M and M

Project Details

Description

Can we use Deep Neural Networks to understand how the mind works? The 5-year ERC grant entitled “Generalisation in Mind and Machine” compares how humans and artificial neural networks generalise across a range of domains, including visual perception, memory, language, reasoning, and game playing.

Why focus on generalisation? Generalisation provides a critical test-bed for contrasting two fundamentally different theories of mind, namely, symbolic and non-symbolic theories of mind. Symbolic representations are compositional (e.g., Fodor and Pylyshyn, 1988) and are claimed to be necessary to generalise “outside the training space” (Marcus,1998, 2017). By contrast, non-symbolic models, including PDP models and most Deep Neural Networks reject the claim that symbolic representations are required to support human-like intelligence. So can non-symbolic neural networks generalise as broadly as humans? If so, this would seriously challenge a core motivation for symbolic theories of mind and brain. For recent discussion on this issue, see Bowers (2017) in Trends in Cognitive Science.

Our research team is carrying out a series of empirical and modelling investigations that explore the generalisation capacities of humans and machines across a wide range of domains. These studies are designed to:

(1) Focus on tasks that require symbols for the sake of generalisation.
(2) Focus on generalisation across a range of domains in which human performance is well characterised, including vision, memory, and reasoning.
(3) Develop new learning algorithms designed to make symbolic systems biologically plausible.
Alternative titleGeneralization in Mind and Machine
StatusFinished
Effective start/end date1/09/1731/08/22

Structured keywords

  • Brain Imaging
  • Cognitive Neuroscience
  • Language
  • Brain and Behaviour
  • Cognitive Science
  • Visual Perception

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