Giovanni Pezzulo

The paper compares visuomotor strategies of experts and novices in a football task, showing that strategy differences reflect distinct generative models under the active inference framework.

This study shows that inferential planning in the frontal cortex generates simultaneous neural representations of sequence elements through probabilistic inference, unifying planning, memory, and motor preparation findings.

The paper develops a neural network model integrating cognitive maps, stochastic computing, and compositional coding to enable flexible planning and problem-solving in energy-efficient hardware.

This study investigates how humans intercept moving objects under varying conditions, finding that predictive mechanisms incorporating gravity priors are used flexibly depending on task demands.

This paper introduces NEBULA, a framework that models whole-brain neural activity and behavior in C. elegans, identifying shared dynamics for prediction and intervention.

The paper investigates how spaced versus massed training affects brain network organization in spatial memory, finding that spaced training promotes more integrated cortical-thalamic networks.

This study shows that false cardiac feedback can alter pain perception and anticipatory heart rate, demonstrating the role of interoceptive inference in shaping both perception and physiological responses.

This study uses virtual reality eye-tracking to show that gaze patterns during planning exhibit hierarchical structure, with specific visual search dynamics linked to successful navigation on complex maps.

This study investigates brain dynamics during natural vision and audition by analyzing information flow between brain regions in sighted, blind, and deaf participants, revealing how distributed processing supports perception.

This study examines how individuals decide to persist or disengage in sequential effort-based tasks, finding that persistence is influenced by task difficulty, skill, fatigue, progress, goal proximity, and early success.

This study investigates how people balance immediate actions with long-term planning in a virtual task, finding that participants plan ahead and adjust decisions based on spatial and embodied constraints.

This paper extends the drift-diffusion model to account for choice-induced preference change by incorporating attribute variability during decision-making.

This study tracks cognitive and motor decline in spatial navigation across the adult lifespan using data from a video game, showing age-related declines in both domains without a shift to alternative strategies.

This study examines how participants coordinate eye and hand movements during problem-solving tasks, revealing hierarchical planning strategies through coarticulation in gaze and cursor movements.

This paper introduces a modeling approach to study embodied decision-making, showing that decision and action processes interact through feedback loops in active inference.

This paper proposes a meta-Reinforcement Learning framework to unify theories of anterior cingulate cortex function in cognitive control and surprise monitoring, showing it explains neuroimaging data better than previous models.

The paper investigates how humans adaptively use cognitive resources in problem-solving tasks requiring different planning depths, finding they select minimum required depths rather than fixed depths.

The paper investigates coarticulation in sport climbing, showing that expert climbers adjust their movements based on future planned actions, while non-climbers do not.

This study investigates how embodied decision-making models adapt to environmental contingencies using active inference in a two-alternative forced choice task.

The paper develops a computational model that distinguishes self-generated from externally caused optic flow using sensorimotor mismatch circuits, validated with mouse data and applicable to primate vision.

This paper proposes a model of needing as an active inference process that reduces pervasive surprise, demonstrating its influence on reward processing and behavior through simulations.

The paper introduces an Active Inference architecture for efficient control of complex kinematic chains by mapping extrinsic to intrinsic coordinates through inference.

This paper proposes an active inference framework for depth estimation that combines action-perception cycles and Predictive Coding principles to improve depth belief accuracy through biologically plausible neural circuits.

The paper shows that goal-directed behavior compresses neural representations of space in the hippocampus and orbitofrontal cortex, with compressed representations predicting successful learning.

This paper proposes a confidence-driven decision-making model that reinterprets confidence as the primary monitored variable during decisions, offering a more parsimonious explanation of existing findings.

This paper proposes a hierarchical generative model of the hippocampal formation that explains generative replay and continual learning through spatial navigation and memory organization.

The paper extends the drift-diffusion model to account for choice-induced preference change by allowing drift rates to vary during decision-making based on attended attributes.

This paper uses Active Inference to simulate and model different forms of interoceptive control, including homeostatic, allostatic, and goal-directed, and their dysfunctions.

The paper shows that neural dynamics in monkey premotor cortex are shared between action execution and observation, with spatial features more accurately encoded than task types.