Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2016 Jan;26(1):1-11.
doi: 10.1093/cercor/bhu160. Epub 2014 Aug 19.

Cortical Brain Activity Reflecting Attentional Biasing Toward Reward-Predicting Cues Covaries With Economic Decision-Making Performance

Affiliations
Free PMC article

Cortical Brain Activity Reflecting Attentional Biasing Toward Reward-Predicting Cues Covaries With Economic Decision-Making Performance

René San Martín et al. Cereb Cortex. .
Free PMC article

Abstract

Adaptive choice behavior depends critically on identifying and learning from outcome-predicting cues. We hypothesized that attention may be preferentially directed toward certain outcome-predicting cues. We studied this possibility by analyzing event-related potential (ERP) responses in humans during a probabilistic decision-making task. Participants viewed pairs of outcome-predicting visual cues and then chose to wager either a small (i.e., loss-minimizing) or large (i.e., gain-maximizing) amount of money. The cues were bilaterally presented, which allowed us to extract the relative neural responses to each cue by using a contralateral-versus-ipsilateral ERP contrast. We found an early lateralized ERP response, whose features matched the attention-shift-related N2pc component and whose amplitude scaled with the learned reward-predicting value of the cues as predicted by an attention-for-reward model. Consistently, we found a double dissociation involving the N2pc. Across participants, gain-maximization positively correlated with the N2pc amplitude to the most reliable gain-predicting cue, suggesting an attentional bias toward such cues. Conversely, loss-minimization was negatively correlated with the N2pc amplitude to the most reliable loss-predicting cue, suggesting an attentional avoidance toward such stimuli. These results indicate that learned stimulus-reward associations can influence rapid attention allocation, and that differences in this process are associated with individual differences in economic decision-making performance.

Keywords: EEG; ERP; N2pc; learning; reward.

Figures

Figure 1.
Figure 1.
Experimental design. (A) In each trial, participants covertly attended to a bilaterally presented cue stimulus pair providing information about the probability of winning on that trial. They then chose between making a large bet (8 points) and a small bet (2 points) by pressing a button corresponding to the side of the screen containing their preference. Feedback was provided as a green box surrounding the wager amount if the participants won the bet and as a red box if the participant lost. (B) The stimulus pair to be presented on each trial was randomly selected from a set of 20 possible pairs that were formed from 5 different novel symbols. These cue symbols, labeled A, B, M, Y, and Z here, were each associated with a relative likelihood of gain versus loss. Each stimulus pair was thus associated with a total probability of winning [P(win)] for that trial, derived from the combination of the gain/loss probabilities of each of the individual cues, as annotated and color-coded in the figure [ranging from blue to red as P(win) decreases]. ERP responses and participants' behavior were independently evaluated for the most reliable gain-predicting cue (i.e., A), the neutral cue (i.e., M), and the most reliable loss-predicting cue (i.e., Z), based on trial-types circled in blue, gray and red, respectively, inside the matrix figure. This blue-gray-red color code is consistently used throughout the figures.
Figure 2.
Figure 2.
Models of attention toward outcome-predicting cues. Three proposed mechanisms by which stimuli might change in attentional salience as a consequence of their associated reward probability (modified from Gottlieb 2012). In this figure, the values on the x-axis (labeled “reward prediction”) correspond to the mean win-probability associated with cues A, M, and Z throughout the experimental session (represented by blue, gray and red dots, respectively). The y-axis represents predictions about the salience of A, M, and Z derived from each of these attentional models. (A) According to the attention for information model, attention would be prioritized toward stimuli that reliably predict upcoming outcomes, regardless of whether that outcome was likely to be positive or negative (i.e., cues A and Z). (B) According to the attention for uncertainty model, attention would be prioritized toward stimuli that are associated with high uncertainty about the upcoming outcomes (i.e., cue M). (C) According to the attention-for-reward model, attention would be prioritized towards stimuli that predict positive outcomes (i.e., A) and relatively shifted away from stimuli predicting negative outcomes (i.e., Z).
Figure 3.
Figure 3.
Choice behavior across time. Participants' bet choices distinguished between cues A, M, and Z early and consistently throughout the experimental session. Specifically, differences in choice behavior for A and Z were already significant during the first block of the session (shaded areas indicate SEM for each trace).
Figure 4.
Figure 4.
N2pc evoked to the 3 cue types A, M, and Z. (A) Visual ERP at channel PO7/PO8 for contralateral (dashed) and ipsilateral (solid) to the gain-predicting cue (A, in blue), the neutral cue (M, in gray), and the loss-predicting cue (Z, in red). (B) Contralateral minus ipsilateral subtraction, yielding the attention-sensitive N2pc component to the gain-predicting cue (A, in blue), the neutral cue (M, in gray), and the loss-predicting cue (Z, in red). Mean amplitudes were computed in the latency range shown by the dashed box. The scalp topography shows the distribution of the N2pc for cue type A in that latency range. Yellow dots show the positions of the PO7/PO8 electrode sites on the scalp.
Figure 5.
Figure 5.
N2pc responses are consistent with the attention-for-reward model in high decision-performance participants. Displayed are the scalp distribution and mean N2pc amplitudes for cues A, M, and Z in a group of high decision-performance participants (n = 15, decision-performance scores at least 0.5 SD above the mean) and a group of low decision-performance participants (n = 11, decision-performance scores at least 0.5 SD below the mean). In high performers, but not in low performers, the N2pc responses were consistent with the attention-for-reward model. (Error bars indicate SEM.)
Figure 6.
Figure 6.
Double dissociation between gain-maximization and loss-minimization. Across participants, gain-maximization scores correlated with the N2pc amplitude for cue A (the larger the N2pc for A, the better the gain-maximizing performance), but showed no significant association with the N2pc amplitude for cue Z. In contrast, loss-minimization scores were negatively correlated with the N2pc amplitude for cue Z (the larger the N2pc for Z, the worst the loss-minimizing performance), but showed no significant association with the N2pc amplitude for cue A. (Error bars indicate SEM.)

Similar articles

See all similar articles

Cited by 11 articles

See all "Cited by" articles

Publication types

Feedback