Understanding the Neuroscience of Habituated Motivation in Virtue Cultivation
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.
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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 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
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
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
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 Redundant information Variation of statistical structure in Learning conditions Uncertainty volatility Choice task 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 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.
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.
