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).

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Calculation of signal detection theory measures

Label noise is an important issue in classification, with many potential negative consequences. For example, the accuracy of predictions may decrease, whereas the complexity of inferred models and the number of necessary training samples may increase. Many works in the literature have been devoted to the study of label noise and the development of techniques to deal with label noise. However, the field lacks a comprehensive survey on the different types of label noise, their consequences and the algorithms that consider label noise. This paper proposes to fill this gap. First, the definitions and sources of label noise are considered and a taxonomy of the types of label noise is proposed. Second, the potential consequences of label noise are discussed. Third, label noise-robust, label noise cleansing, and label noise-tolerant algorithms are reviewed. For each category of approaches, a short discussion is proposed to help the practitioner to choose the most suitable technique in its own particular field of application. Eventually, the design of experiments is also discussed, what may interest the researchers who would like to test their own algorithms. In this paper, label noise consists of mislabeled instances: no additional information is assumed to be available like e.g., confidences on labels.

https://romisatriawahono.net/lecture/rm/survey/machine%20learning/Frenay%20-%20Classification%20in%20the%20Presence%20of%20Label%20Noise%20-%202014.pdf

Classification in the Presence of Label Noise: A Survey

A framework is outlined in which individual faces are assumed to be encoded as a point in a multidimensional space, defined by dimensions that serve to discriminate faces. It is proposed that such a framework can account for the effects of distinctiveness, inversion, and race on recognition of faces. Two specific models within this framework are identified: a norm-based coding model, in which faces are encoded as vectors from a population norm or prototype; and a purely exemplar-based model. Both models make similar predictions, albeit in different ways, concerning the interactions between the effects of distinctiveness, inversion and race. These predictions were supported in five experiments in which photographs of faces served as stimuli. The norm-based coding version and the exemplar-based version of the framework cannot be distinguished on the basis of the experiments reported, but it is argued that a multidimensional space provides a useful heuristic framework to investigate recognition of faces. Fin...

A Unified Account of the Effects of Distinctiveness, Inversion, and Race in Face Recognition:

Selective processing of threat cues in anxiety states

Attention Increases Sensitivity of V4 Neurons

Contents: Foreword. Preface. What Are Statistical Decisions? Non-Parametric Measures of Sensitivity. Gaussian Distributions of Signal and Noise With Equal Variances. Gaussian Distributions of Signal and Noise With Unequal Variances. Conducting a Rating Scale Experiment. Choice Theory Approximations to Signal Detection Theory. Threshold Theory. The Laws of Categorical and Comparative Judgement. Appendices: Answers to Problems. Logarithms. Integration of the Expression for the Logistic Curve. Computer Programmes for Signal Detection Analysis. Tables.

D. McNicol

Papers

A Primer of Signal Detection Theory; Table of D' and β