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Linear time-invariant Systems01:23

Linear time-invariant Systems

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A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
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Residuals and Least-Squares Property01:11

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The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
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Effects of feedback01:24

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Feedback in control systems plays a critical role in shaping various operational parameters, extending beyond simple error reduction to influence stability, bandwidth, gain, impedance, and sensitivity. Understanding these effects requires examining a basic feedback system characterized by defined input, output, error, and feedback signals.
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Chunking and Rehearsal in Sensory Memory01:22

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Improving short-term memory can be achieved through techniques like chunking and rehearsal. Chunking involves organizing information into larger, more manageable units. This technique is particularly useful for information that exceeds the typical memory span of between five and nine items. For instance, logging into an online account with a password like "ta89vq0179gz" involves grouping letters and numbers into three chunks—ta89, vq01, and 79gz. It makes large amounts of...
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Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

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Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
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Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

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Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
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Related Experiment Video

Updated: Jun 27, 2025

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
09:09

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

Published on: September 27, 2024

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Lexical Feedback in the Time-Invariant String Kernel (TISK) Model of Spoken Word Recognition.

James S Magnuson1,2, Heejo You3, Thomas Hannagan4,5

  • 1BCBL: Basque Center on Cognition, Brain & Language, Donostia-San Sebastián, Spain.

Journal of Cognition
|April 29, 2024
PubMed
Summary

The Time-Invariant String Kernel (TISK) model now simulates top-down effects with added lexical feedback, maintaining performance on core spoken word recognition tasks. This enhancement offers efficient, robust processing for cognitive architectures.

Keywords:
Computational modelsfeedbackinteractionneural networksspoken word recognition

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Area of Science:

  • Cognitive Science
  • Computational Linguistics
  • Psychology

Background:

  • The Time-Invariant String Kernel (TISK) model is an interactive activation model for spoken word recognition.
  • TISK offers computational efficiency compared to TRACE by using fewer nodes and connections.
  • The original TISK model lacked lexical feedback, limiting its ability to simulate top-down effects.

Purpose of the Study:

  • To investigate the impact of adding lexical feedback to the TISK model.
  • To determine if TISK with feedback can simulate top-down effects without compromising existing performance.
  • To assess TISK's robustness to noise with and without feedback.

Main Methods:

  • Implemented lexical feedback within the TISK model.
  • Simulated spoken word recognition tasks, including those with noise.
  • Compared performance of TISK with and without feedback to previous findings.

Main Results:

  • TISK with lexical feedback successfully simulates top-down effects.
  • The model retains its ability to account for fundamental spoken word recognition phenomena.
  • TISK demonstrates graceful degradation under noisy input conditions with feedback.

Conclusions:

  • Lexical feedback enhances TISK's capabilities for simulating cognitive processes.
  • Feedback provides an efficient computational basis for robust constraint-based processing in cognitive architectures.
  • The TISK model with feedback offers a viable alternative for modeling spoken word recognition.