Evidence map›Paper›PMID 25309408›Full record

ArticleFrontiers in computational neuroscience2014

Exploration of complex visual feature spaces for object perception.

Daniel D Leeds, John A Pyles, Michael J Tarr

Abstract read
In one paragraph

Article in Frontiers in computational neuroscience, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Daniel D LeedsComputer and Information Science Department, Fordham University Bronx, NY, USA ; Center for the Neural Basis of Cognition, Carnegie Mellon University Pittsburgh, PA, USA.
John A PylesCenter for the Neural Basis of Cognition, Carnegie Mellon University Pittsburgh, PA, USA ; Psychology Department, Carnegie Mellon University Pittsburgh, PA, USA.
Michael J TarrCenter for the Neural Basis of Cognition, Carnegie Mellon University Pittsburgh, PA, USA ; Psychology Department, Carnegie Mellon University Pittsburgh, PA, USA.

Funding

INTERDISCIPLINARY TRAINING IN COMPUTATIONAL NEUROSCIENCET90DA022762 · NIDA · CARNEGIE-MELLON UNIVERSITY · PI KASS, ROBERT E, RUBIN, JONATHAN E. · 2006 to 2020
$2.0M
Using Functional Physiology to Uncover the Fundamental Principles of Visual CorteR01MH084195 · NIMH · CARNEGIE-MELLON UNIVERSITY · PI TARR, MICHAEL J · 2008 to 2011
$1.3M
NIDA NIH HHS T90 DA022762NIMH NIH HHS R01 MH084195
6 · The paper itself

Abstract

The mid- and high-level visual properties supporting object perception in the ventral visual pathway are poorly understood. In the absence of well-specified theory, many groups have adopted a data-driven approach in which they progressively interrogate neural units to establish each unit's selectivity. Such methods are challenging in that they require search through a wide space of feature models and stimuli using a limited number of samples. To more rapidly identify higher-level features underlying human cortical object perception, we implemented a novel functional magnetic resonance imaging method in which visual stimuli are selected in real-time based on BOLD responses to recently shown stimuli. This work was inspired by earlier primate physiology work, in which neural selectivity for mid-level features in IT was characterized using a simple parametric approach (Hung et al., 2012). To extend such work to human neuroimaging, we used natural and synthetic object stimuli embedded in feature spaces constructed on the basis of the complex visual properties of the objects themselves. During fMRI scanning, we employed a real-time search method to control continuous stimulus selection within each image space. This search was designed to maximize neural responses across a pre-determined 1 cm(3) brain region within ventral cortex. To assess the value of this method for understanding object encoding, we examined both the behavior of the method itself and the complex visual properties the method identified as reliably activating selected brain regions. We observed: (1) Regions selective for both holistic and component object features and for a variety of surface properties; (2) Object stimulus pairs near one another in feature space that produce responses at the opposite extremes of the measured activity range. Together, these results suggest that real-time fMRI methods may yield more widely informative measures of selectivity within the broad classes of visual features associated with cortical object representation.

Indexed as

computational modelingintermediate feature representationneuroimagingobject recognitionreal-time stimulus selection

Identifiers

PMID25309408
PMCPMC4162468

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.