Hi!

I'm a third year graduate student in the Linguistics department at the University of Maryland, College Park where I'm also part of the Language Science Center. Broadly, I'm interested in meaning, its acquisition, and the relationship between linguistic and conceptual structure. With tools from formal semantics, psycholinguistics, and psychophysics, I'm looking into the lexical specification and acquisition of different quantifiers, as well as how they interface with extralinguistic cognition. I'm advised by Jeff Lidz and Paul Pietroski.

Research

First- & Second-order Quantifiers: Universal quantifiers like each, every, and all are expressible using the tools of first- or second-order logic. Theorists often abstract away from this distinction, but it's an empirical question whether speakers mentally represent quantifiers in a "format-neutral" way rather than in one particular format. Put another way, is the choice to specify each’s meaning with first- or second-order logic on a par with the choice between two different fonts or is it a psychologically important distinction? Jeff Lidz, Paul Pietroski, Justin Halberda and I argue that the first- / second-order distinction is psychologically realized and has detectible consequences: second-order quantifiers promote representing the items quantified over as a group, which results in better memory for visual properties of groups (like center of mass and approximate cardinality).

More & Most: Relatedly, we've been looking at how the meanings of more and most bias different visual search and memory encoding strategies. One upshot is that when evaluating statements like "more of the dots are blue", adults and kids represent the focused (blue) and non-focused (non-blue) sets and perform a direct comparison. When evaluating statements like "most of the dots are blue", on the other hand, people attend to and represent the focused set (blue dots) and the superset (dots) and perform a proportional comparison. In displays with only two colors, this is a sub-optimal strategy since it introduces more noise into the number estimates than the simple direct comparison would! With Athena Wong, we've begun to extend these predictions to Cantonese quantifiers as well. We take this to be good evidence (1) for a specific meaning specification of proportional quantifiers like most and (2) for the idea that meaning carries some weight in deciding what verification strategy gets deployed. People don't always take the cognitively easiest or otherwise superior route.

Event Concepts & Verb Learning: I'm also working with Laurel Perkins, Mina Hirzel, Alexander Williams, and Jeff Lidz to understand how learners relate syntactic arguments and event participants in verb learning. Do they expect the number of participants perceived in an event to match one-to-one the number of arguments in the clause describing that event? Or do they exploit knowledge of more sophisticated relationships and expect that particular argument positions will name certain participant roles? To this end, we’ve identified events -- like x taking y from z -- that infants plausibly view under a 3-participant concept but that adults often describe with transitive clauses like "The girl took the truck". We’re using these videos to test how learner’s hypotheses about novel verb meanings change depending on what syntactic frame they’re presented in.

Pre-UMD: Before coming to Maryland I studied Cognitive Science at Johns Hopkins. I managed Justin Halberda's Vision lab and worked with him on a number of projects, many of which were related to the Approximate Number System. I was also fortunate enough to work with Akira Omaki and Emily Atkinson on a project investigating the relationship between working memory and parsing.

Presentations

Click the icons for PDFs of abstracts (), slides (), and posters ()

Talks

Knowlton, T., Halberda, J., Pietroski, P., and Lidz, J. (2018) Acquiring the universal quantifiers: every part together or each part on its own? BUCLD 43, Boston, MA. 

Knowlton, T. (2018) Are natural language quantifiers first- or second-order? McDonnell Network Focused Workshop on “The Development of Set and Quantifier Representations”, Johns Hopkins. 

Posters

[Upcoming] Knowlton, T., Pietroski, P., Halberda, J., and Lidz, J. (2019) Representational Format and Universal Quantifiers. LSA Annual Meeting, New York, NY. 

Perkins, L., Knowlton, T., Williams, A., and Lidz, J. (2018) Matching number vs. linking roles: Using 3-participant scene percepts to understand infants’ bootstrapping. BUCLD 43, Boston, MA. 

Knowlton, T., Perkins, L., Williams, A., and Lidz, J. (2018) Getting a grip on infants’ event representations: participant number in TAKE and PICK-UP. XXI ICIS Biennial Meeting, Philadelphia, PA. 

Knowlton, T., Wong, A., