Learning Categories by Creating New Descriptions (Goldstone)

Robert Goldstone, Indiana University, February 1, 2024

ABSTRACT: In Bongard problems, problem-solvers must come up with a rule for distinguishing visual scenes that fall into two categories. Only a handful of examples of each category are presented. This requires the open-ended creation of new descriptions. Physical Bongard Problems (PBPs) require perceiving and predicting the spatial dynamics of the scenes. We compare the performance of a new computational model (PATHS) to human performance. During continual perception of new scene descriptions over the course of category learning, hypotheses are constructed by combining descriptions into rules for distinguishing the categories. Spatially or temporally juxtaposing similar scenes promotes category learning when the scenes belong to different categories but hinders learning when the similar scenes belong to the same category.

Robert Goldstone is a Distinguished Professor in the Department of Psychological and Brain Sciences and Program in Cognitive Science at Indiana University. His research interests include concept learning and representation, perceptual learning, educational applications of cognitive science, and collective behavior.

Goldstone, R. L., Dubova, M., Aiyappa, R., & Edinger, A. (2023). The spread of beliefs in partially modularized communities. Perspectives on Psychological Science, 0(0). https://doi.org/10.1177/17456916231198238

Goldstone, R. L., Andrade-Lotero, E., Hawkins, R. D., & Roberts, M. E. (2023). The emergence of specialized roles within groups. Topics in Cognitive Science, DOI: 10.1111/tops.12644.

Weitnauer, E., Goldstone, R. L., & Ritter, H. (2023). Perception and simulation during concept learning. Psychological Review, https://doi.org/10.1037/rev0000433.

« Apprentissage continu et contrôle cognitif » (Alexandre)

Frédérique Alexandre , Inria, Bordeaux, 14-Dec 2023

Résumé : J’explore la différence entre l’efficacité de l’apprentissage humain et celle des grands modèles de langage en termes de temps de calcul et de coûts énergétiques. L’étude se focalise sur le caractère continu de l’apprentissage humain et les défis associés, tels que l’oubli catastrophique. Deux types de mémoires, la mémoire de travail et la mémoire épisodique, sont examinés. Le cortex préfrontal est décrit comme essentiel pour le contrôle cognitif et la mémoire de travail, tandis que l’hippocampe est central pour la mémoire épisodique. Alexandre suggère que ces deux régions collaborent pour permettre un apprentissage continu et efficace, facilitant ainsi la pensée et l’imagination.

Abstract: I explore the difference between the efficiency of human learning and that of large language models in terms of computational time and energy costs. The study focuses on the continuous nature of human learning and associated challenges, such as catastrophic forgetting. Two types of memory, working memory and episodic memory, are examined. The prefrontal cortex is described as essential for cognitive control and working memory, while the hippocampus is central for episodic memory. Alexandre suggests that these two regions collaborate to enable continuous and effective learning, thus facilitating thought and imagination.

Frédéric Alexandre est directeur de recherche à l’Inria et dirige l’équipe Mnemosyne à Bordeaux, spécialisée en Intelligence Artificielle et Neurosciences Computationnelles. L’équipe étudie les différentes formes de mémoire cérébrale et leur rôle dans des fonctions cognitives telles que le raisonnement et la prise de décision. Ils explorent la dichotomie entre mémoires explicites et implicites et comment elles interagissent. Leurs projets récents s’étendent de l’acquisition du langage à la planification et la délibération. Les modèles créés sont validés expérimentalement et ont des applications médicales, industrielles, ainsi qu’en sciences humaines, notamment en éducation, droit, linguistique, économie, et philosophie.

Frédéric Alexandre. A global framework for a systemic view of brain modeling. Brain Informatics, 2021, 8 (1),

Snigdha Dagar, Frédéric Alexandre, Nicolas P. Rougier. From concrete to abstract rules : A computational sketch. 15th International Conference on Brain Informatics, Jul 2022.

