The development of Bayesian integration in sensorimotor estimation

Chambers, Claire; Sokhey, Taegh; Gaebler‐Spira, Deborah; Kording, Konrad

doi:10.1101/136267

Cited by 8 publications

(9 citation statements)

References 35 publications

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“…For example, prior knowledge acquired in a sensorimotor task does not fully generalize to a perceptual decision-making task that requires the same probabilistic treatment of sensory and prior information (Chambers, Fernandes, & Kording, 2017). Also, we show that young children fail to incorporate experimentally-imposed priors into their sensorimotor estimates and learn to approximate efficient use of statistical information during development (Chambers, Sokhey, Gaebler-Spira, & Kording, 2017). If the underlying neural processes are innately 'Bayes optimal', then behavior should reflect this under different conditions and without learning.…”

mentioning

confidence: 79%

While optimal models are useful, optimality claims are not that common.

Chambers¹,

Kording²

2018

Preprint

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mentioning

confidence: 79%

While optimal models are useful, optimality claims are not that common.

Chambers¹,

Kording²

2018

Preprint

Self Cite

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show abstract

“…For example, prior knowledge acquired in a sensorimotor task does not fully generalize to a perceptual decision-making task that requires the same probabilistic treatment of sensory and prior information (Chambers et al 2017a). Also, we show that young children fail to incorporate experimentally imposed priors into their sensorimotor estimates and learn to approximate efficient use of statistical information during development (Chambers et al 2017b). If the underlying neural processes are innately "Bayes optimal," then behavior should reflect this under different conditions and without learning.…”

Section: Introductionmentioning

confidence: 84%

When the simplest voluntary decisions appear patently suboptimal

Salinas¹,

Seideman²,

Stanford³

2018

Behav Brain Sci

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Rahnev & Denison (R&D) catalog numerous experiments in which performance deviates, often in subtle ways, from the theoretical ideal. We discuss an extreme case, an elementary behavior (reactive saccades to single targets) for which a simple contextual manipulation results in responses that are dramatically different from those expected based on reward maximization – and yet are highly informative and amenable to mechanistic examination.

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“…Everything we have discussed could be framed in terms of a cue-prior combination task rather than a cue-cue combination task by letting all parameters that represent one of the cues in each of our equations represent the prior knowledge instead. For example, many of the studies focusing on integration of sensory and prior information require observers to estimate the location of a hidden target, on a continuous scale, using an uncertain sensory cue and prior knowledge of the target distribution (e.g., Berniker et al, 2010;Tassinari et al, 2006;Vilares et al, 2012;Chambers et al, 2018;Kiryakova et al, 2020;Bejjanki et al, 2016). In some cases, when the target distribution is centered on the middle of the stimulus-response range, it would be difficult to parse how much of the central bias is due to a reliance on prior knowledge or a central tendency bias.…”

Section: Extending the Methods To Model Central Tendency Biases In Tasks Where One Of The Cues Is Prior Knowledgementioning

confidence: 99%

Central tendency biases must be accounted for to consistently capture Bayesian cue combination in continuous response data

et al. 2021

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Observers in perceptual tasks are often reported to combine multiple sensory cues in a weighted average that improves precision—in some studies, approaching statistically optimal (Bayesian) weighting, but in others departing from optimality, or not benefitting from combined cues at all. To correctly conclude which combination rules observers use, it is crucial to have accurate measures of their sensory precision and cue weighting. Here, we present a new approach for accurately recovering these parameters in perceptual tasks with continuous responses. Continuous responses have many advantages, but are susceptible to a central tendency bias, where responses are biased towards the central stimulus value. We show that such biases lead to inaccuracies in estimating both precision gains and cue weightings, two key measures used to assess sensory cue combination. We introduce a method that estimates sensory precision by regressing continuous responses on targets and dividing the variance of the residuals by the squared slope of the regression line, “correcting-out” the error introduced by the central bias and increasing statistical power. We also suggest a complementary analysis that recovers the sensory cue weights. Using both simulations and empirical data, we show that the proposed methods can accurately estimate sensory precision and cue weightings in the presence of central tendency biases. We conclude that central tendency biases should be (and can easily be) accounted for to consistently capture Bayesian cue combination in continuous response data.

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The development of Bayesian integration in sensorimotor estimation

Abstract: If the brain is inherently

Cited by 8 publications

References 35 publications

While optimal models are useful, optimality claims are not that common.

While optimal models are useful, optimality claims are not that common.

When the simplest voluntary decisions appear patently suboptimal

Central tendency biases must be accounted for to consistently capture Bayesian cue combination in continuous response data

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