Models Of The Brain

Question 1

Symbolic logic

Answer 1

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

Question 2

1970s blocks world

Answer 2

“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

Question 3

1990s chess

Answer 3

Known= white rook on A1, white queen on D1

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

Question 4

Conditioning

Answer 4

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

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

Question 5

Cognitive machine

Answer 5

Can do reasoning, learning, perception

Question 6

Data-analysis model

Answer 6

Data driven

Purely descriptive

Question 7

Box and arrow model

Answer 7

Information processing model

Conceptual, implicit assumptions

Question 8

Computational model

Answer 8

Information processing model implemented as a simulation

Explicit assumptions

Various levels of abstractions

Question 9

Explicit vs implicit

Epstein (2008)

Answer 9

When studying cognitive processes, always employ models, often implicit

Computational models make assumptions explicitly

Assumptions can then be tested

Question 10

Prediction

Epstein (2008)

Answer 10

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

Question 11

Explanation

Epstein (2008)

Answer 11

Models can be explanatory even if they are not predictive

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

Question 12

Abstraction and idealisation

Answer 12

Abstracted and idealised models can capture broad trends

Question 13

David Marr

Level of understanding

Answer 13

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?

Question 14

Bottom up approach

Answer 14

Implementation

->

Rules

->

Problem

Question 15

Top down approach

Answer 15

Problem

->

Rules

->

Implementation

Question 16

All levels are important

Krakauer et al (2017)

Answer 16

But experimental techniques favour the implementation level