Evaluating the Impact of Genetic Counseling and Testing with Signal Detection Methods

One measure of the impact of genetic counseling and testing (GCT) is the extent to which it fosters behavioral change that is consistent with mutation status. We describe and illustrate how two different signal detection methods, receiver operating characteristic (ROC) analysis and recursive partitioning, can be used in this context to evaluate the impact of GCT. We analyzed real screening behavior data obtained in the 12 months following GCT for Hereditary Nonpolyposis Colon Cancer (HNPCC) using these two different signal detection approaches. Each approach demonstrated that GCT had an impact on behavioral outcomes, and was effective in fostering behavioral outcomes appropriate to mutation status. The ROC approach demonstrated that GCT was effective because mutation positive and mutation negative individuals could be distinguished on the basis of the number of recommended screening behaviors. The recursive partitioning approach demonstrated that GCT was effective because there were generally high rates of adherence to screening guidelines among subjects. The recursive partitioning technique also identified four subgroups of subjects, each with distinct characteristics, for which tailored interventions could be developed to increase rates of adherence to screening guidelines. Signal detection methods are easily implemented and are useful techniques for evaluating the impact of GCT.     

Signal-detection analyses of conditional discrimination and delayed matching-to-sample performance

Quantitative analyses of stimulus control and reinforcer control in conditional discriminations and delayed matching-to-sample procedures often encounter a problem; it is not clear how to analyze data when subjects have not made errors. The present article examines two common methods for overcoming this problem. Monte Carlo simulations of performance demonstrated that both methods introduced systematic deviations into the results, and that there were genuine risks of drawing misleading conclusions concerning behavioral models of signal detection and animal short-term memory.     

Experiencing the Cross-Sensory Error Signal During Movement Leads to Proprioceptive Recalibration

Abstract

Reaching to targets in a virtual reality environment with misaligned visual feedback of the hand results in changes in movements (visuomotor adaptation) and sense of felt hand position (proprioceptive recalibration). We asked if proprioceptive recalibration arises even when the misalignment between visual and proprioceptive estimates of hand position is only experienced during movement. Participants performed a “shooting task” through the targets with a cursor that was rotated 30° clockwise relative to hand motion. Results revealed that, following training on the shooting task, participants adapted their reaches to all targets by approximately 16° and recalibrated their sense of felt hand position by 8°. Thus, experiencing a sensory misalignment between visual and proprioceptive estimates of hand position during movement leads to proprioceptive recalibration.     

Stopping speech suppresses the task-irrelevant hand

Some situations require one to quickly stop an initiated response. Recent evidence suggests that rapid stopping engages a mechanism that has diffuse effects on the motor system. For example, stopping the hand dampens the excitability of the task-irrelevant leg. However, it is unclear whether this ‘global suppression’ could apply across wider motor modalities. Here we tested whether stopping speech leads to suppression of the task-irrelevant hand. We used Transcranial Magnetic Stimulation over the primary motor cortex with concurrent electromyography from the hand. We found that when speech was successfully stopped the motor evoked potential from the task-irrelevant hand was significantly reduced compared to when the participant failed to stop speaking, or responded on non stop signal trials, or compared to baseline. This shows that when speech is quickly stopped, there is a broad suppression across the motor system. This has implications for the neural basis of speech control and stuttering.     

Happy to See Me,Aren’t You,Sally? Signal Detection Analysis of Emotion Detection in Briefly Presented Male and Female Faces

Using signal detection methods, possible effects of emotion type (happy, angry), gender of the stimulus face, and gender of the participant on the detection and response bias of emotion in briefly presented faces were investigated. Fifty-seven participants (28 men, 29 women) viewed 90 briefly presented faces (30 happy, 30 angry, and 30 neutral, each with 15 male and 15 female faces) answering yes if the face was perceived as emotional and no if it was not perceived as emotional. Sensitivity [d', z(hit rate) minus z(false alarm rate)] and response bias (β, likelihood ratio of "signal plus noise" vs. "noise") were measured for each face combination for each presentation time (6.25, 12.50, 18.75, 25.00, 31.25 ms). The d' values were higher for happy than for angry faces and higher for angry-male than for angry-female faces, and there were no effects of gender-of-participant. Results also suggest a greater tendency for participants to judge happy-female faces as emotional, as shown by lower β values for these faces as compared to the other emotion-gender combinations. This happy-female response bias suggests, at least, a partial explanation to happy-superiority effects in studies where performance is only measured as percent correct responses, and, in general, that women are expected to be happy.     

