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).     

Relationships between self-determination theory and theory of planned behavior applied to physical activity and exercise behavior in chronic pain

This study examined the relationships between self-determination theory (SDT) and theory of planned behavior (TpB) applied to physical activity and exercise behavior (PA&E) in people with chronic pain. Two hundred and eleven adults with chronic musculoskeletal pain (28 males and 183 females, age range 18 to 82 years, mean age 43 years) were recruited from online support groups and clinic networks in the United States. Participants completed SDT measures relevant to PA&E on perceived autonomy support, autonomy, competence, and relatedness, as well as TpB measures relevant to PA&E on intention, attitudes, subjective norms, and perceived behavioral control. Correlational techniques and canonical correlation analysis were performed to examine the relationships and variance within and between theoretical dimensions. Overall, the SDT set accounted for 37% of the TpB variance and the TpB set accounted for 32% of the SDT set variance. The results indicate there are statistical similarities and differences between concepts in SDT and TpB models for PA&E. Using both empirical guidance and clinical expertise, researchers and practitioners should attempt to select and integrate non-redundant and complementary components from SDT, TpB, and other related health behavior theories.     

Signal detection theory with item effects

Applications of signal detection theory (SDT) often involve presentations of different items on each trial, such as slides in a medical imaging study or words in a memory study. If factors particular to the items themselves, apart from being a signal or noise, affect observers’ responses, then ‘item effects’ are present. One way to model these effects is to use a latent continuous variable as an item ‘factor’, such as item ‘difficulty’. Details of SDT models with item effects are clarified via derivations of their implied conditional means, variances, and covariances. Intra-item correlations are defined and suggested as measures of the magnitude of item effects. The SDT-item models are simple random coefficient models and can be fit with standard software. More general models, such as item models with mixing and/or with random observer effects, are also considered.     

Signal detection theory with finite mixture distributions: theoretical developments with applications to recognition memory

An extension of signal detection theory (SDT) that incorporates mixtures of the underlying distributions is presented. The mixtures can be motivated by the idea that a presentation of a signal shifts the location of an underlying distribution only if the observer is attending to the signal; otherwise, the distribution is not shifted or is only partially shifted. Thus, trials with a signal presentation consist of a mixture of 2 (or more) latent classes of trials. Mixture SDT provides a general theoretical framework that offers a new perspective on a number of findings. For example, mixture SDT offers an alternative to the unequal variance signal detection model; it can also account for nonlinear normal receiver operating characteristic curves, as found in recent research.     

Self‐determination theory as a foundation for personality researchers

In this introductory article, we first describe the impetus for this special issue. What made us think that self‐determination theory (SDT) might provide a sort of foundation for the rest of personality psychology? For readers unfamiliar with SDT, we then provide a historical overview that covers the evolution of the six “mini‐theories” that currently compose SDT: cognitive evaluation theory, causality orientations theory, organismic integration theory, basic psychological needs theory, goal contents theory, and relational motivation theory. Following each section are preliminary suggestions about how each mini‐theory might be useful or informative in other branches of personality. This special issue contains nine articles, each of which makes its own attempt to newly link its area of personality research to SDT. Even if SDT is not the appropriate seed for greater consilience in personality psychology, we urge the field not to neglect the search for unifying principles (Sheldon, Cheng, & Hilpert, 2011); it may finally be time to renew the search for a “grand theory” in personality.     

Signal detection theory analyses of semantic priming in word recognition

Word recognition, semantic priming, and cognitive impenetrability research have used signal detection theory (SDT) measures to separate perceptual and postperceptual processes. In the D. Norris (1986) checking model and model simulation (D. Norris, 1995), priming alters only postperceptual word decision criteria: Stimulus-related priming reduces uncertainty, increasing sensitivity; stimulus-unrelated priming increases false alarms more than hits, reducing sensitivity. This work is cited as strong evidence that criterion changes can alter perceptual sensitivity and that SDT is inappropriate for investigating complex cognitive processes. The authors' current SDT ideal observer analysis of the model demonstrates that related priming does not directly alter sensitivity and that unrelated priming increases only false-alarm rate, reducing sensitivity. This analysis provides new perspectives on SDT concepts of complex decision processing.     

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.     

