The role of uncertainty in attentional and choice exploration

Dynamics of visual attention in exploration and exploitation for reward-guided adjustment tasks

The learning process encompasses exploration and exploitation phases. While reinforcement learning models have revealed functional and neuroscientific distinctions between these phases, knowledge regarding how they affect visual attention while observing the external environment is limited. This study sought to elucidate the interplay between these learning phases and visual attention allocation using visual adjustment tasks combined with a two-armed bandit problem tailored to detect serial effects only when attention is dispersed across both arms. Per our findings, human participants exhibited a distinct serial effect only during the exploration phase, suggesting enhanced attention to the visual stimulus associated with the non-target arm. Remarkably, although rewards did not motivate attention dispersion in our task, during the exploration phase, individuals engaged in active observation and searched for targets to observe. This behavior highlights a unique information-seeking process in exploration that is distinct from exploitation.

Enriching Data Science and Health Care Education: Application and Impact of Synthetic Data Sets Through the Health Gym Project

Large-scale medical data sets are vital for hands-on education in health data science but are often inaccessible due to privacy concerns. Addressing this gap, we developed the Health Gym project, a free and open-source platform designed to generate synthetic health data sets applicable to various areas of data science education, including machine learning, data visualization, and traditional statistical models. Initially, we generated 3 synthetic data sets for sepsis, acute hypotension, and antiretroviral therapy for HIV infection. This paper discusses the educational applications of Health Gym's synthetic data sets. We illustrate this through their use in postgraduate health data science courses delivered by the University of New South Wales, Australia, and a Datathon event, involving academics, students, clinicians, and local health district professionals. We also include adaptable worked examples using our synthetic data sets, designed to enrich hands-on tutorial and workshop experiences. Although we highlight the potential of these data sets in advancing data science education and health care artificial intelligence, we also emphasize the need for continued research into the inherent limitations of synthetic data.

Exploration versus exploitation decisions in the human brain: A systematic review of functional neuroimaging and neuropsychological studies

Thoughts and actions are often driven by a decision to either explore new avenues with unknown outcomes, or to exploit known options with predictable outcomes. Yet, the neural mechanisms underlying this exploration-exploitation trade-off in humans remain poorly understood. This is attributable to variability in the operationalization of exploration and exploitation as psychological constructs, as well as the heterogeneity of experimental protocols and paradigms used to study these choice behaviours. To address this gap, here we present a comprehensive review of the literature to investigate the neural basis of explore-exploit decision-making in humans. We first conducted a systematic review of functional magnetic resonance imaging (fMRI) studies of exploration-versus exploitation-based decision-making in healthy adult humans during foraging, reinforcement learning, and information search. Eleven fMRI studies met inclusion criterion for this review. Adopting a network neuroscience framework, synthesis of the findings across these studies revealed that exploration-based choice was associated with the engagement of attentional, control, and salience networks. In contrast, exploitation-based choice was associated with engagement of default network brain regions. We interpret these results in the context of a network architecture that supports the flexible switching between externally and internally directed cognitive processes, necessary for adaptive, goal-directed behaviour. To further investigate potential neural mechanisms underlying the exploration-exploitation trade-off we next surveyed studies involving neurodevelopmental, neuropsychological, and neuropsychiatric disorders, as well as lifespan development, and neurodegenerative diseases. We observed striking differences in patterns of explore-exploit decision-making across these populations, again suggesting that these two decision-making modes are supported by independent neural circuits. Taken together, our review highlights the need for precision-mapping of the neural circuitry and behavioural correlates associated with exploration and exploitation in humans. Characterizing exploration versus exploitation decision-making biases may offer a novel, trans-diagnostic approach to assessment, surveillance, and intervention for cognitive decline and dysfunction in normal development and clinical populations.

Anxiety as a disorder of uncertainty: implications for understanding maladaptive anxiety, anxious avoidance, and exposure therapy

In cognitive-behavioral conceptualizations of anxiety, exaggerated threat expectancies underlie maladaptive anxiety. This view has led to successful treatments, notably exposure therapy, but is not consistent with the empirical literature on learning and choice alterations in anxiety. Empirically, anxiety is better described as a disorder of uncertainty learning. How disruptions in uncertainty lead to impairing avoidance and are treated with exposure-based methods, however, is unclear. Here, we integrate concepts from neurocomputational learning models with clinical literature on exposure therapy to propose a new framework for understanding maladaptive uncertainty functioning in anxiety. Specifically, we propose that anxiety disorders are fundamentally disorders of uncertainty learning and that successful treatments, particularly exposure therapy, work by remediating maladaptive avoidance from dysfunctional explore/exploit decisions in uncertain, potentially aversive situations. This framework reconciles several inconsistencies in the literature and provides a path forward to better understand and treat anxiety.

Chasing Unknown Bandits: Uncertainty Guidance in Learning and Decision Making

In repeated decision problems for which it is possible to learn from experience, people should actively seek out uncertain options, rather than avoid ambiguity or uncertainty, in order to learn and improve future decisions. Research on human behavior in a variety of multiarmed-bandit tasks supports this prediction. Multiarmed-bandit tasks involve repeated decisions between options with initially unknown reward distributions and require a careful balance between learning about relatively unknown options (exploration) and obtaining high immediate rewards (exploitation). Resolving this exploration-exploitation dilemma optimally requires considering not only the estimated value of each option, but also the uncertainty in these estimations. Bayesian learning naturally quantifies uncertainty and hence provides a principled framework to study how humans resolve this dilemma. On the basis of computational modeling and behavioral results in bandit tasks, I argue that human learning, attention, and exploration are guided by uncertainty. These results support Bayesian theories of cognition and underpin the fundamental role of subjective uncertainty in both learning and decision making.

