Reward learning and statistical learning independently influence attentional priority of salient distractors in visual search

Existing research demonstrates different ways in which attentional prioritization of salient nontarget stimuli is shaped by prior experience: Reward learning renders signals of high-value outcomes more likely to capture attention than signals of low-value outcomes, whereas statistical learning can produce attentional suppression of the location in which salient distractor items are likely to appear. The current study combined manipulations of the value and location associated with salient distractors in visual search to investigate whether these different effects of selection history operate independently or interact to determine overall attentional prioritization of salient distractors. In Experiment 1, high-value and low-value distractors most frequently appeared in the same location; in Experiment 2, high-value and low-value distractors typically appeared in distinct locations. In both experiments, effects of distractor value and location were additive, suggesting that attention-promoting effects of value and attention-suppressing effects of statistical location-learning independently modulate overall attentional priority. Our findings are consistent with a view that sees attention as mediated by a common priority map that receives and integrates separate signals relating to physical salience and value, with signal suppression based on statistical learning determined by physical salience, but not incentive salience.

Risk-Promoting Effects of Reward-Paired Cues in Human Sign- and Goal-Trackers

The incentive sensitization theory of addiction proposes that through repeated associations with addictive rewards, addiction-related stimuli acquire a disproportionately powerful motivational pull on behaviour. Animal research suggests trait-like individual variation in the degree of incentive salience attribution to reward-predictive cues, defined phenotypically as sign-tracking (high) and goal-tracking (low incentive salience attribution). While these phenotypes have been linked to addiction features in rodents, their translational validity has been little studied. Here, we examined whether sign- and goal-tracking in healthy human volunteers modulates the effects of reward-paired cues on cost-benefit decision making. Sign-tracking was measured in a Pavlovian conditioning paradigm as the amount of eye gaze fixation on the reward-predictive cue versus the location of impending reward delivery. In Study 1 (Cherkasova et al, 2018), participants were randomly assigned to perform a two-choice lottery task in which rewards were either accompanied (cued, n=63) or unaccompanied (uncued, n=68) by money images and casino jingles. In Study 2, participants (n=58) performed cued and uncued versions of the task in a within-subjects design. Across both studies, cues promoted riskier choice, and both studies yielded evidence of goal-tracking being associated with greater risk-promoting effects of cues. These findings are at odds with the notion of sign-trackers being preferentially susceptible to the influence of reward cues on behavior and point to the role of mechanisms besides incentive salience in mediating such influences.

Computational approaches to predicting treatment response to obesity using neuroimaging

Obesity is a worldwide disease associated with multiple severe adverse consequences and comorbid conditions. While an increased body weight is the defining feature in obesity, etiologies, clinical phenotypes and treatment responses vary between patients. These variations can be observed within individual treatment options which comprise lifestyle interventions, pharmacological treatment, and bariatric surgery. Bariatric surgery can be regarded as the most effective treatment method. However, long-term weight regain is comparably frequent even for this treatment and its application is not without risk. A prognostic tool that would help predict the effectivity of the individual treatment methods in the long term would be essential in a personalized medicine approach. In line with this objective, an increasing number of studies have combined neuroimaging and computational modeling to predict treatment outcome in obesity. In our review, we begin by outlining the central nervous mechanisms measured with neuroimaging in these studies. The mechanisms are primarily related to reward-processing and include “incentive salience” and psychobehavioral control. We then present the diverse neuroimaging methods and computational prediction techniques applied. The studies included in this review provide consistent support for the importance of incentive salience and psychobehavioral control for treatment outcome in obesity. Nevertheless, further studies comprising larger sample sizes and rigorous validation processes are necessary to answer the question of whether or not the approach is sufficiently accurate for clinical real-world application.