Scene Buildup From Latent Memory Representations Across Eye Movements

An unresolved problem in eye movement research is how a representation is constructed on-line from several consecutive fixations of a scene. Such a scene representation is generally understood to be sparse; yet, for meeting behavioral goals a certain level of detail is needed. We propose that this is achieved through the buildup of latent representations acquired at fixation. Latent representations are retained in an activity-silent manner, require minimal energy expenditure for their maintenance, and thus allow a larger storage capacity than traditional, activation based, visual working memory. The latent representations accumulate and interact in working memory to form to the scene representation. The result is rich in detail while sparse in the sense that it is restricted to the task-relevant aspects of the scene sampled through fixations. Relevant information can quickly and flexibly be retrieved by dynamical attentional prioritization. Latent representations are observable as transient functional connectivity patterns, which emerge due to short-term changes in synaptic weights. We discuss how observing latent representations could benefit from recent methodological developments in EEG-eye movement co-registration.