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Updated: Jul 15, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

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Published on: March 2, 2015

Is predictive coding falsifiable?

H Bowman¹, D J Collins², A K Nayak³

¹School of Computing, University of Kent, UK; School of Psychology, University of Birmingham, UK; Wellcome Centre for Human Neuroimaging, UCL, UK.

Neuroscience and Biobehavioral Reviews

|September 25, 2023

Summary

This summary is machine-generated.

Predictive coding theory in neuroscience is challenged by unfalsifiability concerns. This study demonstrates how specific evoked-response patterns, considering precision, can falsify predictive coding, ensuring its scientific validity.

Keywords:

Attention Evoked responses Gain control Predictive coding

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Last Updated: Jul 15, 2025

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Area of Science:

Neuroscience
Cognitive Science
Computational Neuroscience

Background:

Predictive coding is a dominant theory in neuroscience, explaining perception and cognition through prediction errors.
Concerns have been raised regarding the potential unfalsifiability of predictive coding models.
The existence of 'contra-predictive' patterns, where expected stimuli elicit larger responses, challenges basic predictive coding formulations.

Purpose of the Study:

To address the unfalsifiability concerns surrounding predictive coding theory.
To investigate whether specific empirical data can falsify predictive coding.
To examine the role of precision-weighting in explaining contra-predictive patterns.

Main Methods:

Analysis of behavioral and neuroimaging data patterns.
Theoretical examination of predictive coding formulations under varying precision assumptions.
Identification of specific response characteristics (amplitude, latency, frequency) to differentiate explanations.

Main Results:

Contra-predictive patterns, where expected stimuli yield larger evoked responses, are observed.
While increased precision can explain these patterns, it also alters response latency and frequency.
These associated changes in latency and frequency provide empirical markers for falsification.

Conclusions:

Predictive coding is not inherently unfalsifiable.
The interplay of prediction error and precision-weighting can be empirically tested.
Specific patterns in evoked responses, modulated by precision, allow for the falsification of predictive coding, upholding its scientific rigor.