@incollection{lappeEncoding_2024,
author = "Alexander Lappe and A. Bogn{\'a}r and G. G. Nejad and Albert Mukovskiy and Martin A. Giese and Rufin Vogels",
abstract = "The primate visual system has evolved subareas in which neurons appear to respond
more strongly to images of a specific semantic category, like faces or bodies. The computational
processes underlying these regions remain unclear, and there is debate on whether this effect is in
fact driven by semantics or rather by visual features that occur more often among images from
the specific category. Recent works tackling the question of whether the same visual features
drive responses of face-selective cells to face images and non-face images have yielded mixed
results. Here, we report findings on shared encoding of body and object images in body-selective
neurons in macaque superior temporal sulcus. We targeted two fMRI-defined regions, anterior
and posterior body patches in two awake macaques using V probes, recording multi-unit activity
in and around these patches. In a first phase, we recorded responses to a set of 475 images of a
macaque avatar in various poses. We then trained a deep-neural-network based model to predict
responses to these images, and subsequently evaluated the model on two sets of object and body
stimuli consisting of 6857 and 2068 images, respectively. These images comprised a variety of
object types and animal species. After the inference process, we selected the highest and lowest
predicted activator for each recording channel from both object and body images. In a second
phase, we recorded responses of the same multi-units to these stimuli. For analysis, we only kept
those multi-unit sites with high test/retest reliability. Also, we only considered multi-unit sites
for which the selected bodies elicited a significantly higher response than the selected objects.
We then tested whether the high-predicted objects/bodies indeed lead to higher responses at the
corresponding electrode than the low-predicted ones. Across neurons, we found a significant
preference of the high-predicted stimulus for both objects and bodies. The highly-activating
objects consisted of a variety of everyday objects and did not necessarily globally resemble a
body. Furthermore, the correlations between predicted and recorded responses to the objects
were consistently positive for both monkeys and recording areas, meaning that the model was
able to predict responses to objects after having only been trained on images of a macaque
avatar. Our results show that the feature preferences of body-selective neurons are at least
partially shared between bodies and objects. On a larger scope, we provide further evidence that
category selectivity arises due to highly shared visual features among category instances, rather
than semantics.",
booktitle = "Society for Neuroscience",
title = "{E}ncoding of bodies and objects in body-selective neurons",
year = "2024",
}