Assessment of shock discrimination in rats with signal detection theory

Nociception in six rats was evaluated with signal detection theory (TSD) techniques using a modified “yes-no” paradigm. A .1-sec, .075-mA shock was the discriminative stimulus for a choice to one side of a T-maze, and a .1-sec, .25-mA shock (capable of reliably eliciting a “flinch”) was the discriminative stimulus for the opposite choice. Response bias was manipulated through differential reinforcement for the two choices, and signal sensitivity was manipulated by varying the intensity of the higher intensity shock signal. Individual detection ROCs appeared linear with slopes of one when plotted on double-probability scales. Indices of signal sensitivity and response bias were independent, as predicted by TSD. Morphine sulfate (4 mg/kg) significantly reduced d’ but did not affect β, suggesting that, if appropriate parameters are used, TSD analyses may have value in assessing nociception in rats.     

Decision processes in discrimination: fundamental misrepresentations of signal detection theory.

A new approach to studying decision making in discrimination tasks is described that does not depend on the technical assumptions of signal detection theory (e.g., normality of the encoding distributions). In 3 different experiments, results of these new distribution-free tests converge on a single, surprising conclusion: response biases had substantial effects on the encoding distributions but no effect on the decision rule, which was uniformly unbiased in equal and unequal base rate conditions and in symmetric and asymmetric payoff conditions. This seemingly paradoxical result is fundamentally inconsistent with the entire family of signal detection theory models, raising some important questions about the significance of many published results in the human performance literature.     

An application of signal detection theory with finite mixture distributions to source discrimination

A mixture extension of signal detection theory is applied to source discrimination. The basic idea of the approach is that only a portion of the sources (say A or B) of items to be discriminated is encoded or attended to during the study period. As a result, in addition to 2 underlying probability distributions associated with the 2 sources, there is a 3rd distribution that represents items for which sources were not attended to. Thus, over trials, the observed response results from a mixture of an attended (A or B) distribution and a nonattended distribution. The situation differs in an interesting way from detection in that, for detection, there is mixing only on signal trials and not on noise trials, whereas for discrimination, there is mixing on both A and B trials. Predictions of the mixture model are examined for data from several recent studies and in a new experiment.     

Calculation of signal detection theory measures

Signal detection theory (SDT) may be applied to any area of psychology in which two different types of stimuli must be discriminated. We describe several of these areas and the advantages that can be realized through the application of SDT. Three of the most popular tasks used to study discriminability are then discussed, together with the measures that SDT prescribes for quantifying performance in these tasks. Mathematical formulae for the measures are presented, as are methods for calculating the measures with lookup tables, computer software specifically developed for SDT applications, and general purpose computer software (including spreadsheets and statistical analysis software).     

An experiment on shape discrimination and signal detection in octopus

Ten octopuses were trained to perform a successive discrimination between the two shapes shown in Figure I (a). After 7 days of training, when performance was significantly above chance, transfer tests were given with other shapes that were either rotations or parts of the original training shapes. At least six theories have been put forward to explain shape discrimination in the octopus, but none of these are capable of explaining the present results. The transfer tests suggest that the discrimination was performed in terms of component parts of the shapes (vertical bars projecting upwards or downwards), and their relationship to the shape as a whole (terminal or central).

During successive discrimination training the general level of attack varies between animals, and fluctuates from day to day. As a result there are often more attacks on both the positive and negative shapes on some occasions than others, making it difficult to compare the levels of discrimination achieved. It is suggested that the concepts of signal detection theory can help overcome this difficulty. Attacks on the positive shape (“hits”) plotted against attacks on the negative shape (“false positives”) constitute an ROC curve from which a value of d', independant of the general level of attack, can be obtained.     

Threshold nonlinearities and signal detection theory

The review summarizes Kulikowski's contributions to the mechanisms involved in visual detection and discrimination. His findings provide strong evidence for the existence of 'barriers' which block weak activity in the visual system. It is proposed that the properties of these barriers may underlie near-optimal detection in conditions of signal uncertainty.     

A signal detection theory analysis of gap detection in the rat

An innocuous sensory event (a prestimulus) briefly preceding a startle-eliciting stimulus (SES) reduces the amplitude of the elicited reflex. This study used signal detection theory (SDT) techniques to quantify the effects of gaps (pauses in otherwise continuous noise) on the rat’s acoustic startle reflex. Sixteen rats were given four identical test sessions consisting of the randomized presentation of 150 trials of the SES alone and 150 trials of a gap-and-SES combination. Gap duration (1, 2, 4, and 8 msec) varied between sessions. Data analyses based on amplitude, difference scores, percentage scores, and SDT techniques identified similar patterns. The three longest gaps, but not the shortest, were reliably detected, and differences among these three were identified with percentage and SDT analyses. Analyses of amplitude changes over test sessions yielded different patterns for each measure. The results demonstrate that an SDT analysis is a sensitive index of prestimulus effects.     

A signal detection theory analysis of an unconscious perception effect.

The independent observation model (Macmillan & Creelman, 1991) is fitted to detection-identification data collected under conditions of heavy masking. The model accurately predicts a quantitative relationship between stimulus detection and stimulus identification over a wide range of detection performance. This model can also be used to offer a signal detection interpretation of the common finding of above-chance identification following a missed signal. While our finding is not a new one, the stimuli used in this experiment (redundant three-letter strings) differ slightly from those used in traditional signal detection work. Also, the stimuli were presented very briefly and heavily masked, conditions typical in the study of unconscious perception effects.     

