Training attention improves decision making in individuals with elevated self-reported depressive symptoms

Abstract

Depression is often characterized by attentional biases toward negative items and away from positive items, which likely affects reward and punishment processing. Recent work has reported that training attention away from negative stimuli reduced this bias and reduced depressive symptoms. However, the effect of attention training on subsequent learning has yet to be explored. In the present study, participants were required to learn to maximize reward during decision making. Undergraduates with elevated self-reported depressive symptoms received attention training toward positive stimuli prior to performing the decision-making task (n = 20; active training). The active-training group was compared to two other groups: undergraduates with elevated self-reported depressive symptoms who received placebo training (n = 22; placebo training) and a control group with low levels of depressive symptoms (n = 33; nondepressive control). The placebo-training depressive group performed worse and switched between options more than did the nondepressive controls on the reward maximization task. However, depressives that received active training performed as well as the nondepressive controls. Computational modeling indicated that the placebo-trained group learned more from negative than from positive prediction errors, leading to more frequent switching. The nondepressive control and active-training depressive groups showed similar learning from positive and negative prediction errors, leading to less-frequent switching and better performance. Our results indicate that individuals with elevated depressive symptoms are impaired at reward maximization, but that the deficit can be improved with attention training toward positive stimuli.

Figure 1

a) Sample screen shot from the training task. In the placebo training condition the positive and neutral words each preceded the dot-probe with equal probability. In the active training condition the positive word preceded the dot-probe on 85% of trials. b) Sample screen shot from the decision-making task. Participants were told that they were testing two oxygen extraction systems. The oxygen extracted on each trial was shown in the “Current” tank then transferred to the “Cumulative” tank before the next trial began.

Figure 2

Decision-making task reward structure. Depending on their selection, participants received the reward corresponding to Option A or Option B. Option A gave a mean reward of 55 points, while option B gave a mean reward of 65 points.

Figure 3

a) Proportion of optimal selections for the decision-making task. Error bars represent standard error of the mean. b) Proportion of optimal selections for each group averaged across 1000 simulations of the extended RL model. Error bars represent standard errorof the mean.

Figure 4

a) Average number of times that participants in each group switched between reward options across 150 trials. Error bars represent standard error of the mean. b) Average number switches between reward options based on 1000 simulations for each group using the extended RL model. Error bars represent standard error of the mean.

Figure 5

Akaike weights compare goodness of fit for the baseline model, Softmax RL model, and extended RL model. Higher Akaike weights indicate better fit. Error bars represent standard errorof the mean.

Figure 6

Learning rate parameter values for positive and negative reward prediction errors. Error bars represent standard error of the mean.