AphasiaBank

Main Concept Analysis

This page provides links to information about main concept analysis.

Selected articles on main concept analysis:

• Brisebois, Brambati, Jutras, Rochon, Leonard, Zumbansen, Anglade, & Marcotte (2023) -- Adaptation and Reliability of the Cinderella Story Retell Task in Canadian French Persons without Brain Injury
• Dalton & Richardson (2015) -- Core-Lexicon and Main-Concept Production During Picture-Sequence Description in Adults Without Brain Damage and Adults With Aphasia
• Dalton & Richardson (2019) -- A Large-Scale Comparison of Main Concept Production Between Persons With Aphasia and Persons Without Brain Injury
• Greenslade et al. (2020) -- Macrostructural Analyses of Cinderella Narratives in a Large Nonclinical Sample
• Hameister & Nickels (2018) -- The Cat in the Tree -- Using Picture Descriptions to Inform our Understanding of Conceptualisation in Aphasia
• Kong (2009) -- The Use of Main Concept Analysis to Measure Discourse Production in Cantonese-speaking Persons with Aphasia: A Preliminary Report
• Nicholas & Brookshire (1995) -- Presence, Completeness, and Accuracy of Main Concepts in the Connected Speech of NonBrain-Damaged Adults and Adults With Aphasia
• Richardson & Dalton (2016) -- Main Concepts for Three Different Discourse Tasks in a Large Non-clinical Sample
• Richardson & Dalton (2019) -- Main Concepts for Two Picture Description Tasks: An Addition to Richardson and Dalton, 2016
• Richardson et al. (2021) -- Main Concept, Sequencing, and Story Grammar (MSSG) Analyses of Cinderella Narratives in a Large Sample of Persons with aphasia

Main Concepts

Here are links to main concepts for tasks from the AphasiaBank Discourse Protocol. These include instructions for scoring presence, accuracy and completeness.

• Broken Window -- picture description -- Richardson & Dalton, 2016
• Cat in the Tree -- picture description -- Hameister & Nickels, 2018
• Cat in the Tree -- picture description -- Richardson & Dalton, 2019
• Refused Umbrella -- picture description -- Richardson & Dalton, 2019
• Cinderella -- story -- Richardson & Dalton, 2016
• Sandwich Task -- procedural discourse -- Richardson & Dalton, 2016

Scoring

Procedures vary. Examples include:

• Nicholas & Brookshire (1995)
  • AC -- # of accurate and complete concepts -- all essential information is accurate and complete
  • AI -- # of accurate but incomplete concepts -- part of the essential information is accurate, but one or more essential parts are missing
  • IN -- # of inaccurate concepts -- one or more parts of the essential information are inaccurate
  • AB -- # of absent concepts -- none of the essential information is given
• Kong (2009) added 2 newly devised measures to Nicholas and Brookshire (1995) measures
  • Main concept score (MC) = (3 x AC) + (2 x AI) + (1 x IN)
  • # of AC per minute
• Richardson and Dalton (2015) modified Kong (2009) formula
  • Main concept score (MC) = (3 x AC) + (2 x AI) + (2 x IC) + (1 x II), where IC statements contain at least one incorrect piece of information but mentioned all essential elements and II statements contained at least one incorrect essential element and also failed to include at least one essential element

Main Concept Analysis Training Materials -- Richardson & Dalton

The materials here were compiled by Jessica Richardson, Ph.D. and Sarah Grace Dalton, Ph.D., who continue to update these materials regularly. The materials contain:

• Main Concept Training Manual -- with coding and scoring rules, examples, scoresheets, references, and a reading list
• Readings/Resources -- pdf files for relevant, important articles
• FOQUSAphasia Workshop PowerPoint presentation on Main Concept Analysis -- from February 25, 2021
• See web-app description (#2) below for more online training materials.

Automatic Coding and Scoring

1. Using CLAN. It is efficient to use Coder Mode to enter main concepts into a CHAT transcript. You can download this mc.cut file, put it in the folder with the CHAT files you want to code, and then enter the codes into the transcript by following the steps explained in the Coder Mode section of the CLAN manual or viewing the Coder Mode screencast here. This mc.cut file will allow you to code up to 34 main concepts and mark them as AC (Accurate Complete), AI (Accurate Incomplete), IC (Inaccurate Complete), or II (Inaccurate Incomplete). Utterances with no main concept can be coded as NA. If a main concept spans multiple utterances, code it once on the final utterance.

Once the CHAT file has the MC codes on the coder tier, run this CLAN command -- codes filename.cha (or *.cha for all CHAT files in the folder) -- and you'll get a list of all MCs in the sample, the code assigned to each MC, the total number of MCs used in the sample, the total number of each code (AC, AI, IC, II, NA), and a composite score based on the Richardson and Dalton (2015) scoring.

2. Using a web-app. Scoring can be done automatically with this web-app --
https://rb-cavanaugh.shinyapps.io/mainConcept/. Using simple orthographic transcription of the language sample (Broken Window, Refused Umbrella, Cat Rescue, Cinderella, Sandwich), the app provides a summary page with total scores and percentiles based on average norms relative to healthy controls and other individuals with aphasia. It also allows users to download a spreadsheet of their data and a PDF report. Finally, the app includes a training manual with practice transcripts, readings/resources, anda training workshop.

The app was developed by Rob Cavanaugh, Sarah Grace Dalton, and Jessica Richardson with grant support from NIH/NIDCD (Cavanaugh, F31 DC019853-01). Citation for this software and link for source code: Cavanaugh, R., Dalton, S. G., & Richardson, J. (2021). mainConcept: An open-source web-app for scoring main concept analysis. R package version 0.0.1.0000. https://github.com/aphasia-apps/mainConcept . Comments, feedback, and bug-reports can be made on the github page.