Midterm 1

Question 1

What are the different fields that comprise cognitive science?

Answer 1

psychology, cognitive science, computer science, linguistics, anthropology, neuroscience

Question 2

What role do these different fields play in cognitive science?

Answer 2

philosophy: raises questions, defines concepts, gives directions
psychology: focus on mental processes (learning, memory, attention)
neuroscience: focus on the brain (maps mental processes to physical structures)
computer science: creating artificial minds; defines old problems in new ways
linguistics: models language as a cognitive system
anthropology: gives a scope of human culture, behavior, & thought (how different ppl think)

Question 3

Explain multiple realizability

Answer 3

the thesis that the same mental property, state, or event can be implemented by different physical properties, states, or events.

Question 4

Why do we care about multiple realizability in cognitive science?

Answer 4

If the mind and the brain were the same thing, then the field of cognitive science would not be needed

Question 5

Mind

Answer 5

information processor. According to this, it would incorporate some form of mental representation

Question 6

Interdisciplinary

Answer 6

multiple fields comprised

Question 7

Information processing

Answer 7

quantifying thought in terms of information

Question 8

Functionalism

Answer 8

mental states are defined by their functional profiles
what makes something a thought, desire, pain depends not on what biology causes it but more on the physical state or function it causes the body to do

Question 9

Why do we care about representations in cognitive science?

Answer 9

To explain how the mind interacts with the physical world

Question 10

Explain what analog and digital representations are (with examples).

Answer 10

Digital: discrete, categorical (digital clock, color wheel)
Analog: continuous spectrum (analog clock, color spectrum)

Question 11

Define and explain the differences between imagistic, symbolic, and propositional representations (with examples).

Answer 11

Imagistic: a mental image (when you imagine something in your mind’s eye)
Ex: dollar bill
Symbolic: something that stands for something else; relationship between them is arbitrary
Ex: $
propositional: in terms of language
Ex: “money”

Question 12

What is aphantasia, and what does it tell us about mental representation?

Answer 12

Aphantasia: absence of mental image
It is difficult to remember events or faces of people → mental representations help us with cognition in a sense

Question 13

What are Marr’s levels of explanation?

Answer 13

1) Computational
-What is the goal of the system
-What do you think will be accomplished after the computations?
2) Algorithmic
-What steps are taken to accomplish that goal
-What representations are created
3) Implementation
-What is the physical system that executes the steps
-How are the steps executed physically

Question 14

Why do we care about Marr’s levels of explanation in cognitive science?

Answer 14

Breaks down understanding of how the mind receives and processes information

Question 15

Give an example of a system that you could explain with Marr’s levels.

Answer 15

Chess:
Computational
Things needed to know
The goal
The movements
The rules; what are captures

Algorithmic
How do you win?
→ Analyze games and then create a statistical model to see the probability of winning that type of play
→ Study openings

Implementational
Can do it in their head

Question 16

Weber’s law

Answer 16

As relative difference gets smaller, it gets harder are harder to differentiate; can perceive difference above a certain ratio

Question 17

Representational Trade-off

Answer 17

how choosing one representation type over another may bias you towards one algorithm over another and there are a lot of choices at each level for different algorithms that can converge on a single computational goal

Question 18

What is the grounding problem

Answer 18

If there is a “little person” in our brain viewing the world through us, there must be a little person in their brain, and so on
There is “no grounding” for this process

Question 19

Compare and contrast the different types of dualism and monism. How does each approach define the mind?

Answer 19

monism: mental properties and physical features are identical to one another
-idealism: Everything in the universe is mental
or mentally-constructed.
-Physicalism: Everything in the universe is physical
dualism: mental properties and physical things are non-identical
-property dualism: Mind and body have different properties.
-Biological Naturalism: Mental states are not identical to brain states, but they are causally reducible to brain states.
-Epiphenomenalism: Mental states are caused by brain states, but they do not cause anything. They are causal dead ends.
-Panpsychism: Mental states are an inherent property of matter. Everything has a little bit of consciousness but it’s a spectrum.

Question 20

What is qualia, and why is it important for our conception of the mind?

Answer 20

We can’t fully understand a mind from someone else’s perspective because we don’t know how it feels to be someone else

Question 21

What did we learn from Tolman’s rat studies, and why are they important for the history of cognitive science?

Answer 21

-Rats learned without getting rewards
-Empirical scientific study; proved behaviorist wrong
-The rats navigate the maze by:
-response learning: Learning by remembering a
sequence of movements.
-place learning: Learning by remembering the
spatial layout of the maze.

Question 22

Latent learning

Answer 22

Only showing that something has been learned after an incentive has been given

Question 23

What is computational theory of mind?

Answer 23

a family of views that hold that the human mind is an information processing system and that cognition and consciousness together are a form of computation

Question 24

Why do we care about computational theory of mind in cognitive science?

Answer 24

Since it says the brain is like a computer

Question 25

Explain formal systems (and give an example).

Answer 25

Logic
FORM OF ARGUMENT MATTERS; physical system does not matter
You can swap out the arguments
If p is true
AND if p = q
Then q is true

EXAMPLE: Games
Pieces don’t matter; the rules matter
In chess; doesn’t matter what you play it with…just ended the rules

Question 26

Why do we care about formal systems in cognitive science?

