a simple linear representation. Robustness analyses demonstrated that the primary conclusions remained stable across alternative prior specifications, sampling configurations, and posterior initializations. These findings highlight the value of hierarchical Bayesian approaches for modeling individual variability in visual working-memory performance and demonstrate the utility of reproducible computational workflows for evaluating statistical models using open behavioral datasets.

Stroop Interference in Reaction Time and Accuracy: A Behavioral and Computational Analysis

Thu, 11 Jun 2026 00:00:00 +0000

Citation

Jaripour, E. (2026). Stroop Interference in Reaction Time and Accuracy: A Behavioral and Computational Analysis. Research Square Preprint. DOI: 10.21203/rs.3.rs-10287577/v1

Abstract

The Stroop task is a classic paradigm for studying cognitive interference, selective attention, and response competition. By analyzing behavioral data from 81 participants, this study examined how congruent, incongruent, and neutral conditions affect reaction time and accuracy. The dataset contained trial-level responses from a publicly available Stroop experiment. In the preprocess, probe trials, invalid reaction times, and extreme values were removed. In addition, the participant–session structure in the raw files was resolved. The results showed a robust Stroop interference effect. The fastest reaction times were in the congruent condition, and the slowest in the incongruent condition. The neutral condition contained intermediate performances. The mean interference effect is defined as the difference in reaction time between incongruent and congruent trials. It was 106 ms. A paired-samples t-test confirmed a significant difference between incongruent and congruent conditions, with t(80) = 17.77, p < .001, and a large effect size (dz = 1.97). It showed strong interference at the group level. Accuracy remained high across conditions, with little evidence of a speed–accuracy trade-off. Assumption checks supported the use of parametric analysis.