ArticlePLoS computational biology2021
Novelty is not surprise: Human exploratory and adaptive behavior in sequential decision-making.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Novelty as a drive of human exploration in complex stochastic environments.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Article
- Social aloofness is associated with non-social explore-exploit decisions.Communications psychology · 2025Article
- Adaptive Decision-Making "Fast" and "Slow": A Model of Creative Thinking.The European journal of neuroscience · 2025Review
- The CuriousOpen mind : discoveries in cognitive science · 2025Article
- Actor-critic networks with analogue memristors mimicking reward-based learning.Nature machine intelligence · 2025Article
- Perirhinal cortex to the nucleus accumbens circuit in novelty salience following methamphetamine self-administration.Addiction neuroscience · 2024Article
- Individual differences in information demand have a low dimensional structure predicted by some curiosity traits.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Testing the convergent validity, domain generality, and temporal stability of selected measures of people's tendency to explore.Nature communications · 2024Article
- Fast adaptation to rule switching using neuronal surprise.PLoS computational biology · 2024Article
- An Attempt to Explain Visual Aesthetic Appreciation.Integrative psychological & behavioral science · 2023Article
- Reward Maximization Through Discrete Active Inference.Neural computation · 2023Article
- Elephants as an animal model for self-domestication.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Neural spiking for causal inference and learning.PLoS computational biology · 2023Article
- Article
- Striatal dopamine explains novelty-induced behavioral dynamics and individual variability in threat prediction.Neuron · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Classic reinforcement learning (RL) theories cannot explain human behavior in the absence of external reward or when the environment changes. Here, we employ a deep sequential decision-making paradigm with sparse reward and abrupt environmental changes. To explain the behavior of human participants in these environments, we show that RL theories need to include surprise and novelty, each with a distinct role. While novelty drives exploration before the first encounter of a reward, surprise increases the rate of learning of a world-model as well as of model-free action-values. Even though the world-model is available for model-based RL, we find that human decisions are dominated by model-free action choices. The world-model is only marginally used for planning, but it is important to detect surprising events. Our theory predicts human action choices with high probability and allows us to dissociate surprise, novelty, and reward in EEG signals.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.