Highlights d Cortex-wide task-epoch-specific causal neural activity across sensorimotor learning d Rapid inhibitory response of orofacial cortex contributes to delay licking d Secondary whisker motor cortex is a key node converting whisker input to lick plan d Sensory information converges to a focal frontal area critical for delayed response

Surprise-based learning allows agents to rapidly adapt to nonstationary stochastic environments characterized by sudden changes. We show that exact Bayesian inference in a hierarchical model gives rise to a surprise-modulated trade-off between forgetting old observations and integrating them with the new ones. The modulation depends on a probability ratio, which we call the Bayes Factor Surprise, that tests the prior belief against the current belief. We demonstrate that in several existing approximate algorithms, the Bayes Factor Surprise modulates the rate of adaptation to new observations. We derive three novel surprise-based algorithms, one in the family of particle filters, one in the family of variational learning, and one in the family of message passing, that have constant scaling in observation sequence length and particularly simple update dynamics for any distribution in the exponential family. Empirical results show that these surprise-based algorithms estimate parameters better than alternative approximate approaches and reach levels of performance comparable to computationally more expensive algorithms. The Bayes Factor Surprise is related to but different from the Shannon Surprise. In two hypothetical experiments, we make testable predictions for physiological indicators that dissociate the Bayes Factor Surprise from the Shannon Surprise. The theoretical insight of casting various approaches as surprise-based learning, as well as the proposed online algorithms, may be applied to the analysis of animal and human behavior and to reinforcement learning in nonstationary environments.

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Drivers of reinforcement learning (RL), beyond reward, are controversially debated. Novelty and surprise are often used equivocally in this debate. Here, using a deep sequential decision-making paradigm, we show that reward, novelty, and surprise play different roles in human RL. Surprise controls the rate of learning, whereas novelty and the novelty prediction error (NPE) drive exploration. Exploitation is dominated by model-free (habitual) action choices. A theory that takes these separate effects into account predicts on average 73 percent of the action choices of human participants after the first encounter of a reward and allows us to dissociate surprise and novelty in the EEG signal. While the event-related potential (ERP) at around 300ms is positively correlated with surprise, novelty, NPE, reward, and the reward prediction error, the ERP response to novelty and NPE starts earlier than that to surprise.

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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Surprising events trigger measurable brain activity and influence human behavior by affecting learning, memory, and decision-making. Currently there is, however, no consensus on the definition of surprise. Here we identify 16 mathematical definitions of surprise in a unifying framework, show how these definitions relate to each other, and prove under what conditions they are indistinguishable. We classify these surprise measures into four main categories: (i) change-point detection surprise, (ii) information gain surprise, (iii) prediction surprise, and (iv) confidence-correction surprise. We design experimental paradigms where different categories make different predictions: we show that surprise-modulation of the speed of learning leads to sensible adaptive behavior only for change-point detection surprise whereas surprise-seeking leads to sensible exploration strategies only for information gain surprise. However, since neither change-point detection surprise nor information gain surprise perfectly reflect the definition of ‘surprise’ in natural language, a combination of prediction surprise and confidence-correction surprise is needed to capture intuitive aspects of surprise perception. We formalize this combination in a new definition of surprise with testable experimental predictions. We conclude that there cannot be a single surprise measure with all functions and properties previously attributed to surprise. Consequently, we postulate that multiple neural mechanisms exist to detect and signal different aspects of surprise.Author noteAM is grateful to Vasiliki Liakoni, Martin Barry, and Valentin Schmutz for many useful discussions in the course of the last few years, and to Andrew Barto for insightful discussions through and after EPFL Neuro Symposium 2021 on “Surprise, Curiosity and Reward: from Neuroscience to AI”. We thank K. Robbins and collaborators for their publicly available experimental data (Robbins et al., 2018). All code needed to reproduce the results reported here will be made publicly available after publication acceptance. This research was supported by Swiss National Science Foundation (no. 200020_184615). Correspondence concerning this article should be addressed to Alireza Modirshanechi, School of Computer and Communication Sciences and School of Life Sciences, EPFL, Lausanne, Switzerland. E-mail: alireza.modirshanechi@epfl.ch.

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