Biology subjects

Esmaily, J.

Publications and source records attributed to Esmaily, J..

3 recordsLinked to original sources

bioRxiv2025

Interpretable and Brain-Inspired Recurrent Model of Hierarchical Decision-Making Capturing Trial-by-Trial Variability

Humans often make decisions in hierarchical environments, where low-level perceptual judgments inform high-level strategies. Understanding how the brain computationally navigates such multi-level decisions remains an open challenge. While existing models offer statistical insights, they often fall short in capturing the neural mechanisms and trial-by-trial variability underlying individual choices. To address this gap, we developed a neurocomputational model that integrates a biologically inspired attractor network for low-level perceptual decisions with a recurrent neural network (RNN) for high-level strategic adjustments. With minimal architectural constraints, the RNN receives only raw firing rates, feedback, and prior environment, learning to infer environment-switching strategies without explicit access to confidence or stimulus strength. In a hierarchical task combining motion discrimination and bandit decisions (N = 9; ~10,800 trials), the model successfully reproduced three hallmark behavioral patterns observed in humans. Unlike previous models, the model also captured trial-to-trial variability in switching decisions and implicitly learned to estimate decision confidence. For interpretability, we used representational and sensitivity analyses. Representational analyses revealed internal dynamics consistent with evidence accumulation in the anterior cingulate cortex (ACC), while sensitivity analysis identified feedback as the dominant influence on strategy, modulated by recent trial history. This framework combines interpretability and predictive power, moving beyond simple data fitting to provide mechanistic insights into how the brain integrates confidence and feedback to guide adaptive behavior in hierarchical decision-making.

neuroscience↗

bioRxiv2025

Neural and Behavioural Correlates of Variance of Sensory Evidence

Neurobiology of perceptual decisions has largely focused on the neural correlates of the mean strength of sensory evidence. Much less is known about the neural coding of sensory variability. Here, we analyzed the EEG signals obtained from participants who judged the mean orientation of a sequence of gratings with varying variance but constant mean to identify the neural signatures of sensory variance and their relation to individual differences in choice confidence. The neural responses in the stimulus-entrained (4 Hz) and alpha (9-11 Hz) bands tracked variability independently of mean. The frontal and centro-parietal regions demonstrated a quadratic relationship (i.e., strongest responses to intermediate levels of uncertainty) to the standard deviation of the sequence. The occipital response coded the visual stimulus variability linearly. These neural markers of variability were correlated with inter-individual differences in computational components of metacognition. Centro-parietal activity was most predictive of metacognitive sensitivity, aligning with its known role in evidence accumulation. These findings advance our understanding of how the brain dynamically encodes uncertainty and help better characterise the electrophysiological basis of individual differences in metacognitive evaluation.

neuroscience↗

bioRxiv2022

Interpersonal alignment of neural evidence accumulation to social exchange of confidence

Private, subjective beliefs about uncertainty have been found to have idiosyncratic computational and neural substrates yet, humans share such beliefs seamlessly and cooperate successfully. Bringing together decision making under uncertainty and interpersonal alignment in communication, in a discovery plus pre-registered replication design, we examined the neuro-computational basis of the relationship between privately-held and socially-shared uncertainty. Examining confidence-speed-accuracy trade-off in uncertainty-ridden perceptual decisions under social vs isolated context, we found that shared (i.e., reported confidence) and subjective (inferred from pupillometry) uncertainty dynamically followed social information. An attractor neural network model incorporating social information as top-down additive input captured the observed behaviour and spontaneously demonstrated the emergence of social alignment in virtual dyadic simulations. Electroencephalography showed that social exchange of confidence modulated the neural signature of perceptual evidence accumulation in the central parietal cortex and produced a sustained top-down flow of information from prefrontal to parietal cortex. Our findings offer a neural population model for interpersonal alignment of shared beliefs.

neuroscience↗