Prelim 1

Question 1

What is a “homunculus”?

Answer 1

A “homunculus” is a little human. In cognitive science, it refers to an argument that accounts for a phenomenon in terms of the very phenomenon that it is supposed to explain, which results in an infinite regress.

Example #1:

Bert: How do eyes project an image to your brain?

Ernie: Think of it as a little guy in your brain watching the movie projected by your eyes.

Bert: Ok, but what is happening in the little guy in your head’s brain?

Ernie: Well, think of it as a little guy in his brain watching a movie…

Question 2

What is the problem with appealing to a homunculus, as an explanation of mental faculties?

Answer 2

Unilluminating because it simply appeals to another homunculus to explain the problem. Infinite regress.

Question 3

How do functionalism and decompositionism each contribute to a better explanation than appealing to a homunculus?

Answer 3

Functionalism -> Mental states are constituted by their causal (functional) role, not by their material make-up

Ex: what makes something memory (say) is the function it has in the overall system, not that it is the hippocampus

So if two systems have the same function, then they are the same mental capacity

Decompositionism -> Each mental capacity is built up out of other, less intelligent capacities (Reigning solution to the problem of the homunculus)

ex -> langauge comprehension

Thus, both theories provide an alternative to appealing to a homunculus by defining a certain capacity by less intelligent capacities as well as its functional role instead of saying that a capacity is controlled by a homunculus

Question 4

example of decomposition w/ language comprehension

Answer 4

To be able to understand one sentence you have analyze grammar, understand each word

Everything can be reduced

Question 5

Classical reductionism

Answer 5

Special sciences capture truths that can ultimately be restated in terms of lower level sciences, terminating at physics.

Ex: So, Psychology is reduced by Neuroscience just in case the laws of psychology are derivable from those of neuroscience.

Question 6

Mind-brain identity theory (MBIT)

Answer 6

The general claim of MBIT is that for every type of mental state (e.g. episodic memory), there will be a corresponding type of brain state (some configuration of neurons with certain pattern of activity or organization)

Types of mental states = types of brain states. (E.g.memories = certain types of neural structures, which can be found in human brains)

Question 7

Functionalism

Answer 7

Mental states are constituted by their causal (functional) role, not by their material make- up; Basic idea of functionalism is that what makes something memory (say) is the function it has in the overall system

Rejection of MBIT

Question 8

Multiple realizability

Answer 8

When the same type of thing at the higher level (e.g. memory) can be implemented or “realized” in multiple ways at the lower level (e.g. in mammalian brains and in avian brains)

Example:
Every creature in the universe that has memory has to have a hippocampus?

At a minimum, it seems rash to conclude that birds don’t have memory just because they don’t have the same brain structure we do.

More strongly, might think that it’s obvious that birds have memory, so any theory that identifies memory with a brain region is a bad theory.

Question 9

Context sensitivity

Answer 9

Two individuals of the same type at the lower level can be individuals of different types at a higher level, depending on the context.

The same kind of physical object can be a second-hand gear in one context and a minute-hand gear in another context.

Just having a physical description of the gear, without the context, doesn’t tell you whether the gear is a minute-hand gear.

Question 10

Why might functionalism make reductionism unlikely to be true?

Answer 10

In psychology, we want to characterize mechanisms by the functions they serve.

As a result, according to anti-reductionism, we are bound to end up with physically dissimilar mechanisms doing the same job (as in the case of memory in birds/humans).

But the anti-reductionist wager is that just as there is no simple one-one mapping between the kinds of molecular biology and the kinds of biochemistry, we can expect no simple one-one mapping between the categories of psychology and the categories of neuroscience.

Question 11

Validity

Answer 11

An argument is valid if and only if it is impossible for its premises to be true and its conclusion false

Truth gets preserved – with a valid argument you never go from true premises to false conclusions

Question 12

Is this a sound and/or valid argument?

Answer 12

Sound + valid argument (true premises)

Question 13

Is this argument sound and/or valid?

