Week 5.1 Cog Neuro

Question 1

representations

Answer 1

properties that are manifested in cog systems and neural systems

Question 2

single-cell recordings

Answer 2

measure the responsiveness of a neuron to a given stimulus

Question 3

electroencephalography (EEG)

Answer 3

measurements of electrical signals generated by the brain through electrodes places on the scalp

Question 4

event-related potential (ERP)

Answer 4

average change in voltage at the scalp that are linked to the timing of cog events e.g. stimulus, response

Question 5

reaction time

Answer 5

time taken between onset of stimulus and production of behavioural response

Question 6

multi-cell recordings

Answer 6

electrical activity of many individually recorded neurons recorded at electrode(s)
(in terms of action potentials per second)

Question 7

multi-cell recordings

Answer 7

electrical activity of many individually recorded neurons recorded at electrode(s)
(in terms of action potentials per second)

Question 8

grandmother cell

Answer 8

hypothetical neuron that responds to one particular stimulus

Question 9

rate coding

Answer 9

the informational content of a neuron may be related to the number of action potentials per second

Question 10

temporal coding

Answer 10

the synchrony of firing may be used by a pop. of neurons to code the same stimulus

Question 11

mental chronometry

Answer 11

study of the time course of info processing in the nervous system

Question 12

additive factors method

Answer 12

general method for dividing reaction times into diff stages

Question 13

N170

Answer 13

ERP component (neg potential at 170 ms) linked to perceiving facial structure

Question 14

associative priming

Answer 14

reaction times = faster to stimulus X after being presented to stimulus Y (if X and Y previously associated)

Question 15

exogenous

Answer 15

related to properties of the stimulus

Question 16

endogenous

Answer 16

related to properties off the task

Question 17

inverse problem

Answer 17

difficulty of locating the sources of electrical activity from measurements taken at the scalp in ERP research

Question 18

dipole modeling

Answer 18

attempt to solve inverse problem involving assuming how many dipoles (regions of elec. activity) contribute to the signal recorded at the scalp

Question 19

magnetoencephalography (MEG)

Answer 19

noninvasive method for recording magnetic fields generated by the brain at the scalp

Question 20

neuronal activity generates

Answer 20

elec + magnetic fields that can be measured invasively/noninvasively

Question 21

studies of single-cell recordings are based on

Answer 21

measuring number of action potentials generated + provide clues about neurons coding info by measuring their responses to stimuli

Question 22

when populations of neurons are active in synchrony they produce

Answer 22

an active electric field that can be detected at the scalp (EEG)
when many such waves are averaged together + linked to stimulus –> event-related potential (ERP) is obtained

Question 23

neurons communicate with each other by

Answer 23

• receiving electrical potentials (excitatory/inhibitory) from other neurons
• once threshold for excitation is surpasses –> action potential propagates along axon
• this triggers release of neurotransmitters at synapses with other neurons

Question 24

what is firing/spiking

Answer 24

tiggering of an action potential

Question 25

single-cell recording measures

Answer 25

the firing rates of individual neurons in non-human animals

Question 26

EEG and MEG measure the

Answer 26

summed activity of large populations of neurons in humans

Question 27

steps in single-cell recording in animals

Answer 27

• electrodes surgically implanted in brains of experimental animals
• can monitor firing rates of neurons when animal perceives diff stimuli
• provides info on how + where diff classes of stimulus are coded in the brain

Question 28

feline visual system

Answer 28

single-cell recording in cats = instrumental in mapping organisation of visual cortex
- hubel and weisel found certain neurons in primary visual cortex fired strongly when cat saw straight line

Question 29

selectivity

Answer 29

specific neurons respond to particular types of visual stimulus

Question 30

hierarchal organisation

Answer 30

higher-level neurons respond to increasingly complex stimuli

Question 31

sparse coding

Answer 31

neurons at top of hierarchy only respond to one specific stimulus

Question 32

sparse distributed code

Answer 32

particular stimuli causes firing across specific set of neurons

Question 33

steps in electroencephalography - EEG

Answer 33

• direct neural recording = fine-grained info but rarely possible in humans
• place electrodes on scalp + record changes in electrical potentials caused by neural firing in brain
• potential around 2-10 microvolt
• combined response of large numbers of neurons to generate measurable response

Question 34

EEG has poor spatial resolution

Answer 34

• studies typically use 32 or 64 electrodes placed over participant’s head
• electrical signal is conducted through the skull so source may be distant from electrode where it is measured
• EEG most useful for learning when not where neural activity occurs

Question 35

EEG signals are noisy

Answer 35

EEG measures tiny electrical signals
have to compete with noise:
- random neural firing
- elec activity from eye/facial movements
- electrical equipment interference
signal-to-noise ratio = poor
deal with this by averaging over large N

Question 36

event-related potentials (ERPs) are produced by

Answer 36

• diff types of stimuli produce characteristic ERPs at diff points in time
• ERPs given names reflecting polarity and timing (N170, P600, N400) + location (N2pc)
• peaks pos/neg but doesn’t explain significance
• polarity depends on spatial arrangement of neurons

Question 37

using ERPs to track timing

Answer 37

• ERPs often used to track time course of cog processes involved in a task
• components at diff points in time may be influenced by diff factors

Question 38

stages of coding for facial recognition annd associated ERP

Answer 38

1. perceptual coding of face
   N170 (affected by perceptual changes to image)
   - larger for human/animal faces
2. face recognition (identity processing)
   N250 (unaffected by view changes, affected by familiarity)
   - larger with familiar faces
3. person recognition (faces and names)
   P400-600 (affected by both faces and names)
   - similar effects for faces/written names

Question 39

magnetoencephalography (MEG)

Answer 39

same basic principle of EEG
- but signals measured by SQUID sensors recording fluctuations in magnetic field

Pros:
- same temporal resolution as EGG
- better spatial resolution
Cons:
- not widely available
- expensive