The Perception, Neuroimaging, and Modeling lab (PI: Dr. Doby Rahnev, rahnevlab.gatech.edu) is hiring a postdoctoral fellow. The exact topic of research is flexible and could include investigating the neural and/or computational bases of perceptual decision making, metacognition, attention, expectation or learning. A special focus of the lab is how these processes are supported by large distributed brain networks. The Rahnev lab uses a wide range of methods such as fMRI, TMS, psychophysics, computational modeling and concurrent TMS-fMRI. The position is initially for 2 years with a possibility for extension. Candidates will be given the opportunity to conduct studies building on current lab research or developing their own projects ideas. The positions are available immediately. The Rahnev lab, at the Georgia Institute of Technology in Atlanta, has access to exceptional research facilities. The lab space is conveniently located just steps away from a 3T Prisma MRI scanner at the Center for Advanced Brain Imaging (CABI, cabiatl.com). The lab also houses its own TMS equipment and is pioneering the use of concurrent TMS-fMRI that allows TMS to be delivered inside the MRI scanner. Working in the Rahnev lab presents opportunities for collaborations across several Atlanta-based universities including Georgia Tech, Emory and Georgia State. Together, these universities have transformed Atlanta into a hub for psychological and neuroscience research with particular strengths in computational neuroscience, the study of special populations (disease, aging, children), ECoG, concurrent brain stimulation and brain recording, and animal research. Georgia Tech has an attractive campus in the heart of Atlanta, a large, vibrant, multicultural city that boasts impressive cultural, culinary, and entertainment opportunities. The Rahnev lab aims to create a supportive, fun and productive environment. We are especially interested in maintaining our already diverse team and therefore seek applications from qualified individuals from all demographics and backgrounds.

The University of Rochester’s Department of Brain and Cognitive Sciences seeks to hire an outstanding early-career candidate in the area of Human Cognition. Areas of study may center on any aspect of higher-level cognitive processes such as decision-making, learning and memory, concepts, language and communication, development, reasoning, metacognition, and collective cognition. We particularly welcome applications from candidates researching cognition in human subjects through behavioral, computational or neuroimaging methods. Successful candidates will develop a research program that establishes new collaborations within the department and across the university, and will also be part of a university-wide community engaged in graduate and undergraduate education.

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 1st, 2025 and will be located in the Mnemosyne team, in Bordeaux, France.

Metacognition has long been defined as “cognition about cognition”. One of its aspects is the error monitoring ability, which includes being aware of one’s own errors without external feedback. This ability is mostly investigated in two-alternative forced choice tasks, where the performance has all or none nature in terms of accuracy. The previous literature documents the effect of different influences on the error monitoring ability, such as working memory, feedback and sensorimotor involvement. However, these demonstrations fall short of generalizing to the real life scenarios where the errors often have a magnitude and a direction. To bridge this gap, recent studies showed that humans could keep track of the magnitude and the direction of their errors in temporal, spatial and numerical domains in two metrics: confidence and short-long/few-more judgements. This talk will cover how the documented effects that are obtained in the two alternative forced choices tasks apply to the temporal error monitoring ability. Finally, how magnitude and direction monitoring (i.e., confidence and short-long judgements) can be differentiated as the two indices of temporal error monitoring ability will be discussed.

Theories of consciousness are commonly grouped into "first-order" and "higher-order" families. As conventional wisdom has it, many more animals are likely to be conscious if a first-order theory is correct. But two recent developments have put pressure on the first/higher-order distinction. One is the argument (from Shea and Frith) that an effective global workspace mechanism must involve a form of metacognition. The second is Lau's "perceptual reality monitoring" (PRM) theory, a member of the "higher-order" family in which conscious sensory content is not re-represented, only tagged with a temporal index and marked as reliable. I argue that the first/higher-order distinction has become so blurred that it is no longer particularly useful. Moreover, the conventional wisdom about animals should not be trusted. It could be, for example, that the distribution of PRM in the animal kingdom is wider than the distribution of global broadcasting.

