PSYC20007 Lecture Notes - Lecture 1: Turing Test, Situated Cognition, Cognitive Revolution
14 Jun 2018
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Lecture 1 - Tuesday 25 July 2017
PSYC20007 - COGNITIVE PSYCHOLOGY
LECTURE 1
COGNITION
TODAY
•Define cognition
•Understand the computational metaphor of cognition
•Compare and contrast classical computational models of cognition and alternative models
•Define mental representation
•Explore examples of different forms of mental representation
•Symbolic and analogue representations
•Propositional representations and mental imagery
•Dynamic, embodied and situated cognition
•Understand the argument for grounding symbolic representations in embodied representations
WHAT IS COGNITION?
•Alan Turning was 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 (see Chapter 1 of your text), and computation became a
powerful metaphor for cognitive processes (see pages 12-14 of your text). Turing’s work inspired
subsequent innovations in Artificial Intelligence (AI) – a research programme 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 those symbols (syntax) – given this, the ‘programme’
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 programme 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 programme would need to encode all of the knowledge a
human has acquired over a life5me 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 programme 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 questons, and has
prompted cognitive scientists to develop artificial intelligences that attempt to provide answers to
these questions. In this lecture we will explore the classical view of cognition and we will contrast
it with alternative (complementary?) approaches in an attempt to understand the multifaceted
nature of our cognitive processes.
•‘By the end of th century... One will be able to speak of machines thinking without expecting to
be contradicted.’
•Latin cognoscere “to know”
•Oxford Dictionary
•Related to the Ancient Greek verb gnόsko 'I know' (noun: gnόsis = knowledge).
•Cognition is the activity of acquiring, organising and using information to enable adaptive, goal-
directed behaviour
•The study of information processing
•Includes mental processes such as learning, memory, attention, language, reasoning, decision
making.
Lecture 1 - Tuesday 25 July 2017
PSYC20007 - COGNITIVE PSYCHOLOGY
•“The mind is a system that creates representations of the world so that we can act within it to
achieve our goals” -- (Goldstein, page 5).
•Cognitive abilities = Intelligence
•The idea is that, quite simply, we store memories of events experienced to draw on for advice for
future behaviour. Knowledge can be thought of as how we mentally represent concepts and ideas.
•Is all of our knowledge in our head? Or does cognition extend outside the boundaries of your
head? Many theories assume that cognition goes on in individual brains even though the idea
exists of mental representations being shared in a social world.
•Cognitive agents (cognisers):
•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
CLASSICAL COGNITION: COMPUTATIONAL METAPHOR FOR COGNITION
•How has this metaphor been used early on in psychology to help us think about what cognition
is?
•Describes cognition as a flow of information through processing devices that encode, store and
retrieve symbolic representations of knowledge. We can see legitimate similarities between
computers and ourselves. This has an intuitive appeal to it; do we think in words? Or images? Or
both?
•The brain is the hardware
•The mind is the software (programme) that runs on the hardware. This opens an extreme take
on this, that in fact cognition and computation don’t rely on biological brains; they can exist
in other hardware (Eg. Silicon chips). If thinking is just a computational language then there is
no reason that computation couldn’t be
realised in hardware other than a biological
brain.
•Cognition analogous to the operations of a
digital computer
•An information processing model of memory
•Sensory signals provide “input” to the system
•Transduction of sensory signals to a mental
code for central processing
•Further processing (computation) in short-
term/working memory, informed by long term
memory
•Circuit diagram of a classic computational
model of memory. There’s nothing in it about
the brain; we assume these processes occur in the
brain but we can talk about the processes themselves without talking about the hardware that
does it.
CLASSICAL COGNITION
•Thought processes reflect the mental manipulation of symbols according to syntactic rules for
combining those symbols.
•Symbols represent our knowledge of things and events (concepts) and our knowledge of the way
concepts can relate to one another.
•Words and numerals are examples of symbols
•Concepts <dog>
Document Summary
What is cognition: alan turning was 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 (see chapter 1 of your text), and computation became a powerful metaphor for cognitive processes (see pages 12-14 of your text). Turing"s work inspired subsequent innovations in artificial intelligence (ai) a research programme 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. The turing test equates cognition with disembodied linguistic output. The thought experiment has proved useful if only to highlight such questons, and has prompted cognitive scientists to develop artificial intelligences that attempt to provide answers to these questions. We can see legitimate similarities between computers and ourselves.
