models

Question 1

define model

Answer 1

simplified or idealized representation of a thing

Question 2

define statistical model

Answer 2

mathematical relationship between variables, that hold under specific assumptions

Question 3

behaviourism view of cognitions

Answer 3

“black box”
input –> output, unknown what happens in the brain between the two

Question 4

cognitive box and arrow models (and example)

Answer 4

models that describe the relationship between different mental processes → assumption that the mind operates like multi-staged information-processing machines

started simply but can be very complex

e.g. Broadbent (1985) levels of processing of information

Question 5

studying cognitive models

Answer 5

manipulating input and observing output to figure out what occurs in between
e.g. gorilla video experiment about paying attention to the stimuli - why does it change when attention given to it - use Broadbent’s model

Question 6

formal cognitive models

Answer 6

aka computational models
a mathematical description of the relationship between mental processes
usually expressed through computer code
assumptions are explicit
often provides numerical predictions

Question 7

informal cognitive models

Answer 7

verbal description of the relationship between different cognitive procedures
often some assumptions are implicit
often provides only directional predictions

Question 8

define simplification and abstraction

Answer 8

simplification = not going to describe all the info, only critical parts
abstraction = generation of general rules and concepts from specific info

Question 9

applying simplification and abstraction to models

Answer 9

• need to balance simplification and abstraction in models
• depending on what question is being asked or process is being conveyed
• emphasis on certain elements that are the purpose of the model e.g. model train about how the wheels work doesn’t need complex engine
• simple is not a criticism of a model → all models are simple → only bad if it is simple in what it is trying to express
• all models are wrong due to simplification, but are useful

Question 10

predictions and or explanations in models

Answer 10

non-scientific explain after the fact - cannot provide falsifiable predictions (Karl Popper)
scientific models must produce predictions

Question 11

scientific models predictions

Answer 11

can be directional or numerical
directional = one thing is more/less than the other
numerical = use a data set and correlations to predict what would happen with a new data point e.g. find a person’s income and predict their happiness based on previous data –> can extrapolate current data
* numerical predictions can vary in levels of accuracy

Question 12

theoretical vs statistical models explanations

Answer 12

theoretical always provide explanations for data found
goes beyond the statistical models - they only describe the relationship without explanation

Question 13

hierarchy of research (5)

Answer 13

framework
theory
model
hypothesis
data

Question 14

hierarchy of research: framework

Answer 14

conceptual system that defines terms and provides control e.g. cognitive psychology

Question 15

hierarchy of research: theory and model

Answer 15

theory = scientific proposition that provides relations between phenomena e.g. early-selection theory
model = schematic representation of a theory e.g. Broadbent’s filter model

Question 16

hierarchy of research: hypothesis and data

Answer 16

hypothesis = narrow testable statement

data = collected observations often as part of an experiment

Question 17

models: explanation without prediction

Answer 17

models which can predict group differences (very broad) but not individual cases

Question 18

models: prediction without explanation

Answer 18

e.g. can predict whether an individual will get Alzheimer’s even though Alzheimer’s isn’t fully understood yet
e.g. computer models using data to give prediction - more data = more predictive power –> like a “black box” as the operator doesn’t understand what the computer is doing to find this
all statistical models fit this - give data without explanation

Question 19

hierarchy of formal models (6)

Answer 19

framework
theory
specification
implementation
hypothesis
data

same as for research but model is split into specification and implementation

Question 20

formal models: specification

Answer 20

formal description of relations described by a theory e.g. formal model of symbolic representations

Question 21

formal models: implementation

Answer 21

specific instantiation of a specification e.g. computer program which can simulate and predict numerical outputs from an input

Question 22

advantages of formal models (3 - brief)

Answer 22

more accurate predictions
counter-intuitive predictions
explicit assumptions

Question 23

advantages of formal models: more accurate predictions

Answer 23

• easier to reject bad models - see if predictions are unreasonable
• select which experiments to perform - if two models give same prediction, that isn’t useful as we cannot tell which was actually correct - with numerical predictions (e.g. % rates) we can see which got closer
• subtle form of hypothesis testing - how close model is to predicting actual result - not just general trend but closeness to specific number/data
• prediction is more accurate - the model might still be bad and therefore prediction can be wrong even when accurate

Question 24

advantages of formal models: counter-intuitive predictions

Answer 24

• clear description of predictions so when predictions seem wrong it is obvious
• hard with informal to notice when counter-intuitive predictions are made

Question 25

advantages of formal models: explicit assumptions

Answer 25

• reveal unanswered questions, flaws in reasoning, contradictory/unreasonable assumptions
• can make assumptions transparent for others to see e.g. verbal models cannot show what occurs but formal can simulate this

Question 26

limitations of formal models [wont be tested on this]

Answer 26

• requires expertise
• transparency
• best compared against other computational models, not against informal
• numerical predictions can be premature
  changing the model takes time - this can limit progress
• may seem to provide scientific validity even when it isn’t e.g. neural network models
• making a model simulate a cognitive task does not teach us about cognition

Question 27

issue understanding the brain

Answer 27

can only sample small amount of activity from a small brain area

Question 28

break down of brain data to understand it (Marr) (3)

Answer 28

1. computation → problem being solved
2. algorithm → steps/rules to solve problem
3. implementation → actual machinery

focus too much on any of these 3 is bad -> hyperfocus doesn’t allow for interactions between the 3 - creates non specific theories that might not match

Question 29

bottom-up approach to brain data

Answer 29

neuroscience and AI
implementation → rules → problem
example:
machinery of neural circuits → generate algorithm from these → what problems do these algorithms solve

Question 30

Marr (1982) view on approaches to brain data

Answer 30

understand algorithms better by understanding the nature of the problem being solved rather than examining the mechanism
therefore use top-down approach
focus on mechanics takes away from other parts - too small detail without context

Question 31

top-down approach to brain data

Answer 31

Marr’s view - and most cognitive psychologists
problem → rules → implementation

e.g.
problem to solve → what algorithms can solve this → how algorithms are implemented in neural circuits

Question 32

Epstein (2008) - core reading
* misconceptions with modelling (2)
* reasons for modelling (there are 16 total, 5 written here)

Answer 32

goal isn’t always prediction
theories don’t arise from and summarise data - they often precede and guide data collection instead

reasons:
guide data collection, explanations, raise new questions, illuminate core dynamics, suggest analogies

Question 33

Guest and Martin (2021) - core reading
* why is using models helpful
* what is an open theory

Answer 33

models are transparent in how conclusions are drawn –> forced to analyse information closely

open theories are developed explicitly and defined formally
* can recognise flaws in replication
* open theories are better to use for their transparency and what they can tell you about the theory being tested