Abstract

Event Abstract Back to Event The impact of signal relevance and signal probability on detection sensitivity Christopher Summerfield1* and Valentin Wyart1 1 University of Oxford, United Kingdom Classical theories quantify detection sensitivity by adjusting true positive rate by false positive rate. Using this approach, psychophysical studies have repeatedly confirmed that increasing the base rate of occurrence of a signal biases observers to report that a signal is present, but has no influence on detection sensitivity. However, analyses that classify signals as ‘present’ or ‘absent’ in a binary fashion discard trial-by-trial variability in external noise energy that might contribute to perceptual decisions. Here, we estimated detection sensitivity using a new ‘single-trial’ method that capitalises on random variations in the noise energy of the stimulus, as calculated using filters whose properties approximate those of neurons in the early visual system. We used this technique in combination with a new task that orthogonally manipulates the probability that a signal will occur (signal probability) with a cue that indicates the likelihood that it will be relevant for a later response (signal relevance). Using the single-trial technique, we demonstrate that signal probability and relevance both enhance detection sensitivity, but in different ways: signal relevance affects mainly trials were energy levels are higher, and signal probability has the strongest impact on sensitivity for low-energy trials. These effects can be accounted for by a computational model in which increased signal probability augments feedforward drive to the system, whereas increased signal relevance dampens noise in the decision process. These data demonstrate that merely cueing observers that the presence of an oriented Gabor patch is more probable than its absence can enhance perceptual sensitivity, calling into question an assumption that has endured for more than 50 years. Keywords: early visual system, signal probability Conference: XI International Conference on Cognitive Neuroscience (ICON XI), Palma, Mallorca, Spain, 25 Sep - 29 Sep, 2011. Presentation Type: Symposium: Oral Presentation Topic: Symposium 6: Expectation and attention in visual cognition: neural and computational approaches Citation: Summerfield C and Wyart V (2011). The impact of signal relevance and signal probability on detection sensitivity. Conference Abstract: XI International Conference on Cognitive Neuroscience (ICON XI). doi: 10.3389/conf.fnhum.2011.207.00037 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 09 Nov 2011; Published Online: 15 Nov 2011. * Correspondence: Dr. Christopher Summerfield, University of Oxford, Oxford, United Kingdom, christopher.summerfield@psy.ox.ac.uk Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Christopher Summerfield Valentin Wyart Google Christopher Summerfield Valentin Wyart Google Scholar Christopher Summerfield Valentin Wyart PubMed Christopher Summerfield Valentin Wyart Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

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