Abstract
Laboratory computers permit detection and discrimination thresholds to be measured rapidly, efficiently, and accurately. In this paper, the general natures of psychometric functions and of thresholds are reviewed, and various methods for estimating sensory thresholds are summarized. The most efficient method, in principle, using maximum-likelihood threshold estimations, is examined in detail. Four techniques are discussed that minimize the reported problems found with the maximum-likelihood method. A package of FORTRAN subroutines, ML-TEST, which implements the maximum-likelihood method, is described. These subroutines are available on request from the author.
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The development of these subroutines began when the author was guest professor at the Institute for Medical Psychology, Ludwig-Maximilians University, Munich, Federal Republic of Germany, with the support of a research stipend from the Alexander von Humboldt Foundation, Bonn. I thank Ernst Pöppel and Ingo Rentschier for their warm hospitality and the von Humboldt Foundation for its generous and flexible support. Figures were prepared with equipment provided by Grant RR07013-78 from the Biomedical Research Support Grant Program, National Institutes of Health. The Monte Carlo simulations were carried out on the VAX 11/780 computer of the Computing Laboratory for Instruction in Psychological Research (CLIPR) in the Department of Psychology at the University of Colorado.
Those wishing copies of the ML-TEST maximum-likelihood subroutines and demonstration program should send the author a single-sided, single-density floppy disk formatted for the RT-11 operating system.
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Harvey, L.O. Efficient estimation of sensory thresholds. Behavior Research Methods, Instruments, & Computers 18, 623–632 (1986). https://doi.org/10.3758/BF03201438
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DOI: https://doi.org/10.3758/BF03201438