A review of some unusual applications of signal detection theory

Hutchinson, T. P.

doi:10.1007/bf00144302

Cited by 12 publications

(2 citation statements)

References 88 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(For a review of applications of the signal-detection analysis in social psychology, see Martin & Rovira, 1981. For a wide-426 QUANTITATIVE ANALYSIS ranging review of applications in areas from recognition memory to military decisions, see Hutchinson, 1981. ) Virtually all applications outside the behavior-analytic literature have invoked the theory of signal detectability advanced by Swets, Tanner, and Birdsall (1961).…”

Section: Signal Detectionmentioning

confidence: 99%

Quantitative Analysis

Nevin

1984

J Exper Analysis Behavior

View full text Add to dashboard Cite

Quantitative analysis permits the isolation of invariant relations in the study of behavior. The parameters of these relations can serve as higher-order dependent variables in more extensive analyses. These points are illustrated by reference to quantitative descriptions of performance maintained by concurrent schedules, multiple schedules, and signal-detection procedures. Such quantitative descriptions of empirical data may be derived from mathematical theories, which in turn can lead to novel empirical analyses so long as their terms refer to behavioral and environmental events. Thus, quantitative analysis is an integral aspect of the experimental analysis of behavior.

show abstract

Section: Signal Detectionmentioning

confidence: 99%

Quantitative Analysis

Nevin

1984

J Exper Analysis Behavior

View full text Add to dashboard Cite

show abstract

“…Disparate areas of psychological research have adopted SDT as an explanatory framework for a broad range of topics including sensation and perception (Fechner, 1860; Tanner & Swets, 1954), category perception (Macmillan, Kaplan, & Creelman, 1977), recognition memory (Wickelgren & Norman, 1966), attention (Lu & Dosher, 1998), perceptual learning (Dosher & Lu, 1998, 1999), group decision behavior (Sorkin & Dai, 1994; Sorkin, Hays, & West, 2001), neurophysiology (Britten, Shadlen, Newsome, & Movshon, 1992), and clinical applications (McFall & Treat, 1999). Many studies have found application for SDT in areas far beyond traditional psychological studies (Hutchinson, 1981; McClelland, 2011). …”

mentioning

confidence: 99%

Separating decision and encoding noise in signal detection tasks.

Cabrera¹,

Lü²,

Dosher³

2015

Psychological Review

View full text Add to dashboard Cite

In this paper we develop an extension to the Signal Detection Theory (SDT) framework to separately estimate internal noise arising from representational and decision processes. Our approach constrains SDT models with decision noise by combining a multi-pass external noise paradigm with confidence rating responses. In a simulation study we present evidence that representation and decision noise can be separately estimated over a range of representative underlying representational and decision noise level configurations. These results also hold across a number of decision rules and show resilience to rule miss-specification. The new theoretical framework is applied to a visual detection confidence-rating task with three and five response categories. This study compliments and extends the recent efforts of researchers (Benjamin, Diaz, & Wee, 2009; Mueller & Weidemann, 2008; Rosner & Kochanski, 2009, Kellen, Klauer, & Singmann, 2012) to separate and quantify underlying sources of response variability in signal detection tasks.

show abstract

Effective Human Oversight of AI-Based Systems: A Signal Detection Perspective on the Detection of Inaccurate and Unfair Outputs

Langer,

Baum,

Schlicker

2024

Minds & Machines

View full text Add to dashboard Cite

Legislation and ethical guidelines around the globe call for effective human oversight of AI-based systems in high-risk contexts – that is oversight that reliably reduces the risks otherwise associated with the use of AI-based systems. Such risks may relate to the imperfect accuracy of systems (e.g., inaccurate classifications) or to ethical concerns (e.g., unfairness of outputs). Given the significant role that human oversight is expected to play in the operation of AI-based systems, it is crucial to better understand the conditions for effective human oversight. We argue that the reliable detection of errors (as an umbrella term for inaccuracies and unfairness) is crucial for effective human oversight. We then propose that Signal Detection Theory (SDT) offers a promising framework for better understanding what affects people’s sensitivity (i.e., how well they are able to detect errors) and response bias (i.e., the tendency to report errors given a perceived evidence of an error) in detecting errors. Whereas an SDT perspective on the detection of inaccuracies is straightforward, we demonstrate its broader applicability by detailing the specifics for an SDT perspective on unfairness detection, including the need to choose a standard for (un)fairness. Additionally, we illustrate that an SDT perspective helps to better understand the conditions for effective error detection by showing examples of task-, system-, and person-related factors that may affect the sensitivity and response bias of humans tasked with detecting unfairness associated with the use of AI-based systems. Finally, we discuss future research directions for an SDT perspective on error detection.

show abstract

A review of some unusual applications of signal detection theory

Cited by 12 publications

References 88 publications

Quantitative Analysis

Quantitative Analysis

Separating decision and encoding noise in signal detection tasks.

Effective Human Oversight of AI-Based Systems: A Signal Detection Perspective on the Detection of Inaccurate and Unfair Outputs

Contact Info

Product

Resources

About