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3 - Approaching Gradience in Acceptability with the Tools of Signal Detection Theory

from Part I - General Issues in Acceptability Experiments

Published online by Cambridge University Press:  16 December 2021

By
Brian Dillon  and
Matthew W. Wagers
Edited by
Grant Goodall
Grant Goodall
Affiliation:
University of California, San Diego
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Summary

This chapter outlines a framework for using signal detection theory (SDT) to guide the design and analysis of acceptability judgment studies in experimental linguistics. It presents a worked example experiment on the syntactic phenomenon of D-linking (discourse linking) and wh-movement. It shows how to derive common SDT measures (like d_sub_a and s), how to do inferential statistics over those measures, and how to find additional theoretical and practical resources.

Keywords

signal detection theoryacceptability judgmentsD-linkinggradience
Type
Chapter
Information
The Cambridge Handbook of Experimental Syntax , pp. 62 - 96
Publisher: Cambridge University Press
Print publication year: 2021

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References

Aarts, B. (2007). Syntactic gradience: The nature of grammatical indeterminacy. Oxford: Oxford University Press.Google Scholar
Alexopoulou, T. & Keller, F. (2007). Locality, cyclicity, and resumption: At the interface between the grammar and the human sentence processor. Language, 83(1), 110–160.Google Scholar
Almeida, D. (2014). Subliminal wh-islands in Brazilian Portuguese and the consequences for syntactic theory. Revista da ABRALIN, 13(2), 55–91.Google Scholar
Baayen, R. H., Davidson, D. J., & Bates, D. M. (2008). Mixed-effects modeling with crossed random effects for subjects and items. Journal of Memory and Language, 59(4), 390–412.Google Scholar
Bader, M. & Häussler, J. (2010). Toward a model of grammaticality judgments. Journal of Linguistics, 46(2), 273–330.CrossRefGoogle Scholar
Bard, E. G., Robertson, D., & Sorace, A. (1996). Magnitude estimation of linguistic acceptability. Language, 71(2), 32–68.Google Scholar
Bock, K. & Middleton, E. L. (2011). Reaching agreement. Natural Language & Linguistic Theory, 29(4), 1033–1069.Google Scholar
Bürkner, P. C. & Vuorre, M. (2019). Ordinal regression models in psychology: A tutorial. Advances in Methods and Practices in Psychological Science, 2(1), 77–101.CrossRefGoogle Scholar
Clark, H. H. (1973). The language-as-fixed-effect fallacy: A critique of language statistics in psychological research. Journal of Verbal Learning and Verbal Behavior, 12(4), 335–359.CrossRefGoogle Scholar
Cowart, W. (1997). Experimental Syntax. Thousand Oaks, CA: Sage.Google Scholar
DeCarlo, L. T. (2002). Signal detection theory with finite mixture distributions: Theoretical developments with applications to recognition memory. Psychological Review, 109(4), 710.CrossRefGoogle ScholarPubMed
DeLong, E. R., DeLong, D. M., & Clarke-Pearson, D. L. (1988). Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach. Biometrics, 44(3), 837–845.Google Scholar
Dillon, B., Andrews, C., Rotello, C. M., & Wagers, M. (2019). A new argument for co-active parses during language comprehension. Journal of Experimental Psychology: Learning, Memory, and Cognition, 45(7), 1271.Google Scholar
Dillon, B., Staub, A., Levy, J., & Clifton, C. Jr. (2017). Which noun phrases is the verb supposed to agree with? Object agreement in American English. Language, 93(1), 65–96.CrossRefGoogle Scholar
Drummond, A. (2013). Ibex farm. Online server: http://spellout.net/ibexfarm.Google Scholar
Dube, C., Rotello, C. M., & Heit, E. (2010). Assessing the belief bias effect with ROCs: It’s a response bias effect. Psychological Review, 117(3), 831.Google Scholar
Efron, B. & Tibshirani, R. (1993). An Introduction to the Bootstrap. London: Chapman & Hall.Google Scholar
Featherston, S. (2008). Thermometer judgments as linguistic evidence. In Riehl, C. M. & Rothe, A. (eds.), Was ist linguistische Evidenz? Aachen: Shaker Verlag, pp. 69–90.Google Scholar
Featherston, S. (2009). Relax, lean back, and be a linguist. Zeitschrift für Sprachwissenschaft, 28(1), 127–32.Google Scholar
Franck, J. (2011). Reaching agreement as a core syntactic process. Natural Language & Linguistic Theory, 29(4), 1071–1086.Google Scholar
