The neuroscience
of habituated motivation
Alberto Masala (PI), SND, Univ. Paris Sorbonne
Daniel Andler, SND, Univ. Paris Sorbonne
Jean Denizeau, MBB, ICM, Univ. P. & M. Curie
Mathias Pessiglione, MBB, ICM, Univ. P. & M. Curie
Two interlocked aims
• to buttress the Aristotelian theory of
cultivation and motivational habituation
(apprenticeship) by providing a neuroscientific
account of its enabling mechanisms;
• to contribute to the integration of moral
philosophy and cognitive neuroscience in a
novel way, based on a recent turn in cognitive
science.
One question
• Given that
– the virtuous apprenticeship path is often either
not taken or soon abandoned
– the virtuous apprenticeship path is sometimes
taken
• What are the conditions under which
apprenticeship gets underway?
How we propose to answer
• Recently developed models of cognitive architecture—
predictive HBMs—seem precisely poised to provide at
least the beginnings of very different kind of answer, a
naturalistic answer.
• Our team combines the necessary competencies:
– Alberto Masala, philosophy (virtue theory)
– Jean Daunizeau, theoretical neuroscience (Bayesian
models)
– Mathias Pessiglione, biological neuroscience (motivation,
advanced skills acquisition)
– Daniel Andler, philosophy (models in cognitive science)
Overcoming the fragility of virtue
We want to discover, model and test factors that
would unlock our ability to cultivate complex
motivational habits.
Virtue as Skill (MacIntyre, Annas)
Tennis Player
Basic movements &
stereotypical attitudes
Good technique & grasp of
major priorities in a match
Superior technique & deep
understanding
Compassionate Teacher
Beginner
Awkard Interventions & basic
emphaty
Intermediate
Decent coping strategies &
good emphaty
Master
Resolute action & subtle
moral sensitivity
Fragility of motivational habituation
• Losing out to the forces of evil…
Egoism, hedonsim, social pressure, situationist scenarios
• ….and laziness (within non-moral mastery)
Stagnation of professionals
K. A. ERICSSON, The Influence of Experience and Deliberate Practice on the
Development of Superior Expert Performance, 2006
Routinized reactions of experts
M. BILALIC & al. “Inflexibility of experts”, 2008
Failure to transfer knowledge
D. BRANSFORD, L. SCHWARTZ, “Rethinking Transfer”, 1999
Default conservatism: narrow, stagnating
context locked skills built at minimal cost for
specific goals
Right Conditions
Investment in complexity: subtle, flexible
mastery and motivational habituation.
Motivational habituation in Learning Sciences
A.Masala « Mastering Wisdom », in A. Masala & J. Webber, eds.
From Personality to Virtue (OUP forthcoming)
Carl Bereiter & Marlene Scardamalia
Knowledge-Building Communities
Interest in learning and understanding
is instilled through gradual motivational
habituation
Cognitive learning sciences
& psychology of expertise
Our project: computational
neurosciences
More specific definition of the
right apprenticeship conditions:
Improvement over common
sense & phenomenology
Basic obstacles and biases
that stop the apprenticeship
process
The search for neurocognitive mechanisms &
the promise of predictive Bayesianism
• The Bayesian tsunami in cognitive science
• Combining the best of 2 worlds:
– classicism’s ability to deal with complex structured
representations
– connectionism’s ability to account for learning
• HBM: Hierarchical Bayesian Model
• Predictive coding: “Let me guess and if I’m wrong I’ll
make the necessary adjustments”
Friston, K. (2008). Hierarchical Models in the Brain. PLoS Computational Biology, 4(11)
Tenenbaum, J. B., Kemp, C., Griffiths, T. L., & Goodman, N. D. (2011). How to Grow a Mind:
Statistics, Structure, and Abstraction. Science, 331(6022), 1279–1285
HBMs at work
• Starting with the highest (deepest) layer, each layer
issues a prediction on the input of the next one below.
• When the last prediction hits the last layer, the error is
‘lazily’ retropropagated upward
• These ideas have been highly productive in the field of
visual perception, and are now being extended to a wide
variety of higher cognitive tasks, such as categorization,
predictions about everyday events and, importantly,
causal reasoning.
Mathys, C., Daunizeau, J., Friston, K. J., & Stephan, K. E. (2011). A Bayesian foundation
for for individual learning under uncertainty. Frontiers in Human Neuroscience, 5, 39
Understanding Aristotelian
apprenticeship: HBMs’ advantage
• HBMs embody conservatism: deep learning is costly.
• An HBM, exposed in the right conditions to the right
learning regimen, will undergo deep change.
• HBMs can account for inter-individual differences, as
well as temporal intra-individual differences in the
capacity for deep learning.
• HBMs seem to be able to handle in an integrated
manner the motivation and the knowledge
dimensions.
Clark, A. (2013). Whatever next? Predictive brains, situated agents, and the future of
cognitive science. Behavioral and Brain Sciences, 36(03), 181–204
Applying this framework
to our problem
• By no means a trivial task.
• Establishing a conceptual common ground,
between philosophy and neuroscience, from
which to attack this problem, requires a
considerable effort.
• At the same time, we want to provide an
‘existence proof’, showing on a special case
that it can be done and that it is profitable.
Applying this framework
to our problem, #1
• Our long-term goal: identify the subtle factors that mediate
the development of sophisticated skills, in their
interconnected cognitive and motivational dimensions.
• First step: Focus on motivation, and examine what
neuroscience and Bayesian modeling can tell us about akrasia
in normal subjects.
• Two-pronged attack:
– HBM modeling
– Psychological and neuropsychological evidence:
• What role do errors in expectations of effort / reward / delay play in
akrasia ?
• Is there a correlation between types/magnitudes of errors and proneness
to akrasia?
• What can we learn from motivation diminution disorders such as aboulia,
apathy, auto-activation deficit, athymormia or apraxia ?
Putting this intuition to work, #2
• A behavioral experiment along the following
lines, aiming at testing hypotheses bearing on
the conditions under which an akratic bias can
be overcome.
Evaluation task
Determination of preferences
Akratic bias (e.g. effort bias)
Choice task
volatility
Uncertainty
Redundant information
Choice task
Variation of statistical
structure in Learning
conditions
Has akratic bias disappeared?
Potential hurdles
1. The matter of levels: bridging the subpersonal
account of cognitive neuroscience and the
personal account of virtue theory, psychology
and phenomenology.
2. The blending of learning and motivation: despite
its being on the computational neuroscientist’s
horizon as a theoretical possibility, it is not as
yet part of the experimentalist’s mindset.
Help: ideas
1. Levels:
•
•
•
a nagging problem for the entire field of cognitive science
yet the neurocomputational tradition, from Helmholz to
contemporary frameworks, provides hints, both negative (e.g.
McCulloch & Pitts’ ‘logical calculus of the ideas immanent in
nervous activity’) and positive (e.g. Smolensky’s dual system in
“The proper treatment of connectionism”)
neuroscientists’ interest in consciousness puts the (distinct yet
connected) problem on their agenda
2. Learning/motivation: pragmatism and Friston’s “actionoriented predictive processing”:
•
inquiry as the activity of an engaged agent facing a problem and
seeking to restore a state of harmony around her.
Help: people
• the thriving cogsci community in Paris
– with a particularly strong interdisciplinary
tradition (Institute of Cognitive Studies, Ecole
normale supérieure; ICM Pitié, UPMC; etc.)
– a strong neurocomputational school, straddling
physics and neuroscience
– a strong school in philosophy of mind and
philosophy of cognitive science.