Models Of The Brain Flashcards Preview

PSY2002 Cognitive > Models Of The Brain > Flashcards

Flashcards in Models Of The Brain Deck (16)
Loading flashcards...
1
Q

Symbolic logic

A

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

2
Q

1970s blocks world

A

“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
Q

1990s chess

A

Known= white rook on A1, white queen on D1

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

4
Q

Conditioning

A

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

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

5
Q

Cognitive machine

A

Can do reasoning, learning, perception

6
Q

Data-analysis model

A

Data driven

Purely descriptive

7
Q

Box and arrow model

A

Information processing model

Conceptual, implicit assumptions

8
Q

Computational model

A

Information processing model implemented as a simulation

Explicit assumptions

Various levels of abstractions

9
Q

Explicit vs implicit

Epstein (2008)

A

When studying cognitive processes, always employ models, often implicit

Computational models make assumptions explicitly

Assumptions can then be tested

10
Q

Prediction

Epstein (2008)

A

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
Q

Explanation

Epstein (2008)

A

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
Q

Abstraction and idealisation

A

Abstracted and idealised models can capture broad trends

13
Q

David Marr

Level of understanding

A

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
Q

Bottom up approach

A

Implementation

->

Rules

->

Problem

15
Q

Top down approach

A

Problem

->

Rules

->

Implementation

16
Q

All levels are important

Krakauer et al (2017)

A

But experimental techniques favour the implementation level