Alireza Soltani

The paper shows that attention modulates learning rates based on cue-choice alignment and reward, improving discrimination between competing cues.

This study investigates sex differences in reward learning by analyzing task engagement and lapse rates in rats during stimulus- and action-based learning and their reversals.

This study investigates the amygdala's role in dynamic model arbitration under uncertainty by comparing behavior in monkeys with and without amygdala lesions.

The paper investigates how attention interacts with learning strategies in multidimensional reward environments, finding that attention modulates future learning rather than directly affecting decision making.

The paper shows that intrinsic and task-related neural timescales increase hierarchically across mammalian cortices, but thalamic timescales differ from this pattern.

The paper investigates how reward feedback interacts with microstimulation in the frontal eye field to influence saccadic choices in macaques, finding reward-dependent adaptation and no global learning effects.

The paper shows that environmental regularities can bias reward learning by increasing confirmation bias and reducing the dimensionality of learning tasks.

This study examines how training affects timescales of neural dynamics in primate prefrontal cortex and their role in working memory.

This study investigates the distinct roles of vlOFC and BLA in stimulus- and action-based reversal learning under uncertainty using chemogenetic inhibition in rats.

This study investigates how mice and monkeys adjust to different types of uncertainty in reward environments using choice data and reinforcement learning models.

The paper investigates neural mechanisms of value representations and learning strategies in humans using a multi-dimensional task and RNNs, revealing a distributed code and role of inhibitory/excitatory neurons.

The paper introduces entropy-based metrics to predict choice behavior in mice and monkeys, explaining 50% and 41% of matching variance, and develops a more accurate model based on these metrics.

This study investigates how reward influences visual processing and choice in monkeys, finding distinct effects on sensitivity to motion and decision-making.

The paper estimates multiple timescales of neural dynamics and their integration of task-relevant signals across cortex, finding increasing timescales from parietal to prefrontal to cingulate cortex and no correlation with signal selectivity.

This study investigates the roles of the anterior cingulate cortex (ACC) and basolateral amygdala (BLA) in decision confidence and learning under uncertainty using a rat behavioral task.

The paper investigates how low-level neural processes and high-level cognitive processes interact to influence context-dependent choice in humans, finding that both contribute to distinct behavioral patterns.

This study investigates how expected reward influences perceptual decision making and finds that reward effects are better explained by decision-making processes rather than sensory processing.

The paper investigates how humans evaluate complex risky options by showing subjects use integrated value functions for individual outcomes and differential weighting based on reward magnitude or probability for overall value construction.

This study shows that response time patterns reflect learning strategies and reward value construction during complex decision-making tasks.

This study shows that monkeys exhibit flexible risk attitudes, with varying utility and probability weighting functions across tasks, suggesting dynamic construction of reward value rather than fixed traits.

This study investigates how time pressure affects decision-making by comparing sensory integration and dynamic combination of sensory and reward information in humans.

The paper shows humans use feature-based learning in dynamic environments and switch to option-based learning when features predict object values accurately.

This paper shows that reward-dependent metaplasticity allows for both adaptability and precision in learning by separating synaptic states into reservoirs and buffers.

The paper shows that prefrontal neurons enhance spatial representation during cognitive tasks by altering population-level activity rather than individual neuron selectivity.