Loss of control over eating : A systematic review of task based research into impulsive and compulsive processes in binge eating

Recurring episodes of excessive food intake in binge eating disorder can be understood through the lens of behavioral control systems: patients repeat maladaptive behaviors against their explicit intent. Self-report measures show enhanced impulsivity and compulsivity in binge eating (BE) but are agnostic as to the processes that might lead to impulsive and compulsive behavior in the moment. Task-based neurocognitive investigations can tap into those processes. In this systematic review, we synthesize neurocognitive research on behavioral impulsivity and compulsivity in BE in humans and animals, published between 2010-2020. Findings on impulsivity are heterogeneous. Findings on compulsivity are sparse but comparatively consistent, indicating an imbalance of goal-directed and habitual control as well as deficits in reversal learning. We urge researchers to address heterogeneity related to mood states and the temporal dynamics of symptoms, to systematically differentiate contributions of body weight and BE, and to ascertain the validity and reliability of tasks. Moreover, we propose to further scrutinize the compulsivity findings to unravel the computational mechanisms of a potential reinforcement learning deficit.Peer reviewe

Lost in Translation? On the Need for Convergence in Animal and Human Studies on the Role of Dopamine in Diet-Induced Obesity

Purpose of Review: Animal and human studies suggest that diet-induced obesity and plasticity in the central dopaminergic system are linked. However, it is unclear whether observed changes depend on diet or obesity, and whether they are specific to brain regions and cognitive functions. Here, we focus on neural and cognitive changes in frontostriatal circuits. Recent Findings: Both diet and obesity affect dopaminergic transmission. However, site and direction of effects are inconsistent across species and studies. Non-specific changes are observed spanning all frontostriatal loops, from sensory input to motivated behaviour. Given the impact of peripheral signals on central dopaminergic signalling and the interaction between the frontostriatal loops, modulation of dopamine likely propagates through all loops and, thus, affects behaviour on various levels of complexity. Summary: To improve convergence between animal and human studies on diet-induced obesity, animal studies should include sophisticated cognitive measures and diets resembling human obesogenic diets, and human studies should adopt diet interventions and longitudinal designs.Peer reviewe

Measuring self-regulation in everyday life: reliability and validity of smartphone-based experiments in alcohol use disorder

Self-regulation, the ability to guide behavior according to one’s goals, plays an integral role in understanding loss of control over unwanted behaviors, for example in alcohol use disorder (AUD). Yet, experimental tasks that measure processes underlying self-regulation are not easy to deploy in contexts where such behaviors usually occur, namely outside the laboratory, and in clinical populations such as people with AUD. Moreover, lab-based tasks have been criticized for poor test–retest reliability and lack of construct validity. Smartphones can be used to deploy tasks in the field, but often require shorter versions of tasks, which may further decrease reliability. Here, we show that combining smartphone-based tasks with joint hierarchical modeling of longitudinal data can overcome at least some of these shortcomings. We test four short smartphone-based tasks outside the laboratory in a large sample (N = 488) of participants with AUD. Although task measures indeed have low reliability when data are analyzed traditionally by modeling each session separately, joint modeling of longitudinal data increases reliability to good and oftentimes excellent levels. We next test the measures’ construct validity and show that extracted latent factors are indeed in line with theoretical accounts of cognitive control and decision-making. Finally, we demonstrate that a resulting cognitive control factor relates to a real-life measure of drinking behavior and yields stronger correlations than single measures based on traditional analyses. Our findings demonstrate how short, smartphone-based task measures, when analyzed with joint hierarchical modeling and latent factor analysis, can overcome frequently reported shortcomings of experimental tasks

Measuring self-regulation in everyday life: Reliability and validity of smartphone-based experiments in alcohol use disorder

Self-regulation, the ability to guide behavior according to one's goals, plays an integral role in understanding loss of control over unwanted behaviors, for example in alcohol use disorder (AUD). Yet, experimental tasks that measure processes underlying self-regulation are not easy to deploy in contexts where such behaviors usually occur, namely outside the laboratory, and in clinical populations such as people with AUD. Moreover, lab-based tasks have been criticized for poor test-retest reliability and lack of construct validity. Smartphones can be used to deploy tasks in the field, but often require shorter versions of tasks, which may further decrease reliability. Here, we show that combining smartphone-based tasks with joint hierarchical modeling of longitudinal data can overcome at least some of these shortcomings. We test four short smartphone-based tasks outside the laboratory in a large sample (N = 488) of participants with AUD. Although task measures indeed have low reliability when data are analyzed traditionally by modeling each session separately, joint modeling of longitudinal data increases reliability to good and oftentimes excellent levels. We next test the measures' construct validity and show that extracted latent factors are indeed in line with theoretical accounts of cognitive control and decision-making. Finally, we demonstrate that a resulting cognitive control factor relates to a real-life measure of drinking behavior and yields stronger correlations than single measures based on traditional analyses. Our findings demonstrate how short, smartphone-based task measures, when analyzed with joint hierarchical modeling and latent factor analysis, can overcome frequently reported shortcomings of experimental tasks

