I. SYMBOLIC & EMBODIED COGNITION
II. LANGUAGE ENCODES PERCEPTUAL INFORMATION
III. PHYSIOLOGICAL AND COGNITIVE MEASURES OF LEARNING
IV. VIRTUAL & MIXED REALITY
VI. EMBODIED CONVERSATIONAL AGENTS
VII. COHESION AND COHERENCE
VIII. CORPUS AND COMPUTATIONAL LINGUISTICS
IX. MISCELLANEOUS
I. SYMBOLIC & EMBODIED COGNITION
Louwerse, M.M. (2018). Knowing the meaning of a word by the linguistic and perceptual company it keeps. Topics in Cognitive Science, 10, 573–589.
Bernabeu, P., Willems, R., & Louwerse, M.M. (2017). Modality switch effects emerge early and increase throughout conceptual processing: Evidence from ERPs. In Proceedings of 39th Annual Meeting of the Cognitive Science Society (CogSci 2017), pp. 1629-1634. Cognitive Science Society.
Louwerse, M.M. & He, X. (2017). 语言加工中的符号相互依存：语言统计和知觉模拟的交互作用 (Symbol interdependency in language processing: Interactions between language statistics and perceptual simulation). Journal of South China Normal University, 2, 51-60.
Louwerse, M. M., Hutchinson, S., Tillman, R., & Recchia, G. (2015). Effect size matters: the role of language statistics and perceptual simulation in conceptual processing. Language, Cognition and Neuroscience, 30, 4, 430-447.
Tillman, R., Hutchinson, S., & Louwerse, M.M. (2015). How sharp is Occam’s razor? Language statistics in cognitive processing. In D.C. Noelle, R. Dale, A. Warlaumont , J. Yoshimi, T. Matlock, C. D. Jennings, & P.P. Maglio (Eds.), Proceedings of the 37th Annual Conference of the Cognitive Science Society (pp. 2404-2409). Austin, TX: Cognitive Science Society.
Hutchinson, S., Tillman, R., & Louwerse, M. (2014). Quick linguistic representations and precise perceptual representations: Language statistics and perceptual simulations under time constraints. Proceedings of the 36th Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society.
Hutchinson, S.C., Wei, L., & Louwerse, M.M. (2014). Avoiding the language-as-a-fixed-effect fallacy: How to estimate outcomes of linear mixed models.In P. Bello, M. Guarini, M. McShane, & B. Scassellati (Eds.), Proceedings of the 36th Annual Conference of the Cognitive Science Society . Austin, TX: Cognitive Science Society.
Hutchinson, S., & Louwerse, M. M. (2013). Statistical linguistic context and embodiment predict metaphor processing but participant gender determines how much. Cognitive Linguistics, 24, 667–687.
Tillman, R., Langston, W., Louwerse, M. (2013). Attribution of responsibility by Spanish and English speakers: How native Language affects our social judgments. Revista Signos, 46, 408-422.
Louwerse, M.M. & Hutchinson, S. (2012). Neurological evidence linguistic processes precede perceptual simulation in conceptual processing. Frontiers in Psychology, 16, 385. doi: 10.3389/fpsyg.2012.00385.
Louwerse, M. M. (2011). Stormy seas and cloudy skies: conceptual processing is (still) linguistic and perceptual. Frontiers in Psychology: Cognition, 2, 1-4.
Louwerse, M.M. (2011). Symbol interdependency in symbolic and embodied cognition. Topics in Cognitive Science (TopiCS), 3, 273-302.
Hutchinson, S., Johnson, S., & Louwerse, M. M. (2011). A linguistic remark on SNARC: Language and perceptual processes in Spatial-Numerical Association. In L. Carlson, C. Hoelscher, & T. Shipley (Eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society (pp.1313-1318). Austin, TX: Cognitive Science Society.
Louwerse, M.M. & Jeuniaux, P. (2010). The linguistic and embodied nature of conceptual processing. Cognition, 114, 96-104.
Jeuniaux, P., Dale, R., & Louwerse, M.M. (2009). The role of feedback in learning form-meaning mappings. In N.A. Taatgen & H. van Rijn (Eds.), Proceedings of the 31th Annual Conference of the Cognitive Science Society (pp. 1488-1493). Cognitive Science Society.
Louwerse, M.M. & Van Peer, W. (2009). Incorporated means symbolic and embodied. Reply to Geeraerts. In Brone, G. & Vandaele, J. (Eds.), Cognitive poetics (pp. 451-454). Berlin, Germany: De Gruyter.
Louwerse, M.M. & Van Peer, W. (2009). How cognitive is cognitive poetics? The interaction between symbolic and embodied cognition. In Brone, G. & Vandaele, J. (Eds.), Cognitive Poetics (pp. 423-444). Berlin, Germany: De Gruyter.
Louwerse, M. M., & Jeuniaux, P. (2008). Language comprehension is both embodied and symbolic. In M. de Vega, A. Glenberg, & A. C. Graesser (Eds.), Embodiment and meaning: A debate (pp. 309-326). Oxford: Oxford University Press.
