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B exam review > Cognitive Modeling > Flashcards

Cognitive Modeling Flashcards

1
Q

2 types of arrival patterns

A

standard; non-standard

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2
Q

2 types of service patterns

A

state-independent

state-dependent

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3
Q

Three queuing principles

A
  • FCFS
  • LCFS
  • pre-emptive
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4
Q

Processing capacity

A

how many customers can be served at once

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5
Q

A/B/X/Y/Z

A 
A = arrival pattern (M, N, D, G)
B = service pattern (M, N, D, G)
X = processing capacity / number of parallel lines/channels
Y = buffer size
Z = queue principle (FCFS, etc)
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6
Q

Traffic intensity function

A 
P = utilization of server
gamma = arrival rate
mu = service speed/rate
P = gamma/mu
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7
Q

W

A

Wait time

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8
Q

L

A

Expected number of customers

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9
Q

Mental workload

A

directly proportionate to average utilization (linear)

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10
Q

Queuing applications in human performance

A
  • quantitative mechanism to understand human performance
  • experiment may be too hard/expensive/impossible
  • unify many experiments (MHP)
  • used in interface design
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11
Q

Ideal human processing model expectations

A
  • address internal mechanisms (neuroscience/cog structure)
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12
Q

Parietal cortex

A

memory, language

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13
Q

Occipital lobe

A

visual signals

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14
Q

Wernicke’s area / Broca’s area

A

language

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15
Q

Inner ear

A
  • hairs transmit signals

- balance

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16
Q

EEG vs. ERP

A

EEG - no obvious external stimuli

ERP - triggered by event

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17
Q

Reaction time

A

1/mu

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18
Q

RT

A

realization time + wait time

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19
Q

SOA

A

Time between arrival times

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20
Q

Relation between neuroscience and cognitive modeling

A
  • Human behavior is the result of interaction between physical and mental activities in body and brain
  • Human neural system is the ground of cognitive model; we no longer work on the black-box assumption
21
Q

ACT-R evolution

A

HAM->ACT-R 1.0 -> ACT-R/PM

Perceptual and motor from epic

22
Q

QN-MHP

A
  • Cognitive subnetwork

- One serial server (F), other servers are parallel

23
Q

Keys to building your own cognitive simulation model (planning)

A

(1) how to model human operators
(2) how to model interaction between the human operators and the world
(3) how many servers you need
(4) how to connect those servers

24
Q

Keys to building your own cognitive simulation model (coding)

A

(1) processing time of servers -> using Qn-MHP or MHP
(2) Capacity of servers
(3) Response recording (response, workload)

25
Q

Keys to building your own cognitive simulation model (key principles)

A
  • Always set the model’s parameters according to: MHP, Literature, Cognitive mechanisms, experimental settings
  • Never code to match your data
26
Q

Measurements of mental workload

A
  • Performance-based
  • Subjective
  • Psychophysiological
27
Q

Advantages to psychophysiological

A
  • do not add a new task to measure workload

- real-time

28
Q

P300 amplitude

A
  • reliable and real-time index of mental workload
  • ERP
  • a positive component of ERP with latency between 300 and 800
29
Q

Existing adaptive workload management systems

A
  • phone adaptive system
  • voice adaptive system
  • in-vehicle message system
30
Q

How to set parameters

A
  • literature
  • experimental settings
  • model’s initial setting (MHP - ex)
  • free parameters
31
Q

Benefits of math model

A
  • rigorous quantification of the system
  • equations are easy to be reused, extended, transferred
  • produce analytical solutions
32
Q

Disadvantages of math model

A
  • system’s with high complexity are hard to model

- deterministic - only have point estimation

33
Q

Benefits of simulation model

A
  • no limitation of complexity of system to be modeled
  • can be used when math model is NP hard
  • distribution prediction
34
Q

Disadvantages to simulation model

A
  • lots of iterations -> closer to analytical solution

- codes are not easily transferable to math equation

35
Q

Exponential model

A

E(avg-min, 1, min), then change to seconds

36
Q

Core assumptions of EPIC

A
  • no limitation on cognitive capacity

- perceptual motor have limited capacity

37
Q

Core assumptions of ACT-R

A

Cognitive processing serial (capacity = 1)

One production rule fired at a time

38
Q

Core assumption of SOAR

A

no limitations anywhere

39
Q

Frequency in promodel

A

inter-arrival time

40
Q

Processing logic in promodel

A

when your entities arrive at current location, what are the processes? - happens automatically

41
Q

Move logic in promodel

A

happens only when destination server is available

42
Q

Things learned from papers

A
  • must have R^2 and RMS
  • must have experimental validation
  • detailed analysis of human cognition
  • include codes
  • show model and experimental results together
  • no single server model
43
Q

attribute

A

label specific towards this entity

44
Q

Improve model

A
  • parameters consistent with task
  • understand which part of model influences data a lot
  • check R^2 and RMS
  • free parameters
45
Q

How to apply/use model

A
  • Goal 1: improve human performance/reduce workload (use optimization/scheduling to develop new systems)
  • Goal 2: explain more experimental results (change model’s input/output)
46
Q

3 different ways to model human brain network

A

(1) neurological
(2) math
(3) simulation

47
Q

Promodel process

A
  • locations
  • entities
  • processing (processing logic and move logic)
  • arrivals (inter-arrival time)
  • (variables)
  • (general information)/(simulation options)
48
Q

Additional promodel process for clock

A
  • attribute
  • arrays
  • arrival logic
  • processing logic
  • Global variable
49
Q

Johnson’s rule

A

doesn’t know which arrives first, just calculates the optimal

B exam review (7 decks)

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