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Novelty or Surprise?
Open accessAbstract
Novelty and surprise play significant roles in animal behavior and in attempts to understand the neural mechanisms underlying it. They also play important roles in technology, where detecting observations that are novel or surprising is central to many applications, such as medical diagnosis, text processing, surveillance, and security. Theories of motivation, particularly of intrinsic motivation, place novelty and surprise among the primary factors that arouse interest, motivate exploratory or avoidance behavior, and drive learning. In many of these studies, novelty and surprise are not distinguished from one another: the words are used more-or-less interchangeably. However, while undeniably closely related, novelty and surprise are very different. The purpose of this article is first to highlight the differences between novelty and surprise and to discuss how they are related by presenting an extensive review of mathematical and computational proposals related to them, and then to explore the implications of this for understanding behavioral and neuroscience data. We argue that opportunities for improved understanding of behavior and its neural basis are likely being missed by failing to distinguish between novelty and surprise.
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“This underwrites Bayesian experimental design [134] and active learning in statistics [135], intrinsic motivation and artificial curiosity in machine learning and robotics [136, 137, 138, 139, 140].”
From The free energy principle made simpler but not too simple (2023) · cites this paper as [138]
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Acquisition and extinction in autoshaping.
Kakade, S. M., & Dayan, P. (2002) · Psychological Review · doi:10.1037/0033-295x.109.3.533 · Cited by 101 · cited 3×
“Models of classical conditioning based on Bayesian methods, including the Kalman filter, have been proposed that go beyond the account provided by the Rescorla-Wagner model (Dayan et al., 2000; Kakade and Dayan, 2002a; Courville et al., 2004, 2006).”
BackgroundMethodLexical surprisal as a general predictor of reading time
Monsalve, I. F., Frank, S. L., & Vigliocco, G. (2012) · UCL Discovery (University College London) · Cited by 87
Sobótka, S., & Ringo, J. L. (1994) · Brain Research · doi:10.1016/0006-8993(94)90061-2 · Cited by 72 · cited 1×
“Repetition suppression is stimulus spe-cific and has been observed in various types of experiments, from classification (Sobotka and Ringo, 1994) to delayed-matching-to-sample tests (Li et al., 1993).”
MethodTemporal-Difference NetworksPreprint
Sutton, R. S., & Tanner, B. K. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1504.05539 · Cited by 62 · cited 1×
“TD learning is not restricted to predicting reward; the role of reward can be replaced by other stimulus features, and it can be generalized to networks of interrelated predictions (Sutton and Tanner, 2004).”
BackgroundResponse of dopaminergic neurons in cat to auditory stimuli presented across the sleep-waking cycle
Steinfels, G. F., Heym, J. H., Strecker, R. E., & Jacobs, B. L. (1983) · Brain Research · doi:10.1016/0006-8993(83)90917-4 · Cited by 60 · cited 2×
“…experiments are simple light flashes or sudden sounds, and the dopaminergic responses to lights and tones typically persist after many presentations so that talking about novelty of the stimuli does not seem appropriate (Steinfels et al., 1983; Horvitz et al., 1997; Ungless, 2004).”
BackgroundReed, P., Mitchell, C., & Nokes, T. (1996) · Animal Learning & Behavior · doi:10.3758/bf03198952 · Cited by 59
Model Uncertainty in Classical Conditioning
Courville, A. C., Gordon, G. J., Touretzky, D. S., & Daw, N. D. (2003) · Cited by 51
Lepora, N. F., Porrill, J., Yeo, C. H., & Dean, P. (2010) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2010.00140 · Cited by 49 · cited 2×
“…further details and the important role this model has played in the history of animal learning theory (see Schmajuk, 2008, for a review; see also Lepora et al., 2010, and Mannella et al., 2010, for two models that capture the basic brain mechanisms with which classical conditioning is…”
BackgroundMethodMirolli, M., Santucci, V. G., & Baldassarre, G. (2013) · Neural Networks · doi:10.1016/j.neunet.2012.12.012 · Cited by 40
Self-citationRanganath, C., & Paller, K. A. (1999) · Journal of Cognitive Neuroscience · doi:10.1162/089892999563661 · Cited by 37 · cited 1×
“Contextual novelty is often studied in oddball experiments where, for example, sequences of a repeating auditory stimulus (e.g., a simple tone) are interleaved with rare odd signals (e.g., a “moo” of a cow) (Ranganath and Paller, 1999).”
