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Man and machine > Problem 3 > Flashcards

Problem 3 Flashcards

1
Q

According to cognitive scientists, how does the mind work ?

A

People have mental procedures that operate on mental representations to produce thought and action

BUT: different mental pres. foster different mental procedures

ex.: calculations with 1,2,3 are different than with I,II,III

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2
Q

Which methods did scientists use to search for the existence of the human mind ?

A
  1. Experimentation with human participants
  2. Forming + testing computational models intended to be analogous to mental operations
    - -> to create theoretical framework
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3
Q

Central hypothesis of cognitive science

A

Thinking can be best understood in terms of representational structures in the mind + computational procedures that operate on those structures

–> mind = computer program

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4
Q

Computational-representational understanding of mind

CRUM

A

Refers to the approach to understanding the mind based on the central hypothesis

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5
Q

How is a computer an analogy to thinking or using the mind according to CRUM ?

A

Running a program results from applying algorithms to data structures

–> just like thinking results from applying computational procedures to mental procedures

e.g.: mental representations = data structures; computational structures = algorithms

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6
Q

Cognitive theory

A

Consists of a set of representational structures + a set of processes that operate on these structures

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7
Q

Computational model

A

Interprets these structures + processes by analogy with computer programs that consists of data structures + algorithms

–> makes the cognitive theory more precise this way

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8
Q

Software program

A

Refers to the location where the computational model is implemented in a specific programming language

–> this is done to test the model

ex.: programming language like Java

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9
Q

Hardware platform

A

Refers to the location where the software program runs on

ex.: macintosh, IBM

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10
Q

Name the 5 complex criteria for evaluating theories of mental representation that claim to explain thought.

A
  1. Representational power
  2. Computational power
  3. Psychological plausibility
  4. Neurological plausibility
  5. Practical applicability
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11
Q

Representational power

A

How much info a particular kind of representation can express

RESULT: Logical relations are difficult to represent in connectionist NWs

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12
Q

Computational power

A

How mental representations account for 3 important kinds of high-level thinking

  1. Problem solving
    - -> planning, decision, explanation
  2. Learning
  3. Language

–> being able to learn from experience

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13
Q

Psychological plausibility

A

Requires accounting for the

a) qualitative capacities of humans
b) quantitative results of psychological experiments concerning these capacities

–> explaining the ways humans accomplish tasks

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14
Q

Neurological plausibility

A

Using neurological techniques to assess high-level cognition + when and where in the brain certain cognitive tasks are performed

e.g.: EEG

RESULT: Real brain NWs are much more complicated, thus connectionist models are only approx. to the behavior of real neurons

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15
Q

Practical applicability

A

Cognitive science should be able to increase the understanding of how students learn

–> + suggest how to teach them better

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16
Q

In which way is the stud of mind interdisciplinary ?

A

It requires the insights + the diversity of methodologies that have been gained by philosophers, psychologists etc

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17
Q

Connectionist research

A

Emphasizes the importance of connections among simple neuron like structures

–> rebound of the computational model, inspired by the brains neuronal structures

e.g.: units = neurons; degree of activation = frequency with which neurons fire

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18
Q

Local representations

A

Refers to a model where neuron-like structures are given an identifiable interpretation in terms of specifiable concepts or propositions

e.g.: one neuron stands for one thing

19
Q

Distributed representations

A

Refers to a model where NWs learn how to represent concepts or propositions in more complex ways

–> to distribute meaning over complexes of neuron-like structures

THUS: it is trained to accurately respond to stimuli so they can acquire concepts that apply to the stimuli

e.g.: numerous neurons converge to stand for one thing

20
Q

Parallel constraint satisfaction

A

Managing a cognitive task in a way where ones processing simultaneously satisfies numerous constraints

–> both models can be used to perform this

ex.: managing to create the perfect timetable for each student in a school

=> negative vs positive internal constraints; + external constraint

21
Q

Feedforward network

Distributed representations

A

Refers to a NW where info flows upward through it

22
Q

Recurrent network

Distributed representations

A

Refers to a NW where the activation from the output unit feeds back into the input units

23
Q

Synchrony

A

Refers to a technique that links units that represent associated elements

24
Q

Vector

A

Refer to lists of numbers that can be understood as the firing rates of groups of neurons

25
Q

What was the broad goal of cognitive science ?

A

Characterizing the nature of human knowledge

–> forms + contents; how knowledge is

a) used
b) processed
c) acquired

26
Q

Cognitive science

A

Refers to the scientific study of the human mind

–> highly interdisciplinary field, combining ideas + methods from

a) psychology
b) linguistics
c) philosophy

27
Q

The analogy to a computer is useful at all 3 stages of the development of cognitive theories.

Name the 3 stages + why.

A
  1. Discovery
  2. Modification
  3. Evaluation

–> a) check whether program gets same right answers and makes the same mistakes

b) helps to show that postulated representations and processes are realizable plausible

28
Q

What will the degree of activation of the units tell us in local representation ?

A

By this we can judge about the applicability of the concept

–> the truth of proposition

29
Q

External constraint

A

Everything that one didn’t program into the program could be an external constraint

–> everything one didn’t take into consideration

30
Q

Delta rule

A

Refers to a gradient descent learning rule for updating the weights of the inputs to artificial neurons in a single layer neural NW

–> determines how to update a neural NW during a backpropagation step

31
Q

Back propagation

A

Can be used to train the NW by adjusting the weights that connect the different units

–> involves finding a function (Gradient descent) that best maps/allocates a set of inputs to their correct outputs

32
Q

Gradient descent

A

Refers to an algorithm that is used to find the set of weights that minimizes the error

33
Q

Process of learning

A

Minimize its cost function by trying to come as close as possible to the local minimum that is used in the cost function

34
Q

Brain-computer interface

BCI

A

Uses brain activity to control external devices, by amplifying algorithms

–> enables severely disabled patients to interact with the environment

35
Q

Connectionist NWs

A

Refer to model that can stimulate learning via hebbian learning + back propagation

–> useful for understanding psychological processes

36
Q

What do connectionist NWs consist of ?

A
  1. Units
    - -> have a degree of activations, thus only fire when there is a certain amount of frequency w/ which neurons fire
  2. Links
    - -> can be

a) excitatory vs inhibitory
b) one way, thus activation flows from one unit to next
c) symmetric, activation flows back + forth

37
Q

Positive constraints

A

Occur when the link between 2 units is excitatory

38
Q

Negative constraints

A

Occur when the link between 2 units is inhibitory

39
Q

External constraint

A

Involves linking units to special units

–> the special unit refers to one which may affect the unit in a way one did not consider “outsider effect”

40
Q

Negative internal constraints

A

Are represented by inhibitory connections

–> come from incompatibly relaxation, which occurs when 2 actions/goals cannot be satisfied together

41
Q

Positive internal constraints

A

Are represented by excitatory connections

–> come from facilitation relaxation, which occurs if an action facilitates a goal and they both go together

42
Q

Activation functions

A

Refer to mathematical equations that determine the output of a neural NW based on the a given input

–> crucial component of deep learning

43
Q

Graceful degradation

A

Refers to the gradual deterioration in performance of a distributed system, which is also o a characteristic of the human cognitive system

Man and machine (9 decks)

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