Improving the reliability of model-based decision-making estimates in the two-stage decision task with reaction-times and drift-diffusion modeling

A well-established notion in cognitive neuroscience proposes that multiple brain systems contribute to choice behaviour. These include: (1) a model-free system that uses values cached from the outcome history of alternative actions, and (2) a model-based system that considers action outcomes and the transition structure of the environment. The widespread use of this distinction, across a range of applications, renders it important to index their distinct influences with high reliability. Here we consider the two-stage task, widely considered as a gold standard measure for the contribution of model-based and model-free systems to human choice. We tested the internal/temporal stability of measures from this task, including those estimated via an established computational model, as well as an extended model using drift-diffusion. Drift-diffusion modeling suggested that both choice in the first stage, and RTs in the second stage, are directly affected by a model-based/free trade-off parameter. Both parameter recovery and the stability of model-based estimates were poor but improved substantially when both choice and RT were used (compared to choice only), and when more trials (than conventionally used in research practice) were included in our analysis. The findings have implications for interpretation of past and future studies based on the use of the two-stage task, as well as for characterising the contribution of model-based processes to choice behaviour

Adapting the queen square guided self help (QGSH) for functional neurological disorders as a stand-alone intervention: an exonian pilot study

Aim: Functional neurological disorders (FND) are one of the most common presentation in neurology clinics, causing a significant disability and economic burden. Cognitive behavioural therapy (CBT) has one of the best available evidence in managing FND, although access remains limited. Queen Square, London neuropsychiatry experts have established an excellent model for a CBT based, Guided Self Help (GSH) programme, which is preparatory to a multidisciplinary inpatient treatment. It has been shown to have good outcomes. This study was designed to ascertain the feasibility and acceptance of this QGSH model, in an Exonian cohort of FND patients, whilst piloting its stand-alone version, without the inpatient component. Additionally, the study explores the need and types of modifications required for the stand-alone adaptation of QGSH. Method: Consecutive patients referred to Exeter FND Service, between February to June 2020, who had internet access, were offered the QGSH pilot. Patients with a primary mental disorder concurrent drug/alcohol misuse or risk of self-harm or suicide were excluded. Ethics approval was not required. The QGSH intervention constitutes of 11 modules focussing on specific elements crucial to FND management along with homework tasks, delivered by the author, under supervision by QGSH experts. Patients completed Pre and Post-intervention questionnaires as well as structured feedback. Results: Three successive patients with varied FND symptoms were recruited to the pilot between February and June 2020. The baseline health status of these patients was worse as compared to EQ-5D-5L population norms with significant baseline psychiatric comorbidity. Outcome measures used before and after QGSH intervention included PHQ 9, GAD 7, EQ-5D-5L and a locally devised symptom severity questionnaire. Necessary modifications were made to the program based on the patients informal feedback and structured formal feedback was sought in the end. Conclusion: All patients derived some benefit from QGSH and certain modifications were suggested in patient feedback to improve engagement. Despite study limitations, especially small size and the impact of Covid 19 pandemic during the intervention; QGSH model appears acceptable and feasible in an Exonian cohort, however, some modifications are recommended for the stand-alone version to succeed. The recommendations will be presented

How People Use Social Information to Find out What to Want in the Paradigmatic Case of Inter-temporal Preferences.

The weight with which a specific outcome feature contributes to preference quantifies a person's 'taste' for that feature. However, far from being fixed personality characteristics, tastes are plastic. They tend to align, for example, with those of others even if such conformity is not rewarded. We hypothesised that people can be uncertain about their tastes. Personal tastes are therefore uncertain beliefs. People can thus learn about them by considering evidence, such as the preferences of relevant others, and then performing Bayesian updating. If a person's choice variability reflects uncertainty, as in random-preference models, then a signature of Bayesian updating is that the degree of taste change should correlate with that person's choice variability. Temporal discounting coefficients are an important example of taste-for patience. These coefficients quantify impulsivity, have good psychometric properties and can change upon observing others' choices. We examined discounting preferences in a novel, large community study of 14-24 year olds. We assessed discounting behaviour, including decision variability, before and after participants observed another person's choices. We found good evidence for taste uncertainty and for Bayesian taste updating. First, participants displayed decision variability which was better accounted for by a random-taste than by a response-noise model. Second, apparent taste shifts were well described by a Bayesian model taking into account taste uncertainty and the relevance of social information. Our findings have important neuroscientific, clinical and developmental significance

