computational models of unconscious thought

[draggett]

Conscious thought is sequential and open to inspection, leaving a trail (autobiographical memory). However, conscious thought is constrained by extremely limited working memory. Unconscious thought, by contrast, can handle lots of information efficiently in relatively simple ways, but is not open to inspection. This issue gathers together ideas on unconscious thought and how it can be functionally modelled in terms of graph algorithms.

• The Limbic system supports emotional processing providing for emotional control over cognition. Can this be modelled using feed-forward networks with some form of back propagation for training them?
• There is Sharon L. Thompson-Schill's hub and spoke model for integration of information across different cortical regions. This could perhaps be implemented as graph query algorithms with message passing across cortical modules. These algorithms should also relate to the kinds of processing needed for statistical predictions and emotional evaluations.
• Ap Dijksterhuis has investigated the role of unconscious and conscious thought in decision making. He showed that people are able to rank choices involving multiple criteria more effectively using subconscious reasoning than using conscious reasoning, which imposes processing constraints. It should be possible to functionally emulate this for examples used in his paper. In particular, he describes an experiment in which different apartments are described with a set of positive and negative attributes. This suggests the use of limbic system to evaluate the different choices from an emotional perspective.
• Shahram Heshmat has likened the brain to a prediction machine that is continuously trying to predict incoming information based on past experiences. The discrepancy between the predictions made by the brain and the actual sensory input is a source of surprise, drawing conscious attention, and stimulating learning. This suggests the use of a system for statistical predictions of behaviour that is constantly being updated by observations. This needs to be able to work with sparse data, avoiding the need for vast numbers of observations. This relates to n-grams and Markov models, e.g. for predicting the next word based on the preceding words. Another approach is to use LSTM neural networks.
• Natural language understanding involves a process for selecting the word sense and grammatical role for a word given the preceding words, the dialogue history, episodic and semantic memory. Other processes are needed to resolve references from nouns and pronouns, and for selecting between different ways to attach prepositions. An open question is what can be implemented effectively using chunk rules and what needs to be implemented as graph algorithms. Experimental work has shown that people are able to unconsciously learn patterns in artificial languages, see e.g. The role of familiarity in implicit learning.

The next step will be to identify some scenarios for building demos as a means to explore different algorithms. For natural language, I am working on a dialogue with a waiter at a restaurant, as the language usage and meaning are well defined. The Dijksterhuis task of ranking apartments could be a good choice for similar reasons. Further work is needed to identify practical scenarios for emotional reasoning in a social context, and for learning to spot anomalous behaviours.