Attending Globally or Locally: Incidental Learning of Optimal Visual Attention Allocation

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Abstract

Attention allocation determines the information that is encoded into memory. Can participants learn to optimally allocate attention based on what types of information are most likely to change? The current study examined whether participants could incidentally learn that changes to either high spatial frequency (HSF) or low spatial frequency (LSF) Gabor patches were more probable and to use this incidentally learned probability information to bias attention during encoding. Participants detected changes in orientation in arrays of 6 Gabor patches: 3 HSF and 3 LSF. For half of the participants, an HSF patch changed orientation on 75% of the trials, and for the other half, an LSF patch changed orientation on 75% of the trials. Experiment 1 demonstrated a change probability effect and an attention allocation effect. Specifically, change detection performance was highest for the probable-change type, and participants learned to use a global spread of attention (fixating between Gabor patches) when LSF patches were most likely to change and to use a local allocation of attention (fixating directly on Gabor patches) when HSF patches were most likely to change. Experiments 2 and 3 replicated these effects and demonstrated that an internal monitoring system is sufficient for these effects. That is, the effects do not require explicit feedback or point rewards. This study demonstrates that incidental learning of probability information can affect the allocation of attention during encoding and can therefore affect what information is stored in visual working memory.

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