Randa Kassab, Frédéric Alexandre. Pattern Separation in the Hippocampus: Distinct Circuits under Different Conditions. Brain Structure and Function, 2018, 223 (6), pp.2785-2808.

Hugo Chateau-Laurent, Frédéric Alexandre. The Opportunistic PFC: Downstream Modulation of a Hippocampus-inspired Network is Optimal for Contextual Memory Recall. 36th Conference on Neural Information Processing System, Dec 2022.

Pramod Kaushik, Jérémie Naudé, Surampudi Bapi Raju, Frédéric Alexandre. A VTA GABAergic computational model of dissociated reward prediction error computation in classical conditioning. Neurobiology of Learning and Memory, 2022, 193 (107653),

Falsifying the Integrated Information Theory of Consciousness (Hanson)

Jake R Hanson, Sr. Data Scientist, Astrophysics, 07-Dec 2023

VIDEO

Abstract: Integrated Information Theory is a prominent theory of consciousness in contemporary neuroscience, based on the premise that feedback, quantified by a mathematical measure called Phi, corresponds to subjective experience. A straightforward application of the mathematical definition of Phi fails to produce a unique solution due to unresolved degeneracies inherent in the theory. This undermines nearly all published Phi values to date. In the mathematical relationship between feedback and input-output behavior in finite-state systems automata theory shows that feedback can always be disentangled from a system’s input-output behavior, resulting in Phi=0 for all possible input-output behaviors. This process, known as “unfolding,” can be accomplished without increasing the system’s size, leading to the conclusion that Phi measures something fundamentally disconnected from what could ground the theory experimentally. These findings demonstrate that IIT lacks a well-defined mathematical framework and may either be already falsified or inherently unfalsifiable according to scientific standards.

Jake Hanson is a Senior Data Scientist at a financial tech company in Salt Lake City, Utah. His doctoral research in Astrophysics from Arizona State University focused on the origin of life via the relationship between information processing and fundamental physics. He demonstrated that there were multiple foundational issues with IIT, ranging from poorly defined mathematics to problems with experimental falsifiability and pseudoscientific handling of core ideas.

Hanson, J.R., & Walker, S.I. (2019). Integrated information theory and isomorphic feed-forward philosophical zombies. Entropy, 21.11, 1073.

Hanson, J.R., & Walker, S.I. (2021). Formalizing falsification for theories of consciousness across computational hierarchies.Neuroscience of Consciousness, 2021.2, niab014.

Hanson, J.R., & Walker, S.I. (2021). Falsification of the Integrated Information Theory of Consciousness. Diss. Arizona State University, 2021.

Hanson, J.R., & Walker, S.I. (2023). On the non-uniqueness problem in Integrated Information Theory. Neuroscience of Consciousness, 2023.1, niad014.

Rethinking the Physical Symbol Systems Hypothesis (Rosenbloom)

Paul Rosenbloom , Computer Science, USC, 12-Oct 2023

VIDEO

ABSTRACT: It is now more than a half-century since the Physical Symbol Systems Hypothesis (PSSH) was first articulated as an empirical hypothesis. More recent evidence from work with neural networks and cognitive architectures has weakened it, but it has not yet been replaced in any satisfactory manner. Based on a rethinking of the nature of computational symbols – as atoms or placeholders – and thus also of the systems in which they participate, a hybrid approach is introduced that responds to these challenges while also helping to bridge the gap between symbolic and neural approaches, resulting in two new hypotheses, one – the Hybrid Symbol Systems Hypothesis (HSSH) – that is to replace the PSSH and the other focused more directly on cognitive architectures. This overall approach has been inspired by how hybrid symbol systems are central in the Common Model of Cognition and the Sigma cognitive architectures, both of which will be introduced – along with the general notion of a cognitive architecture – via “flashbacks” during the presentation.