Answer 26

The system to maximize reward

Question 27

Describe the Turing machine and explain its impact on cognitive science.

Answer 27

The Turing machine is a hypothetical machine that can compute anything that’s mathematically computable.
Its impact on cognitive science- the functionality of it is compared to the mind? Also, since there are so many steps in order to accomplish a specific task, the Turing machine forces us to consider all the steps taking place within our own mind(even tho it seems simple/natural)?

Question 28

What is an informational system?

Answer 28

The mind is like a calculator- the system is all that matters. Therefore, a simulation of a mind is a mind.

Question 29

What is the Church-Turing thesis, and what are its implications?

Answer 29

no computational procedure is an algorithm if it’s not represented as a turing machine
anything that can be computed, should be computed with a turing machine

Question 30

Turing machine

Answer 30

Hypothetical machine; with infinitely long memory tape
can compute anything that is computable
No computation procedure will be an algorithm if not represented by turing machine
Anything that can be done by a computer can be computed on the turing machine

Question 31

Chomsky hierarchy

Answer 31

Ranks classes of formal grammar, from highest to lowest:
Unrestricted grammar (Turing machines)
Context-sensitive grammar
Context-free grammar
Regular grammar (finite state machines)

Question 32

Explain the Frame Problem as a challenge for artificial intelligence.

Answer 32

How AI cannot deal with uncertainty and irrelevant information because they are programed for specific things and do not have the knowledge to keep on learning unless it is programmed into them

Question 33

Describe the Chinese Room argument and explain its implications for computational theory of mind.

Answer 33

The Chinese Room describes the argument of knowledge vs computing a response. Someone in in a room where they are only surrounded by books that have Chinese phrases and translated responses. On the outside of the room, someone slips them a piece of paper and the person inside the room delivers the correct response. The person isn’t necessarily knowledgeable about Chinese, only how to respond. With the computational theory of mind, it talks about how a computer can interpret information without needing to know what it is.

Question 34

Describe the structure and function of an artificial neural network.

Answer 34

Structure:
nodes: input, hidden, output
connections (between nodes): weights (between 0 and 1) governs the behavior of the network
activation function: sums up input from all connected nodes
when a node activates, it sends something to every single node in the hidden layer
the receiving node sums all the inputs it has, and it has to reach a certain value in order to send it to the next node (similar to neurons)

function: simulates the human brain with nodes as neurons

Question 35

Describe the three main types of learning that neural networks can perform.

Answer 35

Unsupervised learning
Supervised learning
Reinforcement learning

Question 36

Compare and contrast classical and connectionist computing

Answer 36

Why do we care about these approaches in cognitive science?
How are they similar, how are they different?

Question 37

Backpropagation

Answer 37

Related to behaviorism, we base our behavior from reward and punishment
The idea of reinforcement learning, taking what we learning, putting it back to improve

Question 38

Graceful degradation

Answer 38

The ability for a brain or computer or other network to still function even after a portion has been damaged because of how functions are distributed across many units (i believe it comes with connectionism)

Question 39

Describe the debate between Nativism and Empiricism.

Answer 39

Nativism is the idea that you are born with some intelligence compared to Empiricism which is the idea that you are born with a clean slate and learn/develop while growing up

Question 40

What are some methods we can use to test babies’ knowledge? How can we use these methods to make inferences about what babies know?

Answer 40

-head turn paradigm: the amt of time infants look at an unexpected stimulus for or where they look can reveal their expectations on the world
-high amplitude sucking procedure:
-used on infants birth - 4 months old
-capitalizes on infants sucking reflex
-infants hear a sound every time they produce a high amp suck
-can be used to test infants’ discrimination of and preference for a variety of language stimuli

Question 41

Describe “core knowledge” and give some examples.

Answer 41

The idea that infants come into this world with a small sense of numbers and core domains; able to perceive
EX: object permanence

Question 42

Describe how babies learn about their native language (with some examples).

Answer 42

By becoming familiar with it in the womb

Question 43

Describe our innate senses of numbers and how they work.

Answer 43

Innate means you are born with; meaning you are born with the sense of counting up to 3 and beyond that as an infant you won’t understand any number higher

Question 44

Empiricism

Answer 44

The accumulation of knowledge and learning based on experiences
You are born with a clean slate and have acquired traits; developed over time

Question 45

Nativism

Answer 45

You are born with some knowledge

Question 46

Babbling

Answer 46

Milestone in language development typically at 6 months where a baby uses the speech sounds they’ve been learning randomly to babble and eventually can do this while accurately mimicking adult speech rhythms, body language, and other conventions. (He also showed us a computer voice learning to mimic humans at its “babbling” stage)

Question 47

Speech rhythm

Answer 47

How a speaker alternates between stressed and unstressed syllables, why some languages sound “faster” or “slower.” example: English alternates stressed syllables a lot and we sound “slower” for it while a language like Japanese is more likely to keep every syllable equal length which allows them to speak “faster”

Question 48

Subitizing number system

Answer 48

A set of up to 3 objects/numbers, in which we can immediately distinguish between them

Question 49

Approximate number system

Answer 49

Large set of numbers split up and able to tell the ratio