scholarly journals How to Detect a Cuckoo Egg: A Signal‐Detection Theory Model for Recognition and Learning

1999 ◽  
Vol 153 (6) ◽  
pp. 633-648 ◽  
Author(s):  
Miguel A. Rodríguez‐Gironés ◽  
Arnon Lotem
Keyword(s):  
Signal Detection ◽  
Signal Detection Theory ◽  
Detection Theory ◽  
Theory Model ◽  
Detection Theory Model

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.

Robustness of the a b Signal Detection Theory Model of Decision Making

2007 ◽  
Vol 51 (18) ◽  
pp. 1263-1267
Author(s):  
Ernesto A. Bustamante ◽  
Brittany L. Anderson ◽  
Amy R. Thompson ◽  
James P. Bliss ◽  
Mark W. Scerbo
Keyword(s):  
Decision Making ◽  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Signal Detection ◽  
Signal Detection Theory ◽  
Classical Model ◽  
Empirical Evaluation ◽  
Detection Theory ◽  
Theory Model ◽  
Extreme Responses

Bustamante, Fallon, and Bliss (2006) showed that the a b Signal Detection Theory (SDT) model was more parsimonious, generalizable, and applicable than the classical SDT model. Additionally, they demonstrated that both models provided statistically equivalent and uncorrelated measures of sensitivity and bias under ideal conditions. The purpose of this research was to show the robustness of the a b model for handling extreme responses. We conducted an empirical evaluation of operators' decision-making and two Monte Carlo simulations. Results from the empirical study showed that the a b model provided equivalent yet independent measures of decision-making accuracy and bias, whereas the classical model failed to provide independent measures in the presence of extreme responses. The Monte Carlo simulations showed a similar trend for the superiority of the a b model. Results from this research provide evidence to support the use of the a b model instead of the classical model.

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