Attention and associative learning in humans: An integrative review.

Mike E Le Pelley,Chris J Mitchell,David N George,Andy J Wills,Tom Beesley

doi:10.1037/bul0000064

Abstract

This article presents a comprehensive survey of research concerning interactions between associative learning and attention in humans. Four main findings are described. First, attention is biased toward stimuli that predict their consequences reliably (learned predictiveness). This finding is consistent with the approach taken by Mackintosh (1975) in his attentional model of associative learning in nonhuman animals. Second, the strength of this attentional bias is modulated by the value of the outcome (learned value). That is, predictors of high-value outcomes receive especially high levels of attention. Third, the related but opposing idea that uncertainty may result in increased attention to stimuli (Pearce & Hall, 1980), receives less support. This suggests that hybrid models of associative learning, incorporating the mechanisms of both the Mackintosh and Pearce-Hall theories, may not be required to explain data from human participants. Rather, a simpler model, in which attention to stimuli is determined by how strongly they are associated with significant outcomes, goes a long way to account for the data on human attentional learning. The last main finding, and an exciting area for future research and theorizing, is that learned predictiveness and learned value modulate both deliberate attentional focus, and more automatic attentional capture. The automatic influence of learning on attention does not appear to fit the traditional view of attention as being either goal-directed or stimulus-driven. Rather, it suggests a new kind of “derived” attention.

Highlights

Background and Scope of the CurrentArticleThe suggestion that the influence of attention might usefully be studied in the context 3Human attentional learning of learning was alluded to by Pavlov (1927), who noted that presenting a novel stimulus to an animal often elicited DQ RULHQWLQJ UHVSRQVH ZKLFK KH WHUPHG DQ 3LQYHVWLJDWRU\ UHIOH[ ́ S 7KH LPSOLFDWLRQ ZDV WKDW WKLV RULHQWLQJ UHVSRQVH UHIOHFWHG WKH DQLPDO¶V DWWHQWLRQ WR WKH stimulus, with a larger orienting response reflecting greater attention to the stimulus
Most of this work has been in visual search and related tasks requiring allocation of spatial attention, and we focus on this area, though we will cover approaches based on nonspatial tasks
Human attentional learning preferentially allocated to cues that accurately predict their consequences), but relatively little support in human studies for the uncertainty principle

Summary

A Simpler Model

Attention Is Determined by Associative Strength it is straightforward to modify the approach described above so that it is betterequipped to account for both learned value and the predictiveness principle, even after extended training. The resulting model still accounts for most demonstrations of an attentional advantage for predictive over nonpredictive stimuli, because the predictive stimuli in these studies typically have greater associative strength ± since a predictive cue is consistently paired with the same outcome, it will develop a strong association with that outcome. In this alternative model, attention is a direct function of learned value. We turn to a final summary and concluding comments on the literature concerning associative learning and attention

Summary and Conclusions

Findings

Conclusion