Recognition Receiver Operating Characteristic Curves: The Complex Influence of Input Statistics, Memory, and Decision-making

2021 ◽  
pp. 1-24
Author(s):  
Olya Hakobyan ◽  
Sen Cheng
Keyword(s):  
Decision Making ◽  
Recognition Memory ◽  
Receiver Operating Characteristic ◽  
Process Model ◽  
Operating Characteristic ◽  
Roc Curves ◽  
Brain Regions ◽  
Dual Process ◽  
Dual Process Model ◽  
Receiver Operating

Abstract Receiver operating characteristic (ROC) analysis is the standard tool for studying recognition memory. In particular, the curvilinearity and the y-offset of recognition ROC curves have been interpreted as indicative of either memory strength (single-process models) or different memory processes (dual-process model). The distinction between familiarity and recollection has been widely studied in cognitive neuroscience in a variety of conditions, including lesions of different brain regions. We develop a computational model that explicitly shows how performance in recognition memory is affected by a complex and, as yet, underappreciated interplay of various factors, such as stimulus statistics, memory processing, and decision-making. We demonstrate that (1) the factors in the model affect recognition ROC curves in unexpected ways, (2) fitting R and F parameters according to the dual-process model is not particularly useful for understanding the underlying processes, and (3) the variability of recognition ROC curves and the controversies they have caused might be due to the uncontrolled variability in the contributing factors. Although our model is abstract, its functional components can be mapped onto brain regions, which are involved in corresponding functions. This enables us to reproduce and interpret in a coherent framework the diverse effects on recognition memory that have been reported in patients with frontal and hippocampal lesions. To conclude, our work highlights the importance of the rich interplay of a variety of factors in driving recognition memory performance, which has to be taken into account when interpreting recognition ROC curves.

Using Receiver Operating Characteristic (ROC) Analysis to Evaluate Information-Based Decision-Making

2019 ◽  
pp. 764-776 ◽  
Author(s):  
Nan Hu
Keyword(s):  
Decision Making ◽  
Receiver Operating Characteristic ◽  
Roc Analysis ◽  
Operating Characteristic ◽  
Decision Rules ◽  
Scoring Systems ◽  
Roc Curves ◽  
Decision Tools ◽  
Classification Tool ◽  
Receiver Operating

Business operators and stakeholders often need to make decisions such as choosing between A and B, or between yes and no, and these decisions are often made by using a classification tool or a set of decision rules. Decision tools usually include scoring systems, predictive models, and quantitative test modalities. In this chapter, the authors introduce the receiver operating characteristic (ROC) curves and demonstrate, through an example of bank decision on granting loans to customers, how ROC curves can be used to evaluate decision making for information-based decision making. In addition, an extension to time-dependent ROC analysis is introduced in this chapter. The authors conclude this chapter by illustrating the application of ROC analysis in information-based decision making and providing the future trends of this topic.

scholarly journals Familiarity, recollection, and receiver-operating characteristic (ROC) curves in recognition memory

2019 ◽  
Vol 47 (4) ◽  
pp. 855-876
Author(s):  
James F. Juola ◽  
Alexandra Caballero-Sanz ◽  
Adrián R. Muñoz-García ◽  
Juan Botella ◽  
Manuel Suero
Keyword(s):  
Recognition Memory ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Roc Curves ◽  
Receiver Operating

Using Receiver Operating Characteristic (ROC) Analysis to Evaluate Information -Based Decision-Making

2018 ◽  
pp. 2213-2223
Author(s):  
Nan Hu
Keyword(s):  
Decision Making ◽  
Receiver Operating Characteristic ◽  
Roc Analysis ◽  
Operating Characteristic ◽  
Decision Rules ◽  
Scoring Systems ◽  
Roc Curves ◽  
Decision Tools ◽  
Classification Tool ◽  
Receiver Operating

Business operators and stakeholders often need to make decisions such as choosing between A and B, or between yes and no, and these decisions are often made by using a classification tool or a set of decision rules. Decision tools usually include scoring systems, predictive models, and quantitative test modalities. In this chapter, we introduce the receiver operating characteristic (ROC) curves and demonstrate, through an example of bank decision on granting loans to customers, how ROC curves can be used to evaluate decision making for information based decision making. In addition, an extension to time-dependent ROC analysis is introduced in this chapter. We conclude this chapter by illustrating the application of ROC analysis in information based decision making and providing the future trends of this topic.