Testing theories of recognition memory by predicting performance across paradigms

Signal-detection theory (SDT) accounts of recognition judgments depend on the assumption that recognition decisions result from a single familiarity-based process. However, fits of a hybrid SDT model, called dual-process theory (DPT), have provided evidence for the existence of a second, recollection-based process. In 2 experiments, the authors tested predictions of DPT and SDT by comparing the invariance of parameter estimates between yes/no (Y/N) and 2-alternative forced-choice (2AFC) testing paradigms. Both experiments showed DPT recollection estimates in Y/N to be poorly correlated with recollection estimates in 2AFC. In Experiment 2, SDT predictions explained more variance than DPT predictions. The authors evaluate and discuss the extent to which each model possesses theoretical validity versus computational flexibility in curve fitting.     

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.     

VRVision: A new tool for the display of 3-D images in behavioral research

Researchers using three-dimensional (3-D) scenes in their studies often spend a lot of time prerendering the scenes into a set of images for later presentations. To improve efficiency, we have developed a new plugin program for MATLAB that eliminates this step. The program is able to manipulate and present 3-D scenes in real time. It allows fine control over numerous parameters, including the mode of projection, angular rotation, lighting, surface photo textures, and animation. Images can be presented with monoscopic or stereoscopic display. It can also dynamically track viewer position and update and display an appropriate 3-D projection. The program is fully compatible with existing MATLAB utilities such as the Psychophysics Toolbox (psychtoolbox.org). It is made freely available to the research community (www.hive.hull .ac.uk/software/vrvision).     

Clustering, seriation, and subset extraction of confusion data

The study of confusion data is a well established practice in psychology. Although many types of analytical approaches for confusion data are available, among the most common methods are the extraction of 1 or more subsets of stimuli, the partitioning of the complete stimulus set into distinct groups, and the ordering of the stimulus set. Although standard commercial software packages can sometimes facilitate these types of analyses, they are not guaranteed to produce optimal solutions. The authors present a MATLAB *.m file for preprocessing confusion matrices, which includes fitting of the similarity-choice model. Two additional MATLAB programs are available for optimally clustering stimuli on the basis of confusion data. The authors also developed programs for optimally ordering stimuli and extracting subsets of stimuli using information from confusion matrices. Together, these programs provide several pragmatic alternatives for the applied researcher when analyzing confusion data. Although the programs are described within the context of confusion data, they are also amenable to other types of proximity data.     

Spreadsheet signal detection

A number of studies in perception, attention, and memory employ signal detection theory (SDT) to assess the accuracy of an observer’s detection or discrimination performance. Some of the problems that students have with understanding and using SDT are associated with the calculations needed to obtain SDT parameters and predictions. All of these calculations, plus the simulation of SDT processes, can be performed using a spreadsheet application program, such as Excel or Quattro Pro. This paper offers a short tutorial on how to use a spreadsheet program to increase your students’ knowledge and understanding of SDT.     

Meaning making,self‐determination theory,and the question of wisdom in personality

Self‐determination theory (SDT) has advanced the most comprehensive model of motives for human flourishing in the field of personality psychology and beyond. In this article, we evaluate SDT relative to the process of meaning making, particularly from a narrative perspective, showing what SDT can and cannot explain about the construction of self‐identity and its relation to human flourishing. On the one hand, SDT explains how subjective assessments of need fulfillment drive the process of self‐determined living. The internal motives that follow such fulfillment serve as important themes in people's life stories that predict several markers of hedonic and eudaimonic well‐being. On the other hand, SDT's focus on subjective fulfillment limits what SDT can explain about how wisdom, which is a canonical good of both eudaimonia and meaning making, helps people make sense of life's more difficult or unfulfilling events. SDT may facilitate a facet of wisdom that is more subjective and experiential but not the critical facet of wisdom defined by objectively more complex structures of interpretation.     

