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John T. Wixted - One of the best experts on this subject based on the ideXlab platform.
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The forgotten history of Signal Detection Theory.
Journal of experimental psychology. Learning memory and cognition, 2019Co-Authors: John T. WixtedAbstract:Signal Detection Theory is one of psychology's most well-known and influential theoretical frameworks. However, the conceptual hurdles that had to be overcome before the Theory could finally emerge in its modern form in the early 1950s seem to have been largely forgotten. Here, I trace the origins of Signal Detection Theory, beginning with Fechner's (1860/1966) Elements of Psychophysics. Over and above the Gaussian-based mathematical framework conceived by Fechner in 1860, nearly a century would pass before psychophysicists finally realized in 1953 that the distribution of sensations generated by neural noise falls above, not below, the threshold of conscious awareness. An extensive body of single-unit recording and neuroimaging research conducted since then supports the idea that sensory noise yields genuinely felt conscious sensations even in the complete absence of stimulation. That hard-to-come-by insight in 1953 led immediately to the notion of a movable decision criterion and to the methodology of receiver operating characteristic (ROC) analysis. Over the ensuing years, Signal Detection Theory and ROC analysis have had an enormous impact on basic and applied science alike. Yet, in some quarters of our field, that fact appears to be virtually unknown. By tracing both its fascinating origins and its phenomenal impact, I hope to illustrate why no area of experimental psychology should ever be oblivious to Signal Detection Theory. (PsycINFO Database Record (c) 2020 APA, all rights reserved).
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Signal Detection Theory
Wiley StatsRef: Statistics Reference Online, 2014Co-Authors: John T. WixtedAbstract:Signal-Detection Theory is a framework for understanding the process of decision making under conditions of uncertainty. Its essential claim is that subjective evidence for the presence or absence of a stimulus is a continuous variable. That is, evidence comes in degrees and is never entirely present or entirely absent even though the stimulus itself is. Although the average amount of subjective evidence is higher on trials in which the stimulus actually is present, trial-to-trial variability is such that no evidence value perfectly distinguishes stimulus-present trials from stimulus-absent trials. As such, the observer is assumed to select a criterion evidence value above which the stimulus is declared to be present and below which it is declared to be absent. This way of thinking sheds light on an extremely wide range of decision problems ranging from auditory Detection to the Detection of a psychiatric condition, and it offers a measure of discrimination (d′) that, unlike percent correct, is theoretically uncontaminated by response bias. Keywords: Signal-Detection Theory; ′; hit rate; false alarm rate; response bias; ROC curves
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Dual-process Theory and Signal-Detection Theory of recognition memory
Psychological Review, 2007Co-Authors: John T. WixtedAbstract:Two influential models of recognition memory, the unequal-variance Signal-Detection model and a dual-process threshold/Detection model, accurately describe the receiver operating characteristic, but only the latter model can provide estimates of recollection and familiarity. Such estimates often accord with those provided by the remember-know procedure, and both methods are now widely used in the neuroscience literature to identify the brain correlates of recollection and familiarity. However, in recent years, a substantial literature has accumulated directly contrasting the Signal-Detection model against the threshold/Detection model, and that literature is almost unanimous in its endorsement of Signal-Detection Theory. A dual-process version of Signal-Detection Theory implies that individual recognition decisions are not process pure, and it suggests new ways to investigate the brain correlates of recognition memory.
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Encyclopedia of Statistics in Behavioral Science - Signal Detection Theory
Encyclopedia of Statistics in Behavioral Science, 2005Co-Authors: John T. WixtedAbstract:Signal-Detection Theory is a framework for understanding the process of decision making under conditions of uncertainty. Its essential claim is that subjective evidence for the presence or absence of a stimulus is a continuous variable. That is, evidence comes in degrees and is never entirely present or entirely absent even though the stimulus itself is. Although the average amount of subjective evidence is higher on trials in which the stimulus actually is present, trial-to-trial variability is such that no evidence value perfectly distinguishes stimulus-present trials from stimulus-absent trials. As such, the observer is assumed to select a criterion evidence value above which the stimulus is declared to be present and below which it is declared to be absent. This way of thinking sheds light on an extremely wide range of decision problems ranging from auditory Detection to the Detection of a psychiatric condition, and it offers a measure of discrimination (d′) that, unlike percent correct, is theoretically uncontaminated by response bias. Keywords: Signal-Detection Theory; d′; hit rate; false alarm rate; response bias; ROC curves
Lawrence T Decarlo - One of the best experts on this subject based on the ideXlab platform.
