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Aphasia Models

presented by Diane Kendall

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Disclosure Statement:

Financial: Diane Kendall receives an honorarium from MedBridge for this course. Publications by Diane Kendall are referenced in this course.

Non-Financial: Diane Kendall is a CE Content Consultant for MedBridge.

Satisfactory completion requirements: All disciplines must complete learning assessments to be awarded credit, no minimum score required unless otherwise specified within the course.

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Accreditation Check:
In this course, Aphasia Models, Dr. Diane Kendall provides a basic background and definition of aphasia, reviews the model upon which the classical aphasia syndromes originates, discusses how the classical syndromes might be lacking in terms of sensitivity and specificity of linguistic impairment, reviews a connectionist model of language, and discusses how patient errors can aid in the development of a sensitive and specific treatment plan.

Meet Your Instructor

Diane Kendall, PhD, CCC-SLP

Dr. Kendall is a Full Professor at the University of Washington Department of Speech and Hearing Sciences. Her research program is focused on rehabilitation of aphasia. She, along with her colleagues, has created a treatment for linguistic deficits in aphasia called Phonomotor Treatment. Through a Veterans Administration Associate Investigator Award (2000-2002), Career Research Development Award…

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Chapters & Learning Objectives

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1. Background & Definitions

Describe how language is both lateralized and localized. Provide a working definition of aphasia.

2. Classical Aphasia Syndromes

Describe symptoms of Broca's aphasia, Wernicke's aphasia and Conduction aphasia. Provide 3 reasons why the classical aphasia syndromes are not useful in treatment planning.

3. Models

Provide recent evidence based meta-analysis study demonstrating that language is distributed. Describe the processing assumptions of a modular model. Describe the processing assumptions of a distributed model

4. Errors

Ascribe errors (phonologic, semantic, mixed) to Dell's model.Discuss how a distributed model explains errors. Describe the difference between bottom-up and top-down errors.

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