Hierarchical interactions between sensory cortices defy predictive coding
Jacob A. Westerberg; Pieter R. Roelfsema · 2025 · Trends in Cognitive Sciences
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
Perceptual experience depends on recurrent interactions between lower and higher cortices. One theory, predictive coding, posits that feedback from higher to lower brain regions decreases neuronal activity predicted by higher-level representations. Despite the widespread adoption of predictive coding in neuroscience, the correspondence to neurophysiological findings in sensory cortices remains elusive. Here, we review how the canonical patterns of intra- and inter-cortical interactions that occur during perception and shifts of attention deviate from those predicted by predictive coding. We ar
Abstract by Jacob A. Westerberg; Pieter R. Roelfsema, Trends in Cognitive Sciences (2025) — licensed CC BY 4.0.
About to run something similar?
Run an AI Precheck on your own design to catch failure modes like this one before you spend the time. Your first desk check is free.
Related failures
Reproducible brain-wide association studies require thousands of individuals
Negative / Null Result ReportDaily Left Prefrontal Transcranial Magnetic Stimulation Therapy for Major Depressive Disorder
Negative / Null Result ReportEarly Brain Overgrowth in Autism Associated With an Increase in Cortical Surface Area Before Age 2 Years
Negative / Null Result ReportObjective evaluation of anosmia and ageusia in COVID‐19 patients: Single‐center experience on 72 cases
Negative / Null Result ReportPolarity and timing-dependent effects of transcranial direct current stimulation in explicit motor learning
Negative / Null Result ReportThe feedback-related negativity (FRN) revisited: New insights into the localization, meaning and network organization
WASTE indexes this work — it does not host or republish it. Failure-type classification is automated and approximate.
Metadata source: OpenAlex · DOI 10.1016/j.tics.2025.09.018
