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Many real-world decisions involve uncertainty, yet some are governed by logical constraints that make certain outcomes objectively more or less probable. Despite this, people often commit reasoning errors such as the conjunction fallacy, i.e. judging a conjunction of events as more likely than a single constituent event. This fallacy has proven robust across populations and contexts, and difficult to fully mitigate. While previous research has shown that reformulating problems or providing explicit instruction can reduce such errors, these approaches often yield limited success and may not generalize to everyday decision-making, where task structures are ambiguous and feedback is sparse. We investigated whether individuals could reduce conjunction fallacies through repeated exposure to decision tasks paired with minimal outcome feedback. Participants (N = 56) were randomly assigned to a training group, which completed a series of conjunction judgment tasks with feedback, or a control group. Results showed that the training group improved on both trained and untrained conjunction tasks, including those based on real-world and clinical scenarios, while the control group showed no such improvement. No transfer effects were observed for unrelated base-rate tasks. Performance gains were gradual, suggesting that participants developed judgment strategies over time rather than immediately adopting normative rules. These findings demonstrate that conjunction fallacies can be mitigated through self-guided learning with minimal instruction, offering a promising approach to improving probabilistic reasoning.

The aim of this thesis is to investigate the computational mechanisms that drive curiosity, the motivational state for learning for its own sake. Curiosity is operationalized as information-seeking behaviors and self-reported curiosity. A novel mechanism based on resolving uncertainty in episodic events (episodic curiosity) is tested and compared to predominant theories on curiosity. This computational mechanism is then used to investigate how curiosity interacts with other types of motivation, and the effects of curiosity on learning in different knowledge domains. Specifically, individually measured indicators of uncertainty are computed using an information-theoretic quantity of entropy (Shannon, 1948).

The aim of the first study is to compare an existing theory, the Learning Progress hypothesis (Gottlieb & Oudeyer, 2018; Oudeyer et al., 2016), to episodic curiosity account. The experiment takes the form of an asteroid avoidance computer game, where outcome uncertainty can be dispelled by requesting trial-related feedback. The results of this study indicate that a desire to close episodic uncertainty is enough to drive and maintain information-seeking behavior, but learning progress is also a lesser determinant of information-seeking.

In the second study the relationship between uncertainty, curiosity and satisfaction is examined. The experiment tests a predominant theory that curiosity reflects a desire for pleasurable learning. The effect of uncertainty and curiosity on memory encoding is also examined. The results show that satisfaction had a negative association with learning, indicating that the learning resulting from curiosity may not necessarily be pleasurable.

The final study looks at how curiosity integrates with other motivational goals, by aligning curiosity with or against the best option in a two-armed bandit task. Can curiosity distract from, or improve, learning and performance in the bandit task? This study shows that the motivational value from curiosity can integrate with extrinsic task goals to improve or distract from performance.

Humans often appear to desire information for its own sake, but it is presently unclear what drives this desire. The important role that resolving uncertainty plays in stimulating information seeking has suggested a tight coupling between the intrinsic motivation to gather information and performance gains, construed as a drive for long-term learning. Using an asteroid-avoidance game that allows us to study learning and information seeking at an experimental time-scale, we show that the incentive for information-seeking can be separated from a long-term learning outcome, with information-seeking best predicted by per-trial outcome uncertainty. Specifically, participants were more willing to take time penalties to receive feedback on trials with increasing uncertainty in the outcome of their choices. We found strong group and individual level support for a linear relationship between feedback request rate and information gain as determined by per-trial outcome uncertainty. This information better reflects filling in the gaps of the episodic record of choice outcomes than long-term skill acquisition or assessment. Our results suggest that this easy to compute quantity can drive information-seeking, potentially allowing simple organisms to intelligently gather information for a diverse episodic record of the environment without having to anticipate the impact on future performance.