Biology subjects

Razmi, N.

Publications and source records attributed to Razmi, N..

3 recordsLinked to original sources

bioRxiv2026

A normative account of human temporal structure learning

People rapidly recalibrate their expectations about the world in the face of surprising observations. This recalibration should depend on the temporal structure of the environment, however how people should and do learn temporal structures remains unknown. To examine this gap, we developed a Bayesian model that infers temporal structure of the environment directly from observations and makes accurate predictions across qualitatively different environments. We tested predictions of our model in an online behavioral study in which participants predicted outcomes generated according to various temporal structures, and demonstrated that people learned to exploit these structures in a qualitatively similar manner to that of our structure learning model. Furthermore, we show that normative structure learning accounts for puzzling asymmetries previously observed in sequential effects of temporal structures on human learning. Taken together our model and empirical data provide the first general account of how people use temporal structures to interpret unexpected observations in the service of learning.

neuroscience↗

bioRxiv2025

Fluctuations in arousal reflect latent state transitions that facilitate behavioral optimization

People are often faced with surprising events that defy expectations. Such events elicit transient activity in the locus coeruleus/norepinephrine system and elevation of peripheral arousal markers including pupil dilation and the EEG P300, but the function of these signals remains unclear. We propose that they reflect latent state transitions that dynamically control the mental context governing learning and perception. We tested and confirmed five preregistered predictions of this theory using EEG and pupil measurements collected from people performing a color prediction and reproduction task. Task latent state transitions elicited pupil dilation and amplified event-related potentials including the P300. These EEG and pupil measures related to behavioral signatures of latent state updating, including reduced bias and bidirectional adjustment of learning, both across trials and individuals. Our findings support the theory that LC/NE-linked arousal systems optimize behavior by signaling environmental transitions to facilitate rapid adjustments of mental context.

neuroscience↗

bioRxiv2020

Adaptive learning through temporal dynamics of state representation

People adjust their learning rate rationally according to local environmental statistics and calibrate such adjustments based on the broader statistical context. To date, no theory has captured the observed range of adaptive learning behaviors or the complexity of its neural correlates. Here, we attempt to do so using a neural network model that learns to map an internal context representation onto a behavioral response via supervised learning. The network shifts its internal context upon receiving supervised signals that are mismatched to its output, thereby changing the "state" to which feedback is associated. A key feature of the model is that such state transitions can either increase learning or decrease learning depending on the duration over which the new state is maintained. Sustained state transitions that occur after changepoints facilitate faster learning and mimic network reset phenomena observed in the brain during rapid learning. In contrast, state transitions after one-off outlier events are short-lived, thereby limiting the impact of outlying observations on future behavior. State transitions in our model provide the first mechanistic interpretation for bidirectional learning signals, such the p300, that relate to learning differentially according to the source of surprising events and may also shed light on discrepant observations regarding the relationship between transient pupil dilations and learning. Taken together, our results demonstrate that dynamic latent state representations can afford normative inference and provide a coherent framework for understanding neural signatures of adaptive learning across different statistical environments. Significance StatementHow humans adjust their sensitivity to new information in a changing world has remained largely an open question. Bridging insights from normative accounts of adaptive learning and theories of latent state representation, here we propose a feed-forward neural network model that adjusts its learning rate online by controlling the speed of transitioning its internal state representations. Our model proposes a mechanistic framework for explaining learning under different statistical contexts, explains previously observed behavior and brain signals, and makes testable predictions for future experimental studies.

neuroscience↗