Signal detection theory and psychophysics

Ingleby, Jo

doi:10.1016/0022-460x(67)90197-6

Cited by 16 publications

(17 citation statements)

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“…In the SI Appendix ( SI Appendix , SI Methods ), we describe how sensitivity (d′) and criteria at each location are related to and can be estimated from response proportions in the contingency table. We employed the choice criterion (b cc = c-d′/2) as a measure of choice bias toward each location (27). Note that a lower choice criterion signifies a higher bias for reporting changes at that location.…”

Section: Resultsmentioning

confidence: 99%

Subcortical connectivity correlates selectively with attention’s effects on spatial choice bias

Sreenivasan

Sridharan

2019

Proc. Natl. Acad. Sci. U.S.A.

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Neural mechanisms of attention are extensively studied in the neocortex; comparatively little is known about how subcortical regions contribute to attention. The superior colliculus (SC) is an evolutionarily conserved, subcortical (midbrain) structure that has been implicated in controlling visuospatial attention. Yet how the SC contributes mechanistically to attention remains unknown. We investigated the role of the SC in attention, combining model-based psychophysics, diffusion imaging, and tractography in human participants. Specifically, we asked whether the SC contributes to enhancing sensitivity (d′) to attended information, or whether it contributes to biasing choices (criteria) in favor of attended information. We tested human participants on a multialternative change detection task, with endogenous spatial cueing, and quantified sensitivity and bias with a recently developed multidimensional signal detection model (m-ADC model). At baseline, sensitivity and bias exhibited complementary patterns of asymmetries across the visual hemifields: While sensitivity was consistently higher for detecting changes in the left hemifield, bias was higher for reporting changes in the right hemifield. Remarkably, white matter connectivity of the SC with the neocortex mirrored this pattern of asymmetries. Specifically, the asymmetry in SC–cortex connectivity correlated with the asymmetry in choice bias, but not in sensitivity. In addition, SC–cortex connectivity strength could predict cueing-induced modulation of bias, but not of sensitivity, across individuals. In summary, the SC may be a key node in an evolutionarily conserved network for controlling choice bias during visuospatial attention.

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Section: Resultsmentioning

confidence: 99%

Subcortical connectivity correlates selectively with attention’s effects on spatial choice bias

Sreenivasan

Sridharan

2019

Proc. Natl. Acad. Sci. U.S.A.

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“…The ROCs curve was used for the first time by the US army to analyze the detection of RADAR signals related to Japanese aircrafts during World War II (Ingleby, 1967). The aim of the ROC method was to increasing the success rate in the detection of Japanese aircraft from RADAR signals.…”

Section: Blr Classifiermentioning

confidence: 99%

“…The aim of the ROC method was to increasing the success rate in the detection of Japanese aircraft from RADAR signals. Subsequently, it has been used in psychophysics (Ingleby, 1967), medicine (Pepe, 2003;Zweig & Campbell, 1993), and meteorology (Kharin & Zwiers, 2003). However, the first application of the ROC curves in machine learning was carried out by Spackman (1989) for comparing and evaluating different classification algorithms (Spackman, 1989).…”

Section: Blr Classifiermentioning

confidence: 99%

Sinkhole susceptibility mapping: A comparison between Bayes‐based machine learning algorithms

Taheri¹,

Shahabi

Chapi

et al. 2019

Land Degrad Dev

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Land degradation has been recognized as one of the most adverse environmental impacts during the last century. The occurrence of sinkholes is increasing dramatically in many regions worldwide contributing to land degradation. The rise in the sinkhole frequency is largely due to human‐induced hydrological alterations that favour dissolution and subsidence processes. Mitigating detrimental impacts associated with sinkholes requires understanding different aspects of this phenomenon such as the controlling factors and the spatial distribution patterns. This research illustrates the development and validation of sinkhole susceptibility models in Hamadan Province, Iran, where a large number of sinkholes are occurring under poorly understood circumstances. Several susceptibility models were developed with a training sample of sinkholes, a number of conditioning factors, and four different statistical approaches: naïve Bayes, Bayes net (BN), logistic regression, and Bayesian logistic regression. Ten conditioning factors were initially considered. Factors with negligible contribution to the quality of predictions, according to the information gain ratio technique, were discarded for the development of the final models. The validation of susceptibility models, performed using different statistical indices and receiver operating characteristic curves, revealed that the BN model has the highest prediction capability in the study area. This model provides reliable predictions on the future distribution of sinkholes, which can be used by watershed and land use managers for designing hazard and land‐degradation mitigation plans.

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“…The ROC curve is a curve which plots the sensitivity against the specificity. The integral of this curve is the "area under curve" or AUC value which is is equal to the probability that the score of a randomly chosen positive example is higher than that of a randomly chosen negative one [38,39]. AU C = 1.0 corresponds to perfect ranking, while a random ranking has an AU C = 0.5 value on average [35].…”

Section: Performance Evaluationmentioning

confidence: 99%

A One-Class Classification Approach for Protein Sequences and Structures

Bánhalmi

Busa-Fekete

Kégl

2009

Bioinformatics Research and Applications

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Abstract. The One-Class Classification (OCC) approach is based on the assumption that samples are available only from a target class in the training phase. OCC methods have been applied with success to problems where the classes are very different in size. As class-imbalance problems are typical in protein classification tasks, we were interested in testing one-class classification algorithms for the detection of distant similarities in protein sequences and structures. We found that the OCC approach brought about a small improvement in classification performance compared to binary classifiers (SVM, ANN, Random Forest). More importantly, there is a substantial (50 to 100 fold) improvement in the training time. OCCs may provide an especially useful alternative for processing those protein groups where discriminative classifiers cannot be easily trained.

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Signal detection theory and psychophysics

Cited by 16 publications

References 0 publications

Subcortical connectivity correlates selectively with attention’s effects on spatial choice bias

Subcortical connectivity correlates selectively with attention’s effects on spatial choice bias

Sinkhole susceptibility mapping: A comparison between Bayes‐based machine learning algorithms

A One-Class Classification Approach for Protein Sequences and Structures

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