Louwerse, M.M. & Jeuniaux, P. (2008). How fundamental is embodiment to language comprehension? Constraints on embodied cognition. In V.Sloutsky, B. Love, & K. McRae (Eds.), In B. C. Love, K. McRae, & V. M. Sloutsky (Eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society (pp.1313-1318). Austin, TX: Cognitive Science Society.
Louwerse, M.M. (2007). Symbolic or embodied representations: A case for symbol interdependency. In T. Landauer, D. McNamara, S. Dennis, & W. Kintsch (Eds.). Handbook of latent semantic analysis (pp. 107-120). Mahwah, NJ: Erlbaum.
Louwerse, M.M., Cai, Z., Hu, X., Ventura, M., & Jeuniaux, P. (2006). Cognitively inspired natural-language based knowledge representations: Further explorations of Latent Semantic Analysis. International Journal of Artificial Intelligence Tools, 15,1021-1039
Louwerse, M.M. & Ventura, M. (2005). How children learn the meaning of words and how LSA does it (too). Journal of Learning Sciences, 14, 301-309.
Louwerse, M.M., Cai, Z., Hu, X., Ventura, M., & Jeuniaux, P. (2005). The embodiment of amodal symbolic knowledge representations. In I. Russell & Z. Markov (Eds.), Proceedings of the 18th International Florida Artificial Intelligence Research Society (pp. 542-547). Menlo Park, CA: AAAI Press.
Recchia, G. & Louwerse, M.M. (2016). Archaeology through computational linguistics: Inscription statistics predict excavation sites of Indus Valley artifacts. Cognitive Science, 40, 2065-2080.
Tillman, R. & Louwerse, M. (2018). Estimating emotions through language statistics and embodied cognition. Journal of Psycholinguistic Research, 47, 159-167.
Hutchinson, S. Louwerse, M.M. & (2018). Extracting social networks from language statistics. Discourse Processes, 55, 607-618.
Louwerse, M., & Qu, Z. (2016). Estimating valence from the sound of a word: Computational, experimental, and cross-linguistic evidence. Psychonomic Bulletin & Review, 1-7.
Dinnissen, K. & Louwerse, M.M.(2015). The sound of valence: Phonological features predict word meaning. In D.C. Noelle, R. Dale, A. Warlaumont , J. Yoshimi, T. Matlock, C. D. Jennings, & P.P. Maglio (Eds.), Proceedings of the 37th Annual Conference of the Cognitive Science Society (pp. 572-577). Austin, TX: Cognitive Science Society.
Louwerse, M.M. , Raisig, S., Tillman, R. & Hutchinson, S. (2015). Time after time in words: Chronology through language statistics. In D.C. Noelle, R. Dale, A. Warlaumont , J. Yoshimi, T. Matlock, C. D. Jennings, & P.P. Maglio (Eds.), Proceedings of the 37th Annual Conference of the Cognitive Science Society (pp. 1428-1433). Austin, TX: Cognitive Science Society.
Recchia, G., & Louwerse, M. M. (2014). Reproducing affective norms with lexical co-occurrence statistics: Predicting valence, arousal, and dominance. Quarterly Journal of Experimental Psychology, 68 (8), 1584-1598.
Recchia, G., & Louwerse, M. (2014). Grounding the ungrounded: Estimating locations of unknown place names from linguistic associations and grounded representations. Proceedings of the 36th Annual Conference of the Cognitive Science Society (pp. 1270-1275).
Recchia, G., Slater, A. L., & Louwerse, M. (2014). Predicting the good guy and the bad guy: Attitudes are encoded in language statistics. Proceedings of the 36th Annual Conference of the Cognitive Science Society (pp. 1264-1269).
Hutchinson, S., & Louwerse, M. M. (2014). Language statistics explain the spatial-numerical association of response codes. Psychonomic Bulletin and Review, 21, 470-478.
Hutchinson, S., & Louwerse, M. M. (2013). What’s up can be explained by language statistics. In M. Knauff, M. Pauen, N. Sebanz, & I. Washsmuth (Eds.), In P. Bello, M. Guarini, M. McShane, & B. Scassellati (Eds.), Proceedings of the 36th Annual Conference of the Cognitive Science Society . Austin, TX: Cognitive Science Society.
Tillman, R., Hutchinson, S., Jordan, S. & Louwerse, M. M. (2013). Emotion shifts are also language based: An experiment and corpus linguistic study. In M. Knauff, M. Pauen, N. Sebanz, & I. Washsmuth (Eds.), Proceedings of the 35th Annual Conference of the Cognitive Science Society (pp. 3551-3556). Austin, TX: Cognitive Science Society.
Tillman, R., Hutchinson, S., & Louwerse, M. M. (2013). Geographical locations are encoded in statistical linguistic frequencies. In M. Knauff, M. Pauen, N. Sebanz, & I. Washsmuth (Eds.), Proceedings of the 35th Annual Conference of the Cognitive Science Society (pp. 3557-3562). Austin, TX: Cognitive Science Society.
