2016-01-22
Pigeons are well capable to categorize visual stimuli. Now scientists of the biopsychology adopted a reverse engineering approach to study categorization learning in a novel way. Instead of training pigeons on predefined categories, they simply presented stimuli and analyzed neural output in search of categorical clustering on a solely neural level. They presented artificial, easily distinguishable colored shapes and grating while recording from the nidopallium frontolaterale (NFL), a higher visual area in the avian brain. They computed representational dissimilarity matrices to reveal categorical clustering based on the neural data. This revealed that colored shapes and gratings were differentially represented in the brain. This study gives proof-of-concept that this reverse engineering approach – namely reading out categorical information from neural data – can be quite helpful in understanding the neural underpinnings of categorization learning.
Pigeons are well capable to categorize visual stimuli. Now scientists of the biopsychology adopted a reverse engineering approach to study categorization learning in a novel way. Instead of training pigeons on predefined categories, they simply presented stimuli and analyzed neural output in search of categorical clustering on a solely neural level. They presented artificial, easily distinguishable colored shapes and grating while recording from the nidopallium frontolaterale (NFL), a higher visual area in the avian brain. They computed representational dissimilarity matrices to reveal categorical clustering based on the neural data. This revealed that colored shapes and gratings were differentially represented in the brain. This study gives proof-of-concept that this reverse engineering approach – namely reading out categorical information from neural data – can be quite helpful in understanding the neural underpinnings of categorization learning.
Copyright © BioPsy 2023
Last update: Sep 28, 2023