Simulating the computational mechanisms of cognitive and behavioral psychotherapeutic interventions: insights from active inference

Cognitive-behavioral therapy (CBT) leverages interactions between thoughts, feelings, and behaviors. To deepen understanding of these interactions, we present a computational (active inference) model of CBT that allows formal simulations of interactions between cognitive interventions (i.e., cognitive restructuring) and behavioral interventions (i.e., exposure) in producing adaptive behavior change (i.e., reducing maladaptive avoidance behavior). Using spider phobia as a concrete example of maladaptive avoidance more generally, we show simulations indicating that when conscious beliefs about safety/danger have strong interactions with affective/behavioral outcomes, behavioral change during exposure therapy is mediated by changes in these beliefs, preventing generalization. In contrast, when these interactions are weakened, and cognitive restructuring only induces belief uncertainty (as opposed to strong safety beliefs), behavior change leads to generalized learning (i.e., “over-writing” the implicit beliefs about action-outcome mappings that directly produce avoidance). The individual is therefore equipped to face any new context, safe or dangerous, remaining in a content state without the need for avoidance behavior—increasing resilience from a CBT perspective. These results show how the same changes in behavior during CBT can be due to distinct underlying mechanisms; they predict lower rates of relapse when cognitive interventions focus on inducing uncertainty and on reducing the effects of automatic negative thoughts on behavior

Increased decision thresholds trigger extended information gathering across the compulsivity spectrum

Indecisiveness and doubt are cognitive phenotypes of compulsive disorders, including obsessive-compulsive disorder. Little is known regarding the cognitive mechanisms that drive these behaviours across a compulsivity spectrum. Here, we used a sequential information gathering task to study indecisiveness in subjects with high and low obsessive-compulsive scores. These subjects were selected from a large population-representative database, and matched for intellectual and psychiatric factors. We show that high compulsive subjects sampled more information and performed better when sampling was cost-free. When sampling was costly, both groups adapted flexibly to reduce their information gathering. Computational modelling revealed that increased information gathering behaviour could be explained by higher decision thresholds that, in turn, were driven by a delayed emergence of impatience or urgency. Our findings show that indecisiveness generalises to a compulsivity spectrum beyond frank clinical disorder, and this behaviour can be explained within a decision-theoretic framework as arising from an augmented decision threshold associated with an attenuated urgency signal

Active inference, evidence accumulation, and the urn task

Deciding how much evidence to accumulate before making a decision is a problem we and other animals often face, but one that is not completely understood. This issue is particularly important because a tendency to sample less information (often known as reflection impulsivity) is a feature in several psychopathologies, such as psychosis. A formal understanding of information sampling may therefore clarify the computational anatomy of psychopathology. In this theoretical letter, we consider evidence accumulation in terms of active (Bayesian) inference using a generic model of Markov decision processes. Here, agents are equipped with beliefs about their own behavior--in this case, that they will make informed decisions. Normative decision making is then modeled using variational Bayes to minimize surprise about choice outcomes. Under this scheme, different facets of belief updating map naturally onto the functional anatomy of the brain (at least at a heuristic level). Of particular interest is the key role played by the expected precision of beliefs about control, which we have previously suggested may be encoded by dopaminergic neurons in the midbrain. We show that manipulating expected precision strongly affects how much information an agent characteristically samples, and thus provides a possible link between impulsivity and dopaminergic dysfunction. Our study therefore represents a step toward understanding evidence accumulation in terms of neurobiologically plausible Bayesian inference and may cast light on why this process is disordered in psychopathology

Self-esteem depends on beliefs about the rate of change of social approval

A major challenge in understanding the neurobiological basis of psychiatric disorders is rigorously quantifying subjective metrics that lie at the core of mental illness, such as low self-esteem. Self-esteem can be conceptualized as a ‘gauge of social approval’ that increases in response to approval and decreases in response to disapproval. Computational studies have shown that learning signals that represent the difference between received and expected social approval drive changes in self-esteem. However, it is unclear whether self-esteem based on social approval should be understood as a value updated through associative learning, or as a belief about approval, updated by new evidence depending on how strongly it is held. Our results show that belief-based models explain self-esteem dynamics in response to social evaluation better than associative learning models. Importantly, they suggest that in the short term, self-esteem signals the direction and rate of change of one’s beliefs about approval within a group, rather than one’s social position