The University of Southampton
University of Southampton Institutional Repository

Expertise and gambling: Using type-2 signal detection theory to investigate differences between regular gamblers and non-gamblers

Record type: Article

This paper presents an experimental investigation into how individuals make decisions under uncertainty when faced with different payout structures in the context of gambling. Type-2 signal detection theory was utilised to compare sensitivity to bias manipulations between regular non-problem gamblers and non-gamblers in a novel probability-based gambling task. The results indicated that both regular gamblers and non-gamblers responded to the changes of rewards for correct responses (Experiment 1) and penalties for errors (Experiment 2) in setting their gambling criteria, but that regular gamblers were more sensitive to these manipulations of bias. Regular gamblers also set gambling criteria that were more optimal. The results are discussed in terms of an expertise-transference hypothesis.

Full text not available from this repository.

Citation

Lueddeke, S. and Higham, P.A. (1970) Expertise and gambling: Using type-2 signal detection theory to investigate differences between regular gamblers and non-gamblers Quarterly Journal of Experimental Psychology (doi:10.1080/17470218.2011.584631).

More information

Accepted/In Press date: 1 January 1970

Identifiers

Local EPrints ID: 185231
URI: http://eprints.soton.ac.uk/id/eprint/185231
ISSN: 0272-4995
PURE UUID: 21f5ae64-43fd-4f88-8e56-99bdf2530f14

Catalogue record

Date deposited: 10 May 2011 10:07
Last modified: 18 Jul 2017 11:49

Export record

Altmetrics

Contributors

Author: S. Lueddeke
Author: P.A. Higham

University divisions


Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×