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Related Concept Videos

Per-Unit Sequence Models01:26

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An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
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Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
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Parallel Acquisition of Uncorrelated Sequences does Not Provide Firm Evidence for a Modular Sequence-Learning System.

Marius Barth1, Christoph Stahl1, Hilde Haider1

  • 1Department of Psychology, University of Cologne, DE.

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|February 1, 2023
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Sequence learning may not require separate brain modules. A computational model shows a single learning system can explain parallel acquisition of multiple sequences, challenging modularity theories.

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encapsulated processing modulesimplicit learningsingle-system and dual-systems models

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Area of Science:

  • Cognitive Science
  • Neuroscience
  • Computational Modeling

Background:

  • Dual-systems theories propose modular, encapsulated systems for sequence learning.
  • Evidence for modularity often relies on studies of interference between uncorrelated sequences.

Purpose of the Study:

  • To challenge the premise that parallel acquisition of uncorrelated sequences supports modular sequence learning systems.
  • To propose and test an alternative computational model for sequence learning.

Main Methods:

  • Developed a computational model assuming a single learning system.
  • Simulated parallel acquisition of multiple uncorrelated sequences using the model.
  • Compared model predictions to existing empirical findings on sequence learning.

Main Results:

  • The computational model accurately predicted parallel acquisition of multiple uncorrelated sequences.
  • The model's success was attributed to joint representations of stimulus and response features.
  • Results suggest a single learning system can account for observed learning patterns.

Conclusions:

  • Parallel acquisition of uncorrelated sequences does not necessitate modular learning systems.
  • A single learning system with integrated feature representations can explain sequence learning.
  • Findings question the strong modularity assumption in dual-systems theories of sequence learning.