Related Experiment Video
Updated: Feb 19, 2026

Testing for Metacognitive Responding Using an Odor-based Delayed Match-to-Sample Test in Rats
Published on: June 18, 2018
Baseline response rates affect resistance to change
Toshikazu Kuroda1, James E Cook1, Kennon A Lattal1
1West Virginia University.
Abstract:
The effect of response rates on resistance to change, measured as resistance to extinction, was examined in two experiments. In Experiment 1, responding in transition from a variable-ratio schedule and its yoked-interval counterpart to extinction was compared with pigeons. Following training on a multiple variable-ratio yoked-interval schedule of reinforcement, in which response rates were higher in the former component, reinforcement was removed from both components during a single extended extinction session. Resistance to extinction in the yoked-interval component was always either greater or equal to that in the variable-ratio component. In Experiment 2, resistance to extinction was compared for two groups of rats that exhibited either high or low response rates when maintained on identical variable-interval schedules. Resistance to extinction was greater for the lower-response-rate group. These results suggest that baseline response rate can contribute to resistance to change. Such effects, however, can only be revealed when baseline response rate and reinforcement rate are disentangled (Experiments 1 and 2) from the more usual circumstance where the two covary. Furthermore, they are more cleanly revealed when the programmed contingencies controlling high and low response rates are identical, as in Experiment 2.
More Related Videos
Related Concept Videos
Longitudinal Research
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
Regression Toward the Mean
Dose-Response Relationship: Overview
Social Proof
Pharmacodynamic Models: Additive and Proportional Drug Effect Model

