Introduction Flashcards

1
Q

history

A

behaviourism (1950s, 60s) - Skinner, Watson

cognitive science (1970s, 80s)

cognitive neuropsychology

cognitive neuroscience

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2
Q

behaviourism

A

Psychology should only concern itself with observable behaviour, which can be explained without recourse to internal mental events

Behaviourists focused on inputs and outputs

Inputs = external stimuli

Outputs = behaviour

Tried to establish rules governing the translation of inputs into outputs (i.e. learning)

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3
Q

cognitive science

A

Internal mental events are essential for explaining behaviour and can be characterised as the computational operations of a computer program
- Around time when computers popular

Chomsky: Language development cannot be explained in purely behavioural learning terms – too complex for this

Development of computers provided new technological metaphor

Mind as the software running on the hardware of the brain

The specific form of the hardware is irrelevant

Cognitive scientists studied behaviour (reaction times etc.) but inferred the operation of ‘mental modules’
- Often just inference

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4
Q

cognitive neuropsych

A

Behaviour can be explained by internal mental events and these events can be localised to discrete brain regions

Driven by observations of loss of specific functions after defined brain lesions, e.g. hippocampus lesions leading to memory problems

Double dissociation logic

  • 2 patients
  • Patient 1: Lesion to area A: Function X impaired but function Y spared
  • Patient 2: Lesion to area B: Function Y impaired but function X spared
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5
Q

cognitive neuroscience

A

Driven by the development of new tools for measuring and manipulating brain function

At least in its early stages, adopted the logic of cognitive neuropsychology and combined it with the extra precision afforded by these new tools

functional Magnetic Resonance Imaging (fMRI)

Electroencephalography (EEG)

Transcranial Magnetic Stimulation (TMS)

Improved lesion mapping methods (sMRI)

Magnetoencephalography (MEG)

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6
Q

the search for the mind in the brain

A

Can we reduce all mental phenomena to physical (brain) processes?

Perhaps easy to accept that motor function or language might be reducible to neuronal function

But what about intelligence?

Do you think it is plausible that one day we will be able to describe the richness of flexible, intelligent human behaviour in terms of the firing of neurons?

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7
Q

the answer from cog neuroscience

A

Cognitive neuroscience doesn’t require you to accept that complex mental phenomena can be reduced to the firing of individual neurons

Cognitive neuroscience suggests there is an intermediate level of description at the level of neuronal systems

Systems do not necessarily have to be mapped to discrete brain regions (although they may be)

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8
Q

tools of cognitive neuroscience

A

computerised cognitive testing

neuropsychology

fMRI

EEG

MEG

TMS

psychophysiology

monkey single unit (neuron) recordings

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9
Q

computerised cog testing

A

Measuring reaction times (RTs) and/or accuracy in different conditions

Relies on subtraction logic: If you have 2 conditions that differ by a single process, the difference in RT/acc between the two conditions should reflect the operation of that process

  • E.g. Condition 1 (active condition) – decide which of two letters is an X
  • Condition 2 (control condition) – press when you see any letters
  • Condition 1 – Condition 2 = selective attention
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10
Q

neuropsych - tools

A

Inferring the function of brain regions from the pattern of deficit when damaged

Originally double dissociation logic

More recent studies use large samples and lesion mapping techniques

Allows definitive causal statements about brain-behaviour relationships

Problem is the brain regions are so large that such statements sometimes quite imprecise

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11
Q

stroke

A

thrombus in carotid artery breaks off and travels to the cerebral artery in the brain

thrombus lodges in the cerebral artery causing a stroke

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12
Q

fMRI

A

Visualising functional brain activity during task or at rest

Actually measures Blood Oxygen Level Dependent signal (BOLD) – a proxy for neural activity
- Differences in magnetic properties of oxygenated and deoxygenated blood

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13
Q

fMRI adv

A

High spatial resolution (mm3)

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14
Q

fMRI disadv

A

Low temporal resolution (seconds)

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15
Q

univariate fMRI - single DV

A

More accurately “mass univariate”

Brain divided into cubes or “voxels”

‘Activation’ in each voxel is a dependent variable

Each voxel analysed independently of others

End up with a brain map showing which voxels are ‘activated’

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16
Q

multivariate fMRI

A

Brain again divided into voxels

However, this time voxels are not treated independently

Here, we examine patterns of activation across groups of voxels

Hence, sometimes referred to as multivoxel pattern analysis (MVPA)

Computer algorithm (pattern classifier) trained to learn the patterns of neural activation associated with different conditions

Then given a new data set and asked to predict which condition the subject is currently experiencing based on their neural activation patterns

Above chance classification suggests the brain region in question encodes information about the conditions

17
Q

EEG

A

Recording electrical (neuronal) signals from the scalp

18
Q

EEG adv

A

Very fine temporal precision (measurement every ms)

19
Q

EEG disadv

A

Poor spatial resolution – declines as go deeper into the brain

20
Q

2 ways EEG used

A

Event-Related Potentials (ERPs)

Examination of oscillations in different frequency bands (during task or at rest)

21
Q

ERPs

A

Subject performs some task involving repeated trials of 1 or more conditions

EEG response to trials of each condition are averaged together to form an average waveform (an ERP)

E.g. P300 – ‘oddball’ signature - surprise

22
Q

Examination of oscillations in different frequency bands (during task or at rest)

A

EEG recordings occur in rhythmic, repetitive activity patterns

These are described in terms of their frequencies (Hz)

E.g. Gamma, delta, theta, alpha, beta

Different roles for different frequency bands in cognitive processing – e.g. theta in working memory

E.g. synchronization across different brain regions – how they communicate

23
Q

MEG

A

Records magnetic activity from scalp

24
Q

MEG adv

A

High temporal precision (ms)

25
Q

MEG disadv

A

Low spatial resolution (although better than EEG)

26
Q

TMS

A

Magnetic field generator placed on surface of head

This produces electrical currents in the brain region under the coil via electromagnetic induction

The idea is to produce a ‘virtual lesion’ in the brain – one of the only methods of doing this

By delivering a ‘pulse’ time-locked to a specific part of a task that the subject performs, possible to investigate the effects of localised neuronal disruption on specific cognitive processes

27
Q

TMS advs

A

High temporal precision (ms)

Can make inferences about whether brain region is necessary for a particular process

28
Q

TMS disadv

A

Limited to brain regions near the scalp (can’t stimulate subcortical structures)

29
Q

psychophys

A

Recording bodily responses to stressful or emotional events/tasks

E.g. Galvanic Skin Response (GSR) – measures fingertip sweating
- Might not get accurate responses if you simply ask about emotions

30
Q

psychophys adv

A

Direct measure of emotional response (not reliant on self report)

31
Q

psychophys disadvs

A

Assumptions about what measures actually mean

Measures can be quite ‘noisy’

32
Q

monkey single unit (neuron) recordings

A

Monkey (usually macaque) is trained on a computerised task

Then anaesthetised, skull opened and an electrode array inserted into the brain

On awaking, monkey performs trained task while neuronal activity recorded

33
Q

monkey indv unit recordings adv

A

Excellent temporal and spatial resolution

34
Q

monkey indv unit recordings disadv

A

ethical