Artificial intelligence has not achieved defining features of biological intelligence despite models boasting more parameters than neurons in the human brain. In this perspective article, we synthesize historical approaches to understanding intelligent systems and argue that methodological and epistemic biases in these fields can be resolved by shifting away from cognitivist brain-as-computer theories and recognizing that brains exist within large, interdependent living systems. Integrating the dynamical systems view of cognition with the massive distributed feedback of perceptual control theory highlights a theoretical gap in our understanding of nonreductive neural mechanisms. Cell assemblies—properly conceived as reentrant dynamical flows and not merely as identified groups of neurons—may fill that gap by providing a minimal supraneuronal level of organization that establishes a neurodynamical base layer for computation. By considering information streams from physical embodiment and situational embedding, we discuss this computational base layer in terms of conserved oscillatory and structural properties of cortical-hippocampal networks. Our synthesis of embodied cognition, based in dynamical systems and perceptual control, aims to bypass the neurosymbolic stalemates that have arisen in artificial intelligence, cognitive science, and computational neuroscience.
- Award ID(s):
- 1849131
- PAR ID:
- 10352557
- Date Published:
- Journal Name:
- Proceedings of the Ninth Annual Conference on Advances in Cognitive Systems
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
Abstract -
Beyond the matrix: Experimental approaches to studying cognitive agents in social-ecological systemsStudying social-ecological systems, in which agents interact with each other and their environment are important both for sustainability applications and for understanding how human cognition functions in context. In such systems, the environment shapes the agents' experience and actions, and in turn collective action of agents changes social and physical aspects of the environment. Here we review current investigation approaches, which rely on a lean design, with discrete actions and outcomes and little scope for varying environmental parameters and cognitive demands. We then introduce a multiagent reinforcement learning (MARL) approach, which builds on modern artificial intelligence techniques, and provides new avenues to model complex social worlds, while preserving more of their characteristics, and allowing them to capture a variety of social phenomena. These techniques can be fed back to the laboratory where they make it easier to design experiments in complex social situations without compromising their tractability for computational modeling. We showcase the potential MARL by discussing several recent studies that have used it, detailing the way environmental settings and cognitive constraints can lead to the emergence of complex cooperation strategies. This novel approach can help researchers bring together insights from human cognition, sustainability, and AI, to tackle real world problems of social-ecological systems.more » « less
-
null (Ed.)The greater male variability phenomenon predicts that males exhibit larger ranges of variation in cognitive performance compared with females; however, support for this pattern has come exclusively from studies of humans and lacks mechanistic explanation. Furthermore, the vast majority of the literature assessing sex differences in cognition is based on studies of humans and a few other mammals. In order to elucidate the underpinnings of cognitive variation and the potential for fitness consequences, we must investigate sex differences in cognition in non-mammalian systems as well. Here, we assess the performance of male and female food-caching birds on a spatial learning and memory task and a reversal spatial task to address whether there are sex differences in mean cognitive performance or in the range of variation in performance. For both tasks, male and female mean performance was similar across four years of testing; however, males did exhibit a wider range of variation in performance on the reversal spatial task compared with females. The implications for mate choice and sexual selection of cognitive abilities are discussed and future directions are suggested to aid in the understanding of sex-related cognitive variation.more » « less
-
A metacognitive radar switches between two modes of cognition— one mode to achieve a high-quality estimate of targets, and the other mode to hide its utility function (plan). To achieve high-quality es- timates of targets, a cognitive radar performs a constrained utility maximization to adapt its sensing mode in response to a changing target environment. If an adversary can estimate the utility function of a cognitive radar, it can determine the radar’s sensing strategy and mitigate the radar performance via electronic countermeasures (ECM). This article discusses a metacognitive radar that switches between two modes of cognition: achieving satisfactory estimates of a target while hiding its strategy from an adversary that detects cognition. The radar does so by transmitting purposefully designed suboptimal responses to spoof the adversary’s Neyman–Pearson de- tector. We provide theoretical guarantees by ensuring that the Type-I error probability of the adversary’s detector exceeds a predefined level for a specified tolerance on the radar’s performance loss. We illustrate our cognition-masking scheme via numerical examples in- volving waveform adaptation and beam allocation. We show that small purposeful deviations from the optimal emission confuse the adversary by significant amounts, thereby masking the radar’s cognition. Our approach uses ideas from revealed preference in microeconomics and adversarial inverse reinforcement learning. Our proposed algorithms provide a principled approach for system-level electronic counter- countermeasures to hide the radar’s strategy from an adversary. We also provide performance bounds for our cognition-masking scheme when the adversary has misspecified measurements of the radar’s response.more » « less
-
Abstract The remarkable successes of convolutional neural networks (CNNs) in modern computer vision are by now well known, and they are increasingly being explored as computational models of the human visual system. In this paper, we ask whether CNNs might also provide a basis for modeling higher‐level cognition, focusing on the core phenomena of similarity and categorization. The most important advance comes from the ability of CNNs to learn high‐dimensional representations of complex naturalistic images, substantially extending the scope of traditional cognitive models that were previously only evaluated with simple artificial stimuli. In all cases, the most successful combinations arise when CNN representations are used with cognitive models that have the capacity to transform them to better fit human behavior. One consequence of these insights is a toolkit for the integration of cognitively motivated constraints back into CNN training paradigms in computer vision and machine learning, and we review cases where this leads to improved performance. A second consequence is a roadmap for how CNNs and cognitive models can be more fully integrated in the future, allowing for flexible end‐to‐end algorithms that can learn representations from data while still retaining the structured behavior characteristic of human cognition.