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Summary Lectures Behavioural Data Science - 7201703PXY
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Summary Lectures BehaviouralData Science
Lecture 1 – A Dialogue on Theories, Phenomena, and Data..................................................................3
Lecture 2 – Complexity and Network Models........................................................................................7
Lecture 3 – The New World of Behavioural Data...................................................................................8
Lecture 4 – Binary Classification............................................................................................................9
Lecture 5 – Bayesian Inference............................................................................................................13
Lecture 6 – The Ultimate Debate.........................................................................................................15
,
, Lecture 1 – A Dialogue on Theories, Phenomena, and
Data
Overview of the Lecture
Behavioural Data Science: Task and Scope
Interplay between data and theory
o Data
o Phenomena
o Theory
The role of mathematical modelling
What is Behavioural Data Science?
A multidisciplinary scientific field, a merge of statistical analysis, informatics, simulation,
mathematical reasoning, and new data registration techniques.
Understanding, Prediction, and Change
Understanding: construction of psychological theories to explain behaviour
Prediction: application of statistical models to predict behaviour
Change: development of interventions to change behaviour
Control predictable change with intervention, could be troublesome in
psychology
The Complexities of Human Behaviour
But standard methods to study human behaviour are remarkably simple: questionnaires, tests, and
small-scale experiments.
However, recently, new sources of data are being mined and these offer new ways of approaching
old questions.
The Architecture of the Data World
Data representation of observations
When data is represented in a table, rows represent cases, while columns represent
features/properties/attributions
Phenomena robust features of the world
Phenomena are themselves not data, phenomena are evidenced by patterns in the data.
We often need advanced statistical models to “see” the patterns
Theories a set of principles that aims to explain phenomena
You don’t try to explain the data, only the phenomena. You explain features of data.
An In-Depth Example: The Speed-Accuracy Trade-Off
Participants who are doing the lexical decision task are told to do this as quickly and accurately as
possible. They need to decide whether what they’re seeing is an existing word or a non-existing
word.
Global slowing Older adults are typically slower than young adults.
But… older people are more often correct. You need to look at the speed AND accuracy at the same
time in order to determine if there is actually a decline in speed, or that the priorities lie elsewhere
for older/younger people.
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