Paul S. Rosenbloom is Professor Emeritus of Computer Science in the Viterbi School of Engineering at the University of Southern California (USC). His research has focused on cognitive architectures (models of the fixed structures and processes that together yield a mind), such as Soar and Sigma; the Common Model of Cognition (a partial consensus about the structure of a human-like mind); dichotomic maps (structuring the space of technologies underlying AI and cognitive science); “essential” definitions of key concepts in AI and cognitive science (such as intelligence, theories, symbols, and architectures); and the relational model of computing as a great scientific domain (akin to the physical, life and social sciences).

Rosenbloom, P. S. (2023). Rethinking the Physical Symbol Systems Hypothesis. In Proceedings of the 16^th International Conference on Artificial General Intelligence (pp. 207-216). Cham, Switzerland: Springer.

Laird, J. E., Lebiere, C. & Rosenbloom, P. S. (2017). A Standard Model of the Mind: Toward a Common Computational Framework across Artificial Intelligence, Cognitive Science, Neuroscience, and Robotics. AI Magazine, 38, 13-26.

Rosenbloom, P. S., Demski, A. & Ustun, V. (2016). The Sigma cognitive architecture and system: Towards functionally elegant grand unification. Journal of Artificial General Intelligence, 7, 1-103.

Rosenbloom, P. S., Demski, A. & Ustun, V. (2016). Rethinking Sigma’s graphical architecture: An extension to neural networks. Proceedings of the 9^th Conference on Artificial General Intelligence (pp. 84-94).

LLMs are impressive but we still need grounding to explain human cognition (Bergen)

Benjamin Bergen, Cognitive Science, UCSD, 14 sept 2023

VIDEO

ABSTRACT: Human cognitive capacities are often explained as resulting from grounded, embodied, or situated learning. But Large Language Models, which only learn on the basis of word co-occurrence statistics, now rival human performance in a variety of tasks that would seem to require these very capacities. This raises the question: is grounding still necessary to explain human cognition? I report on studies addressing three aspects of human cognition: Theory of Mind, Affordances, and Situation Models. In each case, we run both human and LLM participants on the same task and ask how much of the variance in human behavior is explained by the LLMs. As it turns out, in all cases, human behavior is not fully explained by the LLMs. This entails that, at least for now, we need grounding (or, more accurately, something that goes beyond statistical language learning) to explain these aspects of human cognition. I’ll conclude by asking but not answering a number of questions, like, How long will this remain the case? What are the right criteria for an LLM that serves as a proxy for human statistical language learning? and, How could one tell conclusively whether LLMs have human-like intelligence?

Ben Bergen is Professor of Cognitive Science at UC San Diego, where he directs the Language and Cognition Lab. His research focuses on language processing and production with a special interest in meaning. He’s also the author of ‘Louder than Words: The New Science of How the Mind Makes Meaning‘ and ‘What the F: What Swearing Reveals about Our Language, Our Brains, and Ourselves.’

Trott, S., Jones, C., Chang, T., Michaelov, J., & Bergen, B. (2023). Do Large Language Models know what humans know? Cognitive Science 47(7): e13309.

Chang, T. & B. Bergen (2023). Language Model Behavior: A Comprehensive Survey. Computational Linguistics.

Michaelov, J., S. Coulson, & B. Bergen (2023). Can Peanuts Fall in Love with Distributional Semantics? Proceedings of the 45th Annual Meeting of the Cognitive Science Society. Austin, TX: Cognitive Science Society.

Jones, C., Chang, T., Coulson, S., Michaelov, J., Trott, T., & Bergen, B. (2022). Distributional Semantics Still Can’t Account for Affordances. Proceedings of the 44th Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society.

Cognitive architectures and their applications (Lebière)

Christian Lebière, Carnegie-Mellon, 20 October, 2022

VIDEO

Abstract: Cognitive architectures are computational implementations of unified theories of cognition. Being able to represent human cognition in computational form enables a wide range of applications when humans and machines interact. Using cognitive models to represent common ground between deep learners and human users enables adaptive explanations. Cognitive models representing the behavior of cyber attackers can be used to optimize cyber defenses including techniques such as deceptive signaling. Cognitive models of human-automation interaction can improve robustness of human-machine teams by predicting disruptions to measures of trust under various adversarial situations. Finally, the consensus of 50 years of research in cognitive architectures can be captured in the form of a Common Model of Cognition that can provide a guide for neuroscience, artificial intelligence and robotics.

