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.     

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.     

The mirror effect in recognition memory: data and theory

The mirror effect is a regularity in recognition memory that requires reexamination of current views of memory. Five experiments that further support and extended the generality of the mirror effect are reported. The first two experiments vary word frequency. The third and fourth vary both word frequency and concreteness. The fifth experiment varies word frequency, concreteness, and the subject's operations on the words. The experiments furnish data on the stability of the effect, its relation to response times, its extension to multiple mirror effects, and its extension beyond stimulus variables to operation variables. A theory of the effect and predictions that derive from the theory are presented.     

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.     

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 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.     

The variance theory of the mirror effect in recognition memory

The mirror effect refers to a rather general empirical finding showing that, for two classes of stimuli, the class with the higher hit rates also has a lower false alarm rate. In this article, a parsimonious theory is proposed to account for the mirror effect regarding, specifically, high- and low-frequency items and the associated receiver-operating curves. The theory is implemented in a recurrent network in which one layer represents items and the other represents contexts. It is shown that the frequency mirror effect is found in this simple network if the decision is based on counting the number of active nodes in such a way that performance is optimal or near optimal. The optimal performance requires that the number of active nodes is low, only nodes active in the encoded representation are counted, the activation threshold is set between the old and the new distributions, and normalization is based on the variance of the input. Owing to the interference caused by encoding the to-be-recognized item in several preexperimental contexts, the variance of the input to the context layer is greater for highthan for low-frequency items, which yields lower hit rates and higher false alarm rates for high- than for low-frequency items. Although initially the theory was proposed to account for the mirror effect with respect to word frequency, subsequent simulations have shown that the theory also accounts for strength-based mirror effects within a list and between lists. In this case, consistent with experimental data, the variance theory suggests that focusing attention to the more difficult class within a list affects the hit rate, but not the false alarm rate and not the standard deviations of the underlying density, leading to no mirror effect.     

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).     

The mirror effect and the spacing effect

In the mirror effect, there are fewer false negatives (misses) and false positives (false alarms) for rare (low-frequency) words than for common (high-frequency) words. In the spacing effect, recognition accuracy is positively related to the interval (spacing or lag) between two presentations of an item. These effects are related in that they are both manifestations of a leapfrog effect (a weaker item jumps over a stronger item). They seem to be puzzles for traditional strength theory and at least some current global-matching models. A computational strength-based model (EICL) is proposed that incorporates excitation, inhibition, and a closed-loop learning algorithm. The model consists of three nonlinear coupled stochastic difference equations, one each for excitation (x), inhibition (y), and context (z). Strength is the algebraic sum (i.e., s = x − y + z). These equations are used to form a toy lexicon that serves as a basis for the experimental manipulations. The model can simulate the mirror effect forcedchoice inequalities and the spacing effect for single-item recognition, all parameters are random variables, and the same parameter values are used for both the mirror and the spacing effects. No parameter values varied with the independent variables (word frequency for the mirror effect, lag for the spacing effect), so the model, not the parameters, is doing the work.     

Auditory threshold,signal-to-noise ratio and signal detection theory

The use of Signal Detection Theory has demonstrated that there is not an auditory threshold at the level supposed by classical psychophysics, but has not disproved the existence of an auditory threshold at a lower level. Following the recommended signal detection theory procedure fairly closely, an experiment obtained evidence either of the existence of a signal-to-noise ratio audi tory threshold, or a nonlinear relationship between stimulus and response which could not be statistically distiguished from such a threshold , Using the same apparatus and subjects, audi tory thresholds were also obtained using the classical method of limits. The acoustic pressure for the classical thresholds was about five times as great as for the signal detection theory method.     

The effect of sleep deprivation on signal detection parameters

Twelve subjects performed a high signal rate vigilance task, once after a night's sleep, and once after a night without sleep. Raw scores were transformed into the signal detection parameters, d' and β. After sleep deprivation, detection performance was significantly impaired. This was reflected in a fall of d', whereas β was not significantly altered. Analysing the control data alone for comparison with other vigilance studies revealed a decrement in % signals detected and d', and an increase in β from the first to the second half of the test.     

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.     

On the statistical and theoretical basis of signal detection theory and extensions: Unequal variance, random coefficient, and mixture models

Basic results for conditional means and variances, as well as distributional results, are used to clarify the similarities and differences between various extensions of signal detection theory (SDT). It is shown that a previously presented motivation for the unequal variance SDT model (varying strength) actually leads to a related, yet distinct, model. The distinction has implications for other extensions of SDT, such as models with criteria that vary over trials. It is shown that a mixture extension of SDT is also consistent with unequal variances, but provides a different interpretation of the results; mixture SDT also offers a way to unify results found across several types of studies.     

Functions for traditional and multilevel approaches to signal detection theory

In the present article, functions written in the freeware R are presented that calculate several measures from traditional signal detection theory for each individual in a sample, along with summary statistics for the sample. Bias-corrected and accelerated bootstrap confidence intervals are also produced. Arguments are made for using an alternative approach—multilevel generalized linear models—and a function is presented for it. These functions are part of the R package sdtalt, which is available on the Comprehensive R Archive Network. Recent data from memory recognition studies are used to illustrate these functions.     

The effect of sequential dependencies on some signal detection parameters

The values of various sensitivity and response criterion parameters were calculated under different experimental conditions for both detection and recognition tasks. The effects of feedback and stimulus probability were found to be consistent with previous studies. When the values of the parameters were computed depending on the nature of the immediately preceding trial, the criterion parameters were found to be dependent on the identity of the preceding response, while those measuring sensitivity were dependent on its correctness. The dependence of the sensitivity parameters could be explained as an artifact of statistical bias, while the effect on the criterion parameters appears genuine. The nature of the phenomenon of statistical bias is discussed.     

The effect of cueing in a visual signal detection task

In a signal detection task four subjects were presented with stimulus cards, 50% of which contained a capital O in one of the four quadrants. Fifty per cent of these Os were surrounded by a small rectangle. This small rectangle served as a simultaneous cue, indicating the region where the O could appear. The other 50% were surrounded by a big rectangle encompassing the whole field. Fifty per cent of the blank cards contained a small rectangle, the other 50% a big one. Exposure duration was set at 15 and 20 ms. The results indicated that subjects, for whom these exposure durations led to performance exceeding chance level, used the simultaneous cue for reducing the field to be searched, resulting in higher sensitivity (d') in the small rectangle condition. Results were interpreted in terms of increasing efficiency of search as a result of which a higher quality of the icon was available for making decisions about the presence of the O.     

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).