Protection from uncertainty in the exploration/exploitation trade-off

The exploration/exploitation trade-off (EE trade-off) describes how, when faced with several competing alternatives, decision-makers must often choose between a known good alternative (exploitation) and one or more unknown but potentially more rewarding alternatives (exploration). Prevailing theory on how humans perform the EE trade-off states that uncertainty is a major motivator for exploration: the more uncertain the environment, the more exploration that will occur. The current paper examines whether exploratory behaviour in both choice and attention may be impacted differently depending on whether uncertainty is onset suddenly (unexpected uncertainty), or more slowly (expected uncertainty). It is shown that when uncertainty was expected, participants tended to explore less with their choices, but not their attention, than when it was unexpected. Crucially, the impact of this "protection from uncertainty" on exploration only occurred when participants had an opportunity to learn the structure of the task prior to experiencing uncertainty. This suggests that the interaction between uncertainty and exploration is more nuanced than simply more uncertainty leading to more exploration, and that attention and choice behaviour may index separate aspects of the EE trade-off

Approaches to Learning to Control Dynamic Uncertainty

In dynamic environments, when faced with a choice of which learning strategy to adopt, do people choose to mostly explore (maximizing their long term gains) or exploit (maximizing their short term gains)? More to the point, how does this choice of learning strategy influence one’s later ability to control the environment? In the present study, we explore whether people’s self-reported learning strategies and levels of arousal (i.e., surprise, stress) correspond to performance measures of controlling a Highly Uncertain or Moderately Uncertain dynamic environment. Generally, self-reports suggest a preference for exploring the environment to begin with. After which, those in the Highly Uncertain environment generally indicated they exploited more than those in the Moderately Uncertain environment; this difference did not impact on performance on later tests of people’s ability to control the dynamic environment. Levels of arousal were also differentially associated with the uncertainty of the environment. Going beyond behavioral data, our model of dynamic decision-making revealed that, in actual fact, there was no difference in exploitation levels between those in the highly uncertain or moderately uncertain environments, but there were differences based on sensitivity to negative reinforcement. We consider the implications of our findings with respect to learning and strategic approaches to controlling dynamic uncertainty.This study was supported by the Engineering and Physical Sciences Research Council

The Nature of Belief-Directed Exploratory Choice in Human Decision-Making

In non-stationary environments, there is a conflict between exploiting currently favored options and gaining information by exploring lesser-known options that in the past have proven less rewarding. Optimal decision-making in such tasks requires considering future states of the environment (i.e., planning) and properly updating beliefs about the state of the environment after observing outcomes associated with choices. Optimal belief-updating is reflective in that beliefs can change without directly observing environmental change. For example, after 10 s elapse, one might correctly believe that a traffic light last observed to be red is now more likely to be green. To understand human decision-making when rewards associated with choice options change over time, we develop a variant of the classic “bandit” task that is both rich enough to encompass relevant phenomena and sufficiently tractable to allow for ideal actor analysis of sequential choice behavior. We evaluate whether people update beliefs about the state of environment in a reflexive (i.e., only in response to observed changes in reward structure) or reflective manner. In contrast to purely “random” accounts of exploratory behavior, model-based analyses of the subjects’ choices and latencies indicate that people are reflective belief updaters. However, unlike the Ideal Actor model, our analyses indicate that people’s choice behavior does not reflect consideration of future environmental states. Thus, although people update beliefs in a reflective manner consistent with the Ideal Actor, they do not engage in optimal long-term planning, but instead myopically choose the option on every trial that is believed to have the highest immediate payoff

The Role of the Noradrenergic System in the Exploration–Exploitation Trade-Off: A Psychopharmacological Study

Animal research and computational modeling have indicated an important role for the neuromodulatory locus coeruleus–norepinephrine (LC–NE) system in the control of behavior. According to the adaptive gain theory, the LC–NE system is critical for optimizing behavioral performance by regulating the balance between exploitative and exploratory control states. However, crucial direct empirical tests of this theory in human subjects have been lacking. We used a pharmacological manipulation of the LC–NE system to test predictions of this theory in humans. In a double-blind parallel-groups design (N = 52), participants received 4 mg reboxetine (a selective norepinephrine reuptake inhibitor), 30 mg citalopram (a selective serotonin reuptake inhibitor), or placebo. The adaptive gain theory predicted that the increased tonic NE levels induced by reboxetine would promote task disengagement and exploratory behavior. We assessed the effects of reboxetine on performance in two cognitive tasks designed to examine task (dis)engagement and exploitative versus exploratory behavior: a diminishing-utility task and a gambling task with a non-stationary pay-off structure. In contrast to predictions of the adaptive gain theory, we did not find differences in task (dis)engagement or exploratory behavior between the three experimental groups, despite demonstrable effects of the two drugs on non-specific central and autonomic nervous system parameters. Our findings suggest that the LC–NE system may not be involved in the regulation of the exploration–exploitation trade-off in humans, at least not within the context of a single task. It remains to be examined whether the LC–NE system is involved in random exploration exceeding the current task context

Rostrolateral Prefrontal Cortex and Individual Differences in Uncertainty-Driven Exploration

SummaryHow do individuals decide to act based on a rewarding status quo versus an unexplored choice that might yield a better outcome? Recent evidence suggests that individuals may strategically explore as a function of the relative uncertainty about the expected value of options. However, the neural mechanisms supporting uncertainty-driven exploration remain underspecified. The present fMRI study scanned a reinforcement learning task in which participants stop a rotating clock hand in order to win points. Reward schedules were such that expected value could increase, decrease, or remain constant with respect to time. We fit several mathematical models to subject behavior to generate trial-by-trial estimates of exploration as a function of relative uncertainty. These estimates were used to analyze our fMRI data. Results indicate that rostrolateral prefrontal cortex tracks trial-by-trial changes in relative uncertainty, and this pattern distinguished individuals who rely on relative uncertainty for their exploratory decisions versus those who do not.Video Abstrac

Computational mechanisms of curiosity and goal-directed exploration

Successful behaviour depends on the right balance between maximising reward and soliciting information about the world. Here, we show how different types of information-gain emerge when casting behaviour as surprise minimisation. We present two distinct mechanisms for goal-directed exploration that express separable profiles of active sampling to reduce uncertainty. 'Hidden state' exploration motivates agents to sample unambiguous observations to accurately infer the (hidden) state of the world. Conversely, 'model parameter' exploration, compels agents to sample outcomes associated with high uncertainty, if they are informative for their representation of the task structure. We illustrate the emergence of these types of information-gain, termed active inference and active learning, and show how these forms of exploration induce distinct patterns of 'Bayes-optimal' behaviour. Our findings provide a computational framework for understanding how distinct levels of uncertainty systematically affect the exploration-exploitation trade-off in decision-making

Time pressure changes how people explore and respond to uncertainty

How does time pressure influence exploration and decision-making? We investigated this question with several four-armed bandit tasks manipulating (within subjects) expected reward, uncertainty, and time pressure (limited vs. unlimited). With limited time, people have less opportunity to perform costly computations, thus shifting the cost-benefit balance of different exploration strategies. Through behavioral, reinforcement learning (RL), reaction time (RT), and evidence accumulation analyses, we show that time pressure changes how people explore and respond to uncertainty. Specifically, participants reduced their uncertainty-directed exploration under time pressure, were less value-directed, and repeated choices more often. Since our analyses relate uncertainty to slower responses and dampened evidence accumulation (i.e., drift rates), this demonstrates a resource-rational shift towards simpler, lower-cost strategies under time pressure. These results shed light on how people adapt their exploration and decision-making strategies to externally imposed cognitive constraints