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The SAL Integrated Hybrid Cognitive Architecture

The SAL Integrated Hybrid Cognitive Architecture. Christian Lebiere, Niels Taatgen & John Anderson – Carnegie Mellon University Randall O’Reilly – University of Colorado David Jilk - eCortex. Hybrid Cognitive Architectures. Cognitive invariants: Representation, mechanisms and structure

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The SAL Integrated Hybrid Cognitive Architecture

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  1. The SAL Integrated Hybrid Cognitive Architecture Christian Lebiere, Niels Taatgen & John Anderson – Carnegie Mellon University Randall O’Reilly – University of Colorado David Jilk - eCortex

  2. Hybrid Cognitive Architectures • Cognitive invariants: Representation, mechanisms and structure • Provides general problem solutions for integrating capabilities • Can be symbolic (Soar), connectionist (Leabra), hybrid (ACT-R) • Cognitive systems, like physical systems, have multiple useful levels of description • Different cognitive techniques have distinct and complementary computational properties • Marr’s computational, algorithmic and implementation levels apply to cognition Neuromorphic Computing Workshop

  3. Leabra • Tripartite Architecture • Posterior cortex for sensory-motor process and LTM • Prefrontal cortex for WM and control and basal ganglia for action selection and learning • Hippocampus for rapid learning of new information • Computational properties of distinct brain areas • Specialized for incompatible forms of learning (e.g. rapid in hippocampus, slow in cortex) • Red arrows represent top-down cognitive control • Black arrows for standard neural communication ACT-R Group Meeting

  4. ACT-R • Tight integration of symbolic and statistical • Symbolic level for structured cognition • Statistical level for learning and adaptivity • Massive parallelism within each module • Asynchronous interaction between modules • Limited-capacity module interaction • Central control of cortical areas through procedural module (BG) ACT-R Group Meeting

  5. Four Approaches to Hybridism • The abstractionist approach - Rational Analysis (Anderson, 1990) • Abstract properties of Leabra into subsymbolic algorithms (e.g. RL) • Pro: most efficient; most abstract • Con: is it possible? Is it fully functional? • The reductionist approach - ACT-RN (Lebiere & Anderson, 1993) • Implement ACT-R structure in neural constructs (e.g. ACT-RN) • Pro: most biologically inspired • Con: computationally feasible? Biological accuracy vs functional power? • The modular approach – Initial SAL Integration • Mix ACT-R and Leabra modules in ACT-R-like (or Leabra-like) architecture • Pro: easiest to achieve; specialization argument • Con: Limited leveraging of each framework; computational bottleneck? • The hybrid approach – Future SAL Integration • Mix ACT-R and Leabra within same modules w/ rich, adaptive interface • Pro: most ambitious meaning of hybridism • Con: at best very difficult to achieve ACT-R Group Meeting

  6. Modular Approach - SAL • Leverage common neural localizations of brain functions • Leverages complementary strengths of each system • ACT-R: control • Leabra: learning • Relatively fast and easy to implement • Low bandwidth communication ACT-R Group Meeting

  7. Embodied Cognition • Perform search for objects in virtual environment • Takes instruction, controls navigation, identifies objects • ACT-R handles control and Leabra provides representation • Addresses basic issues like symbol grounding in environment Neuromorphic Computing Workshop

  8. Stream of 20 characters, task it to spot targets (letters) in between distracters (digits). There are either 0, 1, or 2 targets If there are 2 targets, there can be 0-8 distracters in between them Characters are presented 100 ms apart Attentional Blink

  9. Typical result • Proportion where second target is reported correctly • ACT-R model predicts this but fails to predict misorderings at lag 1.

  10. Replace Visual Module with Neural Network

  11. Leabra/ACT-R: Discrete Perception becomes Continuous Classic ACT-R: 2 A B 3 4 8 perceive perceive perceive perceive perceive perceive

  12. Result: Better Data Fit

  13. Key Research Questions for SAL How do symbols arise? How do procedural rules operate?

  14. Conclusions • Cognitive architectures aim to provide general, integrated account of human cognition • Multi-level architectures have the potential to overcome limitations of current approaches • Neural constraints provide essential guidance • Exploration of architectural integration issues provide a way to test neural hypotheses Neuromorphic Computing Workshop

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