Characterizing psychophysical measures of discrimination thresholds and the effects of concentration on...

What is the spatial and temporal nature of odor representations within primary olfactory networks at the threshold of an animal's ability to discriminate? Although this question is of central importance to olfactory neuroscience, it can only be answered in model systems where neural representations can be measured and discrimination thresholds between odors can be characterized. Here, we establish these thresholds for a panel of odors using a Pavlovian paradigm in the moth Manduca sexta. Moths were differentially conditioned to respond to one odor (CS+) but not another (CS-) using undiluted odorants to minimize salience-dependent learning effects. At 24 and 48 h postconditioning, moths were tested for the presence of a conditioned response (CR) with a blank, then the CS+ and CS- (pseudorandomly) across a 5-log step series of increasing concentration. Results identified discrimination thresholds and established that differential CRs to the CS+ and CS- increased with stimulus concentration. Next, 3 separate groups of moths were differentially conditioned at either one-log step below, at, or one log step above the identified discrimination threshold. At 24 and 48 h postconditioning, moths were tested sequentially with a blank, the concentration used for conditioning, and then undiluted odor. Conditioning at one log step below the discrimination threshold established a CR, indicating both stimulus detection and learning, but was insufficient to establish evidence of discrimination. Moths conditioned at the discrimination threshold were able to discriminate but only when stimulated with undiluted odors, indicating learning, but discrimination measures were hampered. When conditioned above the discrimination threshold, moths had no difficulty in discriminating. These results establish methods for psychophysical characterization of discrimination and indicate that differential conditioning at lowered concentrations biases threshold measures.