Louwerse, M. M. & Benesh, N. (2012). Representing spatial structure through maps and language: Lord of the Rings encodes the spatial structure of Middle Earth. Cognitive Science, 36, 1556-69.
Hutchinson, S., & Louwerse, M. M. (2012). The upbeat of language: Linguistic context and perceptual simulation predict processing valence words. In N. Miyake, D. Peebles, & R. P. Cooper (Eds.), Proceedings of the 34th Annual Conference of the Cognitive Science Society (pp. 1709-1714). Austin, TX: Cognitive Science Society.
Hutchinson, S., Datla, V., & Louwerse, M. M. (2012). Social networks are encoded in language. In N. Miyake, D. Peebles, & R. P. Cooper (Eds.), Proceedings of the 34th Annual Conference of the Cognitive Science Society (pp. 491-496). Austin, TX: Cognitive Science Society.
Louwerse, M. M., Hutchinson, S., & Cai, Z. (2012). The Chinese route argument: Predicting the longitude and latitude of cities in China and the Middle East using statistical linguistic frequencies. In N. Miyake, D. Peebles, & R. P. Cooper (Eds.), Proceedings of the 34th Annual Conference of the Cognitive Science Society (pp. 695-700). Austin, TX: Cognitive Science Society.
Tillman, R., Datla, V., Hutchinson, S., & Louwerse, M. M.(2012). From head to toe: Embodiment through statistical linguistic frequencies. In N. Miyake, D. Peebles, & R. P. Cooper (Eds.), Proceedings of the 34th Annual Conference of the Cognitive Science Society (pp. 2434-2439). Austin, TX: Cognitive Science Society.
Louwerse, M.M. & Connell, L. (2011). A taste of words: Linguistic context and perceptual simulation predict the modality of words. Cognitive Science, 35, 381-398.
Louwerse, M.M. & Zwaan, R.A. (2009). Language encodes geographical information. Cognitive Science, 33, 51-73.
Louwerse, M.M. (2008). Embodied representations are encoded in language. Psychonomic Bulletin and Review, 15, 838-844.
III. PHYSIOLOGICAL AND COGNITIVE MEASURES OF LEARNING
Tinga, A. M., de Back, T. T., & Louwerse, M. M. (2019). Non-invasive neurophysiological measures of learning: A meta-analysis. Neuroscience & Biobehavioral Reviews.
Tinga, A.M., Nyklíček, I., Jansen, M.P., de Back, T., Louwerse, M.M. (2018). Respiratory biofeedback does not facilitate lowering arousal in meditation through virtual reality. Applied Psychophysiology and Biofeedback.
Tinga, A. M., Menger, N. S., de Back, T. T., & Louwerse, M. M. (2023). Age Differences in Learning-Related Neurophysiological Changes. Journal of Psychophysiology, 1-14.
Tinga, A.M., Clim, M., de Back, T.T., & Louwerse, M.M. (2021). Measures of prefrontal functional near-infrared spectroscopy in visuomotor learning. Experimental Brain Research, 239, 1061-1072.
Tinga, A. M., de Back, T. T., & Louwerse, M. M. (2020). Neurophysiological changes in visuomotor sequence learning provide insight in general learning processes: Measures of brain activity, skin conductance, heart rate and respiration. International Journal of Psychophysiology, 151, 40-48. doi:10.1016/j.ijpsycho.2020.02.015
Tinga, A. M., de Back, T. T., & Louwerse, M. M. (2020). Non-invasive neurophysiology in learning and training: Mechanisms and a SWOT analysis. Frontiers in Neuroscience, 14, 589. doi:10.3389/fnins.2020.00589
IV. VIRTUAL & MIXED REALITY
De Back, T. T., Tinga, A. M., & Louwerse, M. M. (2021). Learning in immersed collaborative virtual environments: Design and implementation. Interactive Learning Environments, 1–19.
De Back, T. T., Tinga, A. M., & Louwerse, M. M. (2021). CAVE-based immersive learning in undergraduate courses: examining the effect of group size and time of application. International Journal of Educational Technology in Higher Education, 18(1).
De Back, T. de, van Hoef, R., Tinga, A., & Louwerse, M.M. (2018). Presence is key: unlocking performance benefits of immersive virtual reality. In Proceedings of 40th Annual Meeting of the Cognitive Science Society (CogSci 2018), Cognitive Science Society.
De Back, T., Tinga, A., Van Hoef, R., Peters, E., & Louwerse, M. (2018, July). The applicability and benefits of virtual reality for the cognitive sciences. In Proceedings of 40th Annual Meeting of the Cognitive Science Society (CogSci 2018), Cognitive Science Society.
De Back, T. T., Tinga, A. M., Nguyen, P., & Louwerse, M. M. (2020). Benefits of immersive collaborative learning in CAVE-based virtual reality. International Journal of Educational Technology in Higher Education.
V. MULTIMODAL COMMUNICATION
Abney, D. H., Dale, R., Louwerse, M. M., & Kello, C. T. (2018). The bursts and lulls of multimodal interaction: Temporal distributions of behavior reveal differences between verbal and nonverbal communication. Cognitive Science, 143, 1297-1316.
