Understanding the Neuroscience of Habituated Motivation in Virtue Cultivation

 
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)
Basic movements &
stereotypical attitudes
Good technique & grasp of
major priorities in a match
Superior technique & deep
understanding
Awkard Interventions & basic
emphaty
Decent coping strategies &
good emphaty
Resolute action & subtle
moral sensitivity
 
Beginner
 
Intermediate
 
Master
 
Tennis Player
 
Compassionate Teacher
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
 
Investment in complexity
: subtle, flexible
mastery and motivational habituation.
 
Right Conditions
 
Motivational habituation in Learning Sciences
 
A.Masala « Mastering Wisdom », in A. Masala & J. Webber, eds.
From Personality to Virtue
 (OUP forthcoming)
 
 
Knowledge-Building Communities
 
Interest in learning and understanding
is instilled through gradual motivational
habituation
 
Carl Bereiter & Marlene Scardamalia
 
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 i
nter-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
Choice task
Redundant information
Uncertainty
volatility
Choice task
 
Determination of preferences
 
Akratic bias (e.g. effort bias)
 
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 “action-
oriented 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.
Slide Note
Embed
Share

Exploring the intersection of Aristotelian theory, cognitive neuroscience, and moral philosophy, this research delves into the conditions and mechanisms underlying the cultivation of virtuous habits through apprenticeship. By leveraging diverse expertise in philosophy, neuroscience, and cognitive science, the team aims to overcome the fragility of virtue and unlock the potential for developing complex motivational habits. Through a naturalistic approach, they seek to unravel the factors that shape the path from novice to master in various fields, challenging the forces of egoism and stagnation.


Uploaded on Aug 30, 2024 | 2 Views


Download Presentation

Please find below an Image/Link to download the presentation.

The content on the website is provided AS IS for your information and personal use only. It may not be sold, licensed, or shared on other websites without obtaining consent from the author. Download presentation by click this link. If you encounter any issues during the download, it is possible that the publisher has removed the file from their server.

E N D

Presentation Transcript


  1. 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

  2. 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.

  3. 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?

  4. 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)

  5. Overcoming the fragility of virtue We want to discover, model and test factors that would unlock our ability to cultivate complex motivational habits.

  6. Virtue as Skill (MacIntyre, Annas) Tennis Player Compassionate Teacher Basic movements & stereotypical attitudes Awkard Interventions & basic emphaty Beginner Good technique & grasp of major priorities in a match Decent coping strategies & good emphaty Intermediate Superior technique & deep understanding Resolute action & subtle moral sensitivity Master

  7. 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

  8. 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.

  9. 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

  10. Our project: computational neurosciences Cognitive learning sciences & psychology of expertise Basic obstacles and biases that stop the apprenticeship process More specific definition of the right apprenticeship conditions: Improvement over common sense & phenomenology

  11. 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

  12. 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

  13. 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

  14. 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.

  15. 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 ?

  16. 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.

  17. Evaluation task Determination of preferences Akratic bias (e.g. effort bias) Choice task Redundant information Variation of statistical structure in Learning conditions Uncertainty volatility Choice task Has akratic bias disappeared?

  18. 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.

  19. 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 Learning/motivation: pragmatism and Friston s action- oriented predictive processing : inquiry as the activity of an engaged agent facing a problem and seeking to restore a state of harmony around her. 2.

  20. Help: people the thriving cogsci community in Paris with a particularly strong interdisciplinary tradition (Institute of Cognitive Studies, Ecole normale sup rieure; ICM Piti , UPMC; etc.) a strong neurocomputational school, straddling physics and neuroscience a strong school in philosophy of mind and philosophy of cognitive science.

  21. FIN

Related


More Related Content

giItT1WQy@!-/#giItT1WQy@!-/#giItT1WQy@!-/#giItT1WQy@!-/#