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Tailoring a psychophysical discrimination experiment upon assessment of the psychometric function: Predictions and results

Authors :
Andrea Vilardi
Davide Tabarelli
Leonardo Ricci
Source :
AIP Advances, Vol 5, Iss 2, Pp 027121-027121-10 (2015)
Publication Year :
2015
Publisher :
AIP Publishing LLC, 2015.

Abstract

Decision making is a widespread research topic and plays a crucial role in neuroscience as well as in other research and application fields of, for example, biology, medicine and economics. The most basic implementation of decision making, namely binary discrimination, is successfully interpreted by means of signal detection theory (SDT), a statistical model that is deeply linked to physics. An additional, widespread tool to investigate discrimination ability is the psychometric function, which measures the probability of a given response as a function of the magnitude of a physical quantity underlying the stimulus. However, the link between psychometric functions and binary discrimination experiments is often neglected or misinterpreted. Aim of the present paper is to provide a detailed description of an experimental investigation on a prototypical discrimination task and to discuss the results in terms of SDT. To this purpose, we provide an outline of the theory and describe the implementation of two behavioural experiments in the visual modality: upon the assessment of the so-called psychometric function, we show how to tailor a binary discrimination experiment on performance and decisional bias, and to measure these quantities on a statistical base. Attention is devoted to the evaluation of uncertainties, an aspect which is also often overlooked in the scientific literature.

Subjects

Subjects :
Physics
QC1-999

Details

Language :
English
ISSN :
21583226
Volume :
5
Issue :
2
Database :
Directory of Open Access Journals
Journal :
AIP Advances
Publication Type :
Academic Journal
Accession number :
edsdoj.19b4b89477c435d9985c43be62d06f5
Document Type :
article
Full Text :
https://doi.org/10.1063/1.4908271