Comparison of Yes‐no and Latency Measures of Auditory Intensity Discrimination

Abstract

Rats discriminated auditory intensity differences of sinusoids at 3.0 kilohertz in a go/no-go signal detection procedure. Responses to the signal (hits) were reinforced with electrical brain stimulation, and misses produced a brief timeout. On intermixed noise trials, withholding of responses (correct rejections) was reinforced, and false alarms produced the timeout. In two test conditions, the signal was either the louder (100 decibels) or softer (90, 93, 96, or 99 decibels) of the pair of intensities presented within a set of trials. Each animal was first trained with signal value louder or softer, and reversed for the second condition so that the former noise value served as signal. Hits showed shorter latencies than false alarms, regardless of the relative intensity of signal and noise, and the nmagnitude of differentiation was proportional to signal-noise separation. Both hits and false alarms showed longer latencies as the discrimination became more difficult. Isosensitivity contours derived from the latencies showed close similarity across conditions; in comparison, the yes-no measure of detectability, d', showed greater variability. The similarity of latency differentiation across louder and softer signal conditions supports a detection model in which the ob.erver's judgment is controlled by the distance of sensory effect from criterion on each trial, as opposed to the loudness of the tones per se.

Assessing recognition memory using confidence ratings and response times

“…Given that introspective judgements are costly to obtain, largely reflect response fluency (which is usually assumed to be inversely related to response latency), and may be subject to response biases, we assessed to what extent response latencies conveyed similar information to that obtained from introspective judgements. Even though response times have previously been used in several studies to generate ROC functions in perceptual tasks [11,21,[24][25][26][27][28][29][30][31][32][33][34][35][36][37], as far as we are aware, the only published application to recognition memory is a study that investigated performance in a range of memory tasks for four participants [23]. This is particularly surprising given that many studies of recognition memory place a strong focus on the shape of ROC functions in an effort to constrain theoretical accounts and given that response times to recognition decisions feature prominently in attempts to understand recognition memory using sequential sampling models; see [55,56] for recent efforts to validate the unequal-variance assumption in recognition memory (which is usually supported by analyses of the shape of ROC functions) with response times in a diffusion model analysis.…”

“…Historically, a wide variety of dependent variables, including response times [11,[21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38], latency of heart rate increase [27], response frequency [39], and firing rates of individual neurons [32,40], have been used for ROC construction. To construct an ROC function from a dependent variable, one has to assume or establish a mapping between this measure and the evidence for the classification response.…”

The role of trial tracking in rats’ working memory

“…A represents the proportion ofthe area under the receiver operating curve (ROC) and ranges from .50 when the distributions of initial responses to FR I and to FI 10-sec schedules completely overlap to 1.00 when they never overlap. Procedures for calculating A scores were adapted from those used by Green, Terman, and Terman (1982). It should be noted that using initial leverpress latencies to derive A scores, rather than other measures of differential responding to each schedule at S2 (e.g., relative rates of leverpressing), avoids a potential confound between the discriminative control by the SI schedule and the reinforcement control by the S2 schedule.…”

Visual search, visual streams, and visual architectures

“…However, Wolfe, et al (1989) offer no proof that this is the correct explanation. It would be simpler to dispense with focal attention altogether and just assume, as many have done (e.g., Green, Terman, & Terman, 1979), that response time lengthens with smaller sign-noise differences.…”