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Biol Psychol. 2017 Apr;125:45-57. doi: 10.1016/j.biopsycho.2017.02.004. Epub 2017 Feb 28.

Temporal and spatial localization of prediction-error signals in the visual brain.

Author information

1
Institute of Health and Biomedical Innovation, Queensland University of Technology, Australia; York Neuroimaging Centre and Department of Psychology, University of York, UK. Electronic address: patrick.johnston@qut.edu.au.
2
Institute of Health and Biomedical Innovation, Queensland University of Technology, Australia; York Neuroimaging Centre and Department of Psychology, University of York, UK.
3
York Neuroimaging Centre and Department of Psychology, University of York, UK.
4
Brain & Psychological Sciences Research Centre, Swinburne University of Technology, Burwood Road, Hawthorn, VIC 3123, Australia.

Abstract

It has been suggested that the brain pre-empts changes in the environment through generating predictions, although real-time electrophysiological evidence of prediction violations in the domain of visual perception remain elusive. In a series of experiments we showed participants sequences of images that followed a predictable implied sequence or whose final image violated the implied sequence. Through careful design we were able to use the same final image transitions across predictable and unpredictable conditions, ensuring that any differences in neural responses were due only to preceding context and not to the images themselves. EEG and MEG recordings showed that early (N170) and mid-latency (N300) visual evoked potentials were robustly modulated by images that violated the implied sequence across a range of types of image change (expression deformations, rigid-rotations and visual field location). This modulation occurred irrespective of stimulus object category. Although the stimuli were static images, MEG source reconstruction of the early latency signal (N/M170) localized expectancy violation signals to brain areas associated with motion perception. Our findings suggest that the N/M170 can index mismatches between predicted and actual visual inputs in a system that predicts trajectories based on ongoing context. More generally we suggest that the N/M170 may reflect a "family" of brain signals generated across widespread regions of the visual brain indexing the resolution of top-down influences and incoming sensory data. This has important implications for understanding the N/M170 and investigating how the brain represents context to generate perceptual predictions.

KEYWORDS:

EEG; M170; MEG; N170; Perceptual prediction; Predictive coding; Visual perception

PMID:
28257807
DOI:
10.1016/j.biopsycho.2017.02.004
[Indexed for MEDLINE]

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