The LMPCA program: A graphical user interface for fitting the linked-mode PARAFAC-PCA model to coupled real-valued data

In behavioral research, PARAFAC analysis, a three-mode generalization of standard principal component analysis (PCA), is often used to disclose the structure of three-way three-mode data. To get insight into the underlying mechanisms, one often wants to relate the component matrices resulting from such a PARAFAC analysis to external (two-way two-mode) information, regarding one of the modes of the three-way data. To this end, linked-mode PARAFAC-PCA analysis can be used, in which the three-way and the two-way data set, which have one mode in common, are simultaneously analyzed. More specifically, a PARAFAC and a PCA model are fitted to the three-way and the two-way data, respectively, restricting the component matrix for the common mode to be equal in both models. Until now, however, no software program has been publicly available to perform such an analysis. Therefore, in this article, the LMPCA program, a free and easy-to-use MATLAB graphical user interface, is presented to perform a linked-mode PARAFAC-PCA analysis. The LMPCA software can be obtained from the authors at http://ppw.kuleuven.be/okp/software/LMPCA. For users who do not have access to MATLAB, a stand-alone version is provided.     

Editorial: Animal Metaphors

Social dominance theory (SDT) suggests that prejudice legitimizes and maintains the existing social hierarchy. Consistent with an SDT explanation, it was predicted that (a) perceptions of threat to the existing social hierarchy and (b) social dominance orientation (SDO) would be related to stronger beliefs in stereotypes and "legitimizing myths." In addition, this analysis tests SDT's predicted interaction between perceptions of threat and SDO. It was predicted that for high SDO individuals the relation between perceptions of intergroup threat and the endorsement of legitimizing myths would be stronger than for low SDO individuals. These predictions were examined using a national probability General Social Survey archival data set. The results were consistent with our predictions and suggest that social dominance theory compliments and augments other threat theories of prejudice.     

自主－受控动机效应及应用

自我决定论是一种新的动机认知理论,它将动机区分为自主动机和受控动机。由受控动机到自主动机是一个连续体。在这个连续体上依据自主程度的高低分布着5种不同类型的动机。大量的研究表明不同类型动机之间存在不同的效应。而在实际应用中,要实现由受控动机向自主动机的转化,自主支持的环境是被证实的一个重要的因素。未来研究除了在研究方法上加以改进之外,在促进动机内化的具体应用研究方面还要进行更深入的探讨。而国内本土化研究则刚刚起步,有待于加强     

A self-determination process model of physical activity adoption in the context of a randomized controlled trial

Objective: The PA Counseling (PAC) trial tested a primary care intervention using Self-Determination Theory (SDT) as the guiding framework. This paper presents specific results related to SDT constructs in a physical activity (PA) context. Specifically, whether patients who received both brief autonomy supportive PA counseling from their health-care provider and intensive (3 month) autonomy supportive counseling from a PA counselor would report greater levels of autonomy support, autonomous motivation, perceived competence and PA adoption, than patients receiving only brief counseling. In addition, we tested Williams’ SDT process model in the context of PA adoption.Method: Measures of autonomous motivation and perceived competence for PA were measured at baseline and 6 weeks. PA was assessed at baseline and 13 weeks. An autonomy support index was calculated by multiplying minutes of counseling by perceptions of autonomy support. Group differences in autonomy support, autonomous motivation, perceived competence and PA were examined. The SDT process model of PA adoption was tested via path analysis.Results: The results showed higher autonomy support and autonomous motivation at 6 weeks and higher PA levels at 13 weeks for the experimental group. The SDT process model for PA adoption showed that autonomous motivation and perceived competence at 6 weeks significantly predicted 13-week PA for the experimental group.Conclusions: This study provides a rigorous field test of SDT theory in a PA context. It demonstrates the versatility and applicability of the SDT model for health behavior change. SDT-trained PA counselors appear to provide valuable contribution to facilitating patient behavior change, by increasing patient autonomous motivation for PA.     

A multidimensional scaling and semantic differential technique study of the perception of environmental settings

Abstract.— Multidimensional scaling is compared with the semantic differential technique (SDT) as methods for dimensional analysis of the perception of environmental settings. In one experiment employing as stimuli SDT scales used in previous experiments a General Evaluation , a Social Status , a Potency , a Closedness , and an Affection factor were found for both methods. A Unity and a Complexity factor merged into a single factor, an O riginality factor was only found for multidimensional scaling. In a second experiment employing as stimuli perspective drawings of urban and suburban settings, the results differed in that the General Evaluation , the Social Status , the Closedness , and the Unity/Complexity factors were found for the SDT, and only the first two for multidimensional scaling. Further experiments with other stimuli and methods of representation are needed in order to draw firm conclusions regarding the correspondence between SDT and multidimensional scaling results within the area under consideration.