Multimodal Cognitive Architecture: Making Perception More Central to Intelligent Behavior

AAAI Conferences

I propose that the notion of cognitive state be broadened from the current predicate-symbolic, Language-of-Thought framework to a multi-modal one, where perception and kinesthetic modalities participate in thinking. In contrast to the roles assigned to perception and motor activities as modules external to central cognition in the currently dominant theories in AI and Cognitive Science, in the proposed approach, central cognition incorporates parts of the perceptual machinery. I motivate and describe the proposal schematically, and describe the implementation of a bimodal version in which a diagrammatic representation component is added to the cognitive state. The proposal explains our rich multimodal internal experience, and can be a key step in the realization of embodied agents. The proposed multimodal cognitive state can significantly enhance the agent's problem solving. Note: Memory, as well as the information retrieved from memory and from perception, represented in a predicate-symbolic form.

Towards a Quantum-Like Cognitive Architecture for Decision-Making Artificial Intelligence

We propose an alternative and unifying framework for decision-making that, by using quantum mechanics, provides more generalised cognitive and decision models with the ability to represent more information than classical models. This framework can accommodate and predict several cognitive biases reported in Lieder & Griffiths without heavy reliance on heuristics nor on assumptions of the computational resources of the mind. Expected utility theory and classical probabilities tell us what people should do if employing traditionally rational thought, but do not tell us what people do in reality (Machina, 2009). Under this principle, L&G propose an architecture for cognition that can serve as an intermediary layer between Neuroscience and Computation. Whilst instances where large expenditures of cognitive resources occur are theoretically alluded to, the model primarily assumes a preference for fast, heuristic-based processing.

Introduction to Computational Neuroscience

AITopics Original Links

This course gives a mathematical introduction to neural coding and dynamics. Topics include convolution, correlation, linear systems, game theory, signal detection theory, probability theory, information theory, and reinforcement learning. Applications to neural coding, focusing on the visual system are covered, as well as Hodgkin-Huxley and other related models of neural excitability, stochastic models of ion channels, cable theory, and models of synaptic transmission.

Expanding a Standard Theory of Action Selection to Produce a More Complete Model of Cognition

AAAI Conferences

A standard model of how brains produce natural cognition would provide a framework for organizing cognitive neuroscience research. A recent effort (Laird et al., in press) to build on consensus views of cognitive operations and produce a standard model of natural cognition started with common aspects of well-established cognitive architectures ACT-R, Sigma, and SOAR. The model captures scientific consensus on “how” the brain works, but it does not offer a coherent story for “why” the component modules (i.e., working memory, long-term memory, visual and motor areas) exist and interact in the ways described. This manuscript starts with background information on a well-cited theory of action selection, and extends that theory to a fuller explanation of decision-making, action and perception that includes a framework for the elements of cognition.