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an item response model for true false exams based on Signal Detection Theory
Applied Psychological Measurement, 2020Co-Authors: Lawrence T DecarloAbstract:A true–false exam can be viewed as being a Signal Detection task—the task is to detect whether or not an item is true (Signal) or false (noise). In terms of Signal Detection Theory (SDT), examinees...
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Signal Detection Theory with item effects
Journal of Mathematical Psychology, 2011Co-Authors: Lawrence T DecarloAbstract:Abstract 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.
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The mirror effect and mixture Signal Detection Theory.
Journal of experimental psychology. Learning memory and cognition, 2007Co-Authors: Lawrence T DecarloAbstract: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.
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A Latent Class Extension of Signal Detection Theory, with Applications.
Multivariate behavioral research, 2002Co-Authors: Lawrence T DecarloAbstract:A latent class extension of Signal Detection Theory is presented and applications are illustrated. The approach is useful for situations where observers attempt to detect latent categorical events or where the goal of the analysis is to select or classify cases. Signal Detection Theory is shown to offer a simple summary of the observers' performance in terms of Detection and response criteria. Implications of the view via Signal Detection for the training of raters are noted, as are approaches to validating the parameters and classifications. An extension of the Signal Detection model to more than two latent classes, with a simple restriction on the Detection parameters, is introduced. Sample programs to fit the models using software for latent class analysis or software for second generation structural equation modeling are provided.
Arturo Molina - One of the best experts on this subject based on the ideXlab platform.
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technology transfer motivation analysis based on fuzzy type 2 Signal Detection Theory
Ai & Society, 2016Co-Authors: Pedro Ponce, Kenneth Polasko, Arturo MolinaAbstract:This paper presents a complete study based on Signal Detection Theory (SDT) for deciding the motivation factors that motivate academic researchers to participate in the technology transfer process (university---industry relationship). Moreover, this study determines the researchers' perception about the motivations strategies designed in universities. The paper focuses on positive motivation factors such as academic prestige, competition, generation of resources, the solution of complex problems, professional challenge, personal gains, personal gratification and the solution of society problems. The negative motivation factors studied in the paper are as follows: innovation environment, time required, and lack of incentive and fear of contravening university policies. The importance of SDT lies in the fact that it is a Theory that can deal with observer perception and the ways in which choices are made. This paper proposes fuzzy sets type 2 in SDT to expand its potential and understand the decision of the researchers during the technology transfer process under conditions of uncertainty. Although fuzzy type 1 Detection Theory (FDT) allows Signals to overlap (non-binary description), a complete representation of uncertainty is not incorporated. Thus, fuzzy type 2 Signal Detection Theory (FDT2) is proposed to model the uncertainties and noise condition under technology transfer process. High standards of motivation can maintain and attract competent researchers at universities; thus, this paper deals in a deep fashion with all the main aspects about those motivation factors using FDT2.
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Design based on fuzzy Signal Detection Theory for a semi-autonomous assisting robot in children autism therapy
Computers in Human Behavior, 2016Co-Authors: Pedro Ponce, Arturo Molina, Dimitra GrammatikouAbstract:There are different kinds of robots that are used to assist autistic children during therapy; however, there is not a previous evaluation in place to decide if the robot can detect and send social interaction clues to the child in correct manner. Since the Signal Detection and fuzzy Signal Detection theories are well known techniques in human psychology for detecting Signal and noise relationships, this work proposes those techniques as a main tool to identify how effectively stimuli are detected by social robots. Unlike traditional psychophysical approaches, which treat observers as sensors, Signal Detection Theory recognizes that observers are both sensors and decision makers, and that these are distinct processes that can be measured using separate indices, sensitivity and response criterion. Hence, the robot can be defined as an observer using the Signal Detection Theory. This proposal allows to evaluate social robots with human psychology tools in order to improve the human-robot interaction. Thus, the robots accomplish specific social responses that can be a better approach during the autism therapy. Furthermore, the fuzzy Signal Detection Theory (FSDT) applied to social skills can be an enhanced procedure for designing social robots. A semi-autonomous social robot was designed to validate the proposal. A novel methodology for designing social robot based on Signal Detection Theory.The results show an effective method to improve the human robot interaction.The semiautonomous robots reach excellent results for autism therapy.
