Learning
How do learning progress and novelty drive human curiosity?
Open access · cc by · source: Europe PMC
People decide to stop exploring a task when they stop learning from it, and they choose their next task based on both how much they expect to learn and how new it is.
How this study connects
Role on claims
Each row is a claim on a concept or method page where this paper supports, challenges, or qualifies the statement. Roles are hand-checked — not a model guess.
What people choose to practise tracks how much they are still improving.
Exploration is guided by expected learning progress, not only by reward or novelty. Participants were more likely to keep playing with a character while learning progress was high (β = −0.13 for switching as progress fell), and when choosing something new were drawn to both expected learning progress (β = 0.24) and perceptual novelty.
Evidence for the claim as stated.
Whether external feedback helps depends on what it replaces. Curiosity-driven exploration was strongly guided by internally computed learning progress, while in motor sequence learning positive social comparative feedback produced no overall speed or self-efficacy advantage over no feedback, and plain reaction-time feedback without social context slowed learning.
Evidence for the claim as stated.
Curiosity-driven intrinsic exploration is guided by expected learning progress and novelty, with people opting to switch tasks when their progress in learning starts to decline.
Evidence for the claim as stated.
Open questions
Tensions this paper is part of
From concept pages' “where studies disagree.” Disagreement means the same question; scope means different assays, populations, or outcomes.
Whether external feedback helps depends on what it replaces. Curiosity-driven exploration was strongly guided by internally computed learning progress, while in motor sequence learning positive social comparative feedback produced no overall speed or self-efficacy advantage over no feedback, and plain reaction-time feedback without social context slowed learning.
Study at a glance
- Design
- Human experiment — Free exploration among colored characters; GAM predicting termination by learning progress vs novelty
- N
- N=55 · 67 recruited via Sona; 9 failed inclusion, 3 lost to technical error
- Population
- University students participating for course credit
- Outcome
- Whether exploration is terminated by expected learning progress versus perceptual novelty
Structured fields used in claim comparison tables when every cited study has a complete layer.
Inspect source passages
How do learning progress and novelty drive human curiosity?
These passages occur verbatim in the stored paper text. They support the points listed below; they do not verify every statement in the explanation.
55 participants
The final sample consisted of 55 participants (M age = 20.44, SD = 2.85, F = 28, M = 27).
3 different cartoon characters
Participants were presented with three characters that were distinguishable by their color (green, blue, and red) and shape (A).
Key findings
Participants were more likely to keep playing with a character when their learning progress was high, with the probability of switching increasing as learning progress decreased, showing a beta of -0.13. When choosing a new character, participants were significantly drawn to both expected learning progress (beta of 0.24) and perceptual novelty (beta of 0.87). Furthermore, the influence of novelty on their choices decreased over time, while the influence of expected learning progress remained stable.
Methodology
Researchers tested 55 participants in an online game where they selected and tracked 3 different cartoon characters hiding behind a hedge. Each character's hiding spot followed a unique probability distribution with differing levels of noise and volatility. The researchers tracked when participants chose to switch characters and which character they selected next, using computational modeling to analyze how learning progress and novelty affected these choices.
Limitations
First, the study was conducted entirely online with a sample of undergraduate students, which may not generalize to broader or older populations. Second, because the study did not include external rewards, it does not show how curiosity-driven exploration interacts with or trade-offs against extrinsic motivations like money or points. Finally, while the computational model infers internal states of learning progress, it cannot definitively prove these calculations are consciously occurring in the participants' minds.
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