Olfactory Learning-Set Formation in Rats

Science ◽  
1974 ◽  
Vol 185 (4153) ◽  
pp. 796-798 ◽  
Author(s):  
B. M. Slotnick ◽  
H. M. Katz
Keyword(s):  
Olfactory Learning ◽  
Learning Set

Can rats acquire an olfactory learning set?

2000 ◽  
Vol 26 (4) ◽  
pp. 399-415 ◽  
Author(s):  
Burton Slotnick ◽  
Lillian Hanford ◽  
William Hodos
Keyword(s):  
Olfactory Learning ◽  
Learning Set

Olfactory Learning Set in Two Varieties of Domestic Rat

1969 ◽  
Vol 24 (1) ◽  
pp. 3-15 ◽  
Author(s):  
Joseph W. Jennings ◽  
Louis H. Keefer
Keyword(s):  
Discrimination Problem ◽  
Ecological Validity ◽  
Olfactory Learning ◽  
Olfactory Discrimination ◽  
Laboratory Procedure ◽  
Progressive Improvement ◽  
Learning Set ◽  
Stimulus Environment ◽  
Do So

Based on the assumption that the greater the ecological validity of a laboratory procedure, the greater the likelihood of adequately assessing an organism's behavior, two varieties of domestic rats were subjected to a series of olfactory discrimination problems. The results indicate that rats can easily learn a two-element olfactory discrimination problem. With continued experience there was a progressive improvement in their ability to do so which was interpreted as evidence for learning set. A justifiable conclusion is that the appreciation of the behavioral complexities and capacities of infra-human organisms necessitates questioning them in the idiom of their stimulus environment.

Visual Learning set in a California sea lion

2011 ◽  
Author(s):  
Molly McCormley ◽  
Peter Cook ◽  
Madison Miketa ◽  
Colleen Reichmuth
Keyword(s):  
Visual Learning ◽  
Sea Lion ◽  
California Sea Lion ◽  
Learning Set

scholarly journals Predictive olfactory learning in Drosophila

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chang Zhao ◽  
Yves F. Widmer ◽  
Sören Diegelmann ◽  
Mihai A. Petrovici ◽  
Simon G. Sprecher ◽  
...  
Keyword(s):  
Synaptic Plasticity ◽  
Dopaminergic Neurons ◽  
Mushroom Body ◽  
Trace Conditioning ◽  
Fruit Fly ◽  
Olfactory Learning ◽  
Shock Strength ◽  
Behavioral Experiments ◽  
Kenyon Cells ◽  
Output Neurons

AbstractOlfactory learning and conditioning in the fruit fly is typically modelled by correlation-based associative synaptic plasticity. It was shown that the conditioning of an odor-evoked response by a shock depends on the connections from Kenyon cells (KC) to mushroom body output neurons (MBONs). Although on the behavioral level conditioning is recognized to be predictive, it remains unclear how MBONs form predictions of aversive or appetitive values (valences) of odors on the circuit level. We present behavioral experiments that are not well explained by associative plasticity between conditioned and unconditioned stimuli, and we suggest two alternative models for how predictions can be formed. In error-driven predictive plasticity, dopaminergic neurons (DANs) represent the error between the predictive odor value and the shock strength. In target-driven predictive plasticity, the DANs represent the target for the predictive MBON activity. Predictive plasticity in KC-to-MBON synapses can also explain trace-conditioning, the valence-dependent sign switch in plasticity, and the observed novelty-familiarity representation. The model offers a framework to dissect MBON circuits and interpret DAN activity during olfactory learning.

scholarly journals Adverse Selection, Heterogeneous Beliefs, and Evolutionary Learning

2021 ◽  
Author(s):  
Clemens Buchen ◽  
Alberto Palermo
Keyword(s):  
Adverse Selection ◽  
Relative Size ◽  
Nonlinear Dynamic System ◽  
Heterogeneous Beliefs ◽  
Evolutionary Learning ◽  
Common Assumption ◽  
Cobweb Model ◽  
The Common ◽  
Learning Set ◽  
Set Up

AbstractWe relax the common assumption of homogeneous beliefs in principal-agent relationships with adverse selection. Principals are competitors in the product market and write contracts also on the base of an expected aggregate. The model is a version of a cobweb model. In an evolutionary learning set-up, which is imitative, principals can have different beliefs about the distribution of agents’ types in the population. The resulting nonlinear dynamic system is studied. Convergence to a uniform belief depends on the relative size of the bias in beliefs.

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