BackgroundRobinson, S., Sotak, B. N., During, M. J., & Palmiter, R. D. (2006) · Behavioral Neuroscience · doi:10.1037/0735-7044.120.1.000 · Cited by 35 · cited 1×
“Because we know that dopamine is both necessary and sufficient for appetitive instrumental conditioning (Robinson et al., 2006; Zweifel et al., 2009), it is probably safe to assume that it is phasic dopamine that mediates operant conditioning in sensory reinforcement, just as we assume that it is…”
BackgroundNovelty, Information and Surprise
Palm, G. (2012) · Springer eBooks · doi:10.1007/978-3-642-29075-6 · Cited by 34
Novelty Detection as an Intrinsic Motivation for Cumulative Learning Robots
Nehmzow, U., Gatsoulis, Y., Kerr, E., Condell, J. V., Siddique, N. H., & McGinnity, T. M. (2012) · Springer eBooks · doi:10.1007/978-3-642-32375-1_8 · Cited by 21 · cited 1×
“Prominent neural network methods for novelty detection, such as methods based on self-organizing feature maps (Kohonen, 1984; Nehmzow et al., 2013), perform this basic process where the memory scan is performed in parallel by the network.”
MethodA Computational Model of the Amygdala Nuclei’s Role in Second Order Conditioning
Mannella, F., Zappacosta, S., Mirolli, M., & Baldassarre, G. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-69134-1_32 · Cited by 21 · cited 1×
“…important role this model has played in the history of animal learning theory (see Schmajuk, 2008, for a review; see also Lepora et al., 2010, and Mannella et al., 2010, for two models that capture the basic brain mechanisms with which classical conditioning is implemented in, respectively,…”
BackgroundSelf-citationResponse-contingent sensory change in a causally structured environment
Glow, P. H., & Winefield, A. H. (1978) · Animal Learning & Behavior · doi:10.3758/bf03211996 · Cited by 18 · cited 1×
“For example, if pressing a bar results in the switching on of a light, an animal will start to press the bar, much as if the bar-press were to lead to a reward such as food (e.g., Kish, 1955; Williams and Lowe, 1972; Glow and Winefield, 1978; Reed et al., 1996).”
BackgroundGlow, P. H. (1970) · Australian Journal of Psychology · doi:10.1080/00049537008254568 · Cited by 16 · cited 1×
“Further support that surprise and not novelty supports sensory reinforcement comes from the evidence that light offsets are more-or-less as good reinforcers as light onsets (Glow, 1970; Russell and Glow, 1974).”
BackgroundMusic-engendered laughter: An analysis of humor devices in PDQ Bach
Huron, D. (2004) · Cited by 14
Computational models of classical conditioning
Schmajuk, N. A. (2008) · Scholarpedia · doi:10.4249/scholarpedia.1664 · Cited by 9 · cited 2×
“What is now called Kamin blocking is the failure of an animal to learn to elicit a CR when a CS is presented to an animal as part of a compound that includes another CS that had been previously conditioned to elicit a CR (Moore and Schmajuk, 2008).”
BackgroundMethodMoore, J. W., & Schmajuk, N. A. (2008) · Scholarpedia · doi:10.4249/scholarpedia.3542 · Cited by 9 · cited 1×
“What is now called Kamin blocking is the failure of an animal to learn to elicit a CR when a CS is presented to an animal as part of a compound that includes another CS that had been previously conditioned to elicit a CR (Moore and Schmajuk, 2008).”
BackgroundResponse contingent illumination change as a reinforcer in the rat
Williams, D. I., & Lowe, G. (1972) · Animal Behaviour · doi:10.1016/s0003-3472(72)80045-9 · Cited by 5 · cited 1×
“For example, if pressing a bar results in the switching on of a light, an animal will start to press the bar, much as if the bar-press were to lead to a reward such as food (e.g., Kish, 1955; Williams and Lowe, 1972; Glow and Winefield, 1978; Reed et al., 1996).”