Abney, D., Dale, R., Kello, C., & Louwerse, M.M. (2017). Burstiness across multimodal human interaction reveals differences between verbal and non-verbal communication. In Proceedings of 39th Annual Meeting of the Cognitive Science Society (CogSci 2017), pp. 39-44. Cognitive Science Society.
Louwerse, M. M., Dale, R. A., Bard, E. G., Jeuniaux, P. (2012). Behavior matching in multimodal communication is synchronized. Cognitive Science, 36, 1404-1426.
Louwerse, M.M. & Bangerter, A. (2010). Effects of ambiguous gestures and language on the time course of reference resolution. Cognitive Science, 34, 1517-1529.
Louwerse, M.M., Jeuniaux, P., Zhang, B., Wu, J. & Hoque, M.E. (2008). The interaction between information and intonation structure: Prosodic marking of theme and rheme. In B. C. Love, K. McRae, & V. M. Sloutsky (Eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society (pp.1984-1989). Austin, TX: Cognitive Science Society.
Louwerse, M.M., Benesh, N., Hoque, M.E., Jeuniaux, P., Lewis, G. , Wu, J., & Zirnstein, M. (2007). Multimodal communication in face-to-face conversations. In D. S. McNamara & J. G. Trafton (Eds.), Proceedings of the 29th Annual Cognitive Science Society (pp. 1235-1240). Austin, TX: Cognitive Science Society.
Hoque, M.E., Sorower, M.S., Yeasin, M., & Louwerse, M.M. (2007). What speech tells us about discourse: The role of prosodic and discourse features in dialogue act classification. IEEE International Joint Conference on Neural Networks (IJCNN), 2999-3004.
Louwerse, M.M., McNamara, D.S., Graesser, A.C., Lewis, G., & Zirnstein, M. (2006). An eye for an eye, and for other modalities. In Silva, M. & Cox, A. (Eds.), Proceedings of the Cognitive Science Workshop “What have eye movements told us so far, and what is next?” London, University College London.
Louwerse, M.M., Jeuniaux, P., Hoque, M.E., Wu, J., & Lewis, G. (2006). Multimodal communication in computer-mediated map task scenarios. In R. Sun & N. Miyake (Eds.), Proceedings of the 28th Annual Conference of the Cognitive Science Society (pp. 1717-1722). Mahwah, NJ: Erlbaum.
Jeuniaux, P., Louwerse, M.M., & Hu, X. (2006). The role of discourse structure and response time in multimodal communication. In Gratch, J., Young, M., Aylett, R., Ballin, D., & Olivier, P. (Eds.), Proceedings of the 6th International Conference in Intelligent Virtual Agents (pp. 459-460). New York, Springer.
Hoque, M.E., Yeasin, M., & Louwerse, M.M. (2006). Robust recognition of emotion from speech. In Gratch, J., Young, M., Aylett, R., Ballin, D., & Olivier, P. (Eds.), Proceedings of the 6th International Conference in Intelligent Virtual Agents (pp. 42-53). New York, Springer.
Guhe, M., Steedman, M., Bard, E.G., & Louwerse, M.M. (2006). Prosodic marking of contrasts in information structure. In Schlangen, D. (Ed.), Brandial ’06 Proceedings of the 10th Workshop on the Semantics and Pragmatics of Dialogue (SemDial-10) (pp.179-180). Potsdam: Univ. Verlag.
Louwerse, M.M. & Bangerter, A. (2005). Focusing attention with deictic gestures and linguistic expressions. In B. Bara, L. Barsalou, & M. Bucciarelli (Eds.), Proceedings of the Cognitive Science Society (pp. 1331-1336). Mahwah, NJ: Lawrence Erlbaum.
VI. EMBODIED CONVERSATIONAL AGENTS
Dai, L., Jung, M. M., Postma, M., & Louwerse, M. M. (2022). A systematic review of pedagogical agent research: Similarities, differences and unexplored aspects. Computers & Education, 104607.
Louwerse, M.M., Graesser, A.C., McNamara, D.S. & Lu, S. (2010). Embodied conversational agents as conversational partners. Applied Cognitive Psychology, 23, 1244 – 1255.
Louwerse, M.M., Benesh, N., Watanabe, S., Zhang, B., Jeuniaux, P., & Vargheese, D. (2009). The Multimodal Nature of Embodied Conversational Agents. In N.A. Taatgen & H. van Rijn (Eds.), Proceedings of the 31th Annual Conference of the Cognitive Science Society (pp. 1459-1463). Cognitive Science Society.
Louwerse, M.M., Graesser, A.C., Lu, S., & Mitchell, H.H. (2005). Social cues in animated conversational agents. Applied Cognitive Psychology, 19, 1-12.
Graesser, A.C., Lu, S., Jackson, G.T., Mitchell, H., Ventura, M., Olney, A., & Louwerse, M.M. (2004). AutoTutor: A tutor with dialogue in natural language. Behavioral Research Methods, Instruments, and Computers, 36, 180-193.
