Abstract
Generating associations is important for cognitive tasks including language acquisition and creative problem solving. It remains an open question how the brain represents and processes associations. The Remote Associates Test (RAT) is a task, originally used in creativity research, that is heavily dependent on generating associations in a search for the solutions to individual RAT problems. In this work we present a model that solves the test. Compared to earlier modeling work on the RAT, our hybrid (i.e., non-developmental) model is implemented in a spiking neural network by means of the Neural Engineering Framework (NEF), demonstrating that it is possible for spiking neurons to be organized to store the employed representations and to manipulate them. In particular, the model shows that distributed representations can support sophisticated linguistic processing. The model was validated on human behavioral data including the typical length of response sequences and similarity relationships in produced responses. These data suggest two cognitive processes that are involved in solving the RAT: one process generates potential responses and a second process filters the responses.
Highlights
Language acquisition is highly dependent on the ability to create associations (Elman et al, 1997; Rogers and McClelland, 2004), as they are a central means of expanding both vocabulary and syntax (Brown and Berko, 1960; Hills, 2013)
We describe the organization of a neural network to realize the cognitive processes in Remote Associates Test (RAT) memory search
Quantitative comparisons refer to the statistics of responses in terms of the number of correct solutions and the average number of responses for each RAT problem
Summary
Language acquisition is highly dependent on the ability to create associations (Elman et al, 1997; Rogers and McClelland, 2004), as they are a central means of expanding both vocabulary and syntax (Brown and Berko, 1960; Hills, 2013). Because associative mechanisms play such a crucial role in language and human cognition more generally, it is important to understand how the brain might represent, store, and deploy them. In Natural Language Processing (NLP) researchers work on optimal representations for the extraction of information from large corpora of text, as well as algorithms for text comprehension and production. Technology companies such as Facebook and Google are actively researching how to make machines better at understanding human language to improve their services and the efficiency of interactions between machines and humans
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