PSYC20007 Lecture Notes - Lecture 1: Cognitive Revolution, Knowledge Representation And Reasoning, Causal Structure
10 May 2018
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Lecture 1
- Alan Turing (1950): mathematical basis for model computing, digital computers, solved
enigma codes during WWII; a brilliant British mathematician involved in the development
of computer science (and in cracking the Nazi Enigma code during WWII, among other
things), 2012 marked the 100th anniversary of his birth, the invention of the digital
computer went hand in hand with the “cognitive revolution” in psychology and
computation became a powerful metaphor for cognitive processes, human thought
processes can be thought of as just computations that could be undertaken by a digital
computer
- Turing’s work inspired subsequent innovations in AI (a research program dedicated to
developing intelligent machines, modelled on human cognitive processes), the approach
to cognition that developed from this work in AI is referred to as Classical Cognition, in
its strongest form → classical cognition implies that human cognition reflects the
manipulation of symbols according to specified rules for combining this symbols (syntax)
- given this, the program for a human mind could be implemented in a computer, just
as it is implemented in a biological brain
- Despite the early enthusiasm for this approach, no computer program has yet come
close to passing the so-called “Turing Test”, in which a human interrogator attempts to
distinguish between the (text-based) responses of a computer and a human to his/her
(text-based questions)
- The Turing Test equates cognition with disembodied linguistic output
- For a machine to pass a strong version of the Turing test its program would need to
encode all of the knowledge a human has acquired over a lifetime and it would need to
have a procedure for matching any text input with an appropriate response; the scenario
raises questions about how such knowledge could be acquired, and how the knowledge
stored in the program could be meaningful to the computer (although Turing himself was
not concerned with the latter question, he was interested in the first)
- The thought experiment has proved useful if only to highlight such questions, and has
prompted cognitive scientists to develop AI that attempt to provide answers to these
questions
- What is cognition: latin cognoscere “to know”, Oxford dictionary, related to the Ancient
Greek verb gnosko ‘I know’ (noun: gnosis = knowledge)
- Cognition is the activity/process of acquiring, organising and using information to enable
adaptive, goal-directed behaviour
- The study of information processing (comes from idea that thought processes are like
computational processes) - includes mental processes such as learning, memory,
attention, language, reasoning, decision making
- The mind is a system that creates representations of the world so that we can act within
it to achieve our goals (Goldstein) ⇒ focuses on product of cognition which is that our
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minds create representations of the world, store memories of events that we’ve
experienced and able to draw on them to inform future behaviour, mental representation
→ things inside mind that you can retrieve in order to allow you to think about something
that may not actually be present
- To be able to call on something in mind and think about it → must be something in mind
that represents that idea/concept/object
- Mental representations essential for cognition → idea that the mind contains knowledge,
these are the products of cognition; as we go about acquiring and organising information
from world, as we take in sensory data → mind stores that for later use
- Mind represents knowledge, when i know something → stored that information somehow
in mind, something i can draw on at another point even when the thing it is about is no
longer present
- Knowledge = representation, how we mentally represent concepts and ideas
- Cognitive abilities = intelligence
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- Cognitive agents (cognisers): thinking, sentient being; sense and act on the environment
(detect and effect changes in the environment, gain information); construct mental
models to represent the causal structure of their environment; adapt their mental models
in response to feedback from their behaviour; use mental models to guide future
behaviour
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- Classical cognition: the computational metaphor of cognition; cognition as a flow of
information through processing devices that encode, store and retrieve symbolic
representations of knowledge, brain thinks in language of the mind and it is symbolic like
a computer code, (the brain is hardware [cognition and computation doesn’t rely on
biological brains, can have both in other hardware e.g. silicon chips], the mind is
software [program] and has computational language similar to a computer that is neither
native language or any other language we know nor images but some kind of basic
language of the mind); if thinking is just a computational language then there’s no reason
that cognition cannot be realised in some other hardware that isn’t the biological brain;
cognition analogous to the operations of a digital computer
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Document Summary
Given this, the (cid:3247)program(cid:3248) for a human mind could be implemented in a computer, just as it is implemented in a biological brain. The turing test equates cognition with disembodied linguistic output. The thought experiment has proved useful if only to highlight such questions, and has prompted cognitive scientists to develop ai that attempt to provide answers to these questions. What is cognition: latin cognoscere to know , oxford dictionary, related to the ancient. Greek verb gnosko i know" (noun: gnosis = knowledge) Cognition is the activity/process of acquiring, organising and using information to enable adaptive, goal-directed behaviour. Things inside mind that you can retrieve in order to allow you to think about something that may not actually be present. To be able to call on something in mind and think about it must be something in mind that represents that idea/concept/object.
