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Guidance of spatial attention during associative learning: Contributions of predictability and intention to learn
Author(s) -
Do Carmo Blanco Noelia,
Allen John J. B.
Publication year - 2018
Publication title -
psychophysiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.661
H-Index - 156
eISSN - 1469-8986
pISSN - 0048-5772
DOI - 10.1111/psyp.13077
Subject(s) - associative learning , psychology , n2pc , predictability , associative property , covert , association (psychology) , cognitive psychology , outcome (game theory) , cognition , visual attention , neuroscience , linguistics , philosophy , physics , mathematics , mathematical economics , quantum mechanics , pure mathematics , psychotherapist
Expectations of an event can facilitate its neural processing. One of the ways we build expectations is through associative learning. Interestingly, the learning of contingencies between events can also occur without intention. Here, we study feature‐based attention during associative learning, by asking how a learned association between a cue and a target outcome impacts the attention allocated to this outcome. Moreover, we investigate attention in learning depending on the intention to learn the association. We used an associative learning paradigm where we manipulated outcome predictability and intention to learn an association within streams of cue‐target outcome visual stimuli, while stimulus characteristics and probability were held constant. In order to measure the event‐related component N2pc, widely recognized to reflect allocation of spatial attention, every outcome was embedded among distractors. Importantly, the location of the target outcome could not be anticipated. We found that predictable target outcomes showed an increased spatial attention as indexed by a greater N2pc component. A later component, the P300, was sensitive to the intention to learn the association between the cue and the target outcome. The current study confirms the remarkable ability of the brain to extract and update predictive information, in accordance with a predictive‐coding model of brain function. Associative learning can guide a visual search and shape covert attentional selection in our rich environments.