Models in cognitive psychology

Model

A simplified (or idealised) representation of a more complex thing

Models in science

Simplified description of what is actually going on

Model - a simplified or idealised representation of a thing

Statistical model - a mathematical relationship between variable, that hold under specific assumptions

Theoretical models (in cognition) - a description of the relationship between different mental processes, that makes assumptions about the nature of these processes

Theoretical models try to explain something and provide further predictions from these descriptions, statistical models do not

Behaviourism

Behaviourism - the mind is like a black box, we don’t know what is happening inside so we should not care what is happening inside, all we care about is behaviour

• if we change the input (eg. environment, education) and we see a change in behaviour

Cognitive - the mind is a black box and if you change the input and the output change, we can make some predictions about what occurs inside the black box

cognitive box and arrow models

Computational models

Simplified version of the actual process

Know the mind does no actually look like this but it is cimplified to its essential components

Models that describe the relationship between different mental processes, under the assumption that the mind operates like multi-staged info processing machines

Box and arrow models started off simple, but can gradually become quite complex

Cognitive models

Formal cognitive models

Computational modelling

There is fluidity in science

• no competition between the models

Simplification and abstraction

When we create a model, we acknowledge that we’re not going to describe all the info we’re describing, only the parts we think are critical for what we’re trying to represent

Simplification - making something simpler

Abstraction - generating general rules and concepts from specific info

What is the right level of abstraction?

• depends on the question we are asking and / or what we are trying to convey

Prediction and / or explanation

Models in science must produce some predictions

These predictions can be directional or numerical

Model that provide numerical predictions can be more or less accurate

Non-scientific theories explain after the fact but cannot provide falsifiable predictions

Aim is to provide a simple model and check how good it is and develop the model to be more accurate

How do we use models to predict and explain?

Explanation and prediction

Explanation without (exact) prediction

Prediction without explanation

Difference between statistical and theoretical models is they are predicting or predicting and explaining

• statstical model - predict and do no explain

• theoretical - predict and explain

Informal vs formal cognitive models

Informal cognitive models

A verbal description of the relationship between different cognitive procedure

• where often some assumptions are implicit

• often provides only directional predictions

Formal / computational models

A mathematical description of the relationship between different cognitive preocedure, often instantiated via computer program / simulation

• assumptions are explicit

• often provides numerical predictions

How do formal models explain?

A formal model is correct but its specific input is not

Why formal models?

More accurate predictions

By having a numberical simulation, we can see if the model provides unreasonable predictions (easier to reject bad models)

Can help us select which experiments to perform

By having numerical predictions, we can provide a more subtle form of hypothesis testing

• We can see how close a model is to predicting an actual result

Falsifying - if you run a simulation and it tells you this is where you should find differences you can run and experiment to prove this and show this

Counter intuitive predictions

A model can more clearly describe predictions follow from a model

With informal models, its hard to notice when they make counter-intuitive predictions

Formal models clearly produce such predictions

Benefits of explicit assumptions

By making assumptions explicit, we can reveal unanswered questions, flaws in our reasoning, contradictory or unreasonable assumptions

It can make our assumptions transparent for others to see

What are the cons of formal models?

Require substantial expertise

Transparency - therefore transparent mostly for experts

Comparison - be compared against other computational models

Prediction - sometimes numerical predictions are premature

Progress - changing model is costly time-wise, can limit progress

Theory - a computational model may give the semblance of scientific validity (neural network models)

• making a model simulate a cognitive task does not necessarily teach us much about congition

How to understand the brain

Problem - we can only ever hope to sample from a tiny fraction of its activity, in a tiny fraction of a bit of brain

The way to make sense of brain data was to break any brain problem into 3 levels

1. Computation - the problem being solved

2. Algorithm - the steps / rules to solve it

3. Implementation - the actual machinery

Bottom up approach (neuroscience and AI)

Marr

An algorithm is likely to be understoof more readily by understanding the nature of the problem being solved than by examining the mechanism (ans the hardware) in which is it embodied

Comign from the function is easier than coming from the data point

Top down approach