Two-Point Tactual Discrimination: A Signal Detection Approach

1981 ◽  
Vol 52 (2) ◽  
pp. 433-434 ◽  
Author(s):  
Gary W. Guyot ◽  
Floyd D. Johnson ◽  
Collin Weaver
Keyword(s):  
Signal Detection ◽  
Detection Technique ◽  
Detection Approach

This investigation determined if subthreshold two-point aesthesiometer separations could be discriminated from one-point on the dorsal forearm. A fixed-based matching signal-detection technique was employed. The results indicated that subjects can reliably discriminate two-point separations as small as 2 mm when compared to one point.

Two-Point Tactual Discrimination on the Back: A Signal-Detection Approach

1982 ◽  
Vol 54 (3_suppl) ◽  
pp. 1289-1290 ◽  
Author(s):  
Gary W. Guyot ◽  
Floyd D. Johnson ◽  
Collin Weaver
Keyword(s):  
Signal Detection ◽  
Detection Technique ◽  
Detection Approach

This investigation determined if subthreshold two-point aesthesiometer separations could be discriminated from one-point on the back, using a fixed-based matching signal detection technique. Subjects reliably discriminated two-point separations as small as 2 mm from one point on the back. In addition, the back appears to be more sensitive to discriminating two points from one point than the dorsal forearm.

Associations between collectivism and perception of social threat: a signal detection approach

2019 ◽  
Author(s):  
Tuong-Van Vu ◽  
Catrin Finkenauer ◽  
Lydia Krabbendam
Keyword(s):  
Signal Detection ◽  
Response Bias ◽  
Social Adjustment ◽  
Emotional Expressions ◽  
Social Threat ◽  
Threat Detection ◽  
Perceptual Sensitivity ◽  
Detection Approach ◽  
Self Construal ◽  
The Individual

Collectivistic orientation, which entails interdependent self-construal and concern for interpersonal harmony and social adjustment, has been suggested to be associated with detecting emotional expressions that signal social threat than individualistic orientation, which entails independent self-construal. The present research tested if this detection is a result of enhanced perceptual sensitivity or of response bias. We used country as proxy of individualism and collectivism (Country IC), measured IC of individuals with a questionnaire (Individual IC) and manipulated IC with culture priming (Situational IC). Dutch participants in the Netherlands (n = 143) and Chinese participants in China (n = 151) performed a social threat detection task where they had to categorize ambiguous facial expressions as “angry” or “not angry”. As the stimuli varied in degrees of scowling and frequency of presentation, we were able to measure the participants' perceptual sensitivity and response bias following the principles of the Signal Detection Theory. On the Country IC level, the results indicated that individualism-representative Dutch participants had higher perceptual sensitivity than collectivism-representative Chinese participants; whereas, Chinese participants were more biased towards categorizing a scowling face as “angry” than the Dutch (i.e. stronger liberal bias). In both groups, collectivism on the Individual IC was associated with a bias towards recognizing a scowling face as “not angry” (i.e. stronger conservative bias). Culture priming (Situational IC) affected neither perceptual sensitivity nor response bias. Our data suggested that cultural differences were in the form of behavioral tendency and IC entails multiple constructs linked to different outcomes in social threat detection.

Signal detection using multi-channel seismic data

1996 ◽  
Vol 86 (1A) ◽  
pp. 221-231 ◽  
Author(s):  
Gregory S. Wagner ◽  
Thomas J. Owens
Keyword(s):  
Signal Detection ◽  
Seismic Data ◽  
Spatial Coherence ◽  
Vertical Component ◽  
Principal Component ◽  
Minimum Variance ◽  
Data Detection ◽  
Array Data ◽  
Detection Approach ◽  
Sample Covariance

Abstract We outline a simple signal detection approach for multi-channel seismic data. Our approach is based on the premise that the wave-field spatial coherence increases when a signal is present. A measure of spatial coherence is provided by the largest eigenvalue of the multi-channel data's sample covariance matrix. The primary advantages of this approach are its speed and simplicity. For three-component data, this approach provides a more robust statistic than particle motion polarization. For array data, this approach provides beamforming-like signal detection results without the need to form beams. This approach allows several options for the use of three-component array data. Detection statistics for three-component, vertical-component array, and three different three-component array approaches are compared to conventional and minimum-variance vertical-component beamforming. Problems inherent in principal-component analysis (PCA) in general and PCA of high-frequency seismic data in particular are also discussed. Multi-channel beamforming and the differences between principal component and factor analysis are discussed in the appendix.

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