Bayesian surprise attracts human attention
Open accessAbstract
We propose a formal Bayesian definition of surprise to capture subjective aspects of sensory information. Surprise measures how data affects an observer, in terms of differences between posterior and prior beliefs about the world. Only data observations which substantially affect the observer's beliefs yield surprise, irrespectively of how rare or informative in Shannon's sense these observations are. We test the framework by quantifying the extent to which humans may orient attention and gaze towards surprising events or items while watching television. To this end, we implement a simple computational model where a low-level, sensory form of surprise is computed by simple simulated early visual neurons. Bayesian surprise is a strong attractor of human attention, with 72% of all gaze shifts directed towards locations more surprising than the average, a figure rising to 84% when focusing the analysis onto regions simultaneously selected by all observers. The proposed theory of surprise is applicable across different spatio-temporal scales, modalities, and levels of abstraction.
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The degree of surprise about an unexpected answer is a function of learners' confidence in their erroneous belief.
“The degree of surprise about an unexpected answer is a function of learners’ confidence in their erroneous belief, which has been demonstrated both using formal models of surprise (Itti & Baldi, 2009) and empirically for trivia facts questions (Reisenzein, 2000).”
From Predicting as a learning strategy (2021) · cites this paper as Itti & Baldi, 2009
Contextual novelty may be quantified using Bayesian inference as a difference between a prior and posterior world model or as a high prediction error for high-confidence states.
“may be quantified using Bayesian inference as a difference between a prior and posterior world model (Itti and Baldi 2009) or as a high prediction error for high-confidence states (Oudeyer and Kaplan 2007).”
From Information-seeking, curiosity, and attention: computational and neural mechanisms (2013) · cites this paper as Itti and Baldi 2009
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- Authors
- Itti, L., & Baldi, P.
- Affiliations
- University of Southern California (US)
- University of California, Irvine (US)
- Corresponding author
- Itti, Laurent
- Venue
- Vision Research
- Publisher
- Elsevier BV
- Published
- 2009-06
- Volume
- 49
- Issue
- 10
- Pages
- 1295-1306
- ISSN
- 0042-6989 (ISSN-L)
- PMC
- PMC2782645
- OpenAlex
- W2039546655
- Cited by
- 1,224
- References
- 59
- Citations by year
- 2026: 53 · 2025: 63 · 2024: 65 · 2023: 60 · 2022: 93 · 2021: 86 · 2020: 74 · 2019: 94 · 2018: 69 · 2017: 69 · 2016: 61 · 2015: 62 · 2014: 65 · 2013: 93 · 2012: 66
- FWCI
- 71.26
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Vision Research (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Science Foundation
- U.S. National Library of Medicine (T15LM007443)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Keywords
- surprise, Bayesian surprise, attention, gaze shifts, computational model of attention, television viewing, sensory input
- MeSH terms
- Adult, Attention, Bayes Theorem, Exploratory Behavior, Eye Movements, Female, Humans, Male, Models, Psychological, Photic Stimulation, Psychomotor Performance, Psychophysics, Television, Young Adult
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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