Why do you fear the bogeyman? An embodied predictive coding model of perceptual inference
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 the wind?) 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 how you 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.
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The same control loop can perform homeostatic regulation through autonomic reflexes.
“In addition, the same loop can perform homeostatic regulation through autonomic reflexes (Pezzulo, 2013, Seth, 2013, Seth et al., 2012) – a process that has long been understood in terms of error cancellation (Cannon, 1929).”
From Active Inference, homeostatic regulation and adaptive behavioural control (2015) · cites this paper as Pezzulo, 2013
The objective is to suppress interoceptive prediction errors through action.
“The objective is to suppress interoceptive prediction errors (Barrett and Simmons, 2015, Pezzulo, 2013, Seth et al., 2012) through action”
From Active Inference, homeostatic regulation and adaptive behavioural control (2015) · cites this paper as Pezzulo, 2013
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“Understanding how different and heterogeneous elements combine in predictive coding architectures, and their relative importances, can shed light on the architecture of conceptual knowledge (Barsalou, 2008; Pezzulo, 2012; Pezzulo and Castelfranchi, 2009; Pezzulo et al., 2011, 2013).”
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- Authors
- Pezzulo, G.
- Affiliations
- National Research Council (IT)
- Corresponding author
- Pezzulo, Giovanni
- ORCID
- Pezzulo, Giovanni
- Venue
- Cognitive, Affective, & Behavioral Neuroscience
- Published
- 2013-12-05
- Volume
- 14
- Issue
- 3
- Pages
- 902-911
- ISSN
- 1530-7026 (ISSN-L)
- 1531-135X
- OpenAlex
- W2054741347
- Cited by
- 128
- References
- 10
- Citations by year
- 2026: 19 · 2025: 5 · 2024: 16 · 2023: 15 · 2022: 9 · 2021: 12 · 2020: 12 · 2019: 10 · 2018: 7 · 2017: 12 · 2016: 5 · 2015: 3 · 2014: 3
- FWCI
- 0.31
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Cognitive Affective & Behavioral Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Embodied and Extended Cognition
- Health Sciences › Medicine › Psychiatry and Mental health › Psychosomatic Disorders and Their Treatments
- Life Sciences › Neuroscience › Cognitive Neuroscience › Cognitive Science and Education Research
- Keywords
- perceptual inference, interoception, fear, bogeyman, horror, hypothesis testing, mind-body unity, heart rate
- MeSH terms
- Awareness, Bayes Theorem, Fear, Humans, Models, Psychological, Motivation, Perception, Predictive Value of Tests, Sensation
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