Answer 13

Truth preserving… valid, even though false premises and conclusion; so not sound

Question 14

How does the computer model provide an explanation for how a physical system could be rational?

Answer 14

The mind can operate in a truth preserving way because:

1. beliefs are represented in symbol sequences
2. like a computer, these symbols have physical properties that the mind/brain manipulates into other symbols (that also have physical properties of course)
3. the physical manipulations of the symbols are arranged so that they are truth preserving

Question 15

Naturalism

Answer 15

how can a physical system be organized so that its causal processes ensure that if it believes something true, those causal processes will lead to other true beliefs (and not false beliefs)

Question 16

How do turing machines work

Answer 16

The symbols are physical objects with physical characteristics (size, shape, etc.). But the symbols can be interpreted as representing various things.

The rules that govern the physical system (the syntax) are designed such that our interpretation of the symbols (the semantics) will be truth preserving.

What the head does is determined by:
1. the token it finds in the square it’s scanning (e.g., 1, 0) 2. the internal state it is currently in (q0, q1, …)

These factors determine:
1. what token to write on the present square
2. the motion of the head (left, right, halt)
3. what internal state (q0, q1, …) to be in for the next step

Question 17

How can the computer model of the mind explain the productivity of thought?

Answer 17

productivity of thought - there is an infinite number of thoughts that we are capable of thinking

As we’ve seen, computers are symbol manipulating devices. And there are rules that govern how symbols can be manipulated (e.g., replace “Luke” with “someone”)

This paves the way for productivity. For computers can operate on recursive rules

Roughly, a rule is recursive if the category in the “if” part of the rule (e.g, NOUN) also appears in the “then” part of the rule.

Question 18

What are the elements of neural networks?

Answer 18

Connectionism (emphasizes fact that the knowledge is contained in the connections)

Neural nets (emphasizes similarity to actual neural networks)

Parallel distributed processing (emphasizes fact that much of the processing is not serial but simultaneous)

+ Deep learning when lots of inner layers

Question 19

McCulloch & Pitts neuron (pre-wired neural nets)

Answer 19

– Inputs binary (1, 0)

– Each input multiplied by associated weight (e.g., x1 * w1)

– Sum the products of each pair of input * weight (xi * wi)

– Threshold: Neuron fires if that sum exceeds threshold (threshold number can be fraction)

– Output binary: If threshold is exceeded, 1; otherwise, 0

Can capture basic logical relations (AND, OR, NOT) using these kinds of neurons.

Question 20

AND gate

Answer 20

Question 21

Rosenblatt’s perceptron (learning neural network)

adds concept of changing weights to the M-P model

Answer 21

This requires calculating the error:
1. Error = target ([1]) – actual output ([0]). 1 – 0 = 1.

Updating also requires specifying a learning rate “alpha”

2. Alpha = how much are we going to change the weights in response to the error. We’ll say .5.

New weight = old weight + ([learning rate*error] * value of input) =
w1(new) = w1(old) + ([alpha * (target-output)] * x1)

Do the same process for adjust the weight for x2

Repeat this process for each row of the truth table until you get your desired output

Question 22

limitations to perceptron model

Answer 22

Neuron is just a single layer, so it can only do certain logic gates (and, or, not); you can’t do exclusive-or/XOR; way to overcome: testing with a multiple-layer neuron

Question 23

Interactive Activation model

Answer 23

Discovered by Hubel & Wiesel

Certain neurons in visual cortex are highly specialized

Some would selectively respond if organism was presented with vertical line while some were selectively respond with a diagonal line

reproduces the word superiority effect because when there’s a word, there is more activation for the letter unit

Basically since you can have connections to words and letters in both directions, seeing the words creates the creation to the letter, making it easier to remember

how inputs can activate connections

Question 24

What we can learn from the Interactive Activation model

Answer 24

1. Neural net models can naturally capture graded performance (relative speed and accuracy in identifying a letter in a word) given multiple factors being processed in parallel.
2. The model shows how the computation of a perceptual representation of an input (a word) might involve simultaneous processing at multiple levels of abstraction (feature, letter, word)
3. Although one might have thought that people’s recognition of letters depended on rules about the correct spelling, McClelland says that the model shows that this need not be so

