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Post-doctoral researcher/Lecturer
Humboldt-Universität zu Berlin Berlin School of Mind and Brain & Department of Philosophy Unter den Linden 6, 10117 Berlin Contact: ines.hipolito [at] hu-berlin.de |
Affiliate of the Theoretical Neurobiology Group Wellcome Centre for Human Neuroimaging University College London United Kingdom |
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I am a philosopher and dynamical systems theorist whose work ranges from considering the ontological status of representations and models in cognition and science to multi-scale modelling of cognition and neuroimaging datasets. More specifically, the nature, evaluation, testing and fitting of cognitive modelling, with prominence on Bayesian inferential models of brain and behaviour. From a formal perspective, I develop concepts, methods, and theories that comprise investigating cognitive agents as consistent with dynamical systems.
My PhD thesis challenged the traditional view of cognitive systems as reducing to information processors. Two crucial papers offer insights in this direction. The thesis advances a model of action that does not require an optimal control system. The model replaces, not only forward and inverse models with a generative model; but also motor plans (considered to be unrealistic due the required specificity of a plan and the huge number of degrees of freedom of the neuromuscular system), with predictions about proprioception.[1] Instead, perceptual-motor coordination are treated as coordinative structures of single units that operate by the same organizational principles as other non-equilibrium thermodynamic systems; more precisely, self-organization coordination by which different states are treated as dynamical patterns. The thesis also advanced a model of the brain that does away without traditional, fixed modular processors.[2] It uses the formalisms of Markov blankets and active inference to explain and show neuronal dynamics at multiple scales (single neurons, brain regions, and brain-wide networks). This treatment is based upon the canonical micro-circuitry used in empirical studies of effective connectivity, so has tremendous practical applications for neuroimaging. Especially when considered together with the mathematical formalisms we developed using Renormalisation Group[3], as well as with the framework we advance with variational Bayes and information geometry. [4] In my research, I leverage Variational Bayes, Dynamical Causal Modelling, and the Free Energy Principle to investigate to what extent cognition can be understood in terms of unfolding, dynamic interactions that adjust and adapt [7] [8]. |
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Philpapers Pubmed Scholar Academia
There is no fundamental difference between man [human] and [non-human] animals
in their ability to feel pleasure and pain, happiness, and misery
- Charles Darwin
in their ability to feel pleasure and pain, happiness, and misery
- Charles Darwin