In this talk I will present an account of how an agent designed or evolved to be intelligent may come to enjoy subjective experiences. First, the agent is stipulated to be capable of (meta)representing subjective ‘qualitative’ sensory information, in the sense that it can easily assess how exactly similar a sensory signal is to all other possible sensory signals. This information is subjective in the sense that it concerns how the different stimuli can be distinguished by the agent itself, rather than how physically similar they are. For this to happen, sensory coding needs to satisfy sparsity and smoothness constraints, which are known to facilitate metacognition and generalization. Second, this qualitative information can under some specific circumstances acquire an ‘assertoric force’. This happens when a certain self-monitoring mechanism decides that the qualitative information reliably tracks the current state of the world, and informs a general symbolic reasoning system of this fact. I will argue that the having of subjective conscious experiences amounts to nothing more than having qualitative sensory information acquiring an assertoric status within one’s belief system. When this happens, the perceptual content presents itself as reflecting the state of the world right now, in ways that seem undeniably rational to the agent. At the same time, without effort, the agent also knows what the perceptual content is like, in terms of how subjectively similar it is to all other possible precepts. I will discuss the computational benefits of this architecture, for which consciousness might have arisen as a byproduct.

As Socrates said that "I know that I know nothing," our mind's function to be aware of our ignorance is essential for abstract and conceptual reasoning. However, the biological mechanism to enable such a hierarchical thought, or meta-cognition, remained unknown. In the first part of the talk, I will demonstrate our studies on the neural mechanism for metacognition on memory in macaque monkeys. In reality, awareness of ignorance is essential not only for the retrospection of the past but also for the exploration of novel unfamiliar environments for the future. However, this proactive feature of metacognition has been understated in neuroscience. In the second part of the talk, I will demonstrate our studies on the neural mechanism for prospective metacognitive matching among uncertain options prior to perceptual decision making in humans and monkeys. These studies converge to suggest that higher-order processes to self-evaluate mental state either retrospectively or prospectively are implemented in the primate neural networks.

A central issue in reinforcement learning (RL) is the ‘curse-of-dimensionality’, arising when the degrees-of-freedom are much larger than the number of training samples. In such circumstances, the learning process becomes too slow to be plausible. In the brain, higher cognitive functions (such as abstraction or metacognition) may be part of the solution by generating low dimensional representations on which RL can operate. In this talk I will discuss a series of studies in which we used functional magnetic resonance imaging (fMRI) and computational modeling to investigate the neuro-computational basis of efficient RL. We found that people can learn remarkably complex task structures non-consciously, but also that - intriguingly - metacognition appears tightly coupled to this learning ability. Furthermore, when people use an explicit (conscious) policy to select relevant information, learning is accelerated by abstractions. At the neural level, prefrontal cortex subregions are differentially involved in separate aspects of learning: dorsolateral prefrontal cortex pairs with metacognitive processes, while ventromedial prefrontal cortex with valuation and abstraction. I will discuss the implications of these findings, in particular new questions on the function of metacognition in adaptive behavior and the link with abstraction.

Computational and cognitive neuroscience techniques have made great strides towards describing the neural computations underlying perceptual inference and decision-making under uncertainty. These tools tell us how and why perceptual illusions occur, which brain areas may represent noisy information in a probabilistic manner, and so on. However, an understanding of the subjective, qualitative aspects of perception remains elusive: qualia, or the personal, intrinsic properties of phenomenal awareness, have remained out of reach of these computational analytic insights. Here, I propose that metacognitive computations, and the subjective feelings that go along with them, give us a solid starting point for understanding subjective experience in general. Specifically, perceptual metacognition possesses ontological and practical properties that provide a powerful and unique opportunity for studying the studying the neural and computational correlates of subjective experience using established tools of computational and cognitive neuroscience. By capitalizing on decades of developments in formal computational model comparisons as applied to the specific properties of perceptual metacognition, we are now in a privileged position to reveal new and exciting insights about how the brain constructs our subjective conscious experiences.