Search

Abstract: Visual marking (VM) refers to our ability to completely exclude old items from search when new stimuli are presented in our visual field. We examined whether this ability reflects an attentional scan of the old items, possibly allowing observers to apply inhibition of return or maintain a memory representation of already seen locations. In four experiments, we compared performance in two search conditions. In the double-search (DS) condition, we required participants to pay attention to a first set of items by having them search for a target within the set. Subsequently, they had to search a second set while the old items remained in the field. In the VM condition, the participants expected the target only to be in the second (new) set. Selection of new items in the DS condition was relatively poor and was always worse than would be expected if only the new stimuli had been searched. In contrast, selection of the new items in the VM condition was good and was equal to what would be expected if there had been an exclusive search of the new stimuli. These results were not altered when differences in Set 1 difficulty, task switching, and response generation were controlled for. We conclude that the mechanism of VM is distinct from mnemonic and/or serial inhibition-of-return processes as involved in search, although we also discuss possible links to more global and flexible inhibition-of-return processes not necessarily related to search.

The selective attention for identification model (SAIM) is an established model of selective visual attention. SAIM implements translation-invariant object recognition, in scenes with multiple objects, using the parallel distributed processing (PDP) paradigm. Here, we show that SAIM can be formulated as Bayesian inference. Crucially, SAIM uses excitatory feedback to combine top-down information (i.e. object knowledge) with bottom-up sensory information. By contrast, predictive coding implementations of Bayesian inference use inhibitory feedback. By formulating SAIM as a predictive coding scheme, we created a new version of SAIM that uses inhibitory feedback. Simulation studies showed that both types of architectures can reproduce the response time costs induced by multiple objects—as found in visual search experiments. However, due to the different nature of the feedback, the two SAIM schemes make distinct predictions about the motifs of microcircuits mediating the effects of top-down afferents. We discuss empirical (neuroimaging) methods to test the predictions of the two inference architectures.