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We discuss opportunities in applying the resource-rationality framework toward answering questions in emotion and mental health research. These opportunities rely on characterization of individual differences in cognitive strategies; an endeavor that may be at odds with the normative approach outlined in the target article. We consider ways individual differences might enter the framework and the translational opportunities offered by each.

BackgroundMajor depressive disorder (MDD) is a highly heterogeneous condition in terms of symptom presentation and, likely, underlying pathophysiology. Accordingly, it is possible that only certain individuals with MDD are well-suited to antidepressants. A potentially fruitful approach to parsing this heterogeneity is to focus on promising endophenotypes of depression, such as neuroticism, anhedonia, and cognitive control deficits.MethodsWithin an 8-week multisite trial of sertraline v. placebo for depressed adults (n = 216), we examined whether the combination of machine learning with a Personalized Advantage Index (PAI) can generate individualized treatment recommendations on the basis of endophenotype profiles coupled with clinical and demographic characteristics.ResultsFive pre-treatment variables moderated treatment response. Higher depression severity and neuroticism, older age, less impairment in cognitive control, and being employed were each associated with better outcomes to sertraline than placebo. Across 1000 iterations of a 10-fold cross-validation, the PAI model predicted that 31% of the sample would exhibit a clinically meaningful advantage [post-treatment Hamilton Rating Scale for Depression (HRSD) difference ⩾3] with sertraline relative to placebo. Although there were no overall outcome differences between treatment groups (d = 0.15), those identified as optimally suited to sertraline at pre-treatment had better week 8 HRSD scores if randomized to sertraline (10.7) than placebo (14.7) (d = 0.58).ConclusionsA subset of MDD patients optimally suited to sertraline can be identified on the basis of pre-treatment characteristics. This model must be tested prospectively before it can be used to inform treatment selection. However, findings demonstrate the potential to improve individual outcomes through algorithm-guided treatment recommendations.

The mesolimbic dopaminergic system has been implicated in two kinds of reward processing, one in reinforcement learning (e.g prediction error) and another in incentive salience attribution (e.g. cue-reactivity). Both functions have been implicated in alcohol dependence with the former contributing to the persistence of chronic alcohol intake despite severe negative consequences and the latter playing a crucial role in cue-induced craving and relapse. The bicentric study “Learning in alcohol dependence (LeAD)” aims to bridge a gap between these processes by investigating reinforcement learning mechanisms and the influence that Pavlovian cues exert over behavior. We here demonstrate that alcohol dependent subjects show alterations in goal-directed, model-based reinforcement learning (Sebold et al., 2014) and demonstrate that prospective relapsing patients show reductions in the medial prefrontal cortex activation during goal-directed control. Moreover we show that in alcohol dependent patients compared to healthy controls, Pavlovian cues exert pronounced control over behavior (Garbusow et al., 2016). Again, prospective relapsing patients showed increased Nucleus accumbens activation during these cue-induced responses. These findings point to an important role of the mesolimbic dopaminergic system as a predictor of treatment outcome in alcohol dependence.Disclosure of interestThe authors have not supplied their declaration of competing interest.

IntroductionFluid intelligence expresses the capacity for interpretation of novel stimuli and flexible behavioral adaptation to such cues. Phasic dopamine firing closely matches a temporal difference prediction error (PE) signal important for learning and rapid behavioral adaptation. Both fluid intelligence and dopaminergic neurotransmission decline with age. So far, no study investigated the relationship between fluid IQ, PE signal and direct measures of dopaminergic neurotransmission. Here we used a multimodal imaging approach that combines positron emission tomography and functional magnetic resonance imaging.MethodsA group of healthy controls was investigated with both 6-[18F]FluoroDOPA PET and functional MRI with a probabilistic reversal task. The task required a constant behavioral adaptation to changes in reward contingencies, while choosing between two abstract stimuli. A reinforcement learning algorithm was used to compute a trial-by-trial prediction error, which was the used as a regressor in the fMRI data analysis with SPM8.ResultsThe prediction error signal was associated with functional activation in the basal ganglia including the ventral striatum and putamen. Fluid intelligence was associated with the PE signal in the ventral striatum, which correlated with age-related changes in dopamine synthesis capacity in the prefrontal cortex.ConclusionThese findings provide insight into the role of age-related changes in dopaminergic neurotransmission on behavioral adaptation. The multimodal imaging approach allows the characterization of interactions between dopamine metabolism and learning-related neuronal activation and may thus be a useful tool to clarify mechanisms underlying learning and plasticity in old age, which are crucial to our understanding of successful aging.