Associate Professor, Computer Science Dept. & Cognitive Science Prog.
LH215, Indiana University, Bloomington, IN 47405, USA
(812) 855-7078
gasser@indiana.edu

See also: my personal home page

Ph.D., UCLA, 1988

What I want to try to figure out is where human language comes from, especially as it develops over the long term in the language learner, but also as it emerges over the short term in the course of communicative acts. One very influential position is that we are all born with a significant component of language (or of the concepts that language is about) already in our brains, that all that is left is to figure out the details of the particular language or languages that we're being exposed to. This position is based on the premise that the environment provides the language learner with such an impoverished set of linguistic data that the only way a language could be learned is for much of it to already be there. Of course this position is only tenable if human languages can be shown to share a sizable set of properties, and much of the research within this framework has been dedicated to finding that set of properties and showing that they are already in place as language is acquired.

A competing position, which is growing in influence, is based on the premise that there is considerable regularity in the environment, including regularity in the things that language is about, regularity in the language input itself, and unconscious strategies on the part of adults and older children to make language accessible. In place of innate constraints on language, this view posits that learners have access to powerful, general-purpose statistical learning mechanisms, to sophisticated perceptual and motor control systems, and to a propensity for social interaction. The research within this framework has been dedicated to elaborating the capacities of statistical learning, especially algorithms inspired by nervous systems and to studying the nature and the effects on learning of particular kinds oof regularities in the environment.

From early on I have gravitated to the second framework because it seems to me the simpler, default position. It is already obvious from other work in cognitive science that humans are sophisticated statistical learners. If this capacity and other general-purpose ones suffice, then we could do without the innate knowledge of language proposed by the proponents of the other view. In addition, calling something innate leaves open the question of how that innate knowledge gets implemented in neural hardware and, perhaps more significantly for cognitive science, how it could be linked to experience. Finally, the research in the other paradigm just has not been convincing. The search for what is universal, and presumably innate, in languages has had to resort to such abstract constructs that it seems less and less likely that such things have anything to do with what people know about language, let alone with their innate "endowment".

The strategy, then, is to take a phenomenon, a relatively simple linguistic behavior, and attempt to show how a simple statistical learning device, given plausible input, could acquire it. This normally leads to failure, and the simple device gets augmented in ways that make it more powerful. But power comes not from building in the solution to the problem by providing the knowledge that is needed to solve it. Power comes from special-purpose learning mechanisms and from modularity, from components of the system which learn to specialize as they are exposed to input. My recent projects have looked at three separate areas of linguistic behavior from this perspective.

1. How do words and sentences mean what they mean? One fruitful approach to this question is to treat it from the perspective of the young child. For the child, language takes on significance as it is grounded in experience, in perception, action, and affective states. Two questions of interest in this project are:
   1. How do the visual system and spatial language interact in the learning of spatial concepts?
   2. How do simple perceptual inputs, along with particular linguistic tasks, lead to the orders of acquisition for different forms that are observed in children?
   A major collaborator on all of this has been former IU Cognitive Science PhD student Eliana Colunga. I have also worked increasingly closely with IU Psychological & Brain Sciences Professor Linda B. Smith.
2. How do children learn the internal structure of words, how words are composed out of constituent morphemes (morphology) and how the primitive sounds of a language combine with one another (phonology)? My earlier work focused on the kind of computational device that could achieve this learning. The outcome of this project was a neural-network architecture which could be trained to recognize and produce words which are representative of the kinds of morphological combination that occur in the world's languages. One contribution was the discovery that separate modules responsible for learning roots (the basic forms of words) and for learning inflections (the prefixes, suffixes, etc. that get attached to roots) improved performance for all types of morphological combination. An early collaborator on this project was IU Cognitive Science student Chan-do Lee, now on the faculty at Taejon University, South Korea.
3. How are music and language organized in terms of nested periodic beats, and how do people process this structure? Another earlier project was concerned with developing computational models which can track the rhythm of simple musical and linguistic patterns and learn to beat along. Collaborators included former Cognitive Science PhD student Douglas Eck, now at IDSIA in Switzerland, and IU Linguistics and Computer Science Professor Robert F. Port.

Gasser, M. & Colunga, E. (forthcoming). Pattern learning in infants and neural networks. In P. Quinlan (Ed.), Cognitive approaches to developmental psychology. Psychology Press. [PDF]

Gasser, M. (2002). Computational models of language learning. In D. Chalmers, P. Culicover, R. French, R. Goldstone, & L. Nadel (Eds.), Encyclopedia of Cognitive Science. London: Nature Publishing Group. [zipped MS Word]

Gasser, M., Colunga, E. & Smith, L. B. (2001). Developing relations. In Emile van der Zee & Urpo Nikanne (Eds.), Cognitive interfaces: constraints on linking cognitive information, pp. 185-214. Oxford: Oxford University Press. [HTML]

Gasser, M. & Colunga, E. (2000). Babies, variables, and relational correlations. Annual Conference of the Cognitive Science Society, 22, 160-165. [PDF]

Gasser, M., Eck, D, & Port, R. F. (1999). Meter as mechanism: a neural network that learns metrical patterns Connection Science, 11, 187-216. [PDF]

Gasser, M. & Smith, L. B. (1998). Learning nouns and adjectives: a connectionist account. Language and Cognitive Processes, 13, 269-306. [PDF]

Gasser, M. (1997). Transfer in a connectionist model of the acquisition of morphology. In H. Baayen & R. Schroeder (Eds.), Yearbook of Morphology, 1996, pp. 97-116. Dordrecht: Foris. [PDF]

Gasser, M. (1995). Relating comprehension and production in the acquisition of morphology. In C. Koster & F. Wijnen (Eds.), Proceedings of the Groningen Assembly on Language Acquisition, 1995, pp. 197-206. Groningen: Centre for Language and Cognition. [PDF]

Gasser, M. (1992). Learning distributed representations for syllables. Annual Conference of the Cognitive Science Society, 14, 396-401. [PDF]

Gasser, M., & Lee, C.-D. (1991). A short-term memory architecture for the learning of morphophonemic rules. In R. P. Lippmann, J. E. Moody, & D. S. Touretzky (Eds.), Advances in Neural Information Processing Systems 3, 605-611. San Mateo, CA: Morgan Kaufmann.

Gasser, M. (1990). Connectionism and universals of second language acquisition. Studies in Second Language Acquisition, 12, 179-199. [PDF]

Gasser, M. (1989). Robust lexical selection in parsing and generation. Annual Conference of the Cognitive Science Society, 11, 82-89.

Gasser, M. (1985). Amharic -m and -ss: Morphology, theme, and assumed knowledge. Lingua, 65, 51-106.