Academic journal article Cognitive, Affective and Behavioral Neuroscience

Why Do You Fear the Bogeyman? an Embodied Predictive Coding Model of Perceptual Inference

Academic journal article Cognitive, Affective and Behavioral Neuroscience

Why Do You Fear the Bogeyman? an Embodied Predictive Coding Model of Perceptual Inference

Article excerpt

Published online: 5 December 2013

© Psychonomic Society, Inc. 2013

Abstract Why are we scared by nonperceptual entities such as the bogeyman, and why does the bogeyman only visit us during the night? Why does hearing a window squeaking in the night suggest to us the unlikely idea of a thief or a killer? And why is this more likely to happen after watching a horror movie? To answer these and similar questions, we need to put mind and body together again and consider the embodied nature of perceptual and cognitive inference. Predictive coding provides a general framework for perceptual inference; I propose to extend it by including interoceptive and bodily information. The resulting embodied predictive coding inference permits one to compare alternative hypotheses (e.g., is the sound I hear generated by a thief or thewind?) using the same inferential scheme as in predictive coding, but using both sensory and interoceptive information as evidence, rather than just considering sensory events. If you hear a window squeaking in the night after watching a horror movie, you may consider plausible a very unlikely hypothesis (e.g., a thief, or even the bogeyman) because it explains both what you sense (e.g., the window squeaking in the night) and howyou feel (e.g., your high heart rate). The good news is that the inference that I propose is fully rational and gives minds and bodies equal dignity. The bad news is that it also gives an embodiment to the bogeyman, and a reason to fear it.

Keywords Embodied predictive coding . Perceptual inference . Decision-making . Interoception

(ProQuest: ... denotes formulae omitted.)

"There is more wisdom in your body than in your deepest philosophy."

-Friedrich Nietzsche

It is a windy night. You go to sleep a bit shocked because, say, you had a small car accident or just watched a shark attack horror movie. During the night, you hear a windowsqueaking.

In normal conditions, you would attribute this noise to the windy night. But this night, the idea that a thief or even a killer is entering your house jumps into your mind. Normally you would have immediately dismissed this hypothesis, but now it seems quite believable, despite the fact that there have been no thefts in your town in the last few years; suddenly, you find yourself expecting a thief to come out of the shadows. How is this possible?

According to the predictive coding theory (Clark, 2013; Friston, 2005; Rao & Ballard, 1999), the perceptual system is a hierarchical generativemodel that performs a Bayesian form of inference from the available sensory data (say, the sound of a window squeaking) to perceptual and cognitive hypotheses that represent the most likely causes of the data (say, the wind or a thief).

At higher levels, the competing perceptual hypotheses correspond to possible explanations of the sensory stimuli. Let's assume, for simplicity, only two mutually exclusive hypotheses: "It is the wind" and "It is a thief." Because they are mutually exclusive, the probability of the two hypotheses sums to 1 [e.g., if P(wind) = .8, then P(thief) = .2].

These hypotheses compete on the basis of how well they explain the sensory evidence, which in our example is the sound of the window squeaking. We can consider the threelevel predictive coding hierarchy shown in Fig. 1. The arrows indicate that wind and thief can be considered likely causes of the window squeaking, which in turn can be considered the cause of the heard sound. Intuitively, this corresponds to one of my two hypotheses (wind vs. thief) causing the window to squeak, which in turn causes the sound I hear.

Let's assume, once again for simplicity, that I can unequivocally attribute the sound that I hear to a window squeaking, so we can simplify the problem with the two-level hierarchy shown in Fig. 2. Essentially, the predictive coding framework implements the idea of Helmhol tz (1866/1962) that perception is an unconscious inference of the causes of sensation. …

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