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Aberrant reward functioning is implicated in depression. While attention precedes behavior and guides higher-order cognitive processes, reward learning from an attentional perspective – the effects of prior reward-learning on subsequent attention allocation – has been mainly overlooked.
Methods
The present study explored the effects of reward-based attentional learning in depression using two separate, yet complimentary, studies. In study 1, participants with high (HD) and low (LD) levels of depression symptoms were trained to divert their gaze toward one type of stimuli over another using a novel gaze-contingent music reward paradigm – music played when fixating the desired stimulus type and stopped when gazing the alternate one. Attention allocation was assessed before, during, and following training. In study 2, using negative reinforcement, the same attention allocation pattern was trained while substituting the appetitive music reward for gazing the desired stimulus type with the removal of an aversive sound (i.e. white noise).
Results
In study 1 both groups showed the intended shift in attention allocation during training (online reward learning), while generalization of learning at post-training was only evident among LD participants. Conversely, in study 2 both groups showed post-training generalization. Results were maintained when introducing anxiety as a covariate, and when using a more powerful sensitivity analysis. Finally, HD participants showed higher learning speed than LD participants during initial online learning, but only when using negative, not positive, reinforcement.
Conclusions
Deficient generalization of learning characterizes the attentional system of HD individuals, but only when using reward-based positive reinforcement, not negative reinforcement.
In this Element, a framework is proposed in which it is assumed that visual selection is the result of the interaction between top-down, bottom-up and selection-history factors. The Element discusses top-down attentional engagement and suppression, bottom-up selection by abrupt onsets and static singletons as well as lingering biases due to selection-history entailing priming, reward and statistical learning. We present an integrated framework in which biased competition among these three factors drives attention in a winner-take-all-fashion. We speculate which brain areas are likely to be involved and how signals representing these three factors feed into the priority map which ultimately determines selection.
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