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Theory selection and evaluation in case series research.

Matthew Goldrick¹

¹Department of Linguistics, Northwestern University, Evanston, IL, USA. matt-goldrick@northwestern.edu

Cognitive Neuropsychology

|July 4, 2012

Summary

Current case series research in language production needs to focus on contrasting predictions from alternative theories. This approach will improve theory selection and better explain individual differences in language impairments.

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Area of Science:

Psycholinguistics
Cognitive Science
Neuroscience

Background:

Developing scientific theories requires selecting the best theory from alternatives, not just evaluating fit with data.
Case series research in language production currently faces challenges in theory selection and evaluating individual differences.

Purpose of the Study:

To examine current case series research in language production regarding theory selection and individual differences.
To propose methods for improving the theoretical productivity of case series research.

Main Methods:

Simulation results were used to show that existing language production theories do not make contrasting predictions.
Analyses of empirical distributions of individual differences in lexical access impairments were conducted.

Main Results:

Existing theories of language production do not offer contrasting predictions for response distributions.
Claims about individual differences in language impairments are often underconstrained by data.

Conclusions:

Case series research must focus on data aspects that contrast theoretical alternatives for greater theoretical productivity.
Future research should include independent evidence for hypothesized individual differences and explore parametric approaches to constrain individual difference theories.