TY - JOUR

T1 - Learned Predictiveness Models Predict Opposite Attention Biases in the Inverse Base-Rate Effect

AU - Don, Hilary J.

AU - Beesley, Tom

AU - Livesey, Evan J.

N1 - ©American Psychological Association, 2019. This paper is not the copy of record and may not exactly replicate the authoritative document published in the APA journal. Please do not copy or cite without author's permission. The final article is available, upon publication, at:10.1037/xan0000196

PY - 2019/4/1

Y1 - 2019/4/1

N2 - Several attention-based models of associative learning are built upon the learned predictiveness principle, whereby learning is optimized by attending to the most predictive features and ignoring the least predictive features. Despite their functional similarity, these models differ in their formal mechanisms and thus may produce very different predictions in some circumstances. As we demonstrate, this is particularly evident in the inverse base-rate effect. Using simulations with a modified Mackintosh model and the EXIT model, we found that models based on the learned predictiveness principle can account for rare-outcome choice biases associated with the inverse base-rate effect, despite making opposite predictions for relative attention to rare versus common predictors. The models also make different predictions regarding changes in attention across training, and effects of context associations on attention to cues. Using a human causal learning task, we replicated the inverse base-rate effect and a recently reported reduction in this effect when the context is not predictive of the common outcome and used eye-tracking to test model predictions about changes in attention both prior to making a decision, and during feedback. The results support the predictions made by EXIT, where the rare predictor commands greater attention than the common predictor throughout training. In addition, patterns of attention prior to making a decision differed to those during feedback, where effects of using a partially predictive context were evident only prior to making a prediction.

AB - Several attention-based models of associative learning are built upon the learned predictiveness principle, whereby learning is optimized by attending to the most predictive features and ignoring the least predictive features. Despite their functional similarity, these models differ in their formal mechanisms and thus may produce very different predictions in some circumstances. As we demonstrate, this is particularly evident in the inverse base-rate effect. Using simulations with a modified Mackintosh model and the EXIT model, we found that models based on the learned predictiveness principle can account for rare-outcome choice biases associated with the inverse base-rate effect, despite making opposite predictions for relative attention to rare versus common predictors. The models also make different predictions regarding changes in attention across training, and effects of context associations on attention to cues. Using a human causal learning task, we replicated the inverse base-rate effect and a recently reported reduction in this effect when the context is not predictive of the common outcome and used eye-tracking to test model predictions about changes in attention both prior to making a decision, and during feedback. The results support the predictions made by EXIT, where the rare predictor commands greater attention than the common predictor throughout training. In addition, patterns of attention prior to making a decision differed to those during feedback, where effects of using a partially predictive context were evident only prior to making a prediction.

KW - Attention

KW - EXIT

KW - Eye tracking

KW - Inverse base-rate effect

KW - Mackintosh

U2 - 10.1037/xan0000196

DO - 10.1037/xan0000196

M3 - Journal article

C2 - 30869934

AN - SCOPUS:85062999792

VL - 45

SP - 143

EP - 162

JO - Journal of Experimental Psychology: Animal Learning and Cognition

JF - Journal of Experimental Psychology: Animal Learning and Cognition

SN - 2329-8456

IS - 2

ER -