Post-Doctorant F/H Metacognition and Predictive Coding in Bio-Realistic Artificial Intelligence Networks Ajouter aux favoris

Contexte et atouts du poste

About the centre:

The Inria center at the University of Bordeaux is one of the nine Inria centers in France and has

about twenty research teams. The Inria centre is a major and recognized player in the field of

digital sciences. It is at the heart of a rich R&D and innovation ecosystem: highly innovative

SMEs, large industrial groups, competitiveness clusters, research and higher education players,

laboratories of excellence, technological research institute...

Context:

Every year Inria International Relations Department has a few postdoctoral positions in order

to support Inria international collaborations.

The postdoctoral contract will have a duration of 24 months. The default start date is

November 1st, 2026 and not later than January, 1st 2027. The postdoctoral fellow will be

recruited by one of the Inria Centres in France but it is recommended that the time is shared

between France and the partner's country (please note that the postdoctoral fellow has to

start his/her contract being in France and that the visits have to respect Inria rules for

missions).

Mission confiée

Candidates for postdoctoral positions are recruited after the end of their Ph.D. or after a first

post-doctoral period: the latest eligible Ph.D. defense date will vary accordingly

(approximately between November 1, 2023 and January 1, 2024).

In order to encourage mobility, the postdoctoral position must take place in a scientific

environment that is truly different from the one of the Ph.D. (and, if applicable, from the

position held since the Ph.D.); particular attention is thus paid to French or international

candidates who obtained their doctorate abroad.

Principales activités

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.

The Mnemosyne Inria team in Bordeaux and the Machine Learning CWI group in Amsterdam

have a long experience in cognitive computational neuroscience and have launched a project,

MetaBrain, with the aim of specifying the computational mechanisms of metacognition, also

defining relevant tasks to assess the performance of metacognitive models. In this perspective,

the two teams have recently begun considering how predictive coding, a major biologically

founded model of cortical learning and functioning, could be used to design metacognitive

functions (Brucklacher et al., 2025; Dora et al., 2021; Fontaine et al., 2025). Relying on their

past experience, both teams consider applications in the visual domain.

Accordingly, the role of the postdoctoral fellow to be recruited is to participate to the

MetaBrain project, under the following axes:

Axis 1: Specification of Metacognition and its main computational mechanisms:

When several elementary competences have been learned (possibility to associate a response

with a situation), the role of Metacognition (or cognitive control) is to control the

performances of the competences, the selection or the coordination of the most appropriate

ones depending on the context, or the creation of a new one. This 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.

Our two teams have already proposed models that address several aspects of these

mechanisms (Dagar et al., 2021; Kruijne et al., 2020; Nallapu et al., 2019; Van den Berg, 2023)

and are also aware of several other models in the literature proposing other candidate

mechanisms (see for example Alexander and Brown, 2015; Botvinick et al., 2001, Collins et al.,

2012; Domenech et al., 2015; Miller et al, 2024; VanRullen et al., 2021). All these models

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. It will be consequently important

be become familiar with these elements, and propose a synthesis associating both a precise

description of the mechanisms and a map of their functional dependencies.

Axis 2: Integrating the framework of predictive coding:

Most models of metacognition evoked above consider that the elementary competences are

learned and represented in the cortex. Generally, this is done in the models with a classical

associative learning, whereas an alternative to this type of learning, predictive coding, is

developed for some years, along with strong biological and theoretical justifications (Friston,

2003). This formalism has been also studied recently in our teams (Fontaine and Alexandre,

2025; Brucklacher et al., 2025; Dora et al., 2021). It is particularly interesting in the perspective

of metacognition, since it explicitly manipulates error signals and is a strong basis for

generative models. Manipulating precision, a major parameter in predictive coding, could be

an interesting way to address metacognition, generally described as a way to control hyperparameters

in learning models.

A major contribution of this work will be to study this formalism and propose solutions to

integrate it within a metacognitive perspective. A good way to assess such a dual model will

be to implement it within a task, as described below.

Axis 3: 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 mentioned above but they

differently integrate fundamental constituents such as hierarchical and temporal

dependencies. In a view of standardization, 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 Axes 1 and 2

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 teams with novel properties.

This postdoc position is proposed for 24 months, starting on November 1st, 2026 and will be

mainly located in the Mnemosyne team, in Bordeaux, France, with visits to the CWI partner in

Amsterdam. More specifically, in addition to regular visio-meetings, a 2-3-weeks stay in

Amsterdam each year will be organized.

Compétences

We are looking for a talented colleague with confirmed competences in the domain of

Machine Learning. Accordingly, programming skills related to such modeling approaches and

good knowledge about corresponding frameworks and platforms will be necessary. Past

experience in the domain of computational neuroscience and a strong interest in cognitive

neuroscience will be very positively considered.

Avantages

• Restauration subventionnée
• Transports publics remboursés partiellement
• Congés: 7 semaines de congés annuels + 10 jours de RTT (base temps plein) + possibilité d'autorisations d'absence exceptionnelle (ex : enfants malades, déménagement)
• Possibilité de télétravail partiel et aménagement du temps de travail
• Équipements professionnels à disposition (visioconférence, prêts de matériels informatiques, etc.)
• Prestations sociales, culturelles et sportives (Association de gestion des œuvres sociales d'Inria)
• Accès à la formation professionnelle
• Sécurité sociale

Rémunération

The grossly monthly salary will be 2788€ (before social security contribution and witholding tax)