Uri Maoz

Real-time intracortical decoding from human motor cortex reveals a task-independent commitment to act that aligns with conscious intent

This study decodes motor cortex activity in real-time to predict movement onset before action, showing a task-independent commitment to act that aligns with conscious intent.

A neural circuit mechanism for abstract free choice

This study investigates the neural mechanisms of abstract free choice using a delayed-response paradigm and a hierarchical attractor network model, finding that free choices involve slower responses and reduced goal-switching costs.

Probing for Intentions: The Early Readiness Potential does not Reflect Awareness of Motor Preparation

This study investigates the neural basis of the Readiness Potential (RP) and finds it does not reflect awareness of motor preparation, suggesting metacognitive access to stochastic accumulation instead.

Volitional stopping is preceded by a transient beta oscillation

This study shows that beta oscillations in the motor cortex precede stopping in rats, but have limited utility for real-time decoding.

Slow ramping emerges from spontaneous fluctuations in spiking neural networks

The paper identifies a mechanism for slow-ramping signals in neural networks and validates it using human single-neuron recordings.

Pupil dilations prior to freely timed actions reflect the reported timing of conscious intention

This study investigates antecedent pupil dilations (APDs) before voluntary actions and finds they correlate with the timing of conscious intention rather than movement or external stimuli.

Neural precursors of deliberate and arbitrary decisions in the study of voluntary action

The paper compares neural precursors of arbitrary and deliberate decisions, finding readiness potentials before arbitrary decisions but not deliberate ones, suggesting different neural mechanisms.

Preoperative predictions of in-hospital mortality using electronic medical record data

This paper develops a machine learning model using EMR data to predict in-hospital mortality preoperatively, achieving higher accuracy than existing clinical scores.