Congratulations to Professor Jenny Saffran, recipient of the inaugural Jeffrey L. Elman Prize for Scientific Achievement and Community Building of the Cognitive Science Society.
Jenny Saffran received her BA in cognitive science from Brown University, then completed a PhD in psychology at the University of Rochester with Elissa Newport and Richard Aslin. She has been on the faculty of the University of Wisconsin-Madison since 1997. In 2005 she was appointed full professor and in 2018 she became the Vilas Distinguished Achievement Professor.
Saffran’s interests concern a seminal question in cognitive science: How do children acquire language? Acquiring language depends on a combination of innate structure and learning from experience. Saffran and her colleagues developed laboratory methods to study the experiential input to infant language learning to test specific theories about how learning unfolds. Her experimental research demonstrates, quite remarkably, that humans, including infants, acquire language by tracking statistical information available in the environment. For example, infants learn to segment words by relying on statistical probabilities. Across a language, the transitional probability from one sound to the next will generally be greatest when the two sounds follow one another within a word than across words, as when (in English) the sounds ‘preh’ and ‘tee’ are more likely to follow one another within a word (‘pretty’) than are the sounds ‘tee’ and bay’ (although they sometimes do, when we say, ‘pretty baby’).
At the same time, Saffran recognizes that even the most powerful learners are not blank slates, and has sought to delineate constraints on learning. Her experimental results suggest that learners can use statistical cues to acquire hierarchical phrase structure, an abstract component of linguistic syntax. In particular, she has demonstrated that some computations are favored over others, and that these constraints on learning are related to natural language structure. These results support the emerging perspective that, rather than evolving in a vacuum, human languages evolve to fit the human learner. By comparing statistical learning in linguistic versus nonlinguistic domains, her research offers a direct, innovative way to test the hypothesis that the uniqueness of human language resides in the nature of human learning and not in some specialized language-specific modules within the brain.
Saffran is a community builder in her teaching, in her lab, at the University of Wisconsin, and for cognitive science. First, she has worked tirelessly to improve undergraduate students’ experiences at the university. For example, she developed an innovative peer-tutoring program in her large-enrollment Child Development class. Second, as leader of a very active and productive lab, she has built a supportive and nurturing community for training the next generation of scholars. Saffran encourages students to develop their own research questions and ideas, and she serves as a supportive guide along this path. Third, she plays a central role in her department and at her university. For example, she played a key role in the recent reorganization of the Language Sciences program at Wisconsin. Fourth, Saffran is a generous collaborator who has forged close ties with researchers who study developmental disorders, in an effort to understand the underlying causes of language delays and differences, she collaborates with researchers who study animal cognition, and has worked on methodological issues in infant research. In all aspects, Saffran advances the cognitive science community.
This honor will be celebrated at the CogSci 2020 conference in Toronto with a prize and dedicated symposium.
Adapted from the Cognitive Science Society press release issued January 21.