The statistical properties of the survivor interaction contrast

DOI10.1016/j.jmp.2010.06.006zbMath1232.91589OpenAlexW2095677811MaRDI QIDQ708641

James T. Townsend, Joseph W. Houpt

Publication date: 14 October 2010

Published in: Journal of Mathematical Psychology (Search for Journal in Brave)

Full work available at URL: https://works.bepress.com/cgi/viewcontent.cgi?article=1005&context=joseph_houpt

zbMATH Keywords

nonparametrichuman information processingsurvivor interaction contrastmental architecture

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Cognitive psychology (91E10) Psychophysics and psychophysiology; perception (91E30)

Related Items (16)

An examination of parallel versus coactive processing accounts of redundant-target audiovisual signal processing ⋮ Systems factorial technology analysis of mixtures of processing architectures ⋮ Incorrect responses in the response time interaction contrast ⋮ Comparative estimation systems perform under severely limited workload capacity ⋮ Investigating consumer decision strategies with systems factorial technology ⋮ The balance between vision and touch ⋮ Adaptive design for systems factorial technology experiments ⋮ Four-decision tests for stochastic dominance, with an application to environmental psychophysics ⋮ A hierarchical Bayesian approach to distinguishing serial and parallel processing ⋮ Survivor interaction contrast wiggle predictions of parallel and serial models for an arbitrary number of processes ⋮ Reprint of ``Survivor interaction contrast wiggle predictions of parallel and serial models for an arbitrary number of processes ⋮ An extension of SIC predictions to the Wiener coactive model ⋮ Statistical measures for workload capacity analysis ⋮ Nice guys finish fast and bad guys finish last: Facilitatory vs. inhibitory interaction in parallel systems ⋮ Semiparametric Bayesian approaches to systems factorial technology ⋮ A system factorial technology analysis of the size congruity effect: implications for numerical cognition and stochastic modeling

Uses Software

• k.c

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