Light a Form-Stimulus

1911 ◽  
Vol 51 (6) ◽  
pp. 480-480
Author(s):  
William Crocker
Keyword(s):  
1995 ◽  
Vol 18 (4) ◽  
pp. 641-642
Author(s):  
Maartje E. J. Raijmakers ◽  
Peter C. M. Molenaar

AbstractA distinction should be made between the formation of stimulus-driven associations and cognitive concepts. To test the learning mode of a neural network, we propose a simple and classic input-output test: the discrimination shift task. Feed-forward PDP models appear to form stimulus-driven associations. A Hopfield network should be extended to apply the test.


2017 ◽  
Vol 37 (2) ◽  
pp. 212-219
Author(s):  
Shotaro Murata ◽  
Hiroshi Wakata ◽  
Yasuo Oshita ◽  
Takatsugu Murakawa ◽  
Katsunobu Takenaka ◽  
...  

1907 ◽  
Vol 43 (4) ◽  
pp. 251-258 ◽  
Author(s):  
Heinrich Hasselbring
Keyword(s):  

2003 ◽  
Vol 1 (1) ◽  
pp. 147470490300100 ◽  
Author(s):  
Thomas E. Dickins

Humans uniquely form stimulus equivalence (SE) classes of abstract and unrelated stimuli, i.e. if taught to match A with B and B with C, they will spontaneously match B with A, and C with B, (the relation of symmetry), and A with C (transitivity). Other species do not do this. The SE ability is possibly the consequence of a specific selection event in the Homo lineage. SE is of interest because it appears to demonstrate a facility that is core to symbolic behavior. Linguistic symbols, for example, are arbitrarily and symmetrically related to their referent such that the term banana has no resemblance to bananas but when processed can be used to discriminate bananas. Equally when bananas are perceived the term banana is readily produced. This relation is arguably the defining mark of symbolic representation. In this paper I shall detail the SE phenomenon and argue that it is evidence for a cognitive device that I term a General Symbol Machine (GSM). The GSM not only sets the background condition for subsequent linguistic evolution but also for other symbolic behaviors such as mathematical reasoning. In so doing the GSM is not particularly domain-specific. The apparent domain-specificity of, for example, natural language is a consequence of other computational developments. This introduces complexity to evolutionary arguments about cognitive architecture.


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