Fukuda, S., Goodall, G., Michel, D., & Beecher, H. (2012). Is Magnitude Estimation worth the trouble? In Choi, J., Hogue, E. A., Punske, J., Tat, D., Schertz, J., & Trueman, A., eds., Proceedings of the 29th West Coast Conference on Formal Linguistics. Somerville, MA: Cascadilla Proceedings Project, pp. 328–336.Google Scholar
Gahl, S., Jurafsky, D., & Roland, D. (2004). Verb subcategorization frequencies: American English corpus data, methodological studies, and cross-corpus comparisons. Behavior Research Methods, Instruments, & Computers, 36(3), 432–443.Google Scholar
Goodall, G. (2015). The D-linking effect on extraction from islands and non-islands. Frontiers in Psychology, 5, 1493.Google Scholar
Gordon, P. C., Hendrick, R., & Johnson, M. (2001). Memory interference during language processing. Journal of Experimental Psychology: Learning, Memory, and Cognition, 27(6), 1411.Google ScholarPubMed
Hanley, J. A. & McNeil, B. J. (1983). A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology, 148(3), 839–843.CrossRefGoogle ScholarPubMed
Häussler, J., Grant, M., Fanselow, G., & Frazier, L. (2015). Superiority in English and German: Cross‐language grammatical differences? Syntax, 18(3), 235–265.Google Scholar
Hautus, M. J. (1995). Corrections for extreme proportions and their biasing effects on estimated values of d′. Behavior Research Methods, Instruments, & Computers, 27(1), 46–51.Google Scholar
Hautus, M. J. (1997). Calculating estimates of sensitivity from group data: Pooled versus averaged estimators. Behavior Research Methods, Instruments, & Computers, 29(4), 556–562.CrossRefGoogle Scholar
Heit, E. & Rotello, C. M. (2014). Traditional difference-score analyses of reasoning are flawed. Cognition, 131(1), 75–91.Google Scholar
Jaeger, T. F. (2008). Categorical data analysis: Away from ANOVAs (transformation or not) and towards logit mixed models. Journal of Memory and Language, 59(4), 434–446.Google Scholar
Keller, F. (2000). Gradience in grammar: Experimental and computational aspects of degrees of grammaticality. Doctoral dissertation, University of Edinburgh.Google Scholar
Kush, D., Lohndal, T., & Sprouse, J. (2018). Investigating variation in island effects. Natural Language & Linguistic Theory, 36(3), 743–779.Google Scholar
Langsford, S., Perfors, A., Hendrickson, A. T., Kennedy, L. A., & Navarro, D. J. (2018). Quantifying sentence acceptability measures: Reliability, bias, and variability. Glossa: A Journal of General Linguistics, 3(1). DOI: 10.5334/gjgl.396Google Scholar
Lau, J. H., Clark, A., & Lappin, S. (2017). Grammaticality, acceptability, and probability: A probabilistic view of linguistic knowledge. Cognitive Science, 41(5), 1202–1241.Google Scholar
Liddell, T. M. & Kruschke, J. K. (2018). Analyzing ordinal data with metric models: What could possibly go wrong? Journal of Experimental Social Psychology, 79, 328–348.CrossRefGoogle Scholar
Liu, C. C. & Smith, P. L. (2009). Comparing time-accuracy curves: Beyond goodness-of-fit measures. Psychonomic Bulletin & Review, 16(1), 190–203.CrossRefGoogle ScholarPubMed
Loftus, G. R. (1978). On interpretation of interactions. Memory & Cognition, 6(3), 312–319.CrossRefGoogle Scholar
Ma, H., Bandos, A. I., Rockette, H. E., & Gur, D. (2013). On use of partial area under the ROC curve for evaluation of diagnostic performance. Statistics in Medicine, 32(20), 3449–3458.Google Scholar
Macmillan, N. A. & Creelman, C. D. (2005). Detection Theory: A User’s Guide. Mahwah, NJ: Lawrence Erlbaum.Google Scholar
Macmillan, N. A. & Kaplan, H. L. (1985). Detection theory analysis of group data: Estimating sensitivity from average hit and false-alarm rates. Psychological Bulletin, 98(1), 185.Google Scholar
Macmillan, N. A., Rotello, C. M., & Miller, J. O. (2004). The sampling distributions of Gaussian ROC statistics. Perception & Psychophysics, 66(3), 406–421.Google Scholar
Mauner, G. (1995). Examining the empirical and linguistic bases of current theories of agrammatism. Brain and Language, 50(3), 339–368.Google Scholar
McElree, B. (2000). Sentence comprehension is mediated by content-addressable memory structures. Journal of Psycholinguistic Research, 29(2), 111–123.Google Scholar
McElree, B., Foraker, S., & Dyer, L. (2003). Memory structures that subserve sentence comprehension. Journal of Memory and Language, 48(1), 67–91.Google Scholar
Melo, F. (2013). Area under the ROC curve. In Dubitzky, W., Wolkenhauer, O., Cho, K. H., & Yokota, H., eds., Encyclopedia of Systems Biology. New York: Springer New York, pp. 38–39.Google Scholar
