Learning from feedback is independent from feedback visibility, but supported by aperiodic neural activity

Humans and other animals learn from feedback, by tending to repeat rewarded behavior and change behavior that receives negative feedback. Previous findings suggest that feedback does not need to be consciously perceived in order to be effective. Using continuous flash suppression, we presented participants with visible and invisible positive and negative feedback during a time estimation task while recording EEG. Behavioral results showed that both visible and invisible feedback significantly influenced time estimation error and adjustment in trial N+1, suggesting that subliminal reward information can be effectively utilized. Electrophysiological indicators (reward positivity, P3a, theta activity, aperiodic exponent) showed feedback-valence effects, but only when the feedback was visible. Performance in trial N+1 was successfully predicted by the aperiodic exponent only. These findings suggest that (1) behavioral control is independent from conscious perception of feedback signals; (2) the valence sensitivity of electrophysiological indicators is not informative for effective learning or the impact of feedback on behavioral control; and (3) the general neural state, as characterized by the aperiodic exponent, is predictive of the quality of learning from feedback, with steeper exponents providing the most supportive conditions for learning.

Authors

Institutions

Publication Details

Journal
NeuroImage
Published
2026-03-01
DOI
https://doi.org/10.1016/j.neuroimage.2026.121894
Primary Topic
Neural dynamics and brain function
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Learning from feedback is independent from feedback visibility, but supported by aperiodic neural activity

Lorenza S. Colzato, Yanliang Sun, Bernhard Hommel, Dandan Liu et al.
NeuroImage
Neural dynamics and brain function
article

Learning from feedback is independent from feedback visibility, but supported by aperiodic neural activity

Lorenza S. Colzato, Yanliang Sun, Bernhard Hommel, Dandan Liu, Shiwei Jia
article en

Abstract

Humans and other animals learn from feedback, by tending to repeat rewarded behavior and change behavior that receives negative feedback. Previous findings suggest that feedback does not need to be consciously perceived in order to be effective. Using continuous flash suppression, we presented participants with visible and invisible positive and negative feedback during a time estimation task while recording EEG. Behavioral results showed that both visible and invisible feedback significantly influenced time estimation error and adjustment in trial N+1, suggesting that subliminal reward information can be effectively utilized. Electrophysiological indicators (reward positivity, P3a, theta activity, aperiodic exponent) showed feedback-valence effects, but only when the feedback was visible. Performance in trial N+1 was successfully predicted by the aperiodic exponent only. These findings suggest that (1) behavioral control is independent from conscious perception of feedback signals; (2) the valence sensitivity of electrophysiological indicators is not informative for effective learning or the impact of feedback on behavioral control; and (3) the general neural state, as characterized by the aperiodic exponent, is predictive of the quality of learning from feedback, with steeper exponents providing the most supportive conditions for learning.

NeuroImage
Shandong Normal University (CN)
Openalex Percentile: Top 9%
Neural dynamics and brain function
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Learning from feedback is independent from feedback visibility, but supported by aperiodic neural activity — Lorenza S. Colzato, Yanliang Sun, et al. · NeuroImage (2026) | TGRS Research Map | TGRS