Abstract
Working memory (WM) allows observers to hold information in mind to eventually guide behavior. In some cases, information is stored in a sensory-like format so the details of an object can later be distinguished from similar items. In other cases, it is advantageous to use a more motor-like coding scheme, for example, instead of remembering the visual details of your route to work, you remember a series of motor commands. Here, we used fMRI and an inverted encoding model to compare feature representations under conditions encouraging sensory-like or motor-like strategies. Subjects remembered the orientation of a briefly (500ms) presented grating over a variable delay (2–8s). Next, a spinning dial was shown, rotating at a fixed speed for 3s. Subjects pressed a button when the dial matched the remembered orientation; no feedback was given. We manipulated the predictability of the dial starting position and rotation direction. On trials where both were known beforehand, subjects could plan their motor response as soon as the grating appeared, while on trials where one or both were unknown, subjects could not directly divert to a motor plan. We found that during the fully predictable condition, representations in primary motor cortex were poorly modeled by a sensory training set, but were robust when trained and tested within the memory task itself. The other three conditions showed the opposite effect, suggesting the neural code was less sensory-like when responses could be planned in advance. This effect developed gradually along the posterior-anterior axis of the brain. Additionally, when rotation direction was predictable, both neural reconstructions and behavioral responses were biased away from the expected rotation direction, suggesting that subjects were attempting to compensate for their response latency. Overall, these findings demonstrate that the brain can employ multiple complementary strategies for encoding information that are not mutually exclusive.
Acknowledgement: Predoctoral Fellowship from Institute for Neural Computation, UC San Diego