@article{Meyer-Grant2027,

title = {Extreme-Value Signal Detection Theory for Recognition Memory: The Parametric Road Not Taken},

author = {Constantin G. Meyer-Grant and David Kellen and Samuel Harding and Henrik Singmann},

url = {https://osf.io/qhrfj, preprint link},

year  = {2027},

date = {2027-08-01},

urldate = {2025-08-01},

journal = {Psychological Review},

publisher = {OSF},

abstract = {Signal Detection Theory has long served as a cornerstone of psychological research, particularly in recognition memory. Yet its conventional application hinges almost exclusively on the Gaussian assumption—an adherence rooted more in historical convenience than theoretical necessity that comes with a number of well-documented drawbacks. In this work, we critically examine these limitations and introduce a principled parametric alternative: the Gumbel_min model, based on extreme-value distributions of event minima. A key feature of this model is its grounding in a behavioral principle of invariance under uniform choice-set expansions—a prediction we empirically validate in a novel recognition-memory experiment. We further benchmark the Gumbel_min model against its Gaussian counterpart across multiple recognition-memory tasks, including confidence-rating, ranking, forced-choice, and detection-plus-identification paradigms. Our findings highlight the model's parsimonious yet successful characterization of recognition-memory judgments, as well as the utility of its associated discriminability index, g', which can be directly computed from a single pair of hit and false-alarm rates.},

keywords = {critical tests, extreme value distributions, invariance properties, Recognition memory, signal detection theory},

pubstate = {forthcoming},

tppubtype = {article}

}