Detection and Recognition of Multiple Visual Signals in Noise

1989 ◽  
Vol 33 (20) ◽  
pp. 1383-1387 ◽  
Author(s):  
Greg C. Elvers ◽  
Robert D. Sorkin
Keyword(s):  
Signal Detection ◽  
Visual Display ◽  
Detection Theory ◽  
Theory Model ◽  
Visual Signals ◽  
Visual Signal ◽  
Horizontal Line ◽  
Signal Recognition ◽  
Detection Theory Model ◽  
Detection And Recognition

This experiment tested a detection theory model of visual signal detection and recognition. The task employed a visual display consisting of analog gauges arranged in a horizontal line. The signals to be detected and identified were three unique patterns of gauge values embedded in noise. After viewing the display the observers either reported that any of the signals had occurred (1-of-m signal detection) or specified which of the signals (if any) had occurred (1-of-m signal recognition-detection). The results indicated that performance on 1-of-m recognition and detection tasks can be predicted from performance on the component single-signal detection tasks.

A Signal-Detection Theory Based Perspective on Design of Warning

1998 ◽  
Vol 86 (2) ◽  
pp. 720-722 ◽  
Author(s):  
Mark R. Lehto ◽  
Jason D. Papastavrou
Keyword(s):  
Signal Detection ◽  
Signal Detection Theory ◽  
Human Subjects ◽  
Detection Theory ◽  
Theory Model ◽  
Detection Theory Model

The effects of warnings are analyzed using a distributed signal-detection theory model. It is established that selectivity always increases effectiveness. The implications to optimal warning design for intermittent versus continuous hazards are discussed. The changes in the behavior of the 6 human subjects in response to changes in the warning levels are consistent with the predictions of the model.

Robustness of the a b signal detection theory model of decision making

2007 ◽  
Author(s):  
Ernesto A. Bustamante ◽  
Brittany L. Anderson ◽  
Amy R. Thompson ◽  
James P. Bliss ◽  
Mark W. Scerbo
Keyword(s):  
Decision Making ◽  
Signal Detection ◽  
Signal Detection Theory ◽  
Detection Theory ◽  
Theory Model ◽  
Detection Theory Model

scholarly journals Predicting and measuring decision rules for social recognition in a Neotropical frog

2021 ◽  
Author(s):  
James P Tumulty ◽  
Chloe A Fouilloux ◽  
Johana Goyes Vallejos ◽  
Mark A Bee
Keyword(s):  
Signal Detection ◽  
Individual Variation ◽  
Signal Detection Theory ◽  
Decision Rules ◽  
Detection Theory ◽  
Theory Model ◽  
Social Recognition ◽  
Acoustic Properties ◽  
Signal Variation ◽  
Detection Theory Model

Many animals use signals, such as vocalizations, to recognize familiar individuals. However, animals risk making recognition mistakes because the signal properties of different individuals often overlap due to within-individual variation in signal production. To understand the relationship between signal variation and decision rules for social recognition, we studied male golden rocket frogs, which recognize the calls of territory neighbors and respond less aggressively to a neighbor's calls than to the calls of strangers. We quantified patterns of individual variation in acoustic properties of calls and predicted optimal discrimination thresholds using a signal detection theory model of receiver utility that incorporated signal variation, the payoffs of correct and incorrect decisions, and the rates of encounters with neighbors and strangers. We then experimentally determined thresholds for discriminating between neighbors and strangers using a habituation-discrimination experiment with territorial males in the field. Males required a threshold difference between 9% and 12% to discriminate between calls differing in temporal properties; this threshold matched those predicted by a signal detection theory model under ecologically realistic assumptions of infrequent encounters with strangers and relatively costly missed detections of strangers. We demonstrate empirically that receivers group continuous variation in vocalizations into discrete social categories and show that signal detection theory can be applied to investigate evolved decision rules.

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