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PSY2002 Cognitive > Models Of The Brain > Flashcards

Flashcards in Models Of The Brain Deck (16)
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1

Symbolic logic

Creating new knowledge from facts already known -> replace all words with symbols and can make same inference

2

1970s blocks world

“Put small red block on top of the blue block”

Known= green pyramid on top of small red block, green medium block on top of big red block

Inferences= small red block is blocked by green pyramid, move green pyramid to free space

3

1990s chess

Known= white rook on A1, white queen on D1

Inferences= white knight can take black pawn, black bishop can take ...

4

Conditioning

Before = unconditioned stimulus (U) strong connections to response (R)

After = conditioned stimulus (C) strengthened connections to unconditioned stimulus (U)

5

Cognitive machine

Can do reasoning, learning, perception

6

Data-analysis model

Data driven
Purely descriptive

7

Box and arrow model

Information processing model
Conceptual, implicit assumptions

8

Computational model

Information processing model implemented as a simulation

Explicit assumptions

Various levels of abstractions

9

Explicit vs implicit

Epstein (2008)

When studying cognitive processes, always employ models, often implicit

Computational models make assumptions explicitly

Assumptions can then be tested

10

Prediction

Epstein (2008)

A computational model can give specific predictions for the outcome of an experiment

Helps select which experiments to perform

Helps distinguish between different plausible models

11

Explanation

Epstein (2008)

Models can be explanatory even if they are not predictive

Eg computational models of schizophrenia indicate causes without being able to predict individual cases

12

Abstraction and idealisation

Abstracted and idealised models can capture broad trends

13

David Marr

Level of understanding

1) computation -> why (problem)
What is the goal of computation? Why is it appropriate? Logic behind it?

2) algorithm -> what (rules)
How can computational theory be executed? Algorithm, data, representation

3)implementation -> how (physical)
How can representation and algorithm be realised physically?

14

Bottom up approach

Implementation

->

Rules

->

Problem

15

Top down approach

Problem

->

Rules

->

Implementation

16

All levels are important

Krakauer et al (2017)

But experimental techniques favour the implementation level