Biology subjects

Vahabie, A.-h.

Publications and source records attributed to Vahabie, A.-h..

4 recordsLinked to original sources

bioRxiv2026

Controllable Point-Light Displays Implemented in a game engine for biological motion research

Biological motion perception plays a critical role in survival and social communication across species. Point-light displays (PLDs), which represent body movement using only a small set of joint markers, have long served as an effective tool for isolating motion cues from other visual features. However, existing methods for generating PLDs, ranging from filmed actors with reflective markers to markerless motion extraction and motion-capture datasets, present limitations in cost, accessibility, flexibility, or ecological validity. In particular, many laboratories lack the resources to create customizable stimuli that allow systematic manipulation of movement parameters. In this article, we introduce a practical and easily modifiable method for producing fully controllable 3D PLDs using freely available animation rigs and the Unity game engine. Our approach enables real-time control of depth cues and directional motion without the need for motion capture equipment or specialized filming environments. To demonstrate the utility of the method, we conducted a psychophysical experiment comparing the perception of biological and non-biological motion. The results replicate the well-documented forward-motion perceptual bias for biological stimuli and highlight differences in how observers interpret motion direction across stimulus classes. This method offers a convenient, accessible, and adaptable tool for research on motion perception and social cognition.

neuroscience↗

bioRxiv2025

Deformed Probability Estimation in Goal-Directed reinforcement learning model explains anxious-depression dimensions of psychiatric disorders

Psychiatric disorders are complex, multi-dimensional pathologies rooted in diverse cognitive processes. Computational psychiatry aims to reveal distortions in these processes through behavior modeling, providing a deeper understanding of psychiatric disorders. Previous studies, using Daws two-stage task, had linked the imbalance between habitual/model-free and goal-directed/model-based behaviors to disorders with compulsive behaviors and intrusive thoughts. The model-based component relies on the estimation of environmental probabilities. Therefore, we added a well-known deformation in subjective probability estimation to the model and this improved the model fitting. More importantly, the fitted deformation explains some variance of the anxious-depression dimension of psychiatric symptoms. The deformation parameter is aligned with the description-experience gap in decision-making literature. Our results point to subjective possibly distortion as the probable underlying cognitive process of anxiety, apathy, and depression. This study also shows that the inclusion of cognitive biases in modeling can extract the hidden aspects of behavior possibility linked to disorders. Our approach enhances the precision of computational psychiatry and provides deeper insights into the cognitive processes underlying psychiatric symptoms, paving the way for more effective, personalized therapeutic strategies. Significance StatementThis study builds on a previously used model and Data that indicated a correlation between Model-Based preference and certain psychiatric disorders. By adding distortion to the probability estimation, we identified a new parameter correlated with depression and anxiety. The augmented model demonstrates an improved fit to behavior and aligns with Gillans previous findings. Our approach enhances the precision of computational psychiatry and provides deeper insights into the cognitive processes underlying psychiatric symptoms, paving the way for more effective, personalized therapeutic strategies.

neuroscience↗

bioRxiv2021

Optimal Reinforcement Learning with Asymmetric Updating in Volatile Environments: a Simulation Study

AO_SCPLOWBSTRACTC_SCPLOWThe ability to predict the future is essential for decision-making and interaction with the environment to avoid punishment and gain reward. Reinforcement learning algorithms provide a normative way for interactive learning, especially in volatile environments. The optimal strategy for the classic reinforcement learning model is to increase the learning rate as volatility increases. Inspired by optimistic bias in humans, an alternative reinforcement learning model has been developed by adding a punishment learning rate to the classic reinforcement learning model. In this study, we aim to 1) compare the performance of these two models in interaction with different environments, and 2) find optimal parameters for the models. Our simulations indicate that having two different learning rates for rewards and punishments increases performance in a volatile environment. Investigation of the optimal parameters shows that in almost all environments, having a higher reward learning rate compared to the punishment learning rate is beneficial for achieving higher performance which in this case is the accumulation of more rewards. Our results suggest that to achieve high performance, we need a shorter memory window for recent rewards and a longer memory window for punishments. This is consistent with optimistic bias in human behavior.

neuroscience↗

bioRxiv2020

Implicit counterfactual effect in partial feedback reinforcement learning: behavioral and modeling approach

Context by distorting values of options with respect to the distribution of available alternatives, remarkably affects learning behavior. Providing an explicit counterfactual component, outcome of unchosen option alongside with the chosen one (Complete feedback), would increase the contextual effect by inducing comparison-based strategy during learning. But It is not clear in the conditions where the context consists only of the juxtaposition of a series of options, and there is no such explicit counterfactual component (Partial feedback), whether and how the relativity will be emerged. Here for investigating whether and how implicit and explicit counterfactual components can affect reinforcement learning, we used two Partial and Complete feedback paradigms, in which options were associated with some reward distributions. Our modeling analysis illustrates that the model which uses the outcome of chosen option for updating values of both chosen and unchosen options, which is in line with diffusive function of dopamine on the striatum, can better account for the behavioral data. We also observed that size of this bias depends on the involved systems in the brain, such that this effect is larger in the transfer phase where subcortical systems are more involved, and is smaller in the deliberative value estimation phase where cortical system is more needed. Furthermore, our data shows that contextual effect is not only limited to probabilistic reward but also it extends to reward with amplitude. These results show that by extending counterfactual concept, we can better account for why there is contextual effect in a condition where there is no extra information of unchosen outcome.

animal behavior and cognition↗