Receiver Operating Characteristic (ROC) Analysis

2011 ◽  
pp. 1675-1681 ◽  
Author(s):  
Nicolas Lachiche
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Decision Making ◽  
False Alarm ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Roc Curves ◽  
Hit Rate ◽  
Rate Versus ◽  
Receiver Operating

Receiver Operating Characteristic (ROC curves) have been used for years in decision making from signals, such as radar or radiology. Basically they plot the hit rate versus the false alarm rate. They were introduced recently in data mining and machine learning to take into account different misclassification costs, or to deal with skewed class distributions. In particular they help to adapt the extracted model when the training set characteristics differ from the evaluation data. Overall they provide a convenient way to compare classifiers, but also an unexpected way to build better classifiers.

Use of Receiver Operating Characteristic (ROC) Curves to Evaluate Computer Confidence Threshold and Clinical Performance in the Diagnosis of Appendicitis

1978 ◽  
Vol 17 (03) ◽  
pp. 157-161 ◽  
Author(s):  
F. T. De Dombal ◽  
Jane C. Horrocks
Keyword(s):  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Clinical Performance ◽  
Roc Curves ◽  
Final Diagnosis ◽  
Confidence Threshold ◽  
Computer Aided ◽  
Computer Confidence ◽  
Overall Performance ◽  
Receiver Operating

This paper uses simple receiver operating characteristic (ROC) curves (i) to study the effect of varying computer confidence of threshold levels and (ii) to evaluate clinical performance in the diagnosis of acute appendicitis. Over 1300 patients presenting to five centres with abdominal pain of short duration were studied in varying detail. Clinical and computer-aided diagnostic predictions were compared with the »final« diagnosis. From these studies it is concluded the simplistic setting of a 50/50 confidence threshold for the computer program is as »good« as any other. The proximity of a computer-aided system changed clinical behaviour patterns; a higher overall performance level was achieved and clinicians performance levels became associated with the »mildly conservative« end of the computers ROC curve. Prior forecasts of over-confidence or ultra-caution amongst clinicians using the computer-aided system have not been fulfilled.

scholarly journals MicroRNAs-1299, -126-3p and -30e-3p as Potential Diagnostic Biomarkers for Prediabetes

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 949
Author(s):  
Cecil J. Weale ◽  
Don M. Matshazi ◽  
Saarah F. G. Davids ◽  
Shanel Raghubeer ◽  
Rajiv T. Erasmus ◽  
...  
Keyword(s):  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Predictive Ability ◽  
Roc Curves ◽  
Tolerance Test ◽  
Cross Sectional Study ◽  
Oral Glucose ◽  
Cross Sectional ◽  
Receiver Operating

This cross-sectional study investigated the association of miR-1299, -126-3p and -30e-3p with and their diagnostic capability for dysglycaemia in 1273 (men, n = 345) South Africans, aged >20 years. Glycaemic status was assessed by oral glucose tolerance test (OGTT). Whole blood microRNA (miRNA) expressions were assessed using TaqMan-based reverse transcription quantitative-PCR (RT-qPCR). Receiver operating characteristic (ROC) curves assessed the ability of each miRNA to discriminate dysglycaemia, while multivariable logistic regression analyses linked expression with dysglycaemia. In all, 207 (16.2%) and 94 (7.4%) participants had prediabetes and type 2 diabetes mellitus (T2DM), respectively. All three miRNAs were significantly highly expressed in individuals with prediabetes compared to normotolerant patients, p < 0.001. miR-30e-3p and miR-126-3p were also significantly more expressed in T2DM versus normotolerant patients, p < 0.001. In multivariable logistic regressions, the three miRNAs were consistently and continuously associated with prediabetes, while only miR-126-3p was associated with T2DM. The ROC analysis indicated all three miRNAs had a significant overall predictive ability to diagnose prediabetes, diabetes and the combination of both (dysglycaemia), with the area under the receiver operating characteristic curve (AUC) being significantly higher for miR-126-3p in prediabetes. For prediabetes diagnosis, miR-126-3p (AUC = 0.760) outperformed HbA1c (AUC = 0.695), p = 0.042. These results suggest that miR-1299, -126-3p and -30e-3p are associated with prediabetes, and measuring miR-126-3p could potentially contribute to diabetes risk screening strategies.

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