Abstract
Latent semantic analysis (LSA) is a computational model of human knowledge representation that approximates semantic relatedness judgments. Two issues are discussed that researchers must attend to when evaluating the utility of LSA for predicting psychological phenomena. First, the role of semantic relatedness in the psychological process of interest must be understood. LSA indices of similarity should then be derived from this theoretical understanding. Second, the knowledge base (semantic space) from which similarity indices are generated must contain “knowledge” that is appropriate to the task at hand. Proposed solutions are illustrated with data from an experiment in which LSA-based indices were generated from theoretical analysis of the processes involved in understanding two conflicting accounts of a historical event. These indices predict the complexity of subsequent student reasoning about the event, as well as hand-coded predictions generated from think-aloud protocols collected when students were reading the accounts of the event.
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Burgess, C. (2000). Theory and operational definitions in computational memory models: A response to Glenberg and Robertson.Journal of Memory & Language,43, 402–408.
Burgess, C., Livesay, K., &Lund, K. (1998). Explorations in context space: Words, sentences, discourse.Discourse Processes,25, 211–257.
Chi, M. T. H. (1997). Quantifying qualitative analyses of verbal data: A practical guide.Journal of the Learning Sciences,6, 271–315.
Chi, M. T. H. (2000). Self-explaining expository texts: The dual processes of generating inferences and repairing mental models. In R. Glaser (Ed.),Advances in instructional psychology (pp. 161–238). Mahwah, NJ: Erlbaum.
Chi, M. T. H., deLeeuw, N., Chiu, M., &LaVancher, C. (1994). Eliciting self-explanations improves understanding.Cognitive Science,18, 439–477.
Coté, N., &Goldman, S. R. (1999). Building representations of informational text: Evidence from children’s think-aloud protocols. In H. van Oostendorp & S. R. Goldman (Eds.),The construction of mental representations during reading (pp. 169–193). Mahwah, NJ: Erlbaum.
Coté, N., Goldman, S. R., &Saul, E. U. (1998). Students making sense of informational text: Relations between processing and representation.Discourse Processes,25, 1–53.
Foltz, P. W., Britt, M. A., &Perfetti, C. A. (1996). Reasoning from multiple texts: An automatic analysis of readers’ situation models. In G. W. Cottrell (Ed.),Proceedings of the 18th Annual Cognitive Science Conference (pp. 105–115). Mahwah, NJ: Erlbaum.
Foltz, P. W., Gilliam, S., &Kendall, S. (2000). Supporting content-based feedback in online writing evaluation with LSA.Interactive Learning Environments,8, 111–129.
Foltz, P. W., Kintsch, W., &Landauer, T. K. (1998). The measurement of textual coherence with latent semantic analysis.Discourse Processes,25, 285–307.
Glenberg, A. M., &Robertson, D. A. (2000). Symbol grounding and meaning: A comparison of high-dimensional and embodied theories of meaning.Journal of Memory & Language,43, 379–401.
Goldman, S. R., Coté, N. C., &Saul, E. U. (1995). Paragraphing, reader, and task effects on discourse comprehension.Discourse Processes,20, 273–305.
Graesser, A. C., Wiemer-Hastings, P., Wiemer-Hastings, K., Harter, D., Person, N., &the Tutoring Research Group (2000). Using latent semantic analysis to evaluate the contributions of students in AutoTutor.Interactive Learning Environments,8, 129–147.
Kintsch, E., Steinhart, D., Stahl, G., LSA Research Group, Matthews, C., &Lamb, R. (2000). Developing summarization skills through the use of LSA-based feedback.Interactive Learning Environments,8, 87–109.
Kintsch, W. (2001). Predication.Cognitive Science,25, 173–202.
Landauer, T. K., &Dumais, S. T. (1997). A solution to Plato’s problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge.Psychological Review,104, 211–240.
Landauer, T. K., Foltz, P. W., &Laham, D. (1998). An introduction to latent semantic analysis.Discourse Processes,25, 259–284.
Magliano, J., Millis, K., Wiemer-Hastings, K., & McNamara, D. (2001, July).Using LSA to reveal reader strategies. Paper presented at the conference of the Society for Text and Discourse, Santa Barbara, CA.
Shapiro, A. M., &McNamara, D. S. (2000). The use of latent semantic analysis as a tool for the quantitative assessment of understanding and knowledge.Journal of Educational Computing Research,22, 1–36.
Trabasso, T., &Magliano, J. P. (1996). Conscious understanding during comprehension.Discourse Processes,21, 255–287.
Wolfe, M. B. W., Goldman, S. R., Mayfield, C., Meyerson, P. M., & Bloome, D. M. (2000, July).Middle school students’ processing of multiple accounts of an historical event. Paper presented at the meeting of the Society for Text and Discourse, Lyon, France.
Wolfe, M. B. W., Schreiner, M. E., Rehder, B., Laham, D., Foltz, P. W., Kintsch, W., &Landauer, T. K. (1998). Learning from text: Matching readers and texts by latent semantic analysis.Discourse Processes,25, 309–336.
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Wolfe, M.B.W., Goldman, S.R. Use of latent semantic analysis for predicting psychological phenomena: Two issues and proposed solutions. Behavior Research Methods, Instruments, & Computers 35, 22–31 (2003). https://doi.org/10.3758/BF03195494
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DOI: https://doi.org/10.3758/BF03195494