Ryder, J.M., Graesser, A.C., Le Mentec, J.-C., Louwerse, M.M., Karnavat, A., Popp, E.A., & Hu, X. (2004). A Dialog-based Intelligent Tutoring System for Practicing Battle Command Reasoning. Army Research Institute Technical Report). Alexandria, VA: US Army Research Institute for the Behavioral and Social Sciences.
Olney , A., Louwerse, M., Mathews, E., Marineau, J., Hite-Mitchell, H., & Graesser, A. (2003). Utterance Classification in AutoTutor. In J. Burstein & C. Leacock (Eds.), Building Educational Applications using Natural Language Processing: Proceedings of the Human Language Technology – North American Chapter of the Association for Computational Linguistics Conference 2003 Workshop, May 31, (pp. 1-8). Philadelphia: Association for Computational Linguistics.
Louwerse, M.M., Graesser, A.C., & the Tutoring Research Group (2003). Language use in intelligent tutoring systems: mixed-initiative dialog in AutoTutor. In A.K. Noor (Ed.), Proceedings of workshop on Advanced learning technologies and learning networks and their impact on future aerospace workforce (pp. 251-276). Hanover, MD: NASA Center for Aerospace Information (NASA/CP-2003-212437).
Graesser, A.C., Jackson, G.T., Mathews, E.C., Mitchell, H.H., Olney, A., Ventura, M., Chipman, P., Franceschetti, D., Hu, X., Louwerse, M.M., Person, N.K., & TRG (2003). Why/AutoTutor: A test of learning gains from a physics tutor with natural language dialog. In R. Alterman & D. Hirsh (Eds.), Proceedings of the 25th Annual Conference of the Cognitive Science Society (pp. 1-5). Boston, MA: Cognitive Science Society.
Olney, A., Person, N., Louwerse, M., & Graesser, A. (2002). AutoTutor: A conversational tutoring environment. Proceedings of the ACL-02 Demonstration Session (pp. 108–109). Philadelphia: Association for Computational Linguistics.
Marineau, J., Olney, A., Louwerse, M., Person, N., Olde, B., Susarla, S., Chipman, P., Graesser, A.C., & TRG (2002). AutoTutor’s log files and categories of language and discourse. In C.P. Rose & V. Eleven (Eds.), Workshop Proceedings of Empirical Methods for Tutorial Dialogue Systems at IRS 2002 (pp. 85-92). San Sebastian, Spain.
Louwerse, M.M., Graesser, A.C., Olney, A., & the Tutoring Research Group (2002). Good computational manners: Mixed-initiative dialog in conversational agents. In C. Miller, Etiquette for Human-Computer Work. Papers from the 2002 Fall Symposium, Technical Report FS-02-02 (pp. 71-76). Menlo Park, CA: AAAI Press.
Vaitonytė, J., Alimardani, M., & Louwerse, M. M. (2022). Scoping review of the neural evidence on the uncanny valley. Computers in Human Behavior Reports, 100263.
Vaitonytė, J., Alimardani, M., & Louwerse, M. M. (2022). Corneal reflections and skin contrast yield better memory of human and virtual faces. Cognitive Research: Principles and Implications, 7, 1-15.
VII. COHESION AND COHERENCE
Graesser, A.C., McNamara, D.S., & Louwerse, M.M. (2011). Methods of automated text analysis. In R. Barr, M.L. Kamil, P.B. Mosenthal, and P.D. Pearson (Eds.), Handbook of reading research (pp. 34-53). New York: Routledge.
McNamara, D.S., Louwerse, M.M., McCarthy, P.M., & Graesser, A.C. (2010). Coh-Metrix: Capturing linguistic features of cohesion. Discourse Processes, 47, 292 – 330.
Louwerse, M.M., & Jeauniaux, P. (2009). Computational psycholinguistic techniques to measure cohesion in discourse. In J. Renkema (Ed.), Discourse of course (pp. 213-223). Amsterdam: Benjamins.
Graesser, A.C., Louwerse, M.M., McNamara, D., Olney, A., Cai, Z., & Mitchell, H. (2007). Inference generation and cohesion in the construction of situation models: Some connections with computational linguistics. In F. Schmalhofer and C. Perfetti (Eds.), Higher level language processes in the brain: Inferences and comprehension processes (pp. 289-310). Mahwah, NJ: Erlbaum.
McNamara, D.S., Cai, Z., & Louwerse, M.M. (2007). Comparing latent and non-latent measures of cohesion. In T. Landauer, D.S. McNamara, S. Dennis, & W. Kintsch (Eds.), Handbook of latent semantic analysis (pp. 379-400). Mahwah, NJ: Erlbaum.
Louwerse, M.M. & Graesser, A.C. (2006), Macrostructure. In: Keith Brown, (Editorin-Chief) Encyclopedia of Language & Linguistics, Second Edition, 7 (pp. 426-429). Oxford: Elsevier.