BayesSDT: Software for Bayesian inference with signal detection theory

This article describes and demonstrates the BayesSDT MATLAB-based software package for performing Bayesian analysis with equal-variance Gaussian signal detection theory (SDT). The software uses WinBUGS to draw samples from the posterior distribution of six SDT parameters: discriminability, hit rate, false alarm rate, criterion, and two alternative measures of bias. The software either provides a simple MATLAB graphical user interface or allows a more general MATLAB function call to produce graphs of the posterior distribution for each parameter of interest for each data set, as well as to return the full set of posteriorsamples.     

Towards a behavioral theory of bias in signal detection

A behavioral model for performance on signal-detection tasks is presented. It is based on a relation between response and reinforcement ratios which has been derived from both animal and human research on the distribution of behavior between concurrently available schedules of reinforcement. This model establishes the ratio of obtained reinforcements for the choice responses, and not the probability of stimulus presentation, as the effective biaser in signal-detection research. Furthermore, experimental procedures which do not control the obtained reinforcement ratio are shown to give rise to unstable bias contours. Isobias contours, on the other hand, arise only from controlled reinforcement-ratio procedures.     

Process dissociation and mixture signal detection theory

The process dissociation procedure was developed in an attempt to separate different processes involved in memory tasks. The procedure naturally lends itself to a formulation within a class of mixture signal detection models. The dual process model is shown to be a special case. The mixture signal detection model is applied to data from a widely analyzed study. The results suggest that a process other than recollection may be involved in the process dissociation procedure.     

The mirror effect and mixture signal detection theory

The mirror effect for word frequency refers to the finding that low-frequency words have higher hit rates and lower false alarm rates than high-frequency words. This result is typically interpreted in terms of conventional signal detection theory (SDT), in which case it indicates that the order of the underlying old item distributions mirrors the order of the new item distributions. However, when viewed in terms of a mixture version of SDT, the order of hits and false alarms does not necessarily imply the same order in the underlying distributions because of possible effects of mixing. A reversal in underlying distributions did not appear for fits of mixture SDT models to data from 4 experiments.     

A signal detection analysis of gist-based discrimination of genetic breast cancer risk

Pervasive biases in probability judgment render the probability scale a poor response mode for assessing risk judgments. By applying fuzzy trace theory, we used ordinal gist categories as a response mode, coupled with a signal detection model to assess risk judgments. The signal detection model is an extension of the familiar model used in binary choice paradigms. It provides three measures of discriminability—low versus medium risk, medium versus high risk, and low versus high risk—and two measures of response bias. We used the model to assess the effectiveness of BRCA Gist, an intelligent tutoring system designed to improve women’s judgments and understanding of genetic risk for breast cancer. Participants were randomly assigned to the BRCA Gist intelligent tutoring system, the National Cancer Institute (NCI) Web pages, or a control group, and then they rated cases that had been developed using the Pedigree Assessment Tool and also vetted by medical experts. BRCA Gist participants demonstrated increased discriminability for all three risk categories, relative to the control group; the NCI group showed increased discriminability for two of the three levels. This result suggests that BRCA Gist best improved discriminability among genetic risk categories, and both BRCA Gist and the NCI website improved participants’ ability to discriminate, rather than simply shifting their decision criterion. A spreadsheet that fits the model and compares parameters across the conditions can be downloaded from the Behavior Research Methods website and used in any research involving categorical responses.     

Complexity of visual icons studied via signal detection theory

Two investigations on how humans perceive information from visually rendered complex objects, such as military icons (glyphs) were conducted. A signal detection theory framework was employed to evaluate the sensitivity and specificity of human subject performance. The 6 adults tested showed that as complexity increased, their accuracy in performance decreased. Study 1 showed that complex dimensions (features) could not be assigned arbitrarily. Study 2 developed a rank ordering for features of an iconic object.     

Analysis of a two-alternative force-choice signal detection theory model

A basic problem in psychophysics is recovering the mean internal response and noise amplitude from sensory discrimination data. Since these components cannot be estimated independently, several indirect methods were suggested to resolve this issue. Here we analyze the two-alternative force-choice method (2AFC), using a signal detection theory approach, and show analytically that the 2AFC data are not always suitable for a reliable estimation of the mean internal responses and noise amplitudes. Specifically, we show that there is a subspace of internal parameters that are highly sensitive to sampling errors (singularities), which results in a large range of estimated parameters with a finite number of experimental trials. Four types of singular models were identified, including the models where the noise amplitude is independent of the stimulus intensity, a situation often encountered in visual contrast discrimination. Finally, we consider two ways to avoid singularities: (1) inserting external noise to the stimuli, and (2) using one-interval forced-choice scaling methods (such as the Thurstonian scaling method for successive intervals).     

Alternatives to a table of criterion values in signal detection theory

Gardner, Dalsing, Reyes, and Brake (1984) supplied a table of criterion values (β) related to hit and false-alarm rates in signal detection theory. Other methods of calculatingβ are suggested as more accurate alternatives to using that table. A short computer program is provided to calculate β and the sensitivity indexd’.     

Residual sensory capacities of the deaf: a signal detection analysis of a visual discrimination task.

This experiment compared the visual sensory sensitivity of deaf and hearing subjects in a signal detection paradigm. Subjects (ns = 6) were required to give forced-choice responses to a brightness discrimination task under three stimulus probability conditions (0.25, 0.50, and 0.75). A total of 1,800 trials were given to each subject and utilized to construct isosensitivity functions and d' and Beta, indices for sensory sensitivity and response bias, respectively. The results showed that no enhanced sensory sensitivity is present for these deaf children and questions the classical sensory compensation hypothesis. Furthermore, the deaf subjects responded in a relatively bias-free manner to variations in stimulus probability.