Is the Homunculus “Aware” of Sensory Adaptation?

Abstract

Neural activity and perception are both affected by sensory history. The work presented here explores the relationship between the physiological effects of adaptation and their perceptual consequences. Perception is modeled as arising from an encoder-decoder cascade, in which the encoder is defined by the probabilistic response of a population of neurons, and the decoder transforms this population activity into a perceptual estimate. Adaptation is assumed to produce changes in the encoder, and we examine the conditions under which the decoder behavior is consistent with observed perceptual effects in terms of both bias and discriminability. We show that for all decoders, discriminability is bounded from below by the inverse Fisher information. Estimation bias, on the other hand, can arise for a variety of different reasons and can range from zero to substantial. We specifically examine biases that arise when the decoder is fixed, "unaware" of the changes in the encoding population (as opposed to "aware" of the adaptation and changing accordingly). We simulate the effects of adaptation on two well-studied sensory attributes, motion direction and contrast, assuming a gain change description of encoder adaptation. Although we cannot uniquely constrain the source of decoder bias, we find for both motion and contrast that an "unaware" decoder that maximizes the likelihood of the percept given by the preadaptation encoder leads to predictions that are consistent with behavioral data. This model implies that adaptation-induced biases arise as a result of temporary suboptimality of the decoder.

The effect of neural adaptation on population coding accuracy

“…The discrimination threshold follows from the trial-to-trial standard deviation in the estimate, σ (θ) as (10) where D is the discrimination criterion, chosen to be 1, corresponding to an error rate of 76%. The Cramer-Rao bound on the discrimination threshold is given by (Seriès et al 2009) (11) …”

“…Importantly, if the decoder does not have access to the state of adaptation, as we assumed here, its estimate will typically be biased (Seriès et al 2009). For the two decoders, the bias profile was virtually identical, Fig.…”

“…For biased estimators where the bias is nonuniform this is no longer true. Instead, for biased estimators, the Fisher Information forms a bound on the decoder's discrimination threshold (see Section 2 and Seriès et al (2009)). Conveniently this is also the quantity that is typically assessed psychophysically.…”

Visual perception as retrospective Bayesian decoding from high- to low-level features

“…Researchers have also studied decoding by using models to relate neuronal responses to perceptual estimation. Most models posit, explicitly or implicitly, that decoding follows the same low-to high-level hierarchy, often in the form of what we call the absolute-to-relative assumption (2)(3)(4)(5)(6). For example, these models may decode V1 responses to a line into a perceived orientation of, say 51.2°(or a distribution around it).…”

“…Typically, people choose model parameters to simulate observed ordinal discriminability without checking the relationship between absolute and relative/ordinal judgments (2)(3)(4)(5)(6). In particular, the assumption predicts that absolute-judgment distributions fully determine the corresponding relative-judgment distribution, yet no study measured distributions of both absolute and relative judgments to provide a strong test of the assumption.…”