Louwerse, M.M. & Graesser, A.C. (2005). Coherence in discourse. In Strazny, P. (ed.), Encyclopedia of linguistics. (pp. 216-218). Chicago: Fitzroy Dearborn.
Dufty, D.F., Graesser, A.C., Louwerse, M., & McNamara, D.S., (2006). Is it just readability, or does cohesion play a role? In R. Sun & N. Miyake (Eds.), Proceedings of the 28th Annual Conference of the Cognitive Science Society (pp. 1251-1256). Mahwah, NJ: Erlbaum.
Graesser, A.C., McNamara, D.S., Louwerse, M.M., & Cai, Z. (2004). Coh-Metrix: Analysis of text on cohesion and language. Behavior Research Methods, Instruments, and Computers, 36, 193-202.
McNamara, D.S., Floyd, R.G., Best, R., & Louwerse, M. (2004). World knowledge driving young readers’ comprehension difficulties. In Y.B. Yasmin, W.A., Sandoval, N. Enyedy, A.S. Nixon, & F. Herrera (Eds.), Proceedings of the sixth international conference of the learning sciences: Embracing diversity in the learning sciences (pp. 326-333). Mahwah, NJ: Erlbaum.
Louwerse, M.M., McCarthy, P.M., McNamara, D.S., & Graesser, A.C. (2004). Variation in language and cohesion across written and spoken registers. In K. Forbus, D. Gentner, & T. Regier (Eds.), Proceedings of the twenty-sixth annual conference of the Cognitive Science Society (pp. 843-848). Mahwah, NJ: Erlbaum.
Graesser, A.C., McNamara, D.S., & Louwerse, M.M. (2003). What do readers need to learn in order to process coherence relations in narrative and expository text. In A.P. Sweet and C.E. Snow (Eds.), Rethinking reading comprehension (pp. 82-98). New York: Guilford Publications.
Louwerse, M.M. & Mitchell, H.H. (2003). Towards a taxonomy of a set of discourse markers in dialog: a theoretical and computational linguistic account. Discourse Processes, 35, 199-239.
VIII. CORPUS AND COMPUTATIONAL LINGUISTICS
Carvalho, M.B. & Louwerse, M.M. (2017). Grammar-Based and Lexicon-Based Techniques to Extract Personality Traits from Text. In Proceedings of 39th Annual Meeting of the Cognitive Science Society (CogSci 2017), pp. 1727-1732. Cognitive Science Society.
Price, K.W., Meisinger, E.B., Louwerse, M.M. & D’Mello, S. (2015): The contributions of oral and silent reading fluency to reading comprehension. Reading Psychology, 37, 167-201.
Datla, V., Lin, K. & Louwerse, M. (2014). Linguistic features predict the truthfulness of short political statements. International Journal of Computational Linguistics and Applications, 79-94.
Datla, V., King-Ip Lin, & Louwerse, M. M. (2014). Part of speech induction from distributional features: balancing vocabulary and context. In Proceedings of the 27th Florida Artificial Intelligence Research Society Conference (pp.28-32). Menlo Park, CA: AAAI Press.
Datla, V., King-Ip Lin, & Louwerse, M. M. (2014). Linguistic features predict the truthfulness of short political statements. In A. Gelbukh (ed.), Proceedings of the Conference on Intelligent Text Processing and Computational Linguistics. Berlin: Springer Verlag.
Recchia, G. L., & Louwerse, M. M. (2013). A comparison of string similarity measures for toponym matching. Proceedings of ACM SIGSPATIAL CoMP ’13. Orlando, FL: ACM.
Luno, J., Beck, J. G., & Louwerse, M. M. (2013). Tell us your story: Investigating the linguistic features of trauma narrative (pp. 2955-2960). In M. Knauff, M. Pauen, N. Sebanz, & I. Washsmuth (Eds.), Proceedings of the 35th Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society.
Yang, F., Mo, L., Louwerse, M.M. (2012). Effects of local and global context on processing sentences with subject and object relative clauses. Journal of Psycholinguistic Research, 42, 227-237.
Price, K.W., Meisinger, E.B., D’Mello, S.K., Louwerse, M.M. (2012). Silent reading fluency using underlining: Evidence for an alternative method of assessment. Psychology in the Schools, 49, 606–618.
Lin, K., Datla, V., Morrison, L., & Louwerse, M.M. (2011). Using a feedback system to enhance chart note quality in Electronic Health Records. Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW), 649-654.
Louwerse, M.M., Lin, K., Drescher, A., & Semin, G. (2010). Linguistic cues predict fraudulent events in a corporate social network. In S. Ohlsson & R. Catrambone (Eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society (pp. 961-966). Austin, TX: Cognitive Science Society.
Louwerse, M.M., Crossley, S., & Jeuniaux, P. (2008). What if? Conditionals in educational registers. Linguistics and Education, 19, 56–69.
Louwerse, M.M., Benesh, N., Zhang, B. (2008). Computationally discriminating literary from non-literary texts. In S. Zyngier, M. Bortolussi, A. Chesnokova, J. Auracher (Eds.), Directions in empirical literary studies (pp.175-192). Amsterdam: Benjamins.
