Aging of cognitive processes: Modeling large data
Nemanja Vaci
Alpen-Adria University Klagenfurt
[email protected]
October 2016
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Introduction
Cognitive ageing
Increased age: decline of information processing speed (Park & Reuter-Lorenz,
2009)
(Salthouse, 2004)
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Introduction
Cognitive ageing
Everyday life: Motivation, task demand, surrounding, and increased
knowledge about the world (Salthouse, 2004)
Real-life skills: Domain-related knowledge and experience can
compensate for the age-related decline (Waldman & Avolio, 1993)
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Introduction
Model fitting
Adolphe Quetelet - Treatise on Man (Quetelet, 1835)
”Man is born, grows up, and dies, according to certain laws...”
Lehman - Age and Achievement (Lehman, 1953)
Creative people in different areas peak in the middle of the 30s,
after which they observe decline in creative process
Simonton model of career trajectories and landmarks (Simonton, 1977)
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Introduction
Forces of life
Two different forces in life: Development and Aging (Schroots, 2012)
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Introduction
Large data in chess
Chess: Set of cognitive skills in formalized environment
Archival approach: records of players’ current and previous ratings,
number of played games, gender, age, etc. (Howard, 2008)
German chess database: Modeling large database in psychology
(Vaci & Bilalić, 2016)
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Introduction
Is Age Cruel to Experts
Performance of chess players (Elo scores) over their age and whether
functions are positively altered by the overall ability of practitioners (Vaci,
Gula, & Bilalić, 2015)
German database (119,785) (Vaci, Gula, Bilalić, 2014)
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Questions
Raised questions
The cubic functions offer a better fit to the data than quadratic ones,
however, they bring identical problems to the effect interpretation
Gain better understanding how performance changes over the years,
and how expertise changes the process, however, current results are
not directly relatable to the cognitive processes
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Goal
Goals of the current work
Build bayesian hierarchical models behind chess expertise, that is
going to use comparable mathematical functions
More process dependent functions and better estimation of all
phases in the aging curve
Introduce second level model with the data from more basic cognitive
processes (knowledge, intuitive and deliberate decision making,
memory, and motivation)
How more basic functions influence the performance over lifetime
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First Level
Bayesian hierarchical model - current state
Pre-peak increase for the players born between 1970 and 1999
The mathematical functions:
Exponential: = α ∗ exp(β ∗ Age)
Power law: = α ∗ Ageβ
Logarithmic: = α ∗ log(β ∗ Age)
Linear: = α + β ∗ Age
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First Level
Bayesian hierarchical model - current state
We use R with rjags and R2jags package (Plummer, Stukalov, Denwood, 2016)
Updated dataset: 3,325,056 observations (161,346 players)
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First Level
Model comparison
Currently we are using DIC - deviance information criteria to compare
different models
Phase space of model comparison - all models are competing to
explain the data
Minimum description length principle - MDL (geometrical simplicity)
Predictive accuracy of the model
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First Level
Benefit of the new model
Better estimation:
Functions behind the data trends
Moments of the functions
Preserving effects of expertise - relation between development
and aging function
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Second Level
Second level model
Cross-sectional data (Van der Mass & Wagenmakers, 2005)
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Second Level
Second level model
Bayesian path analysis:
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Second Level
Benefit of the second level model
Combine trend data with the longitudinal data in one model
How more basic skills influence and change the performance over the
lifetime
First glimps into the expertise processes that preserve age-related
decline
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Conclusion
Conclusion - up to now
Expertise-related activity and practice influences life-long performance
curves
Indication that expertise preserves performance in later age
Elo scores are not following quadratic funtion and observes
stabilization of decline
Immediate activity delays the age-related decline
The domain-related activities and amount of practice invested
preserves performance in older age
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Conclusion
Perspectives - up from now
Better estimation of performance function across the age
Preserving effects of expertise, all the moments and steps of the
functions
Investigation how more basic processes contribute to the performance
in the case of chess experts
How these processes change age-related function
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Conclusion
Thank you for the attention
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Conclusion
Thank you for the attention
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Conclusion
Mixed-effect model
ypi = β0 + P0p + (β1 + P1p )Agei
+ β2 Agei2 + β3 Agei3 + β4 Gamesi + β5 StalePlay
+ β6 Agei Gamesi + β7 Agei2 Gamesi + β8 Agei3 Gamesi
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Conclusion
Age and activity effect
Expertise-related activity influences change of rating scores differently
2100
FIDE
1800
1600
2050
Rating
2000
Rating
German
1950
1900
1400
Games
30
1200
3
1000
1850
800
1800
1750
600
10
20
30
40
50
Age
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70
80
10
20
30
40
50
60
70
Age
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80
Conclusion
Cohort effects
1800
Rating
1500
1200
Cohort
1980-2007
Probability Density Function
900
1940-1980
1900-1940
0.09
0.06
0.03
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0
15
30
45
60
75
90
Age
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Conclusion
Activity and Age interaction
Rating
1400
2100
0
Number of games
per year
10 20 30 40 50 60
700
10
20
30
40
50
60
70
80
Age
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Conclusion
Cohort effects
2400
2300
Rating
2200
2100
Formula
DWZ
2000
FIDE
1900
1800
1700
1600
10
20
30
40
50
60
70
80
Age
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