BackgroundSome effects of short-term immediate prior exposure to light change on responding for light change
Russell, A., & Glow, P. H. (1974) · Animal Learning & Behavior · doi:10.3758/bf03199191 · Cited by 4 · cited 1×
“Further support that surprise and not novelty supports sensory reinforcement comes from the evidence that light offsets are more-or-less as good reinforcers as light onsets (Glow, 1970; Russell and Glow, 1974).”
Background
Flagged first, then by citations
8 of the 107 references couldn’t be matched to a known paper.
Research works printed but not linked: 4
- Dayan P. Long T. (1998). Statistical models of learnng, in Advances in Neural Information Processing Systems 10: Proceedings of the 1997 Conference, eds Jordan M. I. Kearns M. J. Solla S. A. (Cambridge, MA: MIT Press), 117– 123
- Houk J. C. Adams J. L. Barto A. G. (1995). A model of how the basal ganglia generates and uses neural signals that predict reinforcement, in Models of Information Processing in the Basal Ganglia, eds Houk J. C. Davis J. L. Beiser D. G. (Cambridge, MA: MIT Press), 249– 270
- Kohonen T. (1977). Associative Memory: A System Theoretic Approach. Berlin: Springer-Varlag
- Storck J. Hochreiter S. Schmidhuber J. (1995). Reinforcement-driven information acquisition in non-deterministic environments, in Proceedings of ICANN'95 (Paris), Vol. 2, 159– 164
Cited by
Sorted by citations
REBUS and the anarchic brain: Toward a unified model of the brain action of psychedelics
Carhart-Harris, R. L., & Friston, K. J. (2019) · Pharmacological Reviews · doi:10.1124/pr.118.017160 · Cited by 1,180
Active inference: a process theory
Friston, K., FitzGerald, T., Rigoli, F., Schwartenbeck, P., & Pezzulo, G. (2017) · Neural Computation · doi:10.1162/neco_a_00912 · Cited by 1,156
“…regarded as the drive for novelty-seeking behavior (Wittmann, Daw, Seymour, & Dolan, 2008; Krebs, Schott, Sch¨utze, & D¨uzel, 2009; Schwarten-beck et al., 2013), in which we anticipate uncertainty that can be resolved (e.g., opening a birthday present: see also Barto, Mirolli, & Baldassarre, 2013).”
BackgroundFriston, K., FitzGerald, T., Rigoli, F., Schwartenbeck, P., ODoherty, J., & Pezzulo, G. (2016) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2016.06.022 · Cited by 674
Predictive Processes and the Peculiar Case of Music
Koelsch, S., Vuust, P., & Friston, K. J. (2018) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2018.10.006 · Cited by 587
“, after and before an observation); it is also known as expected Bayesian surprise or information gain [82,83].”
Latent Space Autoregression for Novelty Detection
Abati, D., Porrello, A., Calderara, S., & Cucchiara, R. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00057 · Cited by 508
“According to cognitive psychology [4], novelty can be expressed either in terms of capabilities to remember an event or as a degree of surprisal [39] aroused by its observation.”
Background
Metadata
- Authors
- Barto, A., Mirolli, M., & Baldassarre, G.
- Affiliations
- University of Massachusetts Amherst (US)
- National Research Council (IT)
- Corresponding author
- Barto, Andrew
- Venue
- Frontiers in Psychology
- Publisher
- Frontiers Media SA
- Published
- 2013
- Volume
- 4
- Pages
- 907-907
- ISSN
- 1664-1078 (ISSN-L)
- PMC
- PMC3858647
- OpenAlex
- W2085754318
- Cited by
- 397
- References
- 107
- Citations by year
- 2026: 58 · 2025: 32 · 2024: 44 · 2023: 37 · 2022: 51 · 2021: 30 · 2020: 38 · 2019: 44 · 2018: 18 · 2017: 14 · 2016: 12 · 2015: 10 · 2014: 8 · 2013: 1
- FWCI
- 13.86
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Frontiers in Psychology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- European Commission (231722)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Psychological and Educational Research Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Embodied and Extended Cognition
- Keywords
- novelty, surprise, intrinsic motivation, exploratory behavior, avoidance behavior, learning, psychobiology, novelty detection
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.
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