Literature DB >> 26973168

A method for real-time visual stimulus selection in the study of cortical object perception.

Daniel D Leeds1, Michael J Tarr2.   

Abstract

The properties utilized by visual object perception in the mid- and high-level ventral visual pathway are poorly understood. To better establish and explore possible models of these properties, we adopt a data-driven approach in which we repeatedly interrogate neural units using functional Magnetic Resonance Imaging (fMRI) to establish each unit's image selectivity. This approach to imaging necessitates a search through a broad space of stimulus properties using a limited number of samples. To more quickly identify the complex visual features underlying human cortical object perception, we implemented a new functional magnetic resonance imaging protocol in which visual stimuli are selected in real-time based on BOLD responses to recently shown images. Two variations of this protocol were developed, one relying on natural object stimuli and a second based on synthetic object stimuli, both embedded in feature spaces based on the complex visual properties of the objects. During fMRI scanning, we continuously controlled stimulus selection in the context of a real-time search through these image spaces in order to maximize neural responses across pre-determined 1cm(3) rain regions. Elsewhere we have reported the patterns of cortical selectivity revealed by this approach (Leeds et al., 2014). In contrast, here our objective is to present more detailed methods and explore the technical and biological factors influencing the behavior of our real-time stimulus search. We observe that: 1) Searches converged more reliably when exploring a more precisely parameterized space of synthetic objects; 2) real-time estimation of cortical responses to stimuli is reasonably consistent; 3) search behavior was acceptably robust to delays in stimulus displays and subject motion effects. Overall, our results indicate that real-time fMRI methods may provide a valuable platform for continuing study of localized neural selectivity, both for visual object representation and beyond.
Copyright © 2016 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Computational modeling; Functional magnetic resonance imaging; Object recognition; Real-time signal processing; Real-time stimulus selection

Mesh:

Year:  2016        PMID: 26973168      PMCID: PMC4889505          DOI: 10.1016/j.neuroimage.2016.02.071

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  21 in total

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