Biology subjects

Yazdanpanah, A.

Publications and source records attributed to Yazdanpanah, A..

2 recordsLinked to original sources

bioRxiv2025

Risky Choices After Frontal Brain Injury: Differential Effects in Self vs Other-Decision Contexts

Frontal lobe integrity is crucial for assessing risk and making informed decisions. This study investigated how frontal lobe lesions affect the computational mechanisms underlying risky choice, particularly when decisions impact oneself versus another person. A Patient Group of 20 individuals with frontal cortex damage and a Control Group of 20 matched individuals performed a gambling task, making accept/reject decisions on mixed-outcome gambles for themselves ("Self") or an anonymous other ("Other"). We provide a mechanistic account of choice behavior using Prospect Theory, the leading behavioral model of decision-making under risk, to quantify parameters for utility curvature, loss aversion, and probability weighting. Behaviorally, the Patient Group accepted significantly more disadvantageous gambles for themselves than did the Control Group yet showed a trend toward greater caution when choosing for others. Prospect Theory modeling revealed a specific computational phenotype for this behavior. Compared to the Control Group, the Patient Group exhibited significantly more pronounced utility curvature (lower , {beta}) and more linear, less distorted probability weighting (higher {gamma}). While patients also showed a trend toward lower loss aversion ({lambda}), this difference was not statistically significant. This combination of altered utility and probability processing explains their paradoxical risk-seeking. These findings suggest that frontal cortex damage disrupts the computation of subjective value, leading to a distinctive decision-making profile marked by altered utility curvature and reduced sensitivity to outcome magnitudes. This computational characterization deepens our understanding of frontal lobe contributions to decision-making and can inform targeted rehabilitation strategies.

neuroscience↗

bioRxiv2024

Contribution of statistical learning to mitigating the curse of dimensionality in reinforcement learning

Natural environments are rich with patterns and regularities. Thus, it is not surprising that most animals have evolved neural mechanisms to detect and adapt to these regularities. Such detections and adaptations can, in turn, influence several cognitive functions including attention, perception, and memory, thereby enhancing survival. Here, we investigated whether detecting environmental regularities that are irrelevant to obtaining rewards can influence learning about multi-dimensional choice options--a process often constrained by the scarcity of reward feedback. To explore this, we trained human participants to perform a multidimensional reward- learning task alongside an orthogonal sequence-prediction task. We found that although feature- specific regularities in the sequence-prediction task were not predictive of reward, they incidentally biased participants behavior toward the feature with regularity during the reward- learning task. Fitting choice behavior with various computational models revealed that this effect was more consistent with modulations in learning rather than decision making, as evidenced by higher learning rates for this feature. This was particularly apparent for learning from chosen, rewarded options and unchosen, unrewarded options, demonstrating that environmental regularities can amplify confirmation bias in reward learning. Our results thus extend the notion of confirmation bias in learning about options and actions to their features. Furthermore, they suggest that temporal regularities can influence reward learning by biasing the association of reward with specific features and by enhancing confirmation bias. These effects help reduce dimensionality of the learning task to mitigate the curse of dimensionality in reward learning.

neuroscience↗