Rationale and method Accurately identifying motives to gamble is crucial in the functional analysis of gambling behavior. In this study, a data-driven approach was followed to clarify the factor structure underlying a pool of motives for gambling, selected from the Gambling Motives Questionnaire–Financial (GMQ-F), and the Reasons for Gambling Questionnaire (RGQ), in a sample of regular problem and non-problem gamblers. Additionally, the role of gambling motives in the relationship between root behavioral activation/inhibition systems (BIS/BAS) and gambling severity, frequency, and preferences was explored using structural equation modelling (SEM). Results and conclusions The present study identified Social, Financial, and Fun/thrill-related gambling motives factors, but also a fourth factor in which some positive and negative reinforcement-based motives were grouped into a single and broader Affect regulation factor. This Affect regulation factor shared variance both with BIS and BAS-related measures, and was the only direct predictor of disordered gambling symptoms. The Fun/thrill factor was directly related to frequency of participation in high-arousal, skill-based games, and all factors were related to participation in lower-arousal, chance games (with Social motives negatively predicting both participation in the latter and total severity). In the SEM model, measures of BIS/BAS sensitivity were connected to gambling behavior only through gambling motives. Based on measures of items’ specificity, a shortened Spanish scale (the brief Gambling Motives Inventory, bGMI) is proposed to assess gambling motives in accordance with the observed 4-factor structure.

Background Gambling behavior presents substantial individual variability regarding its severity, manifestations, and psychological correlates. Specifically, differences in emotion regulation, impulsivity, and cognitive distortions have been identified as crucial to describe individual profiles with implications for the prevention, prognosis, and treatment of gambling disorder (GD). Aims and method The aim of the present study was to investigate the associations of gambling-related cognitions (measured according to the GRCS model) with impulsivity (UPPS-P model) and emotion regulation (CERQ model), in a sample of 246 gamblers with different levels of gambling involvement, using mixed-effects modelling to isolate theoretically relevant associations while controlling for the potentially confounding effects of sociodemographic and clinical covariates. Results Affective/motivational dimensions of UPPS-P impulsivity positive urgency and sensation seeking , on the one hand, and CERQ emotion regulation strategies reappraisal , rumination and blaming others , on the other, independently and significantly predicted distorted gambling-related cognitions. Conclusions These results (a) reinforce the ones of previous studies stressing the relevance of emotional and motivational processes in the emergence of gambling-related cognitive distortions; and (b) replicate the seemingly paradoxical finding that gamblers use emotion regulation strategies customarily considered as adaptive (i.e. reappraisal) to strengthen and justify their biased beliefs about gambling outcomes and controllability.

Accurately identifying motives is crucial in the functional analysis of gambling behavior. In this study, a data-driven approach was followed to clarify the factor structure underlying a pool of motives for gambling, selected from the Gambling Motives Questionnaire – Financial (GMQ-F), and the Reasons for Gambling Questionnaire (RGQ), in a sample of regular problem and non-problem gamblers. Additionally, the role of gambling motives in the relationship between root behavioral activation/inhibition systems (BIS/BAS) and gambling severity, frequency, and preferences was explored using structural equation modelling (SEM). Results show that motives variance was best explained by the existence of four factors: financial motives, social motives, affect regulation, and fun/thrill. Importantly, gambling to regulate affect was directly and independently associated with gambling severity. Only the fun/thrill factor was directly related to frequency of participation in high-arousal, skill-based games, whereas all factors were related to participation in lower-arousal, chance games (with social motives negatively predicting both participation in the latter and total severity). Finally, in the SEM model, measures of BIS/BAS sensitivity were connected to gambling behavior only through gambling motives. In summary, in contrast with previous, theory-driven factorizations, there seems to be no clear-cut separation between positive and negative reinforcement-driven motives, and an affect regulation factor emerged as the only one directly associated with the risk of gambling disorder. In accordance with this dual composition, the affect regulation motives factor appears to be fueled by both BIS and BAS. Based on mesures of items’ specificity, a shortened Spanish scale (the brief Gambling Motives Inventory, bGMI) is proposed to assess gambling motives in accordance with the observed 4-factor structure.

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Causal learning is the ability to progressively incorporate raw information about dependencies between events, or between one's behavior and its outcomes, into beliefs of the causal structure of the world. In spite of the fact that some cognitive biases in gambling disorder can be described as alterations of causal learning involving gambling-relevant cues, behaviors, and outcomes, general causal learning mechanisms in gamblers have not been systematically investigated. In the present study, we compared gambling disorder patients against controls in an instrumental causal learning task. Evidence of illusion of control, namely, overestimation of the relationship between one's behavior and an uncorrelated outcome, showed up only in gamblers with strong current symptoms. Interestingly, this effect was part of a more complex pattern, in which gambling disorder patients manifested a poorer ability to discriminate between null and positive contingencies. Additionally, anomalies were related to gambling severity and current gambling disorder symptoms. Gambling-related biases, as measured by a standard psychometric tool, correlated with performance in the causal learning task, but not in the expected direction. Indeed, performance of gamblers with stronger biases tended to resemble the one of controls, which could imply that anomalies of causal learning processes play a role in gambling disorder, but do not seem to underlie gambling-specific biases, at least in a simple, direct way.