T. Florian Jaegerfjaeger@bcs.rochester.edu[personal homepage]
I am interested in how production and comprehension complexity (due to locality; expectation) influences speakers' choice in language variation. I use psycholinguistic experimentation and corpus-based statistical modeling to investigate whether/to what extent speakers use prosodic and syntactic cues to make unexpected information easier to process (predictability; information structure; common ground), and to which extend this is done for their addressees (audience design). For more see my CV.


Klinton Bicknell (BCS)kbicknell@northwestern.edu[personal homepage]
My research seeks to understand the remarkable efficiency of language comprehension, using the tools of probability theory and statistical decision theory as explanatory frameworks. My work suggests that we achieve communicative efficiency by utilizing rich, structured probabilistic information about language: leveraging linguistic redundancy to fill in details absent from the perceptual signal, to spend less time processing more frequent material, and to make predictions about language material not yet encountered.
Bozena Pajak (BCS)bpajak@bcs.rochester.edu[personal homepage]
I am interested in how previously acquired linguistic knowledge affects future language learning. In particular, I adopt a Bayesian perspective on learning, which leads naturally to questions about how learners interpret new language input given their current state of knowledge. My work primarily investigates learning at the phonetic and phonological levels: I use psycholinguistic experiments to study how adults discriminate novel sounds and interpret statistical phonetic regularities in novel language speech given their prior language exposure.
Jenny Roche (BCS)jroche@bcs.rochester.edu[personal homepage]
I am currently interested in interactive communicative behavior during dialogue. My research background has involved varying levels of speech production, from the low-level analysis of affective speech to higher-levels of analysis of language production during dialogue. My most recent projects have included studies involving the production, perception and action on the interpretation of intent (e.g., via affective prosody) and cues to communication breakdown during interactive communication (e.g., alignment and disambiguation during dialogue). Currently, I am exploring other facets of interactive language that involve comprehension and production, as it is influenced by higher-level social factors (e.g., talker specific characteristics).

Grad students

Esteban Buz (BCS)ebuz@bcs.rochester.edu[personal homepage]
My interests are in phonetic production and variation, audience design and computational models of speaker behavior. My current focus is on if speakers behave in a boundedly rational way during phonetic production.
Judith Degen (BCS)jdegen@bcs.rochester.edu[personal homepage]
My research investigates phenomena at the semantics/pragmatics interface. My current focus lies on the processing of scalar implicatures, but I am more generally interested in how speakers and hearers make use of information about the discourse context - especially of each others' intentional states - in comprehension and production. My work combines behavioral, corpus, and formal linguistic methods.
Maryia Fedzechkina (BCS)mashaf@bcs.rochester.edu[personal homepage]
I am interested in the role of processing preferences in language acquisition. I use a combination of computational and behavioral methods to investigate the extent to which processing biases affect language acquisition.
Dave Kleinschmidt (BCS)dkleinschmidt@bcs.rochester.edu[personal homepage]
I'm interested in computational modeling and speech perception, and specifically in developing models of how phonetic categories are learned and deployed that are plausible from linguistic, computational, neural, and developmental perspectives. I'm also interested in how phonetic categories interact with lexical representations.
Ting Qian (BCS)ting.qian@rochester.edu[personal homepage]
I am interested in how people learn information from the environment when the underlying structures in the environment may be changing. Learners in such non-stationary environments are faced with a couple of difficulties. How do they notice the changes in the underlying structure? How quickly should they adapt to the new changes? What should they do with the old information? I study these questions by running game-based experiments and apply Bayesian computational methods to analyze behavioral data.