Purchase this article with an account.
Lynn A. Olzak, Jordan R. Wagge, Robin D. Thomas; Signal detection analyses of an uncertainty discrimination paradigm. Journal of Vision 2006;6(6):193. doi: https://doi.org/10.1167/6.6.193.
Download citation file:
© ARVO (1962-2015); The Authors (2016-present)
An uncertainty discrimination paradigm (Thomas & Olzak, 1996) can be used to assess the independent or non-independent processing of two components in a compound stimulus. In certainty conditions, the observer knows in which component the cue to discrimination will appear. In the uncertainty condition, it can appear in either. If the components are processed by independent mechanisms, performance will be lower in the uncertainty case due to there being two sources of noise in monitored channels, as opposed to a single source in the certainty case. Thomas and Olzak (1996) presented a signal detection decision model that predicted a performance ratio between the uncertainty and the certainty conditions of 2−0.5, or 0.71, if the components are processed independently. Here, we explore the optimality of that model relative to plausible alternatives, and we further determine how to compute d' in a statistically correct way under each model. Finally, we explore the implications under each model for making inference about neural channel interactions.
This PDF is available to Subscribers Only