Rubén Moreno‐Bote

The paper shows that higher metacognitive efficiency reduces confirmation bias in perceptual decision-making by enabling more flexible integration of repeated sensory information.

This study shows that accumulating tokens toward a jackpot reward improves decision-making accuracy and speed in macaques, with neural activity in dACC reflecting reference-dependent value encoding.

This paper introduces a biophysically plausible decision-making model using interacting neural populations in cortical layer 2/3, tested on human and macaque experiments.

The paper examines how confidence relates to oculomotor dynamics during decision-making, finding that fixations between empty locations correlate with confidence and deliberation.

This study investigates how humans prioritize decisions during planning by analyzing gaze and choice behavior in a multi-category task involving subjective value assessments.

This study identifies a neural mechanism in macaque visual cortex that biases activity in area MT by peripheral optic flow, contributing to perceptual biases associated with flow parsing.

The paper investigates how reward rate influences attentional effort in economic decision-making using neural data from macaques.

This paper models how the brain computes delayed consequences in decision-making without explicit feedback, showing how neural dynamics and learning mechanisms adapt over time.

The paper shows that during decision-making, macaques' gaze at empty locations reactivates value encoding in orbitofrontal cortex, influencing choices even after offers are no longer visible.

This study investigates how Drosophila larvae combine sensory signals through probabilistic inference, showing adaptation to signal reliability and modality-dependent mechanisms.

This study examines how decision-making and information sampling interact during human action, finding that sampling movements co-occur with decision processes.

This study investigates the sense of agency for mental actions using a belief-based action-effect paradigm, finding comparable explicit reports and intentional binding effects for motor and mental actions.

This paper demonstrates that covariance-based perceptrons outperform mean-based methods in classifying synthetic time series and speech digits by leveraging second-order statistics in reservoir computing.

The paper investigates how information about visual stimuli is distributed across large neuronal populations in mouse V1, finding sublinear scaling due to correlated noise.

The paper introduces a new classification framework for time series data using covariance patterns in recurrent neuronal networks, showing it can effectively capture second-order statistical features.

This study shows that primate lateral prefrontal cortex encodes working memory representations during naturalistic behavior and that ketamine disrupts this encoding by altering neuronal firing patterns.

The paper models decision-making under limited capacity, showing that optimal strategies shift from breadth to depth as capacity increases.

This study shows humans integrate contextual information hierarchically to estimate reliability and report confidence based on probabilistic inference.

The paper identifies two key features of neural population responses that influence information encoding and behavioral performance.

The paper proposes a neuronal theory explaining sequential economic choice through dynamic attention and value coding mechanisms.