This is shown be the fact that non-words (i.e., strings that violate orthographic rules) can facilitate recognition

Question 25

General intelligence (learned) vs. instinct (innate)

Answer 25

General intelligence allows us to solve problems and draw on lots of different information Animals don’t have intelligence they have instinct

Question 26

“New Look” perception psychology

Answer 26

Top down – intelligence in perception; what you think affects what you perceive Ex: Context affects perception: perceive word as “cat” even though it is not a letter “a”

Question 27

Domain Specificity

Answer 27

module only can be turned on by certain types of inputs EX 1 : emotions → spider (produces more fear) vs. infinity pool, statistically infinity pool is more dangerous EX 2 : facial recognition → facial recognition is not being turned on when looking at an upside down face, THUS face recognition can only be turned on by certain inputs. → we need exactly the right format to turn on

Question 28

Mandatoriness

Answer 28

cannot control if a module applies to a given input. If input fits, module fires → this is the reverse of DS EX 1 : stroop effect, it’s hard to name what color the font is (blue); vision is faster than reading EX 2 : hollow face illusion → since the features of the face are in the right position the face will automatically correct itself (you will see a face even if it is not structurally correct)

Question 29

Informational Encapsulation

Answer 29

The processing within the module cannot be influenced by information from higher-level cognition. Modules can only access information within its own database. All by itself. → Insulated both from what you want to be true, and from your background beliefs EX 1 : visual illusions, you know the truth but you cant stop yourself from seeing it because the module changes for you. Background knowledge is not getting in there and changing the way you see it EX 2 : lexical access, bug insect vs. bug microphone. You used context to figure out what the meaning

Question 30

Explain the lexical disambiguation example

Answer 30

When a word has multiple meanings, you select the most plausible meaning based on the context “Rumor had it that, for years, the government building had been plagued with problems. The man was not surprised when he found several spiders, roaches, and other bugs in the corner of his room. You would assume bugs in this context refers to the insects, not a microphone *But under the hood, the process itself actually seems to be encapsulated. The fact that the right interpretation is rationally obvious does not get into the mechanism that is involved in going to the lexicon. That mechanism activated both the rational interpretation and the terrible interpretation*

Question 31

The virtues of vertical (modular) and horizontal faculties

Answer 31

Vertical -> different modules (i.e. early vision module, lexical access module) Horizontal -> decision made from vertical modules

Question 32

Marr's 1st level

Answer 32

LEVEL 1: Computational/(ecological) descriptions : the what and why Guiding Questions: What is the goal of the system? → “what is the goal of the computation, why is it appropriate, and what is the logic of the strategy by which it can be carried out?” What it does and why, why does it do that instead of other functions → to answer you can use context, what makes the most sense EX. cash register, spider, and fly

Question 33

Ecological examples of Marr's 1st level

Answer 33

Ecological examples: insect and arachnid vision Red-backed jumping spider - has a curious retina formed of two diagonal strips arranged in a V” The goal of the visual system in the jumping spider is to identify mates and distinguish them from prey. If we don’t know this about the spider’s visual system, it will be much harder to figure out how the system works. The fly - one visual system for landing and one visual system for mating Marr maintains that we would have an incomplete picture of spider vision or fly vision if we missed these computational (ecological) level descriptions.

Question 34

Marr's 2nd level

Answer 34

LEVEL 2 : Representation and algorithm : the how Guiding questions : how can this computational [ecological] theory be implemented? In particular, what is the representation for the input and output, and what is the algorithm for the transformation?”

