Abstract
Abstract
Because our environment is not random, it is beneficial to assimilate the statistics of sensory impressions and improve performance, such as visual search for a target object in a cluttered array of nontarget objects (contextual cueing [CC] effect). Computational models of CC have so far focused on predicting the target location from a particular configuration of nontarget items. This contrasts with recent findings according to which display repetitions train human participants’ general procedures for the search task. Here, we test the latter idea by employing a connectionist model of visual search that exclusively learns a search procedure without acquiring any individual display-layout information. We show that an instance of a “learning how” mechanism not only proposes a viable alternative account to existing “learning that” mechanisms, but also generates more plausible key behavioral metrics and exhibits a central bias as an emergent phenomenon of learning-induced plasticity. These findings have implications for models of visual search and artificial intelligence: Learning a procedure from leveraging a task’s structure alone can mimic the effects of top-down modulation of attention while also reducing the need for supervision in learning, thereby making computational models that leverage procedural learning behaviorally more plausible and easier to train.