Creating False Memories in Humans with an Artificial Neural Network: Implications for Theories of Memory Consolidation

Building on the human memory model that consider LTM to be similar to a distributed network (McClelland, McNaughton & O'Reilly, 1995), and informed by the recent solutions to catastrophic forgetting that suppose memories are dynamically maintained in a dual architecture through a memory self-refreshing mechanism (Ans & Rousset, 1997, 2000; Ans et al., 2002, 2004; French, 1997), we checked whether false memories of never seen (target) items can be created in humans by exposure to "pseudo-patterns" generated from random input in an artificial neural network (previously trained on the target items). In a behavioral experiment using an opposition method it is shown that the answer is yes: Though the pseudo-patterns presented to the participants were selected so as to resemble (both at the exemplar and the prototype level) more the control items than the target items, the participants exhibited more familiarity for the target items previously learned by the artificial neural network. This behavioral result analogous to the one found in simulations indicates that humans, like distributed neural networks, are able to make use of the information the memory self-refreshing mechanism is based upon. The implications of these findings are discussed in the framework of memory consolidation.