The Mnemosyne team of the Inria centre of the University of Bordeaux (France) is looking for a talented postdoctoral fellow with confirmed competences in the domain of Machine Learning for the development of a modeling framework of Metacognition . Metacognition is the cognitive process by which, instead of just learning to associate a response or a behavior with a situation, animals (and mainly primates) monitor the functioning (and particularly errors) of simple cognitive processes, learn to inhibit automatic responses and promote instead contextually appropriate behavioral rules. Better understanding and modeling this process is important for several reasons. In cognitive neuroscience , it paves the way to exploring higher cognitive functions like reasoning, imagination and other kinds of deliberation-based thoughts. In Artificial Intelligence , it stands on the same grounds as Generative AI and proposes different processes and algorithms that might remedy several weaknesses of GenAI and suggest innovative brain-inspired extensions. Located in Bordeaux (France), the role of the postdoctoral fellow to be recruited is to participate to a research program, under the following axes: Axis 1: Specification of Metacognition and its main computational mechanisms: Metacognition is generally described through three main mechanisms: (i) the possibility to monitor cues indicating difficulties in the process of problem solving (errors or conflicts between resources), in order to inhibit elementary default responses, (ii) working memory to keep in sustained activity the different aspects to be integrated (goals and subgoals, predictions, constraints) and (iii) cognitive flexibility corresponding to new goals and contextual rules that can be learned and integrated in the process of problem solving. Existing models (including from our team) indicate possible correspondence with cerebral circuitries and adaptive operations. Nevertheless, they are many and split these general mechanisms in different pieces which are not always consistent and may differ under several aspects. A major contribution will be to carry out a thorough analysis of these elements, to propose a synthesis associating both a precise description of the mechanisms and a map of their functional dependencies. Axis 2: Definition of relevant tasks in the domain of visual reasoning: Although many standard tasks have been defined and shared for simple sensorimotor control, it is not yet the case for cognitive control, generally corresponding to much more complex behaviors. A variety of tasks have been proposed in models evoked above but they differently integrate fundamental constituents such as hierarchical and temporal dependencies. In a similar view of standardization as in the axis above, the goal will be consequently to enumerate properties that have to be assessed when developing such metacognitive models and propose or design corresponding tasks. Subsequently, the postdoctoral fellow will work on integrating the insights from Axis 1 and task definitions in this Axis, with an architecture that integrates selected mechanisms from the different frameworks, particularly under the perspective of extending and evaluating models proposed in our team with novel properties. Axis 3: Organization of an international network of collaboration on the topic: We have already begun to identify and contact international (mainly European) teams working on the topic and willing to contribute to the elaboration of such a roadmap, toward more ambitious international projects. A corresponding goal will be to interact with these partners and to help with the preparation of such projects. This postdoc position is proposed for 18 to 24 months, preferably starting on November 1 st , 2025 and will be located in the Mnemosyne team, in Bordeaux, France. Please send CV + motivation letter to [ mailto:Frederic.Alexandre@inria.fr | Frederic.Alexandre@inria.fr ] for preliminary contact and interview. Deadline for application: June 1 st , 2025 References: Dagar, S., Alexandre, F., & Rougier, N. (2022). From Concrete to Abstract Rules: A Computational Sketch. In M. Mahmud, J. He, S. Vassanelli, A. van Zundert, & N. Zhong (Eds.), Brain Informatics . Springer International Publishing. [ https://doi.org/10.1007/978-3-031-15037-1_2 | https://doi.org/10.1007/978-3-031-15037-1_2 ] Kruijne, W., Bohte, S. M., Roelfsema, P. R., & Olivers, C. N. L. (2020). Flexible Working Memory Through Selective Gating and Attentional Tagging. Neural Computation , 33 (1), 1–40. [ https://doi.org/10.1162/neco_a_01339 | https://doi.org/10.1162/neco_a_01339 ] van den Berg, A. R., Roelfsema, P. 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[ https://doi.org/10.1016/j.cobeha.2014.10.007 | https://doi.org/10.1016/j.cobeha.2014.10.007 ] Miller, K., Eckstein, M., Botvinick, M., & Kurth-Nelson, Z. (2024). Cognitive model discovery via disentangled RNNs. Advances in Neural Information Processing Systems , 36 . -- Frederic A LEXANDRE