Attention, predictive learning, and the inverse base-rate effect: Evidence from event-related potentials

Abstract

We report the first electrophysiological investigation of the inverse base-rate effect (IBRE), a robust non-rational bias in predictive learning. In the IBRE, participants learn that one pair of symptoms (AB) predicts a frequently occurring disease, whilst an overlapping pair of symptoms (AC) predicts a rarely occurring disease. Participants subsequently infer that BC predicts the rare disease, a non-rational decision made in opposition to the underlying base rates of the two diseases. Error-driven attention theories of learning state that the IBRE occurs because C attracts more attention than B. On the basis of this account we predicted and observed the occurrence of brain potentials associated with visual attention: a posterior Selection Negativity, and a concurrent anterior Selection Positivity, for C vs. B in a post-training test phase. Error-driven attention theories further predict no Selection Negativity, Selection Positivity or IBRE, for control symptoms matched on frequency to B and C, but for which there was no shared symptom (A) during training. These predictions were also confirmed, and this confirmation discounts alternative explanations of the IBRE based on the relative novelty of B and C. Further, we observed higher response accuracy for B alone than for C alone; this dissociation of response accuracy (B>C) from attentional allocation (C>B) discounts the possibility that the observed attentional difference was caused by the difference in response accuracy.

Selection history alters attentional filter settings persistently and beyond top‐down control

“…Results showed that attention in the search task was strongly biased toward stimuli that observers experienced as relevant for the categorization task, although participants had the opportunity to prepare for short-term suppression of such stimuli (Experiment 1 and 2) or to fully adjust their attentional control settings altogether (Experiment 3). The findings are well in line with other studies reporting a selection bias toward stimuli with high predictive value (Feldmann-W€ ustefeld et al, 2015;Le Pelley, Beesley, & Griffiths, 2011;Le Pelley, Calvini, & Spears, 2013;Lucke, Lachnit, Koenig, & Uengoer, 2013;Mitchell, Griffiths, Seetoo, & Lovibond, 2012;Wills, Lavric, Croft, & Hodgson, 2007;Wills, Lavric, Hemmings, & Surrey, 2014), and further emphasize the role of prior experience in guiding attention. The present study shows that selection history can bias initial attention deployment very persistently, even when this is detrimental to the task performance and cannot be overruled by top-down attentional control, even under ideal preparation conditions.…”

On the formation of context-based person impressions

“…They found that attentional preference for the perfect predictors of rare outcomes predicted stronger inverse base-rate effects. Another inverse base-rate study also demonstrated an attentional preference for rare associations as measured via ERP correlates of selective attention (Wills, Lavric, Hemmings, & Surrey, 2014). Another study that was very similar to the present ones, but showing the inverse baserate effect in the formation of group impressions (i.e., stereotypes), showed that participants paid more attention to rare rather than common behaviors when learning about minority group members (Sherman et al, 2009).…”

Goal‐directed EEG activity evoked by discriminative stimuli in reinforcement learning

“…The selection of time windows for ERP analysis followed two steps. First we identified the components of interest from previous literature analyzing the ERPs elicited by predictive visual stimuli presented in a nonlateralized fashion: N1, SN, P2, SRP, and P3 (Baker & Holroyd, 2009;Dunning & Hajcak, 2007;Holroyd et al 2011;Liao, Gramann, Feng, Deak, & Li, 2011;Schevernels et al, 2014;Wills et al, 2007Wills et al, , 2014. Then, we identified these components from inspection of ERP waveforms and topographical maps.…”

“…For example, people spend longer looking at predictive stimuli than nonpredictive stimuli (Le Pelley, Beesley, & Griffiths, 2011;Wills, Lavric, Croft, & Hodgson, 2007), people are faster to respond to events occurring in the location of predictive stimuli (Le Pelley, Vadillo, & Luque, 2013), and people are faster to learn new information about predictive stimuli (Le Pelley & McLaren, 2003). Notably, using EEG, Wills et al (2007; see also Wills, Lavric, Hemmings, & Surrey, 2014) found that the magnitude of the anterior N1 and selection negativity (SN) ERP component-which has previously been characterized as an index of visual attentional processes (Hillyard & Anllo-Vento, 1998)-differed between predictive and nonpredictive stimuli. However, in all of these prior studies, the relationship between S and R was confounded with the relationship between S and O.…”