How do we use computational
models of cognitive processes?
Tom Stafford
Department of Psychology
University of Sheffield
Redwood Center, UC Berkeley, 19th March 2010
Application of computational and systems neuroscience models to acting
and action-learning
Stafford, T. & Gurney, K.N. (2007), Biologically constrained action
selection improves cognitive control in a model of the Stroop task,
Phil.Trans. of the Royal Society B:, 362 (1485), 1671-1684.
A reintepretation of BG functional anatomy
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of action selection in the basal ganglia. I. A new functional anatomy.
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Against the RPE hypothesis of DA function
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a role in discovering novel actions? Nature Reviews Neuroscience, 7(12),
967.
What algorithms can discover, and store for re-use, novel actions?
EU FP7 Funding : 'Intrinsically Motivated Cumulative Learning in Versatile
Robots' (Principal: Gianluca Baldassarre, ISTCCNR, Rome)
How do we use computational
models of cognitive processes?
Tom Stafford
Department of Psychology
University of Sheffield
Redwood Center, UC Berkeley, 19th March 2010
What is the point of computational modelling?
“...A rather low brow enterprise” (Crick, 1989)
Models cannot tell us anything about the
world... nor can they provide new information
about brain organisation or function
(Segalowtiz & Bernstein, 1997)
F. Crick, The recent excitement about neural networks. Nature 337, 129 (1989).
S. Segalowitz and D. Bernstein, Neural networks and neuroscience: What are
connectionist simulations good for, in The future of the cognitive revolution.,
(Oxford University Press, 1997)
Tritonia = a kind of marine mollusc
A scheme for categorising the
purposes of computational
modelling (Stafford, 2009)
Stafford, T. (2009). What use are computational models of cognitive
processes? In J. Mayor, N. Ruh & K. Plunkett (eds.) Connectionist Models of
Behavior and Cognition II: Proceedings of the 11th Neural Computation and
Psychology Workshop (pp 265-274). World Scientific.
Three claims:
1. There are many purposes for which
you might build a computational model
2. You should say which of these
motivates your model building
3. The best kind of reasons are those
relating to explanations: using
correspondences between model parts to
make or test predictions which bear on
real-world entities
Assumption: modelling as a kind of theory construction
Model purposes (Stafford, 2009)
Exploratory
capacity
data fitting
biological plausibility
reinterpretation
problem definition
Analysis
Integration
Explanation
Understanding explanation using
the “modelling is just tautology”
accusation (Stafford, 2009)
1+2=3
Model purposes (Stafford, 2009)
Exploratory
capacity
data fitting
biological plausibility
reinterpretation
problem definition
Analysis
Integration
Explanation
prediction
testing
sufficiency
existence proof
insufficiency
Understanding explanation using
the “modelling is just tautology”
accusation
1+2=3
“Models aid explanation in the same way as
mathematics: by enhancing our perception
beyond the horizon of individual reason and
intuition”
An empirical investigation into how modelling
work is presented to the scientific community
Aims:
1. Test of the framework proposed by
Stafford(2009)
2. Discover what makes modelling work
accessible to the non-modelling
community of experimentalists/theorists
An empirical investigation into how modelling
work is presented to the scientific community
Objectives:
1. Test if and how this scheme applies to
successful (i.e. highly cited) modelling
papers
2. Identify practices associated with highcitation rates
http://experimentalphilosophy.typepad.com/
http://x-phi.org/
Corpus:
Web of Science searches, sorted by most highly cited for:
1. Neural Computation
2. Connection Science
3. Publication Name=(nature) AND Topic=(computational)
AND Topic=(neuroscience OR psychology)
4. Publication Name=(nature neuroscience) AND
Topic=(computational)
5. Publication Name=(cognitive science) AND
Topic=(model)
and
Papers from the 11th NCPW Conference
Most highly cited papers in selected journals
Question 1: Is making explicit your purpose for
building a model associated with publication in
quality journals and/or higher citation counts?
Assumption: Because you can build models for
lots of different reasons, if you don't say why
you are building a model it cannot be as easily
assessed or used by the non-modelling
community
Mostly, but not always, highly cited papers are
explicit about the purpose of model building
Mean citations not higher for papers which make
explicit their purpose (probably)
Question 2: What purposes are associated with
highly cited papers?
Exploratory
Nat
Conn.
Nature Neuro Cog Sci Sci
Capacity
2
5
4
18
Data fitting
0
1
6
0
Biol. plausibility
0
0
1
2
Reinterpretation
1
0
1
0
Problem-definition
0
0
1
1
Neural NCPW1
Comp
1
22
17
0
6
0
5
0
2
0
0
TOTAL
68
13
8
4
2
95
Analysis
2
1
2
0
0
2
7
7
Integrative
1
5
5
7
3
6
27
27
1
0
2
0
1
4
0
7
2
0
3
6
3
0
0
1
2
3
0
2
1
0
0
0
0
3
1
2
0
0
13
9
17
2
3
44
Explanatory
prediction
testing
sufficiency
existence proof
insufficiency
Exploratory
Nat
Conn.
Nature Neuro Cog Sci Sci
Capacity
2
5
4
18
Data fitting
0
1
6
0
Biol. plausibility
0
0
1
2
Reinterpretation
1
0
1
0
Problem-definition
0
0
1
1
Neural NCPW1
Comp
1
22
17
0
6
0
5
0
2
0
0
TOTAL
68
13
8
4
2
95
Analysis
2
1
2
0
0
2
7
7
Integrative
1
5
5
7
3
6
27
27
1
0
2
0
1
4
0
7
2
0
3
6
3
0
0
1
2
3
0
2
1
0
0
0
0
3
1
2
0
0
13
9
17
2
3
44
Explanatory
prediction
testing
sufficiency
existence proof
insufficiency
Exploratory
Nat
Conn.