Louwerse, M.M. (2007). Disambiguating propositions. Revista Signos, 40, 337-356.
Crossley, S. A., Louwerse, M., & McNamara, D. S. (2008). Identifying linguistic cues that distinguish text types: A comparison of first and second language speakers. Language Research, 44, 361-381.
Louwerse, M.M., Lewis, G. & Wu, J. (2008). Unigrams, bigrams and LSA. Corpus linguistic explorations of genres in Shakespeare’s plays. In Van Peer, W. & Auracher, J. (eds). New directions in literary studies (pp.108-129). Newcastle: Cambridge Scholars Publishing.
Crossley, S.A., Louwerse, M.M., McCarthy, P., & McNamara, D.S. (2007). What is an authentic text: A computational analysis of second language reading texts. Modern Language Journal, 91, 15-30.
Crossley, S.A. & Louwerse, M.M. (2007). Multi-dimensional register classification using collocations. International Journal of Corpus Linguistics, 12, 453–478.
Vanderveen, A., Huff, K., Gierl, M., McNamara, D.S., Louwerse, M.M., & Graesser, A.C. (2007). Developing and validating instructionally relevant reading competency profiles measured by the critical reading section of the SAT. In McNamara, D.S. (Ed.), Reading comprehension strategies: Theory, interventions, and technologies (pp. 137-172). Mahwah, NJ: Erlbaum.
Graesser, A.C., Cai, Z., Louwerse, M., & Daniel, F. (2006). Question Understanding Aid (QUAID): A web facility that helps survey methodologists improve the comprehensibility of questions. Public Opinion Quarterly, 70, 1-20.
Louwerse, M.M. & Van Peer, W. (2006). Thematics. In: Keith Brown, (Editor-in-Chief) Encyclopedia of Language & Linguistics, Second Edition, 12 (pp. 653-658). Oxford: Elsevier.
Louwerse, M.M. & Van Peer (2006). Waar het over gaat in cijfers. Kwantitatieve benaderuingen in tekst- en literatuurwetenschap. [What it is about in numbers: quantitative approaches in text- and literary studies]. Tijdschrift voor Nederlandse Taal- en Letterkunde, 122, 21-35.
McNamara, D.S., Ozuru, Y., Graesser, A.C., & Louwerse, M. (2006). Validating Coh-Metrix. In R. Sun & N. Miyake (Eds.), Proceedings of the 28th Annual Conference of the Cognitive Science Society (pp. 573-578). Mahwah, NJ: Erlbaum.
Louwerse, M.M., Graesser, A.C., McNamara, D.S., Jeuniaux, P., & Yang, F. (2006). Coherence is also in the eye of the beholder. In Silva, M. & Cox, A. (Eds.), Proceedings of the Cognitive Science Workshop “What have eye movements told us so far, and what is next?” London, University College London.
Crossley, S.A., McCarthy, P.M., Lewis, G.A., Dufty, D.F., Louwerse, M.M., & McNamara, D.S. (2006). Detecting manipulated texts. In R. Sun & N. Miyake (Eds.), Proceedings of the 28th Annual Conference of the Cognitive Science Society (p. 2463). Mahwah, NJ: Erlbaum.
Louwerse, M.M. & Crossley, S.A. (2006). Dialog act classification using n-gram algorithms. In Proceedings of the 19th International Florida Artificial Intelligence Research Society.
Penumatsa, P., Ventura, M., Graesser, A.C., Franceschetti, D.R., Louwerse, M., Hu, X., Cai, Z., & the Tutoring Research Group (2004). The right threshold value: What is the right threshold of cosine measure when using latent semantic analysis for evaluating student answers? International Journal of Artificial Intelligence Tools, 12, 257-279.
Louwerse, M.M. (2004). Semantic variation in idiolect and sociolect: Corpus linguistic evidence from literary texts. Computers and the Humanities, 38, 207-221.
Louwerse, M.M. (2004). Un modelo conciso de cohesion en el texto y coherencia en la comprehension [A concise model of cohesion in text and coherence in comprehension]. Revista Signos, 37, 41-58.
Ventura, M., Hu, X., Graesser, A., & Louwerse, M. (2004). The context dependent sentence abstraction model. In K. Forbus, D. Gentner, & T. Regier (Eds.), Proceedings of the twenty-sixth annual conference of the Cognitive Science Society (pp. 1387-1392). Mahwah, NJ: Erlbaum.
Howell, K., Cannon-Bowers, J., Corbett, A., Louwerse, M.M., & Moye, A. (2004). Learning Science and technology R&D: A roadmap to the future of learning. Proceedings for the Frontiers in Education (FIE) 2004 Conference. CD-ROM.
Dufty, D.F., McNamara, D., Louwerse, M., Cai, Z., & Graesser, A.C. (2004). Automated evaluation of aspects of document quality. In S. Tilley & S. Huang (Eds.), Proceedings of the 22nd annual international conference on Documentation (pp. 14-16). New York, ACM.
