Posterior parietal cortex provides an estimation of the location of an object with respect to the body and uses this information to plan appropriate gait changes to negotiate such an obstacle.

Neural populations in PPC dynamically represent motor-like and then sensory-like aspects of brain–computer interface finger movements with a representational structure that matches able-bodied individuals.

Neurons in the macaque posterior parietal cortex behave like an error detector that computes the saccadic error by comparing the intended and the actual saccade end-position signals.

Single neurons in human posterior parietal cortex encode actual and imagined touch within a shared neural substrate.

Seemingly disparate working memory biases, including short-term serial and contraction biases, may arise from a common mechanism via the interaction of multiple networks, each operating over a distinct timescale.

Comparison of feature encoding and top-down cognitive signals gives insight into the role of parietal cortex in mediating decision making.

Quantitative modeling of inactivations shows the prefrontal cortex (but not parietal cortex) of the rat is obligatory for decisions guided by evidence accumulating longer than 240 ms.

Posterior parietal cortex encodes errors in a task-dependent manner while a large array of frontal regions predict subsequent behavioral changes in response to ambiguous errors.

When mice use vision to choose their trajectories, a large fraction of parietal cortex activity can be precisely predicted from navigational attributes such as spatial position and heading.

Laminar fMRI reveals that adaptive processing involves recurrent processing within visual cortex and top-down influences from posterior parietal cortex via feedback.