Attention biases in the inverse base-rate effect persist into new learning

Abstract

The inverse base-rate effect is a tendency to predict the rarer of two outcomes when presented with cues that make conflicting predictions. Attention-based accounts of the effect appeal to prioritised attention to predictors of rare outcomes. Changes in the processing of these cues are predicted to increase the rate at which they are learned about in the future (i.e., their associability). Our previous work has shown that the development of the inverse base-rate effect is accompanied by greater overt attention to the rare predictor while participants made predictions, and during feedback, and these biases change in different ways depending on the stage of training and global base-rate differences. It is unknown whether these gaze patterns reflect the manner in which cues are prioritised for learning or are merely a consequence of learning what the cues predict. This study tested whether the associability of common and rare predictors differed, and if so, how this difference changed as a function of training length and the presence of base-rate differences in the outcomes. Experiment 1 tested cue associability using a second learning task presented after either short or long training. The results suggest an associability advantage for rare predictors that weakens with extended training and is not strongly affected by the presence of global base-rate differences. However, Experiment 2 showed a clear effect of base-rate differences on choice after very brief training, indicating that attention biases as measured by associability change are not sufficient to produce the inverse base-rate effect.

Hearing hooves, thinking zebras: A review of the inverse base-rate effect

“…That is, the more attention paid to a cue, the faster that cue will be learned about in future novel associations. In a recent study, we show greater associability for previously rare predictors (C) than previously common predictors (B; Don & Livesey, 2021). After baserate training, in a new learning phase, previously rare predictors and previously common predictors were presented in compound, and paired with a novel outcome.…”

“…The Mackintosh model has been applied extensively in animal and human learning literature, and has been critical in explaining related biases in learning, such as the learned predictiveness effect (e.g., Le Pelley et al, 2016;Le Pelley & McLaren, 2003;Lochmann & Wills, 2003). While the Mackintosh model can account for choice biases in the inverse base-rate effect, it fails to predict stronger attention to C on AC trials than to B on AB trials (Don et al, 2019a), as well as associability benefits for previously rare predictors (Don & Livesey, 2021).…”

“…The effect of context associations has been studied by equating global outcome frequency (Don & Livesey, 2017;Don et al, 2019a, Don & Livesey, 2021. In typical inverse base-rate designs with multiple instantiations and fewer outcomes than cue compounds, each outcome is always paired with either common compounds or rare compounds.…”