- Recovery of unrehearsed items in connectionist models
- Connection Science
- Volume | Issue number
- 10 | 2
- Pages (from-to)
- Document type
- Faculty of Social and Behavioural Sciences (FMG)
- Psychology Research Institute (PsyRes)
When gradient-descent models with hidden units are retrained on a portion of a previously learned set of items, performance on both the relearned and unrelearned items improves. Previous explanations of this phenomenon have not adequately distinguished recovery, which is dependent on original learning, from generalization, which is independent of original learning. Using a measure of vector similarity to track global changes in the weight state of three-layer networks, we show that (a) unlike in networks without hidden untts, recovery occurs in the absence of generalization in networks with hidden units, and (b) when the conditions of learning are varied, changes in the extent of recovery are reflected in changes in the extent to which the weights move back towards their values held after original learning. The implications of this work for rehabilitation studies, human relearning and models of human long-term memory are also considered.
- go to publisher's site
If you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library, or send a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible.