Lecture 1 Flashcards

1
Q

behavioural data science =

A

Behavioral Data Science is a multidisciplinary
scientific field that aims to facilitate
understanding, prediction, and change of
human behavior through the analysis of
behaviorally defined variables as they arise in
large datasets (“Big Data”), typically gathered
using modern digital technology (e.g., online
or through mobile devices) and analyzed with
techniques for detecting patterns from high-
dimensional data (e.g., machine learning).

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2
Q

belangrijk ding hieraan

A
  1. multidisciplinary field: crossroad of methodology, psychology, mathematical modeling and statistics.
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3
Q

3 doelen van bds

A

facilitate understanding, prediction, and change of human behavior

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4
Q

understanding =

A

CONSTRUCTION OF PSYCHOLOGICAL THEORIES TO EXPLAIN BEHAVIOR

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5
Q

prediction =

A

APPLICATION OF STATISTICAL MODELS TO PREDICT BEHAVIOR

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6
Q

change =

A

DEVELOPMENT OF INTERVENTIONS TO CHANGE BEHAVIOR

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7
Q

Human behaviour is at the root of
many of the most central problems of
our time: COVID-19 spread and
climate change but also war and
famine have important behavioural
components

A

oke

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8
Q

Human behaviour = (Skinner, 1987)

A

“is possibly the most difficult subject ever submitted to scientific analysis”

Yet standard methods to study it are remarkably simple: questionnaires, tests, and small scale experiments

dus concept = complicated, methods zijn (te?) simpel

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9
Q

hoe noemen we de deze era voor social sciences

A

The golden age of social science

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10
Q

twitter experiment segregation

A

Example of a polarized and segregated network on Twitter. The network visualizes retweets of political hashtags from the 2010 US midterm elections. The nodes represent Twitter users and there is a directed edge from node i to node j if user j retweeted user i. Colors represent political preference: red for conservatives and blue for progressives

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11
Q

wat liet twitter zien

A
  1. faster connections between people
  2. but these contacts have the tendency of polarization
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12
Q

variables are…

A

abstract entities!!! they are made up.

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13
Q

phenomena=

A

patterns in data, robust features of the world

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14
Q

data =

A

representations of observations

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15
Q
  • Observation example:
    “Pete correctly solved IQ
    test item 36”
  • Representation: the row
    that represents Pete has a
    1 in the column that
    represents the IQ item
  • Typically, data are
    structured in rows and
    columns, i.e., in a
    spreadsheet
A

oke

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16
Q

rows =

A

cases

17
Q

columns =

A

represent features/properties/attributes

18
Q

verschil data en phenomena

A

Phenomena are not themselves data! Rather, phenomena are evidenced by patterns in the data. Because psychology is very complex, we often need advanced statistical
models to “see” the patterns data = observations. phenomena are robust, does not matter what kind of methods you applied (where, what year etc).

19
Q

voorbeeld phenomena

A

For instance, the positive
manifold of intelligence,
the robust correlation
between insomnia and
depression, the effect of
time pressure on
accuracy

20
Q

positive manifold =

A

refers to the fact that scores on cognitive assessment tend to correlate very highly with each other, indicating a common latent dimension that is very strong.

21
Q

explanatory theory=

A

is a set of principles that aims to explain phenomena

It describes a world in which the phenomena would follow “as a matter of course”

22
Q

Coming up with a good theory is a creative act,
but it can be systematized and practiced
Ideally, in behavioral data science we are after
mathematically formulated models

A

oke

23
Q

dus verschil theory phenomena and data?

A

theories explain phenomena, and phenomena are evidenced by the data

24
Q

Identical twins’ cognitive test scores are more similar
than those of fraternal twins. This feature is best represented as
a) data
b) a phenomenon
c) an explanatory theory

A

b

25
Q

lexical decision task=

A
  • Participants have to decide whether a letter string
    is a word (e.g., tango) or a nonword (e.g., drapa).
  • Participants usually decide by pressing a
    keyboard key with their index fingers.
  • Participants may judge hundreds or even
    thousands of letter strings in a single session.
  • Usually the stimulus set contains 50% words.
  • Performance on this task is supposed to
    measure the ease with which lexical
    representations are activated from memory.
  • For instance, performance is better for high-
    frequency words (e.g., cat) than for low-
    frequency words (e.g., feline).
  • Participants are usually told to do this as
    quickly and accurately as possible.
  • Key dependent variables of interest are
    response time (RT) and accuracy (proportion
    correct responses).
26
Q

lexical decisions task resultaten

A

older adults are slower than younger adults (na 30 ongeveer)

27
Q

how is this result explained on ldt

A

This decrease of response speed is explained by
the “general slowing” hypothesis, which says that
all cognitive processes operate more slowly in
older adults.

Maybe age-related demyelinization harms basic
neural transmission speed?

28
Q

wat als je alleen kijkt naar de response tijd

A

the speed-accuracy trade off wordt dan geen rekening mee gehouden -> daarom moet je ook kijken naar hoeveelheid goed!
dus niet alleen kijken naar response time!!1 dat zou bias zijn voor mensen die rustiger aan doen met betere accuracy.

dus dan process model gebruiken -> bv ratcliffs diffusion model

29
Q

ratcliff diffusion model

A
  • A model that describes how
    noisy evidence is accumulated
    over time.
  • A model that describes the data
    from simple decision making
    experiments (dus gaat over een paar seconden decision making)
  • A model that allows manifest
    behavior to be decomposed into
    latent psychological processes.
30
Q

diffusion process in action

A

door raam licht little particles -> voorbeeld van een random walk.

31
Q

wat is sequential sampling en drift rate

A
  • In the model, noisy information is accumulated over time (=sequential sampling).
  • The deterministic or signal component of this
    noisy process is called the drift rate.
32
Q

stel dat je een woord ziet

A

dan is er alsnog randomheid, maar dan ga je al snel naar een decisison. bij een lastig woord is het moeilijker en dan is er eerst meer randomheid.

33
Q

what do you assess in the model

A

the distance of the boundary to the word to the red line.

34
Q

waar leidt repeated draws to

A

Repeated draws from the underlying lexical
dimension drive a noisy accumulation of
evidence.
After some time the accumulated evidence
reaches a predetermined threshold amount,
and the corresponding response is initiated.