A mechanistic account of bodily resonance and implicit bias

Implicit social biases play a critical role in shaping our attitudes towards other people. Such biases are thought to arise, in part, from a comparison between features of one's own self-image and those of another agent, a process known as 'bodily resonance'. Recent data have demonstrated that implicit bias can be remarkably plastic, being modulated by brief immersive virtual reality experiences that place participants in a virtual body with features of an out-group member. Here, we provide a mechanistic account of bodily resonance and implicit bias in terms of a putative self-image network that encodes associations between different features of an agent. When subsequently perceiving another agent, the output of this self-image network is proportional to the overlap between their respective features, providing an index of bodily resonance. By combining the self-image network with a drift diffusion model of decision making, we simulate performance on the implicit association test (IAT) and show that the model captures the ubiquitous implicit bias towards in-group members. We subsequently demonstrate that this implicit bias can be modulated by a simulated illusory body ownership experience, consistent with empirical data; and that the magnitude and plasticity of implicit bias correlates with self-esteem. Hence, we provide a simple mechanistic account of bodily resonance and implicit bias which could contribute to the development of interventions for reducing the negative evaluation of social out-groups

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

Modelling Rumination as a State-Inference Process

Rumination is a kind of repetitive negative thinking that involves prolonged sampling of negative episodes from one's past, typically prompted by a present negative experience. We model rumination as an attempt at hidden-state inference, formalized as a partially-observable Markov decision process (POMDP). Using this allegorical model, we demonstrate conditions under which continuous, prolonged collection of samples from memory is the optimal policy. Consistent with phenomenological observations from clinical and experimental work, we show that prolonged sampling (i.e., chronic rumination), formalized as needing to sample more evidence before selecting an action, is required when possible negative outcomes increase in magnitude, when states of the world with negative outcomes are a priori more likely, and when samples are more variable than expected. By demonstrating that prolonged sampling may allow for optimal action selection under certain environmental conditions, we show how rumination may be adaptive for solving particular problems

Modelling Rumination as a State-Inference Process

Rumination is a kind of repetitive negative thinking that involves prolonged sampling of negative episodes from one's past, typically prompted by a present negative experience. We model rumination as an attempt at hidden-state inference, formalized as a partially-observable Markov decision process (POMDP). Using this allegorical model, we demonstrate conditions under which continuous, prolonged collection of samples from memory is the optimal policy. Consistent with phenomenological observations from clinical and experimental work, we show that prolonged sampling (i.e., chronic rumination), formalized as needing to sample more evidence before selecting an action, is required when possible negative outcomes increase in magnitude, when states of the world with negative outcomes are a priori more likely, and when samples are more variable than expected. By demonstrating that prolonged sampling may allow for optimal action selection under certain environmental conditions, we show how rumination may be an adaptive for solving particular problems

A Highly Replicable Decline in Mood During Rest and Simple Tasks

Does our mood change as time passes? This question is central to behavioural and affective science, yet it remains largely unexamined. To investigate, we intermixed subjective momentary mood ratings into repetitive psychology paradigms. We demonstrate that task and rest periods lowered participants' mood, an effect we call "Mood Drift Over Time." This finding was replicated in 19 cohorts totaling 28,482 adult and adolescent participants. The drift was relatively large (-13.8% after 7.3 minutes of rest, Cohen's d=0.574) and was consistent across cohorts. Behaviour was also impacted: participants were less likely to gamble in a task that followed a rest period. Importantly, the drift slope was inversely related to reward sensitivity. We show that accounting for time using a linear term significantly improves the fit of a computational model of mood. Our work provides conceptual and methodological reasons for researchers to account for time's effects when studying mood and behaviour

A highly replicable decline in mood during rest and simple tasks

Does our mood change as time passes? This question is central to behavioural and affective science, yet it remains largely unexamined. To investigate, we intermixed subjective momentary mood ratings into repetitive psychology paradigms. Here we demonstrate that task and rest periods lowered participants' mood, an effect we call 'Mood Drift Over Time'. This finding was replicated in 19 cohorts totalling 28,482 adult and adolescent participants. The drift was relatively large (-13.8% after 7.3 min of rest, Cohen's d = 0.574) and was consistent across cohorts. Behaviour was also impacted: participants were less likely to gamble in a task that followed a rest period. Importantly, the drift slope was inversely related to reward sensitivity. We show that accounting for time using a linear term significantly improves the fit of a computational model of mood. Our work provides conceptual and methodological reasons for researchers to account for time's effects when studying mood and behaviour

Self-regulation and alcohol consumption in everyday life

Alcohol use disorder (AUD) is a major contributor to global disability and mortality. Predicting how people transition from occasional to excessive substance use remains a challenge. Previously, AUD has been linked to reduced cognitive control and increased risky decision-making in cross-sectional studies. However, these relationships can reflect changes that could either be a consequence or precedent of substance use. Thus, an untested hypothesis remains whether fluctuations in cognitive control and decision-making may temporally precede fluctuations in substance use. Here, we test this hypothesis based on a unique, preregistered, one-year longitudinal ecological momentary assessment (EMA) study. We employed EMA of real-life alcohol use in combination with a battery of smartphone-based gamified cognitive control and decision-making tests in n=288 participants with AUD. As hypothesized, we found that more risky decision-making (mixed gambles, information sampling) in a given month predicted increased alcohol consumption in the subsequent month. These results are also supported by a mechanistic computational model of information sampling in risky decision-making. Follow-up analyses further supported a specific temporal direction of the effect such that changes in decision-making preceded subsequent drinking but not vice versa. In contrast to measures of decision-making, cognitive control was not linked to subsequent alcohol consumption. However, we found, as hypothesized, that the detrimental intraindividual impact of risky decision-making on subsequent drinking was buffered in individuals with high working memory. In sum, we report first-time real-life longitudinal results from smartphone-based experiments that reveal intraindividual fluctuations in decision-making as a driving mechanism underlying subsequent fluctuations in alcohol consumption. Our smartphone-based experimental readouts can open new avenues for innovative mechanism-based and just-in-time interventions in AUD