Selection history in context: Evidence for the role of reinforcement learning in biasing attention

Abstract

Attention is biased towards learned predictors of reward. The influence of reward history on attentional capture has been shown to be context-specific: When particular stimulus features are associated with reward, these features only capture attention when viewed in the context in which they were rewarded. Selection history can also bias attention, such that prior target features gain priority independently of reward history. The contextual specificity of this influence of selection history on attention has not been examined. In the present study, we demonstrate that the consequences of repetitive selection on attention robustly generalize across context, such that prior target features capture attention even in contexts in which they were never seen previously. Our findings suggest that the learning underlying attention driven by outcome-independent selection history differs qualitatively from the learning underlying value-driven attention, consistent with a distinction between associative and reinforcement learning mechanisms.

Keywords: Associative learning; Attentional capture; Contextual cuing; Selection history; Selective attention.

Figure 1.

Sequence and time course of trial events. (A) The target was defined as a red-or-green circle, and participants reported the identity of the line segment inside of the target (vertical or horizontal) with a key press. Each target color was only ever presented against one of the two background scenes (forest or city street). (B) During the test phase, the target was defined as the unique shape. On half of the trials, one of the non-target items—the distractor—was rendered in the color of a former target from training, presented equally-often on each background scene.

Figure 2.

Behavioral data. (A) Response time by training session. (B) Accuracy by training session. (C) Response time by distractor condition in the test phase. Error bars reflect the within-subjects SEM. **p<0.005 ***p<0.001