Biology subjects

Bonnen, T.

Publications and source records attributed to Bonnen, T..

3 recordsLinked to original sources

bioRxiv2023

Medial temporal cortex supports compositional visual inferences

Perception unfolds across multiple timescales. For humans and other primates, many object-centric visual attributes can be inferred at a glance (i.e., given <200ms of visual information), an ability supported by ventral temporal cortex (VTC). Other perceptual inferences require more time; to determine a novel objects identity, we might need to represent its unique configuration of visual features, requiring multiple glances. Here we evaluate whether perirhinal cortex (PRC), downstream from VTC, supports object perception by integrating over such visuospatial sequences. We first compare human visual inferences directly to electrophysiological recordings from macaque VTC. While human performance at a glance is approximated by a linear readout of VTC, participants radically outperform VTC given longer viewing times (i.e., >200ms). Next, we leverage a stimulus set that enables us to characterize PRC involvement in these temporally extended visual inferences. We find that human visual inferences at a glance resemble the deficits observed in PRC-lesioned human participants. Not surprisingly, by measuring gaze behaviors during these temporally extended viewing periods, we find that participants sequentially sample task-relevant features via multiple saccades/fixations. These patterns of visuospatial attention are both reliable across participants and necessary for PRC-dependent visual inferences. These data reveal complementary neural systems that support visual object perception: VTC provides a rich set of visual features at a glance, while PRC is able to integrate over the sequential outputs of VTC to support object-level inferences.

neuroscience↗

bioRxiv2022

Inconsistencies between human and macaque lesion data can be resolved with a stimulus-computable model of the ventral visual stream

Decades of neuroscientific research has sought to understand medial temporal lobe (MTL) involvement in perception. The field has historically relied on qualitative accounts of perceptual processing (e.g. descriptions of stimuli), in order to interpret evidence across subjects, experiments, and species. Here we use stimulus computable methods to formalize MTL-dependent visual behaviors. We draw from a series of experiments (Eldridge et al., 2018) administered to monkeys with bilateral lesions that include perirhinal cortex (PRC), an MTL structure implicated in visual object perception. These stimuli were designed to maximize a qualitative perceptual property ( feature ambiguity) considered relevant to PRC function. We formalize perceptual demands imposed by these stimuli using a computational proxy for the primate ventral visual stream (VVS). When presented with the same images administered to experimental subjects, this VVS model predicts both PRC-intact and -lesioned choice behaviors; a linear readout of the VVS should be sufficient for performance on these tasks. Given the absence of PRC-related deficits on these ambiguous stimuli, we (Eldridge et al., 2018) originally concluded that PRC is not involved in perception. Here we (Bonnen & Eldridge) reevaluate this claim. By situating these data alongside computational results from multiple studies administered to humans with naturally occurring PRC lesions, this work offers the first formal, cross-species evaluation of MTL involvement in perception. In doing so, we contribute to a growing understanding of visual processing that depends on--and is independent of--the MTL.

neuroscience↗

bioRxiv2020

When the ventral visual stream is not enough: A deep learning account of medial temporal lobe involvement in perception

The medial temporal lobe (MTL) supports a constellation of memory-related behaviors. Its involvement in perceptual processing, however, has been subject to enduring debate. This debate centers on perirhinal cortex (PRC), an MTL structure at the apex of the ventral visual stream (VVS). Here we leverage a deep learning framework that approximates visual behaviors supported by the VVS--i.e. lacking PRC. We first apply this approach retroactively, modeling 30 published visual discrimination experiments: Excluding non-diagnostic stimulus sets, there is a striking correspondence between VVS-modeled and PRC-lesioned behavior, while each are outperformed by PRC-intact participants. We corroborate and extend these results with a novel experiment, directly comparing PRC-intact human performance to electrophysiological recordings from the macaque VVS: PRC-intact participants outperform a linear readout of high-level visual cortex. By situating lesion, electrophysiological, and behavioral results within a shared computational framework, this work resolves decades of seemingly inconsistent findings surrounding PRC involvement in perception.

neuroscience↗