Question 35

Representations and algorithm examples for 2nd level

Answer 35

Basically: i. representation for the input and the output Example -> for the number 4, we could have: hash marks (||||), arabic base 10 (4), arabic base 2 (100), roman (IV) - all diff representations for the same thing *if the representation is fixed this constrains the possible algorithms (hashmarks would just concatenate; arabic numerals can use addition tables) ii. algorithm for manipulating the representations Example -> consider the various algorithms for multiplication: (3 * 5) algorithm 1 -> add the second number to itself as many times as indicated by the first number (5 + 5 + 5) algorithm 2 -> use multiplication

Question 36

Marr's 3rd level

Answer 36

LEVEL 3 : Hardware implementation Guiding questions : how can the representation and algorithm be realized physically? Marr has little to say about this but he basically says that the relation between levels 1 & 2 and the relation between levels 2 & 3 are similar, “the same algorithm may be implemented in quite different technologies” Again the main idea is that there is a lot of Multiple realizability

Question 37

Marr's 1st stage

Answer 37

Level 1: what is the goal of human vision? Goal : to provide the viewer with a description of her surroundings that is “useful to the viewer and not cluttered with irrelevant information” To provide a description of the shapes and positions of things from image intensity values as detected by the photoreceptors in the retina

Question 38

Marr's 2nd stage

Answer 38

Level 2: how does vision work? 1) first you input an image that your retina collects, very very detailed and weird Primal sketch of the scene from image intensities on photoreceptors in the retina. 2) 2.5 D sketch → shows distance between each point in the visual field and the perceiver and orientation. * step 1 and 2 are perceiver oriented 3) Full 3D model → 3D model removed a lot of the extra info(very detailed information about shape collected from the retina) from input image and is now a constant shape and size.

Question 39

Marr's 3rd level of vision

Answer 39

Marr has nothing

Question 40

Incest avoidance (Westermark hypothesis)

Answer 40

evolutionary mechanism to avoid undesirable alleles and phenotypes from remaining in the population

Question 41

Cheater detection

Answer 41

if a rule is perceived as a social contract, then a cheater detection algorithm is activated that searches for information that could detect cheaters; looks for people who have intentionally taken the benefit specified in a social exchange rule without satisfying the requirement

Question 42

Empiricism

Answer 42

caricature: all learning comes from experiences, mind is a blank slate, knowledge comes primarily from sensory experience agree there is an innate learning mechanism but think that processes are domain general

Question 43

Nativism

Answer 43

caricature: the mind comes into being with ideas of god, triangle, etc. already present, the environment plays no role in acquiring ideas believes in an innate learning mechanism + env. may play a role, but believes that processes ae domain specific Rationalism is used as a synonym for nativism - refers to reliance on reason instead of emotion

Question 44

domain general

Answer 44

critical thinking can be applied to any topic in any field (E.g., hypothesis testing (the kind of thing we do in science, Associative learning, Statistical learning)

Question 45

domain specific

Answer 45

contribution to learning, easiest to characterize negatively: the capacity is acquired in a way that CAN’T be explained as a product of domain general learning mechanisms. critical thinking ability is conceptualized as being specific to a particular area There are specific modules to learn different skills (language, auditory processing, etc.)

Question 46

Birdsong as a case for nativism

Answer 46

shows evidence for domain specific learning mechanisms different birds produce different songs * This is a model for how to think about innateness. * The environment plays a critical role, but can’t explain the behavior without some innate contribution that is specific to the task domain (song, even song-in-this-species)

Question 47

Birdsong and its relation to POS

Answer 47

* The white crown sparrow produces distinctive songs in a way that goes beyond the input. * If you gave the same acoustic input to a domain general statistics program, would not get the output we see with the sparrow. * Given the input that the bird gets, must be some species-specific contribution.

Question 48

Statistical inference

Answer 48

(empiricist learning) allows you to make predictions about lots of things -Bulk of science driven by statistical inference -process of drawing conclusions about an underlying population based on a sample of data

Question 49

Transitional probabilities

Answer 49

how likely it is for one sound to follow another (Ex: “pee” has a higher transitional probability than “bee” in the context of hearing “hap”)

Question 50

Saffran & Aslin study

Answer 50

Tried to see whether 8 month old babies could use the statistical information, putting artificial words together After two minutes of exposure, kids were able to tell the difference between patterns; Infants listened longer for “part words” that didn’t respect the word boundaries Conc: Children have general capacities for statistical reasoning (capacity for statistical inference) that can be used to draw conclusions about the world

Question 51

Empiricist account of how learning works

Answer 51

An empiricist theory of learning capacity X holds that we learn this capacity through a general, all purpose learning mechanism it applies to anything. Empiricist learning proceeds by applying domain general learning mechanisms (e.g., statistical inference, hypothesis testing) to environmental input.

