The scaled target learning model: Revisiting learning in the balloon analogue risk task

Dual-process modeling of sequential decision making in the balloon analogue risk task

People are often faced with repeated risky decisions that involve uncertainty. In sequential risk-taking tasks, like the Balloon Analogue Risk Task (BART), the underlying decision process is not yet fully understood. Dual-process theory proposes that human cognition involves two main families of processes, often referred to as System 1 (fast and automatic) and System 2 (slow and conscious). We cross models of the BART with different architectures of the two systems to yield a pool of computational dual-process models that are evaluated on multiple performance measures (e.g., parameter identifiability, model recovery, and predictive accuracy). Results show that the best-performing model configuration assumes the two systems are competitively connected, an evaluation process based on the Scaled Target Learning model of the BART, and an assessment rate that incorporates sensitivity to the trial number, pumping opportunity, and bias to engage in System 1. Findings also shed light on how modeling choices and response times in a dual-process framework can benefit our understanding of sequential risk-taking behavior.

Altered reinforcement learning in Narcolepsy type I and other central disorders of hypersomnolence

Cognitive impairments are described in central disorders of hypersomnolence (CDH), but studies remain very limited and largely focused on narcolepsy type 1 (NT1). The precise nature and origin of these cognitive impairments is poorly understood. Specifically, impaired decision making under ambiguity has been reported in NT1 and suggested to be caused by dysregulation of the direct projections of hypocretin neurons to the dopamine network. However, the decision-making tasks used previously embed different cognitive functions that are difficult to isolate. This study aims to test reinforcement learning in participants with NT1 and with other (non-hypocretin deficient) CDH in a task known to directly depend on the dopamine system. Participants with NT1 (N = 27), other CDH (N = 34, including narcolepsy type 2 and idiopathic hypersomnia, matched with NT1 participants for sleepiness severity), and healthy participants (N = 34) took part in the study. Results showed that all groups had normal and similar positive reinforcement learning, a pattern not suggestive of dopamine deficiency. However, both participants with NT1 and other CDH had decreased learning abilities to avoid losses. This decreased negative reinforcement learning in participants with CDH was associated with the alteration of vigilance. This study provides new insights into the nature of decision making impairment in people with CDH and suggests that these alterations could be minimized by restoring adequate vigilance.

Deterministic and probabilistic regularities underlying risky choices are acquired in a changing decision context

Predictions supporting risky decisions could become unreliable when outcome probabilities temporarily change, making adaptation more challenging. Therefore, this study investigated whether sensitivity to the temporal structure in outcome probabilities can develop and remain persistent in a changing decision environment. In a variant of the Balloon Analogue Risk Task with 90 balloons, outcomes (rewards or balloon bursts) were predictable in the task's first and final 30 balloons and unpredictable in the middle 30 balloons. The temporal regularity underlying the predictable outcomes differed across three experimental conditions. In the deterministic condition, a repeating three-element sequence dictated the maximum number of pumps before a balloon burst. In the probabilistic condition, a single probabilistic regularity ensured that burst probability increased as a function of pumps. In the hybrid condition, a repeating sequence of three different probabilistic regularities increased burst probabilities. In every condition, the regularity was absent in the middle 30 balloons. Participants were not informed about the presence or absence of the regularity. Sensitivity to both the deterministic and hybrid regularities emerged and influenced risk taking. Unpredictable outcomes of the middle phase did not deteriorate this sensitivity. In conclusion, humans can adapt their risky choices in a changing decision environment by exploiting the statistical structure that controls how the environment changes.

Neural signatures of heterogeneity in risk-taking and strategic consistency

People display a high degree of heterogeneity in risk-taking behaviour, but this heterogeneity remains poorly understood. Here, we use a neural trait approach to examine if task-independent, brain-based differences can help uncover the sources of heterogeneity in risky decision-making. We extend prior research in two key ways. First, we disentangled risk-taking and strategic consistency using novel measures afforded by the Balloon Analogue Risk Task. Second, we applied a personality neuroscience framework to explore why personality traits are typically only weakly related to risk-taking behaviour. We regressed participants' (N = 104) source localized resting-state electroencephalographic activity on risk-taking and strategic consistency. Results revealed that higher levels of resting-state delta-band current density (reflecting reduced cortical activation) in the left dorsal anterior cingulate cortex and the left dorsolateral prefrontal cortex were associated with increased risk-taking and decreased strategic consistency, respectively. These results suggest that heterogeneity in risk-taking behaviour is associated with neural dispositions related to sensitivity to the risk of loss, whereas heterogeneity in strategic consistency is associated with neural dispositions related to strategic decision-making. Finally, extraversion, neuroticism, openness, and self-control were broadly associated with both of the identified neural traits, which in turn mediated indirect associations between personality traits and behavioural measures. These results provide an explanation for the weak direct relationships between personality traits and risk-taking behaviour, supporting a personality neuroscience framework of traits and decision-making.