Modelling Cognitive Processes

Question 1

Why do we need modelling?

Answer 1

Cognition = noisy ie

Searching for signal in noise - targets from distractors

Memory/perception/etc tasks - how to detect the target? how easy is it? does it stand out?

Decision tasks - when to say yes vs no

Question 2

What does modelling help us understand?

Answer 2

Separates decisional from non-decisional processes - what affects the former or latter?

Helps interpret scientific data, distinguish between theories

Helps compare participants and controls

Helps us learn/improve/optimise the science and our own cognitive performance

Question 3

What are three prerequisites for modelling?

Answer 3

Need to have a good understanding of how the cognitive process operates

Need to understand what a particular modelling method requires

Need to have meaningful data to input and an appropriate way of processing it (in order to get meaningful output)

Question 4

What is psychophysics?

Answer 4

The relationship between stimuli in the environment and sensations

Assumptions similar those underlying nearly all experimental psychology - behaviours help us infer mental states

Close links with neuroscience ie neural mechanisms of vision and their links to behaviour

Gustav Fechner

Question 5

What is Detection Theory? (Signal detection theory)

Answer 5

WW2 radar origins - enemy plane or something innocuous? Important to know to minimise death/waste of resources - correct hits (H) and correct rejections (CR) vs false alarms (FA) and misses (M) - maximise first two, minimise second two

Optimise: discrimination = what is signal vs noise?
Criterion setting = amount of evidence needed to say yes

Question 6

How do you calculate discrimination?

Answer 6

Hit rate (HR) - probability of saying yes when a signal is presented = H/(H+M)

False alarm rate (FAR) = probability of saying yes when noise is presented = FA/(FA+CR)

Question 7

What is a z score?

Answer 7

How many standard deviations +/- from the mean of a normal distribution (mean at 0) is the HR and FAR away

Question 8

What is d’?

Answer 8

A measure of discrimination

d’ = z(HR) – z(FAR)

The higher the d’ = greater the distance between distributions and the greater the discriminatory ability

Question 9

What is c?

Answer 9

A measure of bias; the distance between the criterion and the intersection of the distribution

c = -0.5 [z(HR) + z(FAR)]

The higher the c, the less a person is willing to say yes

Question 10

What is the relationship between d’ and c? (discrimination and bias)

Answer 10

Thought to be independent of one another; though some debate as to whether this always holds

Question 11

What are three other assumptions necessary for detection theory?

Answer 11

That d’ and c are independent

That the distributions (of H/M/FA/CR) are normal (though other models exist for nonnormal distributions)

The evidence that feeds into the dimension is graded

Question 12

What can vary in a detection theory distribution?

Answer 12

Signal and noise distributions - equal variance signal detection models assume both distributions are the same; unequal variance models assume one has greater variance than the other

Unequal models - eg in recognition memory - variance of target/signal = greater than the lure/noise because targets can vary so much; requires d(a) instead of d’

Question 13

What relevance does detection theory have to real life?

Answer 13

Use in eye witness testimony to establish best protocol for presenting suspects ie sequential vs simultaneous procedures

Question 14

What should all lineups be? (and not be?)

Answer 14

Fair - all people presented are reasonably similar to one another

Accompanied by unbiased instruction - no forcing of choice, informing that culprit may not be present

Should not be a showup ie presenting with one face and asking if they are the culprit

Question 15

What possible decisions can be made when choosing from a line up?

Answer 15

1. Picking a person:
   correct = culprit
   incorrect = person is an innocent suspect
   incorrect = person is a foil/not a suspect
2. Rejecting the line up:
   correct = when person is an innocent suspect
   incorrect = when person is the culprit

Detection theory modelling can be used to optimise the correct rejections/identifications and minimise errors

Question 16

What does modelling suggest the best lineup type is?

Answer 16

Historically - simultaneous = worse (higher H but also FAs) - people less able to distinguish between seen/unseen lineup members = worse discriminabiltiy

Used to use a ‘diagnosticity ratio’ but this confounded criterion with discriminabiltiy (how conservative/liberal someone is with choosing vs how good someones memory is)

Different methods (Mickes/Flowe/Wixted 2012) found better discriminabiltiy with simultaneous line-ups under new methods - though contested

Basically - the choice varies with policy, culprit walking free vs innocent person being convicted - liberal vs conservative criterion

Question 17

What relevance is detection theory to optimising performance?

Answer 17

Negative scoring on exams:

Assumption - penalties for guesses as they reflect luck rather than knowledge

However - guesses can reflect partial knowledge and metacognition (abilities to evaluate own knowledge and use of strategy) meaning final scoring is no longer an assessment of knowledge

Question 18

What are dual vs single process models of recognition memory?

Answer 18

Dual - familiarity and recognition are distinct processes; familiarity is graded so can be modelled using a SDT-related tool; recollection is all or none

Single - all memory processes feed into one memory evidence dimension

Question 19

What are Receiver Operating Characteristic (ROC) curves?

Answer 19

SDT-related tool for theory testing

Binary responses (yes/no, old/new etc) can be scaled using confidence ratings ie moderately confident yes or very confident yes etc - for each point on the scale, you can calculate HR and FAR for all answers with that level of confidence or higher - HR and FAR pairs are at different levels of bias and can be plotted on a curve

(I dont know what the fuck i’ve just written here, should have gone to this lecture…)

Question 20

How do you interpret ROC curves?

Answer 20

Area under the curve (AUC) = measure of discrimination

Larger the area = better people discriminate between signal and noise

Points on the curve = criteria for subsequent responses ie criterion for very confident no or higher

Show how discrimination varies at different levels of bias (criterion setting) - how people are willing to say yes

Question 21

What are zROC curves?

Answer 21

HRs and FARs can be converted into zROCs which provide info about the ratio of variances of the two distributions (equal variance = slope of 1; unequal = different to 1) and whether the distributions are normal (normal zROCs = linear; non-normal = curvilinear

Question 22

What are some examples of zROC curves?

Answer 22

Dual process models of recollection and familiarity:

Yonelinas et al (2002) - hippocampal lesion studies:
Hippocampus implicated in recollection - controls = curvilinear zROCs stemming from a recollective process shown by the line bending with the most confident responses compared to the lesion patients who showed a linear response

However - memory strength can affect zROC shape: weaker = linear, with a slope of 1 (equal variance); stronger = increased and unequal variance ie many strong memories and people make unnecessary distinctions between moderate and very certain yes which produces an artefact: curvilinear zROCs

Question 23

What is a diffusion model?

Answer 23

Ratcliff, McKoon et al; came after SDT but also deals with signal detection

Computationally complicated but captures data complexity better ie better at dealing with temporality

Question 24

What are some assumptions and limitations of diffusion models?

Answer 24

Assume evidence accumulation over time = noisy process

Separate the quality of evidence from the criteria and from non-decision processes ie stimulus encoding or response execution

Limited as are mostly applicable to fast decisional processes

Question 25

How do diffusion models work?

Answer 25

z is the starting point of data accumulation; a and 0 are criterion which data accumulates towards, when reached a response is initiated; v is the drift rate/rate of accumulation and represents the quality of evidence

a, 0, z and v vary across tasks/conditions - criteria can move towards/away from starting point concurrently or separately

Starting point can move up or down depending on initial bias

Drift rate varies and variability tends to be normally distributed

Drift rate criterion = what is considered old and what new?

Question 26

What can be modelled using diffusion models?

Answer 26

Effects of ageing on memory and lexical decisions

Single neuron recordings in brightness recognition tasks in macaques
ERP data etc

ROCs can also be produced from diffusion models by manipulating decision boundaries +/- drift rate criterion