Generating controlled image sets in cognitive neuroscience research.

Details

Ressource 1Download: serval:BIB_10090AF07CFC.P001 (339.25 [Ko])
State: Public
Version: author
License: Not specified
It was possible to publish this article open access thanks to a Swiss National Licence with the publisher.
Serval ID
serval:BIB_10090AF07CFC
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Generating controlled image sets in cognitive neuroscience research.
Journal
Brain Topography
Author(s)
Knebel J.F., Toepel U., Hudry J., le Coutre J., Murray M.M.
ISSN
0896-0267 (Print)
ISSN-L
0896-0267
Publication state
Published
Issued date
2008
Peer-reviewed
Oui
Volume
20
Number
4
Pages
284-289
Language
english
Notes
Publication types: Journal Article ; Research Support, Non-U.S. Gov't
Publication Status: ppublish
Abstract
The investigation of perceptual and cognitive functions with non-invasive brain imaging methods critically depends on the careful selection of stimuli for use in experiments. For example, it must be verified that any observed effects follow from the parameter of interest (e.g. semantic category) rather than other low-level physical features (e.g. luminance, or spectral properties). Otherwise, interpretation of results is confounded. Often, researchers circumvent this issue by including additional control conditions or tasks, both of which are flawed and also prolong experiments. Here, we present some new approaches for controlling classes of stimuli intended for use in cognitive neuroscience, however these methods can be readily extrapolated to other applications and stimulus modalities. Our approach is comprised of two levels. The first level aims at equalizing individual stimuli in terms of their mean luminance. Each data point in the stimulus is adjusted to a standardized value based on a standard value across the stimulus battery. The second level analyzes two populations of stimuli along their spectral properties (i.e. spatial frequency) using a dissimilarity metric that equals the root mean square of the distance between two populations of objects as a function of spatial frequency along x- and y-dimensions of the image. Randomized permutations are used to obtain a minimal value between the populations to minimize, in a completely data-driven manner, the spectral differences between image sets. While another paper in this issue applies these methods in the case of acoustic stimuli (Aeschlimann et al., Brain Topogr 2008), we illustrate this approach here in detail for complex visual stimuli.
Keywords
Brain/physiology, Brain Mapping, Cognition/physiology, Cognitive Science, Food, Humans, Imagination, Pattern Recognition, Visual, Photic Stimulation/methods, Research, Set (Psychology), Spectrum Analysis
Pubmed
Web of science
Open Access
Yes
Create date
27/01/2009 12:18
Last modification date
25/09/2019 6:08
Usage data