Question 52

Does the Poverty of stimulus argument conclude that language competence requires a domain-general or domain-specific learning mechanism?

Answer 52

POS argument (Chomsky) concludes that: The poverty of the stimulus argument is the claim that primary linguistic data (i.e. the linguistic utterances heard by a child) do not contain enough information to uniquely specify the grammar used to produce them. Contradicts empiricist theory regarding language learning; there are some innate features to language learning Language competence must require a domain specific learning mechanism

Question 53

“H1”

Answer 53

Process the declarative from beginning to end (left to right), word by word, until reaching the first occurrence of the words is, will, etc. transpose this occurrence to the beginning (left), forming the associated interrogative ignores the structure of the sentence, what the words mean Domain general, sentence doesn’t make sense, only pay attention to major words

Question 54

“H2”

Answer 54

BETTER DESCRIBES how statements are converted into question Process the declarative from beginning to end (left to right), word by word, until reaching the first occurrence of the auxiliary is, will, etc. **following the first noun phrase of the declarative **, transpose this occurrence to the beginning (left), forming the associated interrogative pays attention to statement or question, more abstract, cares about the order Domain specific, sentences start to make sense, when you grow older

Question 55

On Chomsky’s picture, language learning depends on a mechanism that is: innate, domain-specific, and constitutes a “universal grammar.” What do each of those claims mean?

Answer 55

Innate = natural domain specific = language acquisition universal grammar = kids already know how grammar/linguistic structure works without being taught

Question 56

What are the steps in a Poverty of stimulus argument?

Answer 56

1) Specify piece of knowledge (i.e. H2) 2) Identify some indispensable input for acquiring the knowledge via domain general learning (If the child has already settled on a general theory of what kinds of grammars are appropriate, she might not need specific evidence about declarative/interrogative for “The man who is tall is here”) 3) Show that this indispensable input is inaccessible to the learner (i.e. "Where’s the little blue crib that was in the house before?") 4) Show that the knowledge is acquired at a young age (and in particular, before the indispensable input is accessible) (i.e. Although children never make this mistake: “Is the man who tall is here?” they do make this one: “Is the man who is tall is here?”)

Question 57

Elements of classical conditioning using Pavlov’s dog experiment

Answer 57

Neutral Stimulus (NS): sound of the bell Unconditioned Stimulus (UCS): food Unconditioned Response (UCR): salivation Conditioned Stimulus (CS): bell Conditioned Response (CR): salivation

Question 58

Extinction

Answer 58

Presenting the conditioned stimulus without the US generates a new inhibitory connection. This eventually is strong enough to block the response Getting used to something, responding less

Question 59

Generalization

Answer 59

Pavlov found that when he stimulated other parts of the dog's body, there was still a good deal of salvation for parts that were close to the thigh, and this dropped off significantly as the stimulation occurred to more removed body parts

Question 60

Discrimination

Answer 60

Pavlov investigated this first by giving the same exact tone paired with food hundreds of times. The dogs still tended to generalize. Then he tried a more contrastive method where he would present a tone (say, 1000 Hz ) paired with food and he would alternately present a slightly different tone (e.g., 900 Hz) without food. Although the dogs at first show generalization, they gradually restricted their response to the more precise stimulus.

Question 61

Spontaneous Recovery

Answer 61

But, crucially, the initial excitatory connection remains to some extent. That's why, according to Pavlov, it's easier to reactivate.

Question 62

Contiguity theory of classical conditioning

Answer 62

Whether an association is formed between two events depends on how close they are in time. posits that classical conditioning is effective only when the conditioned stimulus and unconditioned stimulus follow one another closely in time

Question 63

How does blocking pose a problem for the contiguity theory?

