William T. Newsome

The paper decodes real-time decision variables from motor cortex activity and shows their link to choice behavior and decision dynamics.

This study identifies stable, separable representations of value and choice in OFC during decision-making using novel analysis methods.

The paper examines how transcriptomic types of mouse prefrontal cortex projection neurons relate to axonal projections and encoding properties across cognitive tasks, finding redundant but quantitatively biased task signal encoding.

The paper examines how choice representations are structured in primate prefrontal cortex, finding that diverse neural responses reflect persistent population patterns rather than transient dynamics.

This study shows transcranial ultrasound can bias decision-making in non-human primates by modulating oculomotor circuits, with effects comparable to neuromodulatory drugs.

The paper shows that model selection in neural data can be unreliable due to brittleness, where small data changes affect model rankings, and suggests ways to address this issue.

This study examines how decision variables are represented in neural populations of PMd and M1 during a motor task, showing differences in timing and strength between areas and replicating findings with a computational model.

This study trains a neural network to perform 20 cognitive tasks and finds that it develops functionally specialized clusters and compositional task representations.

The paper shows that deviations from the matching law in foraging behavior reflect an optimal strategy using multi-timescale learning to balance bias and variance under uncertainty.