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Summary Task 6 - Surfing The Edge Of Chaos

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Summary of Task 6 in Man and Machine

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  • October 30, 2023
  • 11
  • 2022/2023
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SURFING THE EDGE OF CHAOS
MIND: REVIEW & EVALUATION – CHAPTER 8 (THAGARD)

THE ACHIEVEMENTS OF COGNITIVE SCIENCE

 Scientific understanding of problem solving, learning, language much better now than 50
years ago
 BUT still no unified theory of cognitive science that explains full range of psychological
phenomena
 To understand mind we need to take all theories into account
 Progress will require more than isolated efforts of researchers in a particular
discipline
 Different versions of CRUM have illuminated different aspects of applied thinking


COMPARATIVE EVALUATION

REPRESENTATIONAL POWER

 Formal logic – considerable representational power, operators & quantifiers
 Computer models with rules, concepts, analogies, images, connections – difficulty representing
proposition such as: “No X are Y for some X”
 Connectionism – more flexible in capturing broader range of sensory experience than
verbal representations
 Struggled with how simple neuronlike units can represent complex relations


Logic Rules Concepts Analogies Images

Abandon Collects Collects Own special packaging
expressiveness of package of package of – intimately
logic for more info about a info about a connected with
computational kind of thing situation sensory functions
power (vision)

Concise, independent Bundle info together in organised structures
representations

NO CURRENT COMPUTATIONAL MODEL HAS REPRESENTATIONAL POWER TO CAPTURE ALL OF HUMAN THOUGHT

 Unified theory of mental representation needs structures that among them have
 Sensory richness of images & connections
 The organising capabilities of concepts, analogues & pictures
 The verbal expressiveness of rules & propositions in formal logic

COMPUTATIONAL POWER

 Need to be concerned with speed & flexibility & abstract computational potential

,  Rule-based systems (heuristics) superior performance in many domains


Problem  Many problem solutions better described in terms of schema application,
solving analogical mapping, parallel constraint satisfaction (than as heuristics in
rule-based system)

Learning  Neither rule-based chunking nor connectionist weight adjustment
captures full range of human learning capabilities

Language  Cognitive science lacks comprehensive theory of language learning &
use

PSYCHOLOGICAL PLAUSIBILITY

 Each of 6 approaches has inspired psychological experiments & computational models
 Different experimental results fit best with different representational theories

NEUROLOGICAL PLAUSIBILITY

 New scanning techniques  cognitive neuroscience fastest developing part of
cognitive science
 Connectionist models gain some neurological plausibility from analogy between
artificial neural networks & brain

PRACTICAL APPLICABILITY

 Can proceed in more piecemeal fashion, selectively applying insights from different
approaches where they appear relevant


Education  Rules, concepts, analogies contributed to understanding how people
solve problems
 Connectionist ideas are starting to have impact on educational theory
& practice

Design  Mostly improved through rules, concepts, analogies, images in creative
design

Intelligent  Most expert-systems: rule-based systems
systems  Some case-based (analogical) & connectionist systems are proving
increasingly useful

CHALLENGES OF COGNITIVE SCIENCE

7 important challenges for CRUM

1. The brain challenge – CRUM ignores crucial facts about how thinking is performed by the
brain
2. The emotion challenge – CRUM neglects important role of emotions in human thinking

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