BACKWARD INHIBITORY LEARNING IN HONEYBEES - A BEHAVIORAL-ANALYSIS OF REINFORCEMENT PROCESSING

Citation
F. Hellstern et al., BACKWARD INHIBITORY LEARNING IN HONEYBEES - A BEHAVIORAL-ANALYSIS OF REINFORCEMENT PROCESSING, Learning & memory, 4(5), 1998, pp. 429-444
Citations number
51
Categorie Soggetti
Psychology, Experimental",Neurosciences
Journal title
ISSN journal
10720502
Volume
4
Issue
5
Year of publication
1998
Pages
429 - 444
Database
ISI
SICI code
1072-0502(1998)4:5<429:BILIH->2.0.ZU;2-D
Abstract
One class of theoretical accounts of associative learning suggests tha t reinforcers are processed according to learning rules that minimize the predictive error between the expected strength of future reinforce ment and its actual strength. The omission of reinforcement in a situa tion where it is expected leads to inhibitory learning of stimuli indi cative for such a violation of the prediction. There are, however, res ults indicating that inhibitory learning can also be induced by other mechanisms. Here, we present data from olfactory reward conditioning i n honeybees that show that (1) one- and multiple-trial backward condit ioning results in conditioned inhibition (CI); (2) the inhibition is m aximal for a 15-sec interval between US and CS; (3) there is a nonmono tonic dependency on the degree of CI from the US-CS interval during ba ckward pairing; and (4) the prior association of context stimuli with reinforcement is not necessary for the development of CI. These result s cannot be explained by models that only minimize a prediction error. Rather, they are consistent with models of associative learning that, in addition, assume that learning depends on tile temporal overlap of a CS with two processes, a fast excitatory and a slow inhibitory one, both evoked by a reinforcer. The findings from this behavioral analys is of reinforcement processing are compared with the known properties of an individual, identified neuron involved in reinforcement processi ng in the bee brain, to further understand the mechanisms underlying p redictive reward learning.