|Author:||Robert D. Hawkins,Gordon H. Bower|
|Title:||Computational Models of Learning in Simple Neural Systems (Psychology of Learning and Motivation, Vol 23 : Advances in Research and Theory) (v. 23)|
|Format:||docx azw rtf lrf|
|ePUB size:||1503 kb|
|FB2 size:||1964 kb|
|DJVU size:||1956 kb|
|Publisher:||Academic Pr (November 1, 1989)|
by Robert D. Hawkins. Published November 1989 by Academic Press.
Psychology of Learning and Motivation, Vol. 23 : Computational Models of Learning in Simple Neural Systems. Book in the The Psychology of Learning & Motivation Series). by Robert D.
htm last update: 3/3/2019.
These methods include technological advances such as tactile cueing and warning systems, as well as often overlooked psychological approaches for improving concentration. The series The Psychology of Learning and Motivation (PLM) had its first volume published in 1967, making 2017 the 50th anniversary of the series.
Shelve The Psychology of Learning and Motivation, Volume 23: Computational Models of Learning in Simple Neural Systems.
This work offers information on recent advances in the psychology of learning and motivation. Among the topics covered are the deriving of categories to achieve goals, the application of category knowledge in unsupervised domains and spatial mental models. Categories: Biology\Biochemistry. Volume 18. Contributors to this volume. Marilyn Jager Adams Martin D. S. Braine Lee R. Brooks Matthew Erdelyi Peter C. Holland Robert Hokon Donald Homa Larry L. Jacoby Donald F. Kendrick Brian J. Reiser Mark Rilling Barbara Rumain Gene P. Sackett Thomas B. Stonebraker.
This volume of The Psychology of Learning and Motivation represents a slight departure from the traditional composition of such volumes.
Indispensable to all psychologists interested in the experimental study of the phenomena of learning and motivation. -CONTEMPORARY PSYCHOLOGY.
Of learning and motivation. ent task is merely a compounding of the simple verification task we have been considering so far. T h e predictions from this extended model are straightforward in the case of Same RTs. Since a correct Same response indicates that each test word has been verified as an instance of the specified category, we would expect Same RT to decrease with the relatedness of each test word to the category. There is a limit to what can be learned from analyses that treat all property statements as if they had the same semantic structure.