The effect of psychological stress and expectation on auditory perception: A signal detection analysis

Both psychological stress and predictive signals relating to expected sensory input are believed to influence perception, an influence which, when disrupted, may contribute to the generation of auditory hallucinations. The effect of stress and semantic expectation on auditory perception was therefore examined in healthy participants using an auditory signal detection task requiring the detection of speech from within white noise. Trait anxiety was found to predict the extent to which stress influenced response bias, resulting in more anxious participants adopting a more liberal criterion, and therefore experiencing more false positives, when under stress. While semantic expectation was found to increase sensitivity, its presence also generated a shift in response bias towards reporting a signal, suggesting that the erroneous perception of speech became more likely. These findings provide a potential cognitive mechanism that may explain the impact of stress on hallucination‐proneness, by suggesting that stress has the tendency to alter response bias in highly anxious individuals. These results also provide support for the idea that top‐down processes such as those relating to semantic expectation may contribute to the generation of auditory hallucinations.     

Modeling response signal and response time data

The diffusion model (Ratcliff, 1978) and the leaky competing accumulator model (LCA, Usher & McClelland, 2001) were tested against two-choice data collected from the same subjects with the standard response time procedure and the response signal procedure. In the response signal procedure, a stimulus is presented and then, at one of a number of experimenter-determined times, a signal to respond is presented. The models were fit to the data from the two procedures simultaneously under the assumption that responses in the response signal procedure were based on a mixture of decision processes that had already terminated at response boundaries before the signal and decision processes that had not yet terminated. In the latter case, decisions were based on partial information in one variant of each model or on guessing in a second variant. Both variants of the diffusion model fit the data well and both fit better than either variant of the LCA model, although the differences in numerical goodness-of-fit measures were not large enough to allow decisive selection between the models.     

Stimulus disparity and punisher control of human signal-detection performance

The present experiment examined the effects of varying stimulus disparity and relative punisher frequencies on signal detection by humans. Participants were placed into one of two groups. Group 3 participants were presented with 1:3 and 3:1 punisher frequency ratios, while Group 11 participants were presented with 1:11 and 11:1 punisher frequency ratios. For both groups, stimulus disparity was varied across three levels (low, medium, high) for each punisher ratio. In all conditions, correct responses were intermittently reinforced (1:1 reinforcer frequency ratio). Participants were mostly biased away from the more punished alternative, with more extreme response biases found for Group 11 participants compared to Group 3. For both groups, estimates of discriminability increased systematically across the three disparity levels and were unaffected by the punisher ratios. Likewise, estimates of response bias and sensitivity to the punisher ratios were unaffected by changes in discriminability, supporting the assumption of parameter invariance in the Davison and Tustin (1978) model of signal detection. Overall, the present experiment found no relation between stimulus control and punisher control, and provided further evidence for similar but opposite effects of punishers to reinforcers in signal-detection procedures.     

A Signal Detection Model for Multiple-Choice Exams

A model for multiple-choice exams is developed from a signal-detection perspective. A correct alternative in a multiple-choice exam can be viewed as being a signal embedded in noise (incorrect alternatives). Examinees are assumed to have perceptions of the plausibility of each alternative, and the decision process is to choose the most plausible alternative. It is also assumed that each examinee either knows or does not know each item. These assumptions together lead to a signal detection choice model for multiple-choice exams. The model can be viewed, statistically, as a mixture extension, with random mixing, of the traditional choice model, or similarly, as a grade-of-membership extension. A version of the model with extreme value distributions is developed, in which case the model simplifies to a mixture multinomial logit model with random mixing. The approach is shown to offer measures of item discrimination and difficulty, along with information about the relative plausibility of each of the alternatives. The model, parameters, and measures derived from the parameters are compared to those obtained with several commonly used item response theory models. An application of the model to an educational data set is presented.     

Monaural Loudness Adaptation for Middle-Intensity Middle-Frequency Signals: The Importance of Measurement Technique

Using the Simple Adaptation technique (SA) and the Ipsilateral Comparison Paradigm (ICP), the authors studied monaural loudness adaptation to a middle-intensity [60 dB(A)] tone at signal frequencies of 250, 1000, and 4000 Hz in the left and right ears. Adaptation effects were absent when the SA procedure was used. However, they were observed uniformly across all frequency values with the ICP, a result that challenges the assertion in the literature, on the basis of SA measures, that loudness adaptation for middle-intensity signals occurs only at frequencies above 4000 Hz. The ICP features periodic intensity modulations (±10 dB relative to the base signal) to accommodate listeners' needs for referents by which they can gauge subtle changes in the loudness of the adapting tone, a key component that is missing in the SA method. Adaptation effects in this investigation were similar in both ears, supporting the equal susceptibility assumption common in loudness adaptation studies.