Nature Neuro Cog Sci Sci
Capacity
2
5
4
18
Data fitting
0
1
6
0
Biol. plausibility
0
0
1
2
Reinterpretation
1
0
1
0
Problem-definition
0
0
1
1
Neural NCPW1
Comp
1
22
17
0
6
0
5
0
2
0
0
TOTAL
68
13
8
4
2
95
Analysis
2
1
2
0
0
2
7
7
Integrative
1
5
5
7
3
6
27
27
1
0
2
0
1
4
0
7
2
0
3
6
3
0
0
1
2
3
0
2
1
0
0
0
0
3
1
2
0
0
13
9
17
2
3
44
Explanatory
prediction
testing
sufficiency
existence proof
insufficiency
Exploratory
Nat
Conn.
Nature Neuro Cog Sci Sci
Capacity
2
5
4
18
Data fitting
0
1
6
0
Biol. plausibility
0
0
1
2
Reinterpretation
1
0
1
0
Problem-definition
0
0
1
1
Neural NCPW1
Comp
1
22
17
0
6
0
5
0
2
0
0
TOTAL
68
13
8
4
2
95
Analysis
2
1
2
0
0
2
7
7
Integrative
1
5
5
7
3
6
27
27
1
0
2
0
1
4
0
7
2
0
3
6
3
0
0
1
2
3
0
2
1
0
0
0
0
3
1
2
0
0
13
9
17
2
3
44
Explanatory
prediction
testing
sufficiency
existence proof
insufficiency
A good fit is not enough
Roberts & Pashler (2000)
How Persuasive is a good
fit: a Comment on Theory
Testing. Psychological
Review, 107(2), 358-367.
Exploratory
Nat
Conn.
Nature Neuro Cog Sci Sci
Capacity
2
5
4
18
Data fitting
0
1
6
0
Biol. plausibility
0
0
1
2
Reinterpretation
1
0
1
0
Problem-definition
0
0
1
1
Neural NCPW1
Comp
1
22
17
0
6
0
5
0
2
0
0
TOTAL
68
13
8
4
2
95
Analysis
2
1
2
0
0
2
7
7
Integrative
1
5
5
7
3
6
27
27
1
0
2
0
1
4
0
7
2
0
3
6
3
0
0
1
2
3
0
2
1
0
0
0
0
3
1
2
0
0
13
9
17
2
3
44
Explanatory
prediction
testing
sufficiency
existence proof
insufficiency
Exploratory
Nat
Conn.
Nature Neuro Cog Sci Sci
Capacity
2
5
4
18
Data fitting
0
1
6
0
Biol. plausibility
0
0
1
2
Reinterpretation
1
0
1
0
Problem-definition
0
0
1
1
Neural NCPW1
Comp
1
22
17
0
6
0
5
0
2
0
0
TOTAL
68
13
8
4
2
95
Analysis
2
1
2
0
0
2
7
7
Integrative
1
5
5
7
3
6
27
27
1
0
2
0
1
4
0
7
2
0
3
6
3
0
0
1
2
3
0
2
1
0
0
0
0
3
1
2
0
0
13
9
17
2
3
44
Explanatory
prediction
testing
sufficiency
existence proof
insufficiency
Exploratory
Nat
Conn.
Nature Neuro Cog Sci Sci
Capacity
2
5
4
18
Data fitting
0
1
6
0
Biol. plausibility
0
0
1
2
Reinterpretation
1
0
1
0
Problem-definition
0
0
1
1
Neural NCPW1
Comp
1
22
17
0
6
0
5
0
2
0
0
TOTAL
68
13
8
4
2
95
Analysis
2
1
2
0
0
2
7
7
Integrative
1
5
5
7
3
6
27
27
1
0
2
0
1
4
0
7
2
0
3
6
3
0
0
1
2
3
0
2
1
0
0
0
0
3
1
2
0
0
13
9
17
2
3
44
Explanatory
prediction
testing
sufficiency
existence proof
insufficiency
Conclusions
1. Most models are built for exploratory rather than
explanatory purposes
2. Highly cited modelling papers are built for a diversity of
reasons
3. Importance of modelling for providing theory integration
and novel frameworks unanticipated by Stafford (2009)
4. Importance of modelling providing new
methods/techniques unanticipated by Stafford (2009)
Better quality journals have higher (but still <0.5)
proportion of 'explanatory' models
“Advice to the young modeller”?
Anything goes?
Improvements to the method
Double, blind, rating of papers
Analysis of modelling papers cited
outside of modelling journals (i.e. by experimentalists)
Analysis of papers which combine modelling and
experimental work from the same lab
Analysis of modelling papers which built on / test
existing models
THE END
t.stafford@shef.ac.uk
“The [theorist] will endeavor not to show us a
commonplace photograph of life, but to give us
a presentment of it which shall be more
complete, more striking, more cogent than
reality itself. To tell everything is out of the
question; it would require at least a volume for
each day to enumerate the endless
insignificant
incidents which crowd our existence. A choice
must be made—and this is the first blow to the
theory of ‘the whole truth’.”
- Guy de Maupassant
"I also suspect that within most modellers a
frustrated mathematician is trying to unfold his
wings. It is not enough to make something that
works. How much better if it can be shown to
embody some powerful general principle for
handling information, expressible in a deep
mathematical form, if only to give an air of
intellectual respectability to an otherwise
rather
low-brow enterprise.”
- Crick (1989) “The recent excitement about
neural networks” Nature, 337, 129-132