Bayesian approaches to sensory integration for motor control

Berniker, Max; Kording, Konrad

doi:10.1002/wcs.125

Cited by 54 publications

(55 citation statements)

References 67 publications

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“…Thus, it is also possible that the perception of causality between an action and its outcome is one of the decisive factors for the acquisition of their statistical relation. Other lines of evidence have suggested that causality inference between events plays critical roles in the optimal integration of cross-sensory signals (Körding et al, 2007; Sato et al, 2007; Berniker and Körding, 2011). As such, we suggest that the perception of causality between an action and its outcome at least partly underlies the acquisition of the statistical relation between them, though we need to empirically dissociate the contribution of action from non-action factors to the acquisition of an action-effect relation.…”

Section: Discussionmentioning

confidence: 99%

Side effect of acting on the world: acquisition of action-outcome statistic relation alters visual interpretation of action outcome

Kawabe¹

2013

Front. Hum. Neurosci.

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Humans can acquire the statistical features of the external world and employ them to control behaviors. Some external events occur in harmony with an agent's action, and thus, humans should also be able to acquire the statistical features between an action and its external outcome. We report that the acquired action-outcome statistical features alter the visual appearance of the action outcome. Pressing either of two assigned keys triggered visual motion whose direction was statistically biased either upward or downward, and observers judged the stimulus motion direction. Points of subjective equality (PSE) for judging motion direction were shifted repulsively from the mean of the distribution associated with each key. Our Bayesian model accounted for the PSE shifts, indicating the optimal acquisition of the action-effect statistical relation. The PSE shifts were moderately attenuated when the action-outcome contingency was reduced. The Bayesian model again accounted for the attenuated PSE shifts. On the other hand, when the action-outcome contiguity was reduced, the PSE shifts were greatly attenuated, and however, the Bayesian model could not accounted for the shifts. The results indicate that visual appearance can be modified by prediction based on the optimal acquisition of action-effect causal relation.

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Section: Discussionmentioning

confidence: 99%

Side effect of acting on the world: acquisition of action-outcome statistic relation alters visual interpretation of action outcome

Kawabe¹

2013

Front. Hum. Neurosci.

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“…The Bayesian paradigm has been viewed as an extension of logic that enables reasoning with propositions whose truth or falsity is uncertain [54,55]. In cognitive science, it has successfully accounted for a wide range of observations, including cue combination in humans [5,4,1,56,3,6,7,2], sensorimotor control [8,9,57], integration of temporal statistics [35,34,10,11,36], perceptual multistability [58,39,40], and various aspects of cognition [59,60,61,38,62].…”

Section: Online Bayesian Inferencementioning

confidence: 99%

Human Inference in Changing Environments with Temporal Structure

Prat-Carrabin

Wilson

Cohen

et al. 2019

Preprint

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To make informed decisions in natural environments that change over time, humans must update their beliefs as new observations are gathered. Studies exploring human inference as a dynamical process that unfolds in time have focused on situations in which the statistics of observations are history-independent. Yet temporal structure is everywhere in nature, and yields history-dependent observations. Do humans modify their inference processes depending on the latent temporal statistics of their observations? We investigate this question experimentally and theoretically using a change-point inference task. We show that humans adapt their inference process to fine aspects of the temporal structure in the statistics of stimuli. As such, humans behave qualitatively in a Bayesian fashion, but, quantitatively, deviate away from optimality. Perhaps more importantly, humans behave suboptimally in that their responses are not deterministic, but variable. We show that this variability itself is modulated by the temporal statistics of stimuli. To elucidate the cognitive algorithm that yields this behavior, we investigate a broad array of existing and new models that characterize different sources of suboptimal deviations away from Bayesian inference. While models with 'output noise' that corrupts the responseselection process are natural candidates, human behavior is best described by sampling-based inference models, in which the main ingredient is a compressed approximation of the posterior, represented through a modest set of random samples and updated over time. This result comes to complement a growing literature on sample-based representation and learning in humans.

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“…the prior) with a measured observation (the likelihood) to update predictions of the current state of the system (the posterior probability). Kalman filters have found several applications, for example, in aeronautics and in humanoid robotics (see references in Berniker and Körding, 2011) and they have been used also in neuroscience, to develop neuro-physiological models of motor controls (Berniker and Körding, 2011).…”

Section: A Bayesian Sequential-sampling Model Of Choicementioning

confidence: 99%

Computational mate choice: Theory and empirical evidence

Castellano

Cadeddu

Cermelli

2012

Behavioural Processes

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a b s t r a c tThe present review is based on the thesis that mate choice results from information-processing mechanisms governed by computational rules and that, to understand how females choose their mates, we should identify which are the sources of information and how they are used to make decisions. We describe mate choice as a three-step computational process and for each step we present theories and review empirical evidence. The first step is a perceptual process. It describes the acquisition of evidence, that is, how females use multiple cues and signals to assign an attractiveness value to prospective mates (the preference function hypothesis). The second step is a decisional process. It describes the construction of the decision variable (DV), which integrates evidence (private information by direct assessment), priors (public information), and value (perceived utility) of prospective mates into a quantity that is used by a decision rule (DR) to produce a choice. We make the assumption that females are optimal Bayesian decision makers and we derive a formal model of DV that can explain the effects of preference functions, mate copying, social context, and females' state and condition on the patterns of mate choice. The third step of mating decision is a deliberative process that depends on the DRs. We identify two main categories of DRs (absolute and comparative rules), and review the normative models of mate sampling tactics associated to them. We highlight the limits of the normative approach and present a class of computational models (sequential-sampling models) that are based on the assumption that DVs accumulate noisy evidence over time until a decision threshold is reached. These models force us to rethink the dichotomy between comparative and absolute decision rules, between discrimination and recognition, and even between rational and irrational choice. Since they have a robust biological basis, we think they may represent a useful theoretical tool for behavioural ecologist interested in integrating proximate and ultimate causes of mate choice.

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Bayesian approaches to sensory integration for motor control

Cited by 54 publications

References 67 publications

Side effect of acting on the world: acquisition of action-outcome statistic relation alters visual interpretation of action outcome

Side effect of acting on the world: acquisition of action-outcome statistic relation alters visual interpretation of action outcome

Human Inference in Changing Environments with Temporal Structure

Computational mate choice: Theory and empirical evidence

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