exam 2

Question 1

what is one use for both language and music?

Answer 1

communication

Question 2

are language and music processed separately or together in the brain?

Answer 2

separately

Question 3

what types of music are most easily recognized across cultures?

Answer 3

dance, lullaby (and healing and love, but less obvious)

Question 4

music + language (theory that these developed from a common precursor)

Answer 4

musilanguage

Question 5

speech vs. music (4 each)

Answer 5

speech: timbre, pitch, rapid processing (20-40ms), left hemisphere
music: timbre, pitch, slower but precise changes, right hemisphere

Question 6

how do we read a spectrogram?

Answer 6

x = time, y= frequency, z = energy/power/intensity (color)

Question 7

what is the speech-to-song illusion?

Answer 7

when a phrase was repeated enough, it started to sound like a tune

Question 8

mapping drequencies (apex, basilar membrane, base)

Answer 8

apex: low frequencies (least stiff)
basilar membrane: vibrates up and down
base: high frequencies (stiffest)

Question 9

maps by frequency in the auditory system

Answer 9

tonotopic maps (similar to retinotopic maps in the visual system)

Question 10

types of brain areas (music and language) (5)

Answer 10

temporal modulation (doesn’t like sounds that stay the same for a long time), likes pitch, speech-selective component (reacts to structure, not meaning), music-selective component (responds to music sounds), language-selective component

Question 11

types of aphasia (2)

Answer 11

Broca’s aphasia: trouble producing speech (comprehension intact)
Wernicke’s aphasia: trouble comprehending speech (production intact, word salad)

Question 12

amusia

Answer 12

fine-fitch discrimination

Question 13

does musical meaning fit into syntax and semantics?

Answer 13

no; music is less strict than language

Question 14

what brain areas respond to groove (music)?

Answer 14

motor and reward

Question 15

are concepts fixed?

Answer 15

no

Question 16

neural effect of expertise

Answer 16

higher activity in right FFA (and in high-level visual cortex) for expertise-related category

Question 17

what does the greebles study show us?

Answer 17

FFA can become selective for categories that are completely novel and not relevant for naturalistic perception (but will not activate more than it does with faces)

Question 18

neural coupling/inter-subject correlation (ISC)

Answer 18

how aligned our brains are to each other while performing a (highly complex) task; brain alignment increases with mutual understanding; strength of neural alignment with experts predicts exam scores

Question 19

generative vs. discriminative inference

Answer 19

generative: use information from prior stages to determine what is happening in the world; can estimate a boundary from prior knowledge
discriminative: what you think the world should look like; determines whether features are dissimilar enough to be categorized differently; doesn’t use prior knowledge to help with categorization, but can learn from prior experience

Question 20

inference

Answer 20

the act of computing unknown/latent/hidden variables, given observed variable

Question 21

[inference by] sampling

Answer 21

a way of approximating a result using prior beliefs without having to use probability distributions (active neurons = 1, inactive neurons = 0)

Question 22

Marr’s level of analysis

Answer 22

computational –> representation/algorithm –> implementation; interdisciplinary connections across levels needed to understand what’s going on in the brain

Question 23

principal component analysis (PCA)

Answer 23

reduce dimensionality + reduce feature collinearity + increase useful variance + reduce noise; ignore dimensions/parts of our data that do not maximize variance (want 2-dimensional data)

Question 24

encoding model

Answer 24

estimating the semantic tuning of each voxel in the brain (how the brain will respond to each stimulus)

Question 25

key take-away of semantic category lecture

Answer 25

category information is widely distributed across many areas of the cortex; stimulus-selective regions are "selectivity peaks" in the distribution

Question 26

modern neural networks

Answer 26

larger versions of earlier networks (more layers ~ deep)

Question 27

AlexNet

Answer 27

the first neural network that could distinguish between thousands of items

Question 28

deep neural networks (DNNs)

Answer 28

good prediction for human behavior; greatly improved the ability to predict performance, judgments, and error patterns compared to e.g., HMAX; capture an internal structure/similarity pattern (RSA) that matches better to our cognition

Question 29

representational similarity analysis (RSA)

Answer 29

quantifying the joint similarity structure of a set of items; the more similar results are, the more likely that those artificial models are doing the same thing; internal structure/similarity pattern captured by DNNs that matches better to our cognition

Question 30

a theory of how categorization works

Answer 30

categories as multidimensional Twizzlers; if two things have features in common, different regions might respond almost the same and you can no longer tell them apart in said region

Question 31

deepfakes

Answer 31

mimic appearance, voice, mannerisms, etc.; can recreate content that maintains features enough that you can fool the sensory system of a cognitive animal (e.g., humans)

Question 32

word embedding models

Answer 32

capture semantic relationships between concepts; words that co-occur are often encoded similarly in hidden layer