Answer 63

Blocking occurs because the new neutral stimulus is irrelevant to the prediction. The unconditioned stimulus is already predicted given the conditioned stimulus. No matter how close in time the new stimulus is presented, it will not form an association because the individual has already associated the original conditioned stimulus with the conditioned response.

Question 64

What is an intuitive explanation for why blocking doesn’t generate classical conditioning?

Answer 64

First, the animal is trying to identify predictive cues. Second, once a predictive cue is known, there is no need to continue trying to identify other predictors that happen at the same time.

Question 65

Rescorla Wagner model

Answer 65

Basic idea: R & W proposed that the strength of conditioning depends on the degree of surprise; learning rate differs in different conditions (e.g. depending on the intensity of shock)

Question 66

Garcia effect

Answer 66

phenomenon in which conditioned taste aversions develop after a specific food becomes associated with a negative reaction, such as nausea or vomiting tastes were preferentially associated with sickness and auditory and visual cues were preferentially associated with shock

Question 67

Latent learning

Answer 67

type of observational learning where the learner doesn’t have to participate in a lesson for learning to occur

Question 68

Model-Based learning

Answer 68

learning to attain a goal, learning a value of the outcome you are getting The models learn what the effect is going to be of taking an particular action in a particular state.

Question 69

Model-Free learning

Answer 69

not trying to get value but more based on habit, more automatic For instance, after getting food from pushing a blue lever several times, the reinforcement learning system might come to assign a positive value to the action of pushing a blue lever, with no foresight. We do not explicitly learn transition probabilities or reward functions. We only try to learn the Q-values of actions, or only learn the policy. Essentially, we just learn the mapping from states to actions, maybe modelling how much we're expecting to get in the long run. The algorithm learns directly when to take what action.

Question 70

The elements of Bayes’ theorem

Answer 70

Posterior Probability -> P(A|B) - probability of A being true given that B is true Likelihood -> P(B|A) - probability that we would find B given that A were true Prior Probability -> P(A) - probability that A is true (same for P(B))

Question 71

Features of human word learning

Answer 71

A few instances often adequate Easily learn extensions of overlapping categories Learn without negative evidence

Question 72

The size principle

Answer 72

“the preference for smaller consistent hypotheses over larger hypotheses increases exponentially with the number of examples, and the most restrictive consistent hypothesis is strongly favored” This principle follows from Bayesian techniques and provides a way of explaining such learning As the number of times of something occurring increases, argument is stronger; Increasing the size of a certain outcome, decreases the size of having other alternatives

Question 73

Explain how the size principle predicts the pattern we see in word learning

Answer 73

If the only example of a “fep” is a Dalmatian, that should provide support for interpreting “fep” as Dalmatian but also dog. But if shown 3 examples of “feps” and all are Dalmatians, that should provide stronger support for interpreting “fep” as Dalmatian rather than dog.

Question 74

Overhypothesis

Answer 74

assume that there would be only be green marbles in the bag containing a green marble and subsequently other bags will contain uniform colored marbles

Question 75

Episodic memory

Answer 75

memory for particular experiences that actually happened

Question 76

Semantic memory

Answer 76

memory for general knowledge/facts

Question 77

Working memory

Answer 77

retention 15-30 sec; capacity is limited (7 +/- 2 novel units); unrehearsed info is lost

Question 78

Components of Working Memory

Answer 78

central executive (attention control system), visuospatial sketchpad (visual-spatial working memory), phonological loop (verbal working memory) All accommodates different types of info we encounter, all for processing diff information ex: visual, spatial, verbal

Question 79

What kind of events interfere with consolidation and reconsolidation?

Answer 79

Stress, new information, emotional experiences

Question 80

Consolidation (cellular/synaptic)

Answer 80

process of going short term to long term memory Process depends on neurons generating new proteins

Question 81

Reconsolidation

Answer 81

reactivation of memory Turning inactive memories to active state, form of “updating”