From exploration to exploitation: a shifting mental mode in late life development

Changes in cognition, affect, and brain function combine to promote a shift in the nature of mentation in older adulthood, favoring exploitation of prior knowledge over exploratory search as the starting point for thought and action. Age-related exploitation biases result from the accumulation of prior knowledge, reduced cognitive control, and a shift toward affective goals. These are accompanied by changes in cortical networks, as well as attention and reward circuits. By incorporating these factors into a unified account, the exploration-to-exploitation shift offers an integrative model of cognitive, affective, and brain aging. Here, we review evidence for this model, identify determinants and consequences, and survey the challenges and opportunities posed by an exploitation-biased mental mode in later life.

An examination of active inference in autistic adults using immersive virtual reality

The integration of prior expectations, sensory information, and environmental volatility is proposed to be atypical in Autism Spectrum Disorder, yet few studies have tested these predictive processes in active movement tasks. To address this gap in the research, we used an immersive virtual-reality racquetball paradigm to explore how visual sampling behaviours and movement kinematics are adjusted in relation to unexpected, uncertain, and volatile changes in environmental statistics. We found that prior expectations concerning ball 'bounciness' affected sensorimotor control in both autistic and neurotypical participants, with all individuals using prediction-driven gaze strategies to track the virtual ball. However, autistic participants showed substantial differences in visuomotor behaviour when environmental conditions were more volatile. Specifically, uncertainty-related performance difficulties in these conditions were accompanied by atypical movement kinematics and visual sampling responses. Results support proposals that autistic people overestimate the volatility of sensory environments, and suggest that context-sensitive differences in active inference could explain a range of movement-related difficulties in autism.

Reversing the relationship between a nontarget cue and the outcome facilitates subsequent human predictive learning

Two experiments were conducted to test the effect of experiencing associative interference on later learning. A predictive learning task was used in which human participants had to evaluate whether plants would grow or not (Outcome) after being watered with different fertilizers (Cues). Experiment 1 found that the increase in the prediction error produced by following a pre-exposed nontarget cue by the outcome, facilitated subsequent acquisition of the relationship between the pre-exposed target cue and the outcome. Experiment 2 compared whether learning about the target cue was differentially affected by experiencing two types of associative interference with the nontarget cue: Pairing the pre-exposed cue with the outcome and presenting the cue without outcome after being paired with it. The experience of associative interference with nontarget cues similarly facilitated subsequent learning about the target cue, regardless of the direction of the change in the nontarget cue-outcome relationship. It is suggested that the increase in prediction error produced by the experience of associative interference may lead to a general increase in attention that facilitates subsequent learning.

A rise in prediction error increases attention to irrelevant cues

We investigated whether a sudden rise in prediction error widens an individual's focus of attention by increasing ocular fixations on cues that otherwise tend to be ignored. To this end, we used a discrimination learning task including cues that were either relevant or irrelevant for predicting the outcomes. Half of participants experienced contingency reversal once they had learned to predict the outcomes (reversal group, n = 30). The other half experienced the same contingencies throughout the task (control group, n = 30). As participants' prediction accuracy increased, they showed a decrease in the number of fixations directed to the irrelevant cues. Following contingency reversal, participants in the reversal group showed a drop in accuracy, indicating a rise in prediction error, and fixated on the irrelevant cues more often than participants in the control group. We discuss the results in the context of attentional theories of associative learning.

Uncertainty modulates value-driven attentional capture

The majority of previous studies on the value modulation of attention have shown that the magnitude of value-driven attentional bias correlates with the strength of reward association. However, relatively little is known about how uncertainty affects value-based attentional bias. We investigated whether attentional capture by previously rewarded stimuli is modulated by the uncertainty of the learned value without the influence of the strength of reward association. Participants were instructed to identify the line orientation in the target color circle. Importantly, each target color was associated with a different level of uncertainty by tuning the variation in reward delivery (Experiment 1) or reward magnitude (Experiment 2). Attentional interference for uncertainty-related distractors was greater than that for certainty distractors in Experiments 1 and 2. In addition, uncertainty-induced attentional bias disappeared earlier than attentional bias for certainty. The study demonstrated that uncertainty modulates value-based attentional capture in terms of strength and persistence, even when the effect of expected value remains constant.

Uncertainty in learning, choice, and visual fixation

Uncertainty plays a critical role in reinforcement learning and decision making. However, exactly how it influences behavior remains unclear. Multiarmed-bandit tasks offer an ideal test bed, since computational tools such as approximate Kalman filters can closely characterize the interplay between trial-by-trial values, uncertainty, learning, and choice. To gain additional insight into learning and choice processes, we obtained data from subjects' overt allocation of gaze. The estimated value and estimation uncertainty of options influenced what subjects looked at before choosing; these same quantities also influenced choice, as additionally did fixation itself. A momentary measure of uncertainty in the form of absolute prediction errors determined how long participants looked at the obtained outcomes. These findings affirm the importance of uncertainty in multiple facets of behavior and help delineate its effects on decision making.