d'plus: A program to calculate accuracy and bias measures from detection and discrimination data.

The program d'plus calculates accuracy (sensitivity) and response-bias parameters using Signal Detection Theory. Choice Theory, and 'nonparametric' models. is is appropriate for data from one-interval, two- and three-interval forced-choice, same different, ABX, and oddity experimental paradigms.

Triangles in ROC space: History and theory of "nonparametric" measures of sensitivity and response bias.

Can accuracy and response bias in two-stimulus, two-response recognition or detection experiments be measured nonparametrically? Pollack and Norman (1964) answered this question affirmatively for sensitivity, Hodos (1970) for bias: Both proposed measures based on triangular areas in receiver-operating characteristic space. Their papers, and especially a paper by Grier (1971) that provided computing formulas for the measures, continue to be heavily cited in a wide range of content areas. In our sample of articles, most authors described triangle-based measures as making fewer assumptions than measures associated with detection theory. However, we show that statistics based on products or ratios of right triangle areas, including a recently proposed bias index and a not-yetproposed but apparently plausible sensitivity index, are consistent with a decision process based on logistic distributions. Even the Pollack and Norman measure, which is based on non-right triangles, is approximately logistic for low values of sensitivity. Simple geometric models for sensitivity and bias are not nonparametric, even if their implications are not acknowledged in the defining publications.

PEST reduces bias in forced choice psychophysics.

Observers performed several different detection tasks using both the PEST adaptive psychophysical procedure and a fixed-level (method of constant stimuli) psychophysical procedure. In two experiments, PEST runs targeted at P (C) = 0.80 were immediately followed by fixed-level detection runs presented at the difficulty level resulting from the PEST run. The fixed-level runs yielded P (C) about 0.75. During the fixed-level runs, the probability of a correct response was greater when the preceding response was correct than when it was wrong. Observers, even highly trained ones, perform in a nonstationary manner. The sequential dependency data can be used to determine a lower bound for the observer's "true" capability when performing optimally; this lower bound is close to the PEST target, and well above the forced choice P (C). The observer's "true" capability is the measure used by most theories of detection performance. A further experiment compared psychometric functions obtained from a set of PEST runs using different targets with those obtained from blocks of fixed-level trials at different levels. PEST results were more stable across observers, performance at all but the highest signal levels was better with PEST, and the PEST psychometric functions had shallower slopes. We hypothesize that PEST permits the observer to keep track of what he is trying to detect, whereas in the fixed-level method performance is disrupted by memory failure. Some recently suggested "more virulent" versions of PEST may be subject to biases similar to those of the fixed-level procedures.(ABSTRACT TRUNCATED AT 250 WORDS)

Auditory phase and frequency discrimination: a comparison of nine procedures.

Two auditory discrimination tasks were thoroughly investigated: discrimination of frequency differences from a sinusoidal signal of 200 Hz and discrimination of differences in relative phase of mixed sinusoids of 200 Hz and 400 Hz. For each task psychometric functions were constructed for three observers, using nine different psychophysical measurement procedures. These procedures included yes-no, two-interval forced-choice, and various fixed- and variable-standard designs that investigators have used in recent years. The data showed wide ranges of apparent sensitivity. For frequency discrimination, models derived from signal detection theory for each psychophysical procedure seem to account for the performance differences. For phase discrimination the models do not account for the data. We conclude that for some discriminative continua the assumptions of signal detection theory are appropriate, and underlying sensitivity may be derived from raw data by appropriate transformations. For other continua the models of signal detection theory are probably inappropriate; we speculate that phase might be discriminable only on the basis of comparison or change and suggest some tests of our hypothesis.