A bit about Daniel Freudenthal
I’m a psychologist with an interest in the simulation of cognitive processes, in particular language acquisition. The main focus of my work is on MOSAIC, a computational model that is used to investigate how children’s early multi-word speech is shaped by the statistical properties of the language they hear. Simulations with MOSAIC have revolved around children’s increasing ability to correctly inflect verbs. Across a range of languages, MOSAIC produces the same types of errors at rates comparable to those in children because it is sensitive to the distributional statistics of the input to which it is exposed in a manner that places great weight on the last items in the speech stream (i.e. has an utterance-final bias, or strong recency effect).
My Role in LuCiD
Within LuCID, I am involved in two strands of research, both of which focus on the German language. The first involves learning the categories of words (e.g. verb or noun) on the basis of the lexical contexts (preceding and following words) in which they occur. A specific focus in this project is to investigate how processing biases and developmental variation affect the categorization process.
The second strand of research involves investigating ways in which MOSAIC can be extended to account for atypical language development. Children with Specific Language Impairment (SLI) are slow to acquire language and, depending on the language, have difficulty with verb inflection. This project examines how details of the inflectional paradigm pose differential challenges for learners of different languages.
LuCiD publications (17) by Daniel Freudenthal
Bidgood, A., Pine, J., Rowland, C., Sala, G., Freudenthal, D. & Ambridge, B. (2021). Verb argument structure overgeneralisations for the English intransitive and transitive constructions: Grammaticality judgments and production priming. Language and Cognition Language and Cognition 13, 397–437.
Freudenthal, D., Ramscar, M., Leonard, L. B. & Pine, J. M. (2021). Simulating the acquisition of verb inflection in typically developing children and children with Developmental Language Disorder in English and Spanish. Cognitive Science, 45(3), e12945.
Pine, J. M., Freudenthal, D. & Gobet, F. (2020). Understanding the cross-linguistic pattern of verb-marking error in typically developing children and children with Developmental Language Disorder: Why the input matters. In C. F. Rowland, B. Ambridge, A. L. Theakston & K. E. Twomey (Eds.), Current perspectives on child language acquisition: How children use their environment to learn. Amsterdam: John Benjamins.
Freudenthal, D., Pine, J. M., & Gobet, F. (2019). Learning cross-linguistic word classes through developmental distributional analysis. In A. Goel, C. Seifert & C. Freska (Eds.), Proceedings of the 41st Annual Conference of the Cognitive Science Society (pp.1773-1779). Montreal, QB: Cognitive Science Society.
Graf, E., Theakston, A., Freudenthal, D. & Lieven, E. (2019). The subject-object asymmetry revisited: Experimental and computational approaches to the role of information structure in children’s argument omissions. IEEE Transactions on Cognitive and Developmental Systems.
Freudenthal, D., Pine, J. M., & Gobet, F. (2018). A computational model of the acquisition of German case. In T. T. Rogers, M. Rau, X. Zhu, & C. W. Kalish (Eds.), Proceedings of the 40th Annual Conference of the Cognitive Science Society (pp. 1687-1692). Austin, TX: Cognitive Science Society.
Freudenthal, D., Pine, J.M. & Gobet, F. (2017). Cross-linguistic learning of word classes from distributional information. Paper presented at the Third LuCiD Language and Communication Development Conference, Lancaster, UK.
Freudenthal, D., Pine, J. M. & Gobet, F. (2016). Incorporating defaulting effects into MOSAIC: Building a two-factor model of the Optional Infinitive stage. Paper presented at the the 2nd LuCiD Language and Communicative Development Conference, Manchester, UK.
Freudenthal, D., Pine, J. M., Jones, G. & Gobet, F. (2016). Simulating developmental changes in noun richness through performance-limited distributional analysis. In A. Papafragou, D. Grodner, D. Mirman, & J. C. Trueswell, (Eds.), Proceedings of the 38th Annual Conference of the Cognitive Science Society (pp. 602-607). Austin, TX: Cognitive Science Society.
Freudenthal, D., Pine, J. M., Jones, G. & Gobet, F. (2016). Developmentally plausible learning of word categories from distributional statistics. In A. Papafragou, D. Grodner, D. Mirman, & J. C. Trueswell, (Eds.), Proceedings of the 38th Annual Conference of the Cognitive Science Society (pp. 674-679). Austin, TX: Cognitive Science Society.
Ambridge, B., Bidgood, A., Pine, J. M., Rowland, C. F. & Freudenthal, D. (2016). Is passive syntax semantically constrained? Evidence from adult grammaticality judgment and comprehension studies. Cognitive Science, 40(6): 1435-59.
Freudenthal, D., Alishahi, A. (2015). Computational Models of Language Acquisition Encyclopedia of Language Development , pp. 92-97.
Freudenthal, D., Pine, J. M., Jones, G. & Gobet, F. (2015). Defaulting effects contribute to the simulation of cross-linguistic differences in Optional Infinitive errors. In D. C. Noelle, R. Dale, A. S. Warlaumont, J. Yoshimi, T. Matlock, C. D. Jennings & P. P. Maglio (Eds.), Proceedings of the 37th Annual Meeting of the Cognitive Science Society (pp. 746-751). Austin, TX: Cognitive Science Society.
Theakston, A., Ibbotson, P., Freudenthal, D., Lieven, E. and Tomasello, M. (2015). Productivity of Noun Slots in Verb Frames. Cognitive Science, 39 (6), 1369-1395.
Freudenthal, D., Pine, J. M., Jones, G. & Gobet, F. (2015). Simulating the cross-linguistic pattern of Optional Infinitive errors in children's declaratives and Wh- questions. Cognition, 143, 61-76.
Ambridge, B., Bidgood, A., Twomey, K. E., Pine, J. M., Rowland, C. F. & Freudenthal, D. (2015). Preemption versus Entrenchment: Towards a construction-general solution to the problem of the retreat from verb argument structure overgeneralization. PLoS ONE, 10 (4): e0123723.
Jones, G., Gobet, F., Freudenthal, D., Watson, S. E. & Pine, J. M. (2014). Why computational models are better than verbal theories: the case of nonword repetition. Developmental Science, 17(2) 298-310.