Pazzaglia, A. M., Dube, C., & Rotello, C. M. (2013). A critical comparison of discrete-state and continuous models of recognition memory: Implications for recognition and beyond. Psychological Bulletin, 139(6), 1173.Google Scholar
Ratcliff, R., McKoon, G., & Tindall, M. (1994). Empirical generality of data from recognition memory receiver-operating characteristic functions and implications for the global memory models. Journal of Experimental Psychology: Learning, Memory, and Cognition, 20(4), 763.Google ScholarPubMed
Ratcliff, R., Sheu, C. F., & Gronlund, S. D. (1992). Testing global memory models using ROC curves. Psychological Review, 99(3), 518.Google Scholar
Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J. C., & Müller, M. (2011). pROC: An open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics, 12(1), 77.Google Scholar
Rotello, C. M., Heit, E., & Dubé, C. (2015). When more data steer us wrong: Replications with the wrong dependent measure perpetuate erroneous conclusions. Psychonomic Bulletin & Review, 22(4), 944–954.Google Scholar
Schütze, C. T. (1996). The Empirical Base of Linguistics: Grammaticality Judgments and Linguistic Methodology. Chicago: University of Chicago Press.Google Scholar
Schütze, C. T. & Sprouse, J. (2014). Judgment data. In Podesva, R. & Sharma, D., eds., Research Methods in Linguistics. Cambridge: Cambridge University Press, pp. 27–50.Google Scholar
Sorace, A. & Keller, F. (2005). Gradience in linguistic data. Lingua, 115(11), 1497–1524.CrossRefGoogle Scholar
Sprouse, J. (2011). A test of the cognitive assumptions of magnitude estimation: Commutativity does not hold for acceptability judgments. Language 87(2), 274–288.Google Scholar
Sprouse, J. & Almeida, D. (2012). Assessing the reliability of textbook data in syntax: Adger’s Core Syntax. Journal of Linguistics, 48, 609–652.CrossRefGoogle Scholar
Sprouse, J. & Almeida, D. (2017). Design sensitivity and statistical power in acceptability judgment experiments. Glossa: A Journal of General Linguistics, 2(1), 1–32. DOI:10.5334/gjgl.236Google Scholar
Sprouse, J., Caponigro, I., Greco, C., & Cecchetto, C. (2016). Experimental syntax and the variation of island effects in English and Italian. Natural Language & Linguistic Theory, 34(1), 307–344.Google Scholar
Sprouse, J., Schütze, C. T., & Almeida, D. (2013). A comparison of informal and formal acceptability judgments using a random sample from Linguistic Inquiry 2001–2010. Lingua, 134, 219–248.Google Scholar
Sprouse, J., Wagers, M., & Phillips, C. (2012). A test of the relation between working-memory capacity and syntactic island effects. Language, 88, 82–123.Google Scholar
Sprouse, J., Yankama, B., Indurkhya, S., Fong, S., & Berwick, R. C. (2018). Colorless green ideas do sleep furiously: gradient acceptability and the nature of the grammar. Linguistic Review, 35(3), 575–599.CrossRefGoogle Scholar
Stevens, S. S. (1956). The direct estimation of sensory magnitudes: Loudness. American Journal of Psychology, 69(1), 1–25.Google Scholar
Stevens, S. S. (1960). The psychophysics of sensory function. American Scientist, 48(2), 226–253.Google Scholar
Theodoridis, S. & Koutroumbas, K. (2008). Pattern Recognition. Burlington, MA: Academic Press.Google Scholar
Venkatraman, E. S. (2000). A permutation test to compare receiver operating characteristic curves. Biometrics, 56, 1134–1138.Google Scholar
Wagers, M. (2013). Memory mechanisms for wh-dependency formation and their implications for islandhood. In Sprouse, J. & Hornstein, N. (eds.), Experimental Syntax and Island Effects. Cambridge: Cambridge University Press, pp. 161–185.Google Scholar
Wagers, M. & Dillon, B. (in prep). Which sentences do speakers favor? ROC analysis of d-linking in filler–gap integration.Google Scholar
Wagers, M. W. & Phillips, C. (2014). Going the distance: memory and control processes in active dependency construction. The Quarterly Journal of Experimental Psychology, 67(7), 1274–1304.Google Scholar
Warstadt, A., Singh, A., & Bowman, S. R. (2018). Neural network acceptability judgments. arXiv preprint arXiv:1805.12471.Google Scholar
Weskott, T. & Fanselow, G. (2011). On the informativity of different measures of linguistic acceptability. Language, 87(2), 249–273.CrossRefGoogle Scholar

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