Analysis of signal detection data

Mike,

I would add the following paper, which discusses Bayesian SDT models.
Rouder's website may have software to estimate the models.


@ARTICLE{RouLu05,
?author = {J. N. Rouder and J. Lu},
?title = {An introduction to {B}ayesian hierarchical models with an
application in the theory of signal detection},
?journal = {Psychonomic Bulletin and Review},
?year = {2005},
?volume = {12},
?pages = {573-604}
}


Ed

--
Ed Merkle, PhD
Assistant Professor
Department of Psychology
Wichita State University
Wichita, KS, USA 67260
http://psychology.wichita.edu/merkle



-----Original Message-----
From: r-sig-mixed-models-bounces at r-project.org on behalf of Mike Lawrence
Sent: Sun 11/14/2010 9:58 PM
To: r-sig-mixed-models at r-project.org
Subject: [R-sig-ME] Analysis of signal detection data


I know mixed effects modelling can handle binomially distributed
error, but is there any way to handle this sort of signal detection
data? My first thought is that glmmer with 4 categories corresponding
to the hit, miss, false alarm, and correction categorization of
responses, but I don't immediately see how this would properly connect
the hit-vs-miss data to reflect a hit rate and the
false-alarm-vs-correct-rejection data to reflect a FA rate.

Thoughts?

Mike

--
Mike Lawrence
Graduate Student
Department of Psychology
Dalhousie University

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~ Certainty is folly... I think. ~