Showing papers by "James L. McClelland published in 1999"
TL;DR: Neural models assist in characterizing the processes carried out by cortical and hippocampal memory circuits by addressing issues including recognition and recall dynamics, sequences of activity as the unit of storage, and consolidation of intermediate-term episodic memory into long-term memory.
149 citations
TL;DR: This work aims to demonstrate the importance of knowing the carrier and removal status of canine coronavirus, as a source of infection for other animals, not necessarily belonging to the same breeds.
91 citations
TL;DR: This chapter discusses the reason of difficulty in paired associate learning in amnesics, and experimental support for the idea that forcing amnesICS to make their own responses to items leads to interference.
Abstract: Publisher Summary This chapter discusses the reason of difficulty in paired associate learning in amnesics. In an experiment described in the chapter, the subject receives a list of, say, 12 word pairs (including, for example, locomotive-dishtowel and table-banana, among others). After a slight delay, the experimenter presents the first word in one of the pairs, and asks the subject to recall the word that was previously paired with it in the experiment. Because of the subject's amnesia, the subject may not remember even that there was a list of word pairs. Nevertheless, as is standard in paired-associate learning, the subject is encouraged to guess a response. Given the arbitrary pairing of the words, table is unlikely to come to mind in the context of banana as a cue, and hence the stimulus is likely to elicit some other response. If learning is Hebbian, it is this response that will be strengthened, thereby leading to interference. There is experimental support for the idea that forcing amnesics to make their own responses to items leads to interference.
73 citations
Proceedings Article•
29 Nov 1999TL;DR: A psychophysical law that describes the influence of stimulus and context on perception is examined using neural network models defined via stochastic differential equations and shows that the law is related to a condition named channel separability and has little to do with the existence of feedback connections.
Abstract: We examine a psychophysical law that describes the influence of stimulus and context on perception. According to this law choice probability ratios factorize into components independently controlled by stimulus and context. It has been argued that this pattern of results is incompatible with feedback models of perception. In this paper we examine this claim using neural network models defined via stochastic differential equations. We show that the law is related to a condition named channel separability and has little to do with the existence of feedback connections. In essence, channels are separable if they converge into the response units without direct lateral connections to other channels and if their sensors are not directly contaminated by external inputs to the other channels. Implications of the analysis for cognitive and computational neurosicence are discussed.