Sufficient Reliability of the Behavioral and Computational Read-Outs of a Probabilistic Reversal Learning Task

Task-based measures that capture neurocognitive processes can help bridge the gap between brain and behavior. To transfer tasks to clinical application, reliability is a crucial benchmark because it poses an upper bound to potential correlations with other variables (e.g., symptom or brain data). However, the reliability of many task-readouts is low. In this study, we scrutinized the re-test reliability of a probabilistic reversal learning task (PRLT) that is frequently used to characterize cognitive flexibility in psychiatric populations. We analyzed data from N=40 healthy subjects, who completed the PRLT twice. We focused on how individual metrics are derived, i.e., whether data was partially pooled across participants and whether priors were used to inform estimates. We compared the reliability of the resulting indices across sessions, as well as the internal consistency of a selection of indices. We find good to excellent reliability for behavioral indices as derived from mixed-effects models that include data from both sessions. The internal consistency was good to excellent. For indices derived from computational modelling, we find excellent reliability when using hierarchical estimation with empirical priors and including data from both sessions. Our results indicate that the PRLT is well equipped to measure individual differences of cognitive flexibility in reinforcement learning. However, this depends heavily on hierarchical modelling of the longitudinal data (whether sessions are modelled separately or jointly), on estimation methods, and the combination of parameters included in computational models. We discuss implications for the applicability of PRLT indices in psychiatric research and as diagnostic tools

Biased or noisy? Motivational biases and decision noise across development

Learning and decision-making undergo substantial developmental changes. In adolescents, both specific developmental changes of choice behavior, such as in motivational choice bias, and generally higher levels of decision noise have been observed. However, it remains unknown whether these two observations are independent or related. A not yet investigated possibility is that the specific development of motivational choice bias might depend on decision noise. We examined 93 participants (12 – 42 years) with a motivational Go/NoGo task, assessing ‘Pavlovian’ choice bias and disentangling it from instrumental learning biases. Participants performed two more reinforcement learning (RL) tasks to test cross-task generalization of computational parameters such as decision noise. Using mixed-effects models, we find an age-related increase in Pavlovian choice bias while instrumental learning bias did not change with age. Implementing a novel adaptation of a computational RL model with outcome-specific noise parameters (‘feedback sensitivity’) showed increases in Pavlovian choice bias and sensitivity for positive feedback with age. Beyond these within-task developmental age effects, noise levels are, firstly, strongly correlated across RL tasks and, secondly, mediate age dependent performance gain and more sophisticated RL processes, i.e., model-based control over choices. Taken together, our findings provide novel insights into the computational processes underlying developmental changes in decision-making: namely a vital role of seemingly unspecific changes in noise in the specific development of more complex learning and choice components. Studying the neurocomputational mechanisms of how varying levels of noise impact distinct aspects of learning and decision processes may also be key to better understand the developmental onset of psychiatric diseases

Diminished reinforcement sensitivity in adolescence is associated with enhanced response switching and reduced coding of choice probability in the medial frontal pole

Precisely charting the maturation of core neurocognitive functions such as reinforcement learning (RL) and flexible adaptation to changing action-outcome contingencies is key for developmental neuroscience and adjacent fields like developmental psychiatry. However, research in this area is both sparse and conflicted, especially regarding potentially asymmetric development of learning for different motives (obtain wins vs avoid losses) and learning from valenced feedback (positive vs negative). In the current study, we investigated the development of RL from adolescence to adulthood, using a probabilistic reversal learning task modified to experimentally separate motivational context and feedback valence, in a sample of 95 healthy participants between 12 and 45. We show that adolescence is characterized by enhanced novelty seeking and response shifting especially after negative feedback, which leads to poorer returns when reward contingencies are stable. Computationally, this is accounted for by reduced impact of positive feedback on behavior. We also show, using fMRI, that activity of the medial frontopolar cortex reflecting choice probability is attenuated in adolescence. We argue that this can be interpreted as reflecting diminished confidence in upcoming choices. Interestingly, we find no age-related differences between learning in win and loss contexts.Peer reviewe