Christian Lebière is a Research Faculty member in the Psychology Department at Carnegie Mellon University. His main research interests are cognitive architectures and their applications to psychology, artificial intelligence, human-computer interaction, decision-making, intelligent agents, network science, cognitive robotics and neuromorphic engineering.

Cranford, E. A., Gonzalez, C., Aggarwal, P., Tambe, M., Cooney, S., & Lebiere, C. (2021). Towards a cognitive theory of cyber deception. Cognitive Science, 45(7), e13013.

Cranford, E., Gonzalez, C., Aggarwal, P., Cooney, S., Tambe, M., & Lebiere, C. (2020). Adaptive cyber deception: Cognitively informed signaling for cyber defense.

Lebiere, C., Blaha, L. M., Fallon, C. K., & Jefferson, B. (2021). Adaptive cognitive mechanisms to maintain calibrated trust and reliance in automation. Frontiers in Robotics and AI, 8, 652776.

Laird, J. E., Lebiere, C., & Rosenbloom, P. S. (2017). A standard model of the mind: Toward a common computational framework across artificial intelligence, cognitive science, neuroscience, and robotics. AI Magazine, 38(4), 13-26.

Lebiere, C., Pirolli, P., Thomson, R., Paik, J., Rutledge-Taylor, M., Staszewski, J., & Anderson, J. R. (2013). A functional model of sensemaking in a neurocognitive architecture. Computational Intelligence and Neuroscience, 2013.

Constraining networks biologically to explain grounding (Pulvermüller)

Friedemann Pulvermueller, FU Berlin, 3 December, 2020

VIDEO

Abstract: Meaningful use of symbols requires grounding in action and perception through learning. The mechanisms of this sensorimotor grounding, however, are rarely specified in mechanistic terms; and mathematically precise formal models of the relevant learning processes are scarce. As the brain is the device that is critical for mechanistically supporting and indeed implementing grounding, modelling needs to take into account realistic neuronal processes in the human brain. This makes it desirable to use not just ‘neural’ networks that are vaguely similar to some aspects of real networks of neurons, but models implementing constraints imposed by neuronal structure and function, that is, biologically realistic learning and brain structure along with local and global structural connectivity and functional interaction. After discussing brain constraints for cognitive modelling, the talk will focus on the biological implementation of grounding, in order to address the following questions: Why do the brains of humans — but not those of their closest relatives — allow for verbal working memory and learning of huge vocabularies of symbols? Why do different word and concept types seem to depend on different parts of the brain (‘category-specific’ semantic mechanisms)? Why are there ‘semantic and conceptual hubs’ in the brain where general semantic knowledge is stored — and why would these brain areas be different from those areas where grounding information is present (i.e., the sensory and motor cortices)? And why should sensory deprivation shift language and conceptual processing toward ‘grounding areas’ — for example toward the visual cortex in the blind? I will argue that brain-constrained modelling is necessary to answer (some of) these questions and, more generally, to explain the mechanisms of grounding.

Friedemann Pulvermüller is professor in the neuroscience of language and pragmatics at the Freie Universität Berlin, where he also directs the ‘Brain Language Laboratory’.

Carota, F., Nili, H., Kriegeskorte, N., & Pulvermüller, F. (2023). Experientially-grounded and distributional semantic vectors uncover dissociable representations of conceptual categories. Language, Cognition and Neuroscience, 1-25.

Pulvermüller, F., Garagnani, M., & Wennekers, T. (2014). Thinking in circuits: Towards neurobiological explanation in cognitive neuroscience. Biological Cybernetics, 108(5), 573-593. doi: 10.1007/s00422-014-0603-9