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<b><i>Background:</i></b> The Dietary Fat and Free Sugar-Short Questionnaire (DFS) is a cost- and time-efficient self-report screening instrument to estimate dietary intake of saturated fat and free sugar. To date, only the English version has been psychometrically evaluated. We assessed the psychometric characteristics of the German version of the DFS in individuals with normal weight, overweight, and obesity. <b><i>Method:</i></b> Sixty-five adult participants completed a German translation of the DFS and a validated food frequency questionnaire (FFQ). We correlated participants’ percentage of energy intake from saturated fat and free sugar from the FFQ with the DFS scores. To establish test-retest reliability, participants completed the DFS a second time. To investigate convergent validity, we correlated participants’ DFS scores with self-reported eating behavior and sensitivity to reward. <b><i>Results:</i></b> DFS scores correlated with percentage of energy from free sugar (<i>rs</i> = 0.443) and saturated fatty acids (<i>rs =</i>0.258) but not with non-target nutrients. The correlation between DFS scores and percentage energy from free sugar was not moderated by body mass index (BMI), whereas the correlation with percentage energy from saturated fat slightly decreased with BMI. Intra-class correlation as an indicator of test-retest reliability was 0.801. DFS scores correlated significantly with restraint of eating behavior (<i>rs</i> = –0.380) and feelings of hunger (<i>rs</i> = 0.275). Correlations of the DFS score with disinhibited eating and sensitivity to rewards failed to be significant. <b><i>Conclusion:</i></b> Our data indicate that the German version of the DFS might be a psychometrically sound self-report instrument to estimate saturated fat and free sugar intake of German adults.

To understand the dysfunctional mechanisms underlying maladaptive reasoning of psychosis, computational models of decision making have widely been applied over the past decade. Thereby, a particular focus has been on the degree to which beliefs are updated based on new evidence, expressed by the learning rate in computational models. Higher order beliefs about the stability of the environment can determine the attribution of meaningfulness to events that deviate from existing beliefs by interpreting these either as noise or as true systematic changes (volatility). Both, the inappropriate downplaying of important changes as noise (belief update too low) as well as the overly flexible adaptation to random events (belief update too high) were theoretically and empirically linked to symptoms of psychosis. Whereas models with fixed learning rates fail to adjust learning in reaction to dynamic changes, increasingly complex learning models have been adopted in samples with clinical and subclinical psychosis lately. These ranged from advanced reinforcement learning models, over fully Bayesian belief updating models to approximations of fully Bayesian models with hierarchical learning or change point detection algorithms. It remains difficult to draw comparisons across findings of learning alterations in psychosis modeled by different approaches e.g., the Hierarchical Gaussian Filter and change point detection. Therefore, this review aims to summarize and compare computational definitions and findings of dynamic belief updating without perceptual ambiguity in (sub)clinical psychosis across these different mathematical approaches. There was strong heterogeneity in tasks and samples. Overall, individuals with schizophrenia and delusion-proneness showed lower behavioral performance linked to failed differentiation between uninformative noise and environmental change. This was indicated by increased belief updating and an overestimation of volatility, which was associated with cognitive deficits. Correlational evidence for computational mechanisms and positive symptoms is still sparse and might diverge from the group finding of instable beliefs. Based on the reviewed studies, we highlight some aspects to be considered to advance the field with regard to task design, modeling approach, and inclusion of participants across the psychosis spectrum. Taken together, our review shows that computational psychiatry offers powerful tools to advance our mechanistic insights into the cognitive anatomy of psychotic experiences.