A computational model of perceptual expectation effect based on neural coding principles

Abstract

Prior expectation affects posterior perceptual experience. This contextual bias is called expectation effect. Previous studies have observed two different patterns of expectation effect: contrast and assimilation. Contrast magnifies the perceived incongruity, and assimilation diminishes the incongruity. This study proposes a computational model that explains the conditions of contrast and assimilation based on neural coding principles. This model proposed that prediction error, uncertainty, and external noise affected the expectation effect. Computer simulations with the model show that the pattern of expectation effect shifted from assimilation to contrast as the prediction error increased, uncertainty decreased the extent of the expectation effect, and external noise increased the assimilation. We conducted an experiment on the size–weight illusion (SWI) as a case of the cross‐modal expectation effect and discussed correspondence with the simulation. We discovered conditions where the participants perceived bigger object to be heavier than smaller one, which contradicts to conventional SWI. Practical applications Expectation effect in sensory perception represents a perceptual bias caused by prior expectation, such as illusions and cross‐modality. The computational model proposed in this study guides researchers and practitioners who investigate this bias in sensory studies to set a hypothesis with appropriate experimental factors. For example, the model suggests that prediction error can be used as a main factor to identify a condition at which assimilation switches over to contrast. The model provided how expectation uncertainty and noise of stimulus affect the switchover point of prediction error and extent of expectation effect. Uncertainty, which may differ from person to person, can be used as a factor to explain personal differences in the extent of expectation effect.

A methodology for extracting expectation effect in user-product interactions for multisensory experience design

“…Meanwhile, for the responses on "noisy" feeling, expectation effect could not be determined. A previous study suggested that uncertain expectations provide only a small extent of expectation effect (Yanagisawa, 2016a). As assumed, the participants hardly predicted the "noisy" feeling of sound based only on body size.…”

“…Consequently, tolerance decreases. (Yanagisawa, 2016a) found that the pattern of expectation effect shifted from assimilation to contrast as the prediction error (the difference between expected and actual value) by using a computational model of expectation effect. More than certain prediction error causes contrast, whereas small prediction error causes assimilation.…”

“…The user's mental model is built based on past experiences, whereby knowledge affects and changes each mental process. A mental model can bias a perceived feature as an expectation effect (Yanagisawa, 2016a;Yanagisawa andTakatsuji, 2015a, 2015b). Yanagisawa (2016a) formalized a computational model for expectation effect shown in Figure 4.…”

How does expectation affect sensory experience?

“…However, the mechanisms and conditions governing the contrasting and assimilating patterns are not yet clearly elucidated. In this study, I proposed a mathematical model of the expectation effect that explains the conditions of contrast and assimilation by applying neural coding principles, such as Bayesian decoding and the efficient encoding principles [1]. Based on the proposed model, I conducted computer simulations of the expectation effect and obtain an accurate hypothesis of the conditions of assimilation and contrast.…”

Multisensory Human-Food Interaction

“…The role of higher-level cognitive factors (e.g., consumer expectations) should also be considered . It has been argued that under the appropriate conditions, both sensory congruency and sensory incongruity between what is expected and what is experienced while tasting can bring positive or negative reactions to the overall tasting experience Yanagisawa, 2016). Visual cues (particularly color) set sensory and hedonic expectations regarding the likely taste and flavor properties of food and drink items.…”