When we remember a stimulus we rarely maintain a full fidelity representation of the observed item. Our working memory instead maintains a mixture of the observed feature values and categorical/gist information. I will discuss evidence from computational models supporting a mix of categorical and fine-detail information in working memory. Having established the need for two memory formats in working memory, I will discuss whether categorical and fine-detailed information for a stimulus are represented separately or as a single unified representation. Computational models of these two potential cognitive structures make differing predictions about the pattern of responses in visual working memory recall tests. The present study required participants to remember the orientation of stimuli for later reproduction. The pattern of responses are used to test the competing representational structures and to quantify the relative amount of fine-detailed and categorical information maintained. The effects of set size, encoding time, serial order, and response order on memory precision, categorical information, and guessing rates are also explored. (This is a 60 min talk).

Visual knowledge obtained from our lifelong experience of the world plays a critical role in our ability to build short-term memories. We propose a mechanistic explanation of how working memory (WM) representations are built from the latent representations of visual knowledge and can then be reconstructed. The proposed model, Memory for Latent Representations (MLR), features a variational autoencoder with an architecture that corresponds broadly to the human visual system and an activation-based binding pool of neurons that binds items’ attributes to tokenized representations. The simulation results revealed that shape information for stimuli that the model was trained on, can be encoded and retrieved efficiently from latents in higher levels of the visual hierarchy. On the other hand, novel patterns that are completely outside the training set can be stored from a single exposure using only latents from early layers of the visual system. Moreover, the representation of a given stimulus can have multiple codes, representing specific visual features such as shape or color, in addition to categorical information. Finally, we validated our model by testing a series of predictions against behavioral results acquired from WM tasks. The model provides a compelling demonstration of visual knowledge yielding the formation of compact visual representation for efficient memory encoding.