PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 13916461

Response latency as a function of reinforcement schedule.

W C STEBBINS·R N LANSON
View original source ↗Export metadata ↓

Abstract

Four Ss were trained to press and hold down a telegraph key in the presence of a light. Subsequent release of the key during a tone was followed by water reinforcement. The schedule of reinforcement for key release was varied, and its effects on the latency (RT) of key release to the tone were studied. Both median RT and variability of RT were found to be inversely related to frequency of reinforcement as determined by the schedule.

Explore related subjects

LearningReaction TimeReinforcement ScheduleReinforcement, PsychologyResearchLEARNINGJournal Article

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

W C STEBBINS, R N LANSON. 1962. Response latency as a function of reinforcement schedule.. https://doi.org/10.1901/jeab.1962.5-299

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2006

A statistical property of multiagent learning based on Markov decision process.

We exhibit an important property called the asymptotic equipartition property (AEP) on empirical sequences in an ergodic multiagent Markov decision process (MDP). Using the AEP which facilitates the analysis of multiagent learning, we give a statistical property of multiagent learning, such as reinforcement learning (RL), near the end of the learning process. We examine the effect of the conditions among the agents on the achievement of a cooperative policy in three different cases: blind, visible, and communicable. Also, we derive a bound on the speed with which the empirical sequence converges to the best sequence in probability, so that the multiagent learning yields the best cooperative result.

Learning↗
PubMed2006

Second order neurons and learning in Cohen-Grossberg networks.

The well known Cohen-Grossberg network is modified to include second order neural interconnections and also to have a learning component. Sufficient conditions are obtained for the existence of a globally exponentially stable equilibrium. The model provides a two-fold generalization of the Cohen-Grossberg network in the sense if one removes the learning component, then one gets a network with second order synaptic interactions; if both the learning component and the second order interactions are removed, then the model reduces to the standard Cohen-Grossberg network.

Learning↗