Higher visual areas act like domain-general filters with strong selectivity and functional specialization
Meenakshi Khosla; Leila Wehbe · 2026 · Nature Communications
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
Neuroscientific studies rely heavily on a-priori hypotheses, which can bias results toward existing theories. Here, we use a hypothesis-neutral approach to study category selectivity in higher visual cortex. Using only stimulus images and their associated fMRI activity, we constrain randomly initialized neural networks to predict voxel activity. Despite no category-level supervision, units in the trained networks act as detectors for semantic concepts like 'faces' or 'words', providing solid empirical support for categorical selectivity. Importantly, this selectivity is mostly maintained when
Abstract by Meenakshi Khosla; Leila Wehbe, Nature Communications (2026) — 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 10.1038/s41467-026-73938-9