The Bayesian brain: the role of uncertainty in neural coding and computation

Knill, David C.; Pouget, Alexandre

doi:10.1016/j.tins.2004.10.007

Cited by 1,949 publications

(1,681 citation statements)

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“…A Bayesian cognitive machinery of mating decision implies that female nervous system represents information about prospective mates in terms of conditional probability density functions. Recent studies in neuroscience provide evidence consistent with the hypothesis that perceptual computations in humans or non-human primates use these types of internal representations (Knill and Pouget, 2004) and that decisions arise by integrating information over time according to a Bayesian updating mechanism (Körding, 2007;Yang and Shadlen, 2007). It is not clear, however, to what extent these findings may be generalized to other animals with much simple nervous systems.…”

Section: Introductionmentioning

confidence: 92%

“…Female mating decisions depend on several male traits, often involving different sensory modalities (reviews in Bro-Jorgensen, 2010;Candolin, 2003;Hebets and Papaj, 2005;Jennions and Petrie, 1997) and females may integrate these different sources of information in at least three different ways: (i) they may form a one-dimension representation of male attractiveness, as assumed by Eq. (1); (ii) they may form a multi-dimension representation of male attractiveness, but use a single decision variable (Knill and Pouget, 2004); or (iii) they may integrate different sources of information into different DVs, hierarchically ordered according to a priority rule (Candolin, 2003). Now, we consider the simple case in which females possess a one-dimension preference function and use a single DV in mating decision.…”

Section: Acquisition Of Evidence: the Computational Mechanisms Of Permentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 92%

Section: Acquisition Of Evidence: the Computational Mechanisms Of Permentioning

confidence: 99%

Computational mate choice: Theory and empirical evidence

Castellano

Cadeddu

Cermelli

2012

Behavioural Processes

View full text Add to dashboard Cite

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“…More recently, it has been proposed that this intuitive idea can be captured in terms of hierarchical Bayesian inference, using generative models with predictive coding or free-energy minimisation; and that this is the main neurocomputational principle for the brain's perception of the environment as well as its learning of new contingencies (Ballard, Hinton, & Sejnowski, 1983;Dayan, Hinton, Neal, & Zemel, 1995;Friston, 2002;Friston, 2003;Friston, 2005;Kawato, Hayakawa, & Inui, 1993;Kersten, Mamassian, & Yuille, 2004;Knill & Pouget, 2004;Mumford, 1992;Murray, Schrater, & Kersten, 2004;Rao & Ballard, 1999).…”

Section: Introductionmentioning

confidence: 99%

Predictive coding explains binocular rivalry: An epistemological review

2008

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a b s t r a c tBinocular rivalry occurs when the eyes are presented with different stimuli and subjective perception alternates between them. Though recent years have seen a number of models of this phenomenon, the mechanisms behind binocular rivalry are still debated and we still lack a principled understanding of why a cognitive system such as the brain should exhibit this striking kind of behaviour. Furthermore, psychophysical and neurophysiological (single cell and imaging) studies of rivalry are not unequivocal and have proven difficult to reconcile within one framework. This review takes an epistemological approach to rivalry that considers the brain as engaged in probabilistic unconscious perceptual inference about the causes of its sensory input. We describe a simple empirical Bayesian framework, implemented with predictive coding, which seems capable of explaining binocular rivalry and reconciling many findings. The core of the explanation is that selection of one stimulus, and subsequent alternation between stimuli in rivalry occur when: (i) there is no single model or hypothesis about the causes in the environment that enjoys both high likelihood and high prior probability and (ii) when one stimulus dominates, the bottom-up, driving signal for that stimulus is explained away while, crucially, the bottom-up signal for the suppressed stimulus is not, and remains as an unexplained but explainable prediction error signal. This induces instability in perceptual dynamics that can give rise to perceptual transitions or alternations during rivalry.

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“…Forced choice methods have now standardized threshold determination in several fields of psychophysics (Treutwein 1995;Leek 2001;Knill and Pouget 2004), and these techniques are now the standard method of assessing motion perception thresholds Grabherr et al 2008;Gu et al 2008;MacNeilage et al 2010;Mallery et al 2010). These techniques have the advantage of largely eliminating the tendency to bias one choice, but make the assumption that the choices have equal thresholds.…”

Section: Introductionmentioning

confidence: 99%

Directional Asymmetries and Age Effects in Human Self-Motion Perception

Roditi

Crane

2012

JARO

109

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Directional asymmetries in vestibular reflexes have aided the diagnosis of vestibular lesions; however, potential asymmetries in vestibular perception have not been well defined. This investigation sought to measure potential asymmetries in human vestibular perception. Vestibular perception thresholds were measured in 24 healthy human subjects between the ages of 21 and 68 years. Stimuli consisted of a single cycle of sinusoidal acceleration in a single direction lasting 1 or 2 s (1 or 0.5 Hz), delivered in sway (leftright), surge (forward-backward), heave (up-down), or yaw rotation. Subject identified self-motion directions were analyzed using a forced choice technique, which permitted thresholds to be independently determined for each direction. Non-motion stimuli were presented to measure possible response bias. A significant directional asymmetry in the dynamic response occurred in 27% of conditions tested within subjects, and in at least one type of motion in 92% of subjects. Directional asymmetries were usually consistent when retested in the same subject but did not occur consistently in one direction across the population with the exception of heave at 0.5 Hz. Responses during null stimuli presentation suggested that asymmetries were not due to biased guessing. Multiple models were applied and compared to determine if sensitivities were direction specific. Using Akaike information criterion, it was found that the model with direction specific sensitivities better described the data in 86% of runs when compared with a model that used the same sensitivity for both directions. Mean thresholds for yaw were 1.3±0.9°/s at 0.5 Hz and 0.9±0.7°/s at 1 Hz and were independent of age. Thresholds for surge and sway were 1.7±0.8 cm/s at 0.5 Hz and 0.7±0.3 cm/s at 1.0 Hz for subjects G50 and were significantly higher in subjects 950 years old. Heave thresholds were higher and were independent of age.

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The Bayesian brain: the role of uncertainty in neural coding and computation

Cited by 1,949 publications

References 51 publications

Computational mate choice: Theory and empirical evidence

Computational mate choice: Theory and empirical evidence

Predictive coding explains binocular rivalry: An epistemological review

Directional Asymmetries and Age Effects in Human Self-Motion Perception

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