Monday, May 4, 2015

Postdoctoral position, Center for Language Science, The Pennsylvania State University


The Center for Language Science (CLS) at The Pennsylvania State University (http://cls.psu.edu/) invites applications for a postdoctoral position. The CLS is home to a cross-disciplinary research program that includes the NSF training program, Partnerships for International Research and Education (PIRE): Bilingualism, mind, and brain: An interdisciplinary program in cognitive psychology, linguistics, and cognitive neuroscience. The program provides training in research on bilingualism that includes an international perspective and that exploits opportunities for collaborative research conducted with one of our international partner sites in the UK (Bangor, Wales), Germany (Mannheim), Spain (Granada and Tarragona), The Netherlands (Nijmegen), Sweden (Lund), and China (Hong Kong and Beijing) and in conjunction with our two domestic partner sites at Haskins Labs and the VL2 Science of Learning Center at Gallaudet University. The successful postdoctoral candidate will have an opportunity to engage in collaborative research within the Center`s international network.

We welcome applications from candidates with preparation in any of the disciplines that contribute to our program. The successful candidate will benefit from a highly interactive group of faculty whose interests include bilingual language processing, language acquisition in children and adults, and language contact, among other topics. Applicants with interests in these topics and with an interest in extending their expertise within experimental psycholinguistics and cognitive neuroscience are particularly welcome to apply. There is no expectation that applicants will have had prior experience in research on bilingualism but we expect candidates to make a commitment to gain expertise in research on bilingualism and also in using neuroscience methods, including both fMRI and ERPs. There is also a possibility of teaching one course during the academic year in the Program in Linguistics.

Questions about faculty research interests may be directed to relevant core training faculty: Psychology: Michele Diaz, Judith Kroll, Ping Li, Janet van Hell, and Dan Weiss; Spanish: Rena Torres Cacoullos, Matt Carlson, Giuli Dussias, John Lipski, Marianna Nadeu, and Karen Miller; Communication Sciences and Disorders: Carol Miller and Chaleece Sandberg; German: Carrie Jackson, Mike Putnam, and Richard Page; French: Marc Authier and Lisa Reed. Administrative questions can be directed to the Director of the Center for Language Science, Judith Kroll:  jfk7@psu.edu. More information about the Center for Language Science (CLS), about the PIRE program, and faculty research programs can be found at http://cls.psu.edu or http:// pire.la.psu.edu.

The initial appointment will be for one year, with a possibility of renewal for a second year depending on the availability of funds. Salary follows NSF/NIH guidelines. The PIRE funding requires that we restrict the search to US citizens only. Applicants should upload a CV, several reprints or preprints, and a statement of research interests. This statement should indicate two or more core faculty members as likely primary and secondary mentors and should describe the candidate`s goals for research and training during a postdoctoral position, including previous experience and directions in which the candidate would like to develop his/her expertise in the language science of bilingualism. Candidates interested in gaining teaching experience should include information on teaching experience and preparation.

Additionally, applicants should arrange for three letters of recommendation to be sent separately to Sharon Elder at sle9@psu.edu.  Review of applications will begin immediately and continue until the position is filled.  The appointment can begin as early as August 1, 2015 but no later than October 1, 2015. Candidates must have completed their Ph.D by the time of appointment.  Apply online at https://psu.jobs/job/57207

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Penn State is an equal opportunity, affirmative action employer, and is committed to providing employment opportunities to all qualified applicants without regard to race, color, religion, age, sex, sexual orientation, gender identity, national origin, disability or protected veteran status.

Friday, May 1, 2015

Is there an evolutionary model for language? The case for "Within Species Comparative Computational Neuroscience"

Comparative Neuroscience is entrenched in our methodological psyche. We regularly use phylogenetically related animals (mice, cats, monkeys) as model systems for understanding our own brain.  Hubel and Wiesel shared a Nobel Prize "for their discoveries concerning information processing in the visual system" not "the cat visual system" (their model animal) because we believe evolution conserves neurocomputational principles including coding strategies, architectures and so on.  Studying mice, cats, and monkeys, we believe, teaches us about the human brain.

For decades, centuries maybe, language scientists have lamented the lack of an animal model for language.  In fact, this was our excuse for why vision scientists seem to have made so much more progress in mapping the neural foundation of their system than ours.  But is it really the case that we don't have an animal model?  Some researchers will quickly point out that birdsong or ultrasonic vocalizations in mice can provide a useful model.

But I suggest we can do better or a least do more by looking for evolutionary homologies to our language system not in other species but in our own brain.

Here's the basic idea:

(1) Neural systems, like the species they inhabit, have a long evolutionary history.
(2) The evolution of neural subsystems (vision, hearing, olfaction, memory, emotion, social cognition, language ...) was not uniform but more klugey.
(3) The evolution of a given subsystem builds on its neurocomputational ancestor systems.
(4) Therefore, just like we find homologies in structural or functional design of related species that reflect their evolutionary lineage, we should find neural homologies in computations and architectures that reflect their neurocomputational lineage.

Language is an interesting case because it evolved so recently compared to other neural systems. Consider that the earliest estimates for the first stages of language evolution are in the range of 1.75 Mya and more typical estimates are roughly equivalent to the appearance of H. Sapiens about 100,000 years ago.  But even if we assume the rudiments of a neural system for language was developing 2 Mya it is quite clear that this system evolved in the context of an already rich neurocomputational system with highly developed sensory and motor, memory, conceptual, and social systems in place.  Specifically, our lineage split with our very bright primate cousin, the chimpanzee, ~5 Mya, which leaves at least an additional 3 million years of brain evolution between our common ancestor with chimps and the (earliest stages) of language evolution.

What this means is that language circuits likely built on top of, and therefore should show homologies to, other systems in our own brain.  And this opens the door to a Comparative Computational Neuroscience program for language: looking to non-linguistic neural systems for clues to the brain organization for language.

This is precisely the approach that gave rise to the Dual Stream model for language, which argues for a homologous organization between language and non-linguistic sensory systems such as the dual stream models of vision and hearing (see here for similar arguments).  Recent work suggesting shared computational principles behind motor control and linguistic processes in speech production is another example.  The fact that we finding what look like homologies provides some evidence that this approach might hold promise.

This does not mean that language can be reduced to sensorimotor circuits any more than the human mind can be "reduced" to the macaque's.  The approach is in fact quite agnostic to the degree of specialization of a system compared to its neurocomputational cousins, making it a potentially useful methodological framework for both the language-is-special and the language-is-not-special crowds.  All it really says is that we can learn something about language systems from studying hearing or vision or motor control, just like we can learn something about human vision from studying cats.