Cai, Z., McNamara, D. S., Louwerse, M. M., Hu, X., Rowe, M. P., & Graesser, A.C. (2004). NLS: A non-latent similarity algorithm. In K. D. Forbus, D. Gentner, T. Regier (Eds.), Proceedings of the 26th Annual Conference of the Cognitive Science Society (pp. 180-185). Mahwah, NJ: Erlbaum.
Graesser, A.C., Louwerse, M.M., Burger, J., Carroll, J. et al. (2003). Question generation and answering systems: R&D for technology-enabled learning systems. Research roadmap for Federation of American Sciences.
Hu, X., Cai, Z., Franceschetti, D., Penumatsa,P., Graesser, A.C., Louwerse, M.M., McNamara, D.S., & TRG (2003). LSA: The first dimension and dimensional weighting. In R. Alterman and D. Hirsh (Eds.), Proceedings of the 25th Annual Conference of the Cognitive Science Society (pp. 587-592). Boston, MA: Cognitive Science Society.
Hu, X., Cai, Z., Graesser, A.C., Louwerse, M.M., Penumatsa, P., Olney, A. & the Tutoring Research Group (2003). An improved LSA algorithm to evaluate student contributions in tutoring dialogue. In G. Gottlob & T. Walsh (Eds.), Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence (pp.1489-1491). San Francisco: Morgan Kaufmann.
Louwerse, M.M. (2001). An analytic and cognitive parameterization of coherence relations. Cognitive Linguistics, 12, 291–315.
Louwerse, M.M. & Van Peer, W. (2002). Introduction. In M. M. Louwerse & W. van Peer (eds.), Thematics: Interdisciplinary Studies (pp. 1-17). Amsterdam/Philadelphia, John Benjamins.
Louwerse, M.M. (2002). Computational retrieval of themes. In M. M. Louwerse & W. van Peer (eds.), Thematics: Interdisciplinary studies (pp. 189-212). Amsterdam/Philadelphia, John Benjamins.
Jackson, T., Mitchell, H.H., Graesser, A.C., & Louwerse, M. (2002). Improving conversational interaction for intelligent tutoring systems. The 4th Annual Memphis Area Engineering and Science Conference Proceedings.
Graesser, A.C., Hu, X., Olde, B.A., Ventura, M., Olney, A., Louwerse, M., Franceschetti, D.R., & Person, N. (2002). Implementing latent semantic analysis in learning environments with conversational agents and tutorial dialog. . In W.G. Gray and C.D. Schunn (Eds.) Proceedings of the 24th Annual Meeting of the Cognitive Science Society (p. 37). Mahwah, NJ: Erlbaum.
Louwerse, M.M. (2001). Context in causal and diagnostic readings: Cognitive evidence from eye tracking. In: Degand, L., Bestgen, Y., Spooren, W., & Waes, L. (Eds.). Multidisciplinary Approaches to Discourse (pp. 11-26). Amsterdam & Muenster, Uitgaven Stichting Neerlandistiek VU, Nodus.
Graesser, A.C., Karnavat, A.B., Daniel, F.K., Cooper, E., Whitten, S.N., & Louwerse, M. (2001). A computer tool to improve questionnaire design. In Statistical Policy Working Paper 33, Federal Committee on Statistical Methodology (pp. 36-48). Washington, DC: Bureau of Labor Statistics.
Graesser, A.C., Hu, X., Susarla, S., Harter, D., Person, N., Louwerse, M., Olde, B., & the TRG (2001). AutoTutor: An intelligent tutor and conversational tutoring scaffold. Papers from the Workshop on ‘Tutorial Dialog Systems’ at the Artificial Intelligence in Education 2001 Conference (pp.47-49). San Antonio, TX.
Louwerse, M.M. (1999). The source of coherence: Why semantic vs. pragmatic is not part of a cognitive approach to a parameterisation of coherence relations. Working notes. International Workshop on Text Representation (pp. 55-61). University of Edinburgh, July 7-9 1999.
Louwerse, M.M. (1997). Inleiding [Introduction]. In Vladimir Propp, De morfologie van het toversprookje. Vormleer van een genre [The Morphology of the Folktale. Formal Study of a Genre; transl. M. M. Louwerse]. Utrecht, Het Spectrum.
Louwerse, M.M. (1999). Computationele modellen in de literatuurwetenschap: bereken maar! [Computational models in literary studies: Count on it!] Frame, 3, 38-57.
Louwerse, M.M. (1999). Computational thematics: Where to start? Journal of Literary Semantics, 28, 1-19.
IX. MISCELLANEOUS
Mitchell, H.H., Graesser, A.C., Louwerse, M.M. (2010). The effect of context on humor: A constraint-based model of verbal jokes. Discourse Processes, 47, 104 – 129.
Louwerse, M.M. & Kuiken, D. (2004). The effects of personal involvement in narrative discourse. Discourse Processes, 38, 169-172.
Louwerse, M.M. (1997). Bits and pieces: Toward an interactive classification of folktales. Journal of Folklore Research, 34, 245-249.