Keeping track of promised rewards: Obesity predicts enhanced flexibility when learning from observation

Meemken, Marie-Theres; Kube, Jana; Wickner, Carolin; Horstmann, Annette

doi:10.1016/j.appet.2018.08.029

Cited by 19 publications

(15 citation statements)

References 28 publications

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“…This was present across action and valence conditions and accompanied by an increased model-derived learning rate in individuals with obesity. We previously also found evidence for an increased reversal learning performance in obese individuals under specific task-conditions (Meemken et al, 2018). However, our current results stand in contrast with several other previous studies, which found an impaired performance when learning to predict food (Zhang et al, 2014) and non-food reinforcement (Coppin et al, 2014;Kastner et al, 2017;Mathar et al, 2017;Kube et al, 2018).…”

Section: Discussioncontrasting

confidence: 99%

Enhanced Go and NoGo Learning in Individuals With Obesity

Kube

Wiencke

Hahn

et al. 2020

Front. Behav. Neurosci.

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Overeating in individuals with obesity is hypothesized to be partly caused by automatic action tendencies to food cues that have the potential to override goal-directed dietary restriction. Individuals with obesity are often characterized by alterations in the processing of such rewarding food, but also of non-food stimuli, and previous research has suggested a stronger impact on the execution of goal-directed actions in obesity. Here, we investigated whether Pavlovian cues can also corrupt the learning of new approach or withdrawal behavior in individuals with obesity. We employed a probabilistic Pavlovian-instrumental learning paradigm in which participants (29 normal-weight and 29 obese) learned to actively respond (Go learning) or withhold a response (NoGo learning) in order to gain monetary rewards or avoid losses. Participants were better at learning active approach responses (Go) in the light of anticipated rewards and at learning to withhold a response (NoGo) in the light of imminent punishments. Importantly, there was no evidence for a stronger corruption of instrumental learning in individuals with obesity. Instead, they showed better learning across conditions than normal-weight participants. Using a computational reinforcement learning model, we additionally found an increased learning rate in individuals with obesity. Previous studies have mostly reported a lower reinforcement learning performance in individuals with obesity. Our results contradict this and suggest that their performance is not universally impaired: Instead, while previous studies found reduced stimulus-value learning, individuals with obesity may show better action-value learning. Our findings highlight the need for a broader investigation of behavioral adaptation in obesity across different task designs and types of reinforcement learning.

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Section: Discussioncontrasting

confidence: 99%

Enhanced Go and NoGo Learning in Individuals With Obesity

Kube

Wiencke

Hahn

et al. 2020

Front. Behav. Neurosci.

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“…Garofalo and colleagues found that sign-tracking individuals were particularly susceptible to PIT in comparison to goal-trackers and that this effect increased with probability of reward delivery. This is especially interesting, as we found improved flexibility in people with obesity in a reversal learning task [62]. We argued that people with obesity exhibited an improved focus on the outcomes of each trial and were thus superior in keeping track of contingency changes.…”

Section: Discussionmentioning

confidence: 87%

Appetitive Pavlovian-to-Instrumental Transfer in Participants with Normal-Weight and Obesity

Meemken

Horstmann

2019

Nutrients

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Altered eating behavior due to modern, food-enriched environments has a share in the recent obesity upsurge, though the exact mechanisms remain unclear. This study aims to assess whether higher weight or weight gain are related to stronger effects of external cues on motivation-driven behavior. 51 people with and without obesity completed an appetitive Pavlovian-to-Instrumental Transfer (PIT) paradigm. During training, button presses as well as presentation of fractal images resulted in three palatable and one neutral taste outcome. In the subsequent test phase, outcome-specific and general behavioral bias of the positively associated fractal images on deliberate button press were tested under extinction. While all participants showed signs of specific transfer, general transfer was not elicited. Contrary to our expectations, there was no main effect of weight group on PIT magnitude. Participants with obesity exhibited higher scores in the Three-Factor Eating Questionnaire Disinhibition scale, replicating a very robust effect from previous literature. Individual Restraint scores were able to predict body-mass index (BMI) change after a three-year period. Our data indicate that PIT is an important player in how our environment influences the initiation of food intake, but its effects alone cannot explain differences in—or future development of—individual weight.

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“…Whereas the HCP neurocognitive test battery is extensive, some cognitive functions previously associated with BMI are not addressed in this test battery. For example, performance on the probabilistic learning task, a measurement of positive and negative outcome learning, was reported to be impaired in people with obesity . Furthermore, tasks measuring response inhibition, such as the stop signal task, go/no go task, and Stroop task, have been inversely associated with body weight status and eating behaviors associated with overeating but were not included in the HCP battery.…”

Section: Discussionmentioning

confidence: 99%

A Brief Neuropsychological Battery for Measuring Cognitive Functions Associated with Obesity

Dalenberg

2019

Obesity

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Objective Although ample evidence links obesity to cognitive dysfunction, the trajectory of cognitive change, the underlying mechanisms, and the involvement of related factors, such as metabolic disease and diet, remain unclear. To support further investigations of BMI and cognition, this study aimed to create a concise test battery to be used in future trials. Methods Twenty neurocognitive measures were regressed on BMI in the Human Connectome Project Healthy Young Adult S1200 data release by using linear mixed models and by adjusting for major confounders. Measures were then identified by using least absolute shrinkage and selection operator regression analysis to select tests most strongly associated with BMI. To guide further test selection, the explained variance for each variable was visualized in the final model. Results BMI was negatively associated with seven neurocognitive measures. Variable selection yielded a model that included years of education and, in order of model weight, delay discounting, the relational task, the Penn Progressive Matrices test, the oral reading recognition test, the Variable Short Penn Line Orientation test, and the Penn Word Memory test. Conclusions This research resulted in an approximate 40‐minute test battery for the BMI‐cognition relationship in young adults that can be used in trials investigating the interrelationship between obesity and cognitive performance.

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Keeping track of promised rewards: Obesity predicts enhanced flexibility when learning from observation

Cited by 19 publications

References 28 publications

Enhanced Go and NoGo Learning in Individuals With Obesity

Enhanced Go and NoGo Learning in Individuals With Obesity

Appetitive Pavlovian-to-Instrumental Transfer in Participants with Normal-Weight and Obesity

A Brief Neuropsychological Battery for Measuring Cognitive Functions Associated with Obesity

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