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Kelly, M. A.; Arora, Nipun; West, Robert L.; Reitter, David – Cognitive Science, 2020
We demonstrate that the key components of cognitive architectures (declarative and procedural memory) and their key capabilities (learning, memory retrieval, probability judgment, and utility estimation) can be implemented as algebraic operations on vectors and tensors in a high-dimensional space using a distributional semantics model.…
Descriptors: Memory, Cognitive Processes, Cognitive Structures, Models
Friedman, Scott; Forbus, Kenneth; Sherin, Bruce – Cognitive Science, 2018
People use commonsense science knowledge to flexibly explain, predict, and manipulate the world around them, yet we lack computational models of how this commonsense science knowledge is represented, acquired, utilized, and revised. This is an important challenge for cognitive science: Building higher order computational models in this area will…
Descriptors: Models, Cognitive Science, Scientific Concepts, Cognitive Structures
Lake, Brenden M.; Lawrence, Neil D.; Tenenbaum, Joshua B. – Cognitive Science, 2018
Both scientists and children make important structural discoveries, yet their computational underpinnings are not well understood. Structure discovery has previously been formalized as probabilistic inference about the right structural form--where form could be a tree, ring, chain, grid, etc. (Kemp & Tenenbaum, 2008). Although this approach…
Descriptors: Discovery Learning, Intuition, Bias, Computation
Stocco, Andrea – Cognitive Science, 2018
Several attempts have been made previously to provide a biological grounding for cognitive architectures by relating their components to the computations of specific brain circuits. Often, the architecture's action selection system is identified with the basal ganglia. However, this identification overlooks one of the most important features of…
Descriptors: Cognitive Structures, Brain, Biology, Anatomy
Tabor, Whitney; Cho, Pyeong W.; Dankowicz, Harry – Cognitive Science, 2013
Human participants and recurrent ("connectionist") neural networks were both trained on a categorization system abstractly similar to natural language systems involving irregular ("strong") classes and a default class. Both the humans and the networks exhibited staged learning and a generalization pattern reminiscent of the…
Descriptors: Learning Processes, Task Analysis, Systems Approach, Geometric Concepts
Cooper, Richard P. – Cognitive Science, 2007
It has been suggested that the enterprise of developing mechanistic theories of the human cognitive architecture is flawed because the theories produced are not directly falsifiable. Newell attempted to sidestep this criticism by arguing for a Lakatosian model of scientific progress in which cognitive architectures should be understood as theories…
Descriptors: Cognitive Structures, Cognitive Processes, Models, Scientific Concepts
Reynolds, Jeremy R.; Zacks, Jeffrey M.; Braver, Todd S. – Cognitive Science, 2007
People tend to perceive ongoing continuous activity as series of discrete events. This partitioning of continuous activity may occur, in part, because events correspond to dynamic patterns that have recurred across different contexts. Recurring patterns may lead to reliable sequential dependencies in observers' experiences, which then can be used…
Descriptors: Prediction, Models, Mathematical Models, Simulation
Larkey, Levi B.; Markman, Arthur B. – Cognitive Science, 2005
Similarity underlies fundamental cognitive capabilities such as memory, categorization, decision making, problem solving, and reasoning. Although recent approaches to similarity appreciate the structure of mental representations, they differ in the processes posited to operate over these representations. We present an experiment that…
Descriptors: Cognitive Processes, Cognitive Structures, Cognitive Mapping, Models