@article {Kumano12192, author = {Hironori Kumano and Yuki Suda and Takanori Uka}, title = {Context-Dependent Accumulation of Sensory Evidence in the Parietal Cortex Underlies Flexible Task Switching}, volume = {36}, number = {48}, pages = {12192--12202}, year = {2016}, doi = {10.1523/JNEUROSCI.1693-16.2016}, publisher = {Society for Neuroscience}, abstract = {Switching behavior based on multiple rules is a fundamental ability of flexible behavior. Although interactions among the frontal, parietal, and sensory cortices are necessary for such flexibility, little is known about the neural computations concerning context-dependent information readouts. Here, we provide evidence that neurons in the lateral intraparietal area (LIP) accumulate relevant information preferentially depending on context. We trained monkeys to switch between direction and depth discrimination tasks and analyzed the buildup activity in the LIP depending on task context. In accordance with behavior, the rate of buildup to identical visual stimuli differed between tasks and buildup was prominent only for the stimulus dimension relevant to the task. These results indicate that LIP neurons accumulate relevant information depending on context to decide flexibly where to move the eye, suggesting that flexibility is, at least partly, implemented in the form of temporal integration gain control.SIGNIFICANCE STATEMENT Flexible behavior depending on context is a hallmark of human cognition. During flexible behavior, the frontal and parietal cortices have complex representations that hinder efforts to conceptualize their underlying computations. We now provide evidence that neurons in the lateral intraparietal area accumulate relevant information preferentially depending on context. We suggest that behavioral flexibility is implemented in the form of temporal integration gain control in the parietal cortex.}, issn = {0270-6474}, URL = {https://www.jneurosci.org/content/36/48/12192}, eprint = {https://www.jneurosci.org/content/36/48/12192.full.pdf}, journal = {Journal of Neuroscience} }