Working memory provides flexible storage of information in service of upcoming behavioral goals. Some models propose specific fixed loci and mechanisms for the storage of visual information in working memory, such as sustained spiking in parietal and prefrontal cortex during working memory maintenance. An alternative view is that information can be remembered in a flexible format that best suits current behavioral goals. For example, remembered visual information might be stored in sensory areas for easier comparison to future sensory inputs, or might be re-coded into a more abstract action-oriented format and stored in motor areas. Here, we tested this hypothesis using a visuo-spatial working memory task where the required behavioral response was either known or unknown during the memory delay period. Using functional magnetic resonance imaging (fMRI) and multivariate decoding, we found that there was less information about remembered spatial position in early visual and parietal regions when the required response was known versus unknown. Furthermore, a representation of the planned motor action emerged in primary somatosensory, primary motor, and premotor cortex during the same task condition where spatial information was reduced in early visual cortex. These results suggest that the neural networks supporting working memory can be strategically reconfigured depending on specific behavioral requirements during a canonical visual working memory paradigm.

Visual cortex contains regions of selectivity for domains of ecological importance. Food is an evolutionarily critical category whose visual heterogeneity may make the identification of selectivity more challenging. We investigate neural responsiveness to food using natural images combined with large-scale human fMRI. Leveraging the improved sensitivity of modern designs and statistical analyses, we identify two food-selective regions in the ventral visual cortex. Our results are robust across 8 subjects from the Natural Scenes Dataset (NSD), multiple independent image sets and multiple analysis methods. We then test our findings of food selectivity in an fMRI “localizer” using grayscale food images. These independent results confirm the existence of food selectivity in ventral visual cortex and help illuminate why earlier studies may have failed to do so. Our identification of food-selective regions stands alongside prior findings of functional selectivity and adds to our understanding of the organization of knowledge within the human visual system.

Navigating through natural environments requires localizing objects along three distinct spatial axes. Information about position along the horizontal and vertical axes is available from an object’s position on the retina, while position along the depth axis must be inferred based on second-order cues such as the disparity between the images cast on the two retinae. Past work has revealed that object position in two-dimensional (2D) retinotopic space is robustly represented in visual cortex and can be robustly predicted using a multivariate encoding model, in which an explicit axis is modeled for each spatial dimension. However, no study to date has used an encoding model to estimate a representation of stimulus position in depth. Here, we recorded BOLD fMRI while human subjects viewed a stereoscopic random-dot sphere at various positions along the depth ( z ) and the horizontal ( x ) axes, and the stimuli were presented across a wider range of disparities (out to ∼40 arcmin) compared to previous neuroimaging studies. In addition to performing decoding analyses for comparison to previous work, we built encoding models for depth position and for horizontal position, allowing us to directly compare encoding between these dimensions. Our results validate this method of recovering depth representations from retinotopic cortex. Furthermore, we find convergent evidence that depth is encoded most strongly in dorsal area V3A.