Date of Award

Spring 2017

Degree Name

Doctor of Philosophy

Department

Psychology

First Advisor

Dr. Alan D. Poling

Second Advisor

Dr. Cynthia Pietras

Third Advisor

Dr. Ron Van Houten

Fourth Advisor

Dr. Timothy Edwards

Keywords

Scent-detection, discrimination training, rats, generalization, odors

Abstract

The global illicit trade in tobacco products leads to an overall increase in the availability of tobacco, and this increase in tobacco availability and consumption undermines effective health, safety, and taxation policies in place to protect current and future populations. Dogs working at ports and customs have been trained to detect tobacco products and research has shown rats can detect tobacco-soaked filters (Mahoney et al., 2014). Cigarette smoking is the most common form of tobacco use, and cigarettes are the most commonly trafficked product in the illicit tobacco trade. In the current study, rats were trained to respond to filter samples of 21 cigarette brands and not to respond to filter samples of controls (e.g., coffee, tape). Training resulted in average hit rates ranging from 91% to 100% and false alarm rates ranging from 2% to 5%. A series of tests were then conducted with 15 untrained cigarette brands to measure generalization. Two tests conducted after concurrent training resulted in hits on 38% and 49% of generalization samples. These results indicate modest generalization from trained to untrained cigarette brands, with performance improving as the number of brands trained increased. After training cigarette brands in succession the hit rate on generalization samples reached 67%. The findings of this study suggest that preparing samples by pulling air from a container through a filter is an effective method for training cigarette scent-detection discrimination. Further research is needed before pouched rats can be employed as illicit tobacco-detection animals in practical applications, as performance did not exceed a mean hit rate of 49% on novel brands of cigarettes.

Access Setting

Dissertation-Open Access

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