Pedro Ponce - One of the best experts on this subject based on the ideXlab platform.
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technology transfer motivation analysis based on fuzzy type 2 Signal Detection Theory
Ai & Society, 2016Co-Authors: Pedro Ponce, Kenneth Polasko, Arturo MolinaAbstract:This paper presents a complete study based on Signal Detection Theory (SDT) for deciding the motivation factors that motivate academic researchers to participate in the technology transfer process (university---industry relationship). Moreover, this study determines the researchers' perception about the motivations strategies designed in universities. The paper focuses on positive motivation factors such as academic prestige, competition, generation of resources, the solution of complex problems, professional challenge, personal gains, personal gratification and the solution of society problems. The negative motivation factors studied in the paper are as follows: innovation environment, time required, and lack of incentive and fear of contravening university policies. The importance of SDT lies in the fact that it is a Theory that can deal with observer perception and the ways in which choices are made. This paper proposes fuzzy sets type 2 in SDT to expand its potential and understand the decision of the researchers during the technology transfer process under conditions of uncertainty. Although fuzzy type 1 Detection Theory (FDT) allows Signals to overlap (non-binary description), a complete representation of uncertainty is not incorporated. Thus, fuzzy type 2 Signal Detection Theory (FDT2) is proposed to model the uncertainties and noise condition under technology transfer process. High standards of motivation can maintain and attract competent researchers at universities; thus, this paper deals in a deep fashion with all the main aspects about those motivation factors using FDT2.
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Design based on fuzzy Signal Detection Theory for a semi-autonomous assisting robot in children autism therapy
Computers in Human Behavior, 2016Co-Authors: Pedro Ponce, Arturo Molina, Dimitra GrammatikouAbstract:There are different kinds of robots that are used to assist autistic children during therapy; however, there is not a previous evaluation in place to decide if the robot can detect and send social interaction clues to the child in correct manner. Since the Signal Detection and fuzzy Signal Detection theories are well known techniques in human psychology for detecting Signal and noise relationships, this work proposes those techniques as a main tool to identify how effectively stimuli are detected by social robots. Unlike traditional psychophysical approaches, which treat observers as sensors, Signal Detection Theory recognizes that observers are both sensors and decision makers, and that these are distinct processes that can be measured using separate indices, sensitivity and response criterion. Hence, the robot can be defined as an observer using the Signal Detection Theory. This proposal allows to evaluate social robots with human psychology tools in order to improve the human-robot interaction. Thus, the robots accomplish specific social responses that can be a better approach during the autism therapy. Furthermore, the fuzzy Signal Detection Theory (FSDT) applied to social skills can be an enhanced procedure for designing social robots. A semi-autonomous social robot was designed to validate the proposal. A novel methodology for designing social robot based on Signal Detection Theory.The results show an effective method to improve the human robot interaction.The semiautonomous robots reach excellent results for autism therapy.
Lisa Feldman Barrett - One of the best experts on this subject based on the ideXlab platform.
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“Utilizing” Signal Detection Theory
Psychological science, 2014Co-Authors: Spencer K. Lynn, Lisa Feldman BarrettAbstract:What do inferring what a person is thinking or feeling, judging a defendant's guilt, and navigating a dimly lit room have in common? They involve perceptual uncertainty (e.g., a scowling face might indicate anger or concentration, for which different responses are appropriate) and behavioral risk (e.g., a cost to making the wrong response). Signal Detection Theory describes these types of decisions. In this tutorial, we show how incorporating the economic concept of utility allows Signal Detection Theory to serve as a model of optimal decision making, going beyond its common use as an analytic method. This utility approach to Signal Detection Theory clarifies otherwise enigmatic influences of perceptual uncertainty on measures of decision-making performance (accuracy and optimality) and on behavior (an inverse relationship between bias magnitude and sensitivity optimizes utility). A "utilized" Signal Detection Theory offers the possibility of expanding the phenomena that can be understood within a decision-making framework.