More Like this

1. Thinking in PolAR pictures: Using rotation-friendly mental images to solve Leiter-R Form Completion

   Palmer, Joshua H.; Kunda, Maithilee (January 2018, Proceedings of the ... AAAI Conference on Artificial Intelligence)

   The Leiter International Performance Scale-Revised (Leiter-R) is a standardized cognitive test that seeks to "provide a nonverbal measure of general intelligence by sampling a wide variety of functions from memory to nonverbal reasoning." Understanding the computational building blocks of nonverbal cognition, as measured by the Leiter-R, is an important step towards understanding human nonverbal cognition, especially with respect to typical and atypical trajectories of child development. One subtest of the Leiter-R, Form Completion, involves synthesizing and localizing a visual figure from its constituent slices. Form Completion poses an interesting nonverbal problem that seems to combine several aspects of visual memory, mental rotation, and visual search. We describe a new computational cognitive model that addresses Form Completion using a novel, mental-rotation-friendly image representation that we call the Polar Augmented Resolution (PolAR) Picture, which enables high-fidelity mental rotation operations. We present preliminary results using actual Leiter-R test items and discuss directions for future work. 

   more » « less
2. AI and Cognitive Testing: A New Conceptual Framework and Roadmap

   Kunda, Maithilee (January 2019, Proceedings of the 41st Annual Conference of the Cognitive Science Society)

   Understanding how a person thinks, i.e., measuring a single individual’s cognitive characteristics, is challenging because cognition is not directly observable. Practically speaking, standardized cognitive tests (tests of IQ, memory, attention, etc.), with results interpreted by expert clinicians, represent the state of the art in measuring a person’s cognition. Three areas of AI show particular promise for improving the effectiveness of this kind of cognitive testing: 1) behavioral sensing, to more robustly quantify individual test-taker behaviors, 2) data mining, to identify and extract meaningful patterns from behavioral datasets; and 3) cognitive modeling, to help map ob- served behaviors onto hypothesized cognitive strategies. We bring these three areas of AI research together in a unified conceptual framework and provide a sampling of recent work in each area. Continued research at the nexus of AI and cognitive testing has potentially far-reaching implications for society in virtually every context in which measuring cognition is important, including research across many disciplines of cognitive science as well as applications in clinical, educational, and workforce settings. 

   more » « less
3. Neurodynamical Computing at the Information Boundaries of Intelligent Systems

   https://doi.org/10.1007/s12559-022-10081-9  

   Monaco, Joseph_D; Hwang, Grace_M (December 2022, Cognitive Computation)

   Abstract 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. 

   more » « less
4. Computational metacognition

   Cox, Michael T.; Mohammad, Zahiduddin; Kondrakunta, Sravya; Gogineni, Venkatsampath R.; Dannenhauer, Dustin; Larue, Othalia (November 2021, Proceedings of the Ninth Annual Conference on Advances in Cognitive Systems)

   Computational metacognition represents a cognitive systems perspective on high-order reasoning in integrated artificial systems that seeks to leverage ideas from human metacognition and from metareasoning approaches in artificial intelligence. The key characteristic is to declaratively represent and then monitor traces of cognitive activity in an intelligent system in order to manage the performance of cognition itself. Improvements in cognition then lead to improvements in behavior and thus performance. We illustrate these concepts with an agent implementation in a cognitive architecture called MIDCA and show the value of metacognition in problem-solving. The results illustrate how computational metacognition improves performance by changing cognition through meta-level goal operations and learning. 

   more » « less
5. Individual differences in judging similarity between semantic relations

   Ichien, N.; Lu, H.; & Holyoak, K. J. (January 2019, Proceedings of the Annual Conference of the Cognitive Science Society)

   The ability to recognize and make inductive inferences based on relational similarity is fundamental to much of human higher cognition. However, relational similarity is not easily defined or measured, which makes it difficult to determine whether individual differences in cognitive capacity or semantic knowledge impact relational processing. In two experiments, we used a multi-arrangement task (previously applied to individual words or objects) to efficiently assess similarities between word pairs instantiating various abstract relations. Experiment 1 established that the method identifies word pairs expressing the same relation as more similar to each other than to those expressing different relations. Experiment 2 extended these results by showing that relational similarity measured by the multi-arrangement task is sensitive to more subtle distinctions. Word pairs instantiating the same specific subrelation were judged as more similar to each other than to those instantiating different subrelations within the same general relation type. In addition, Experiment 2 found that individual differences in both fluid intelligence and crystalized verbal intelligence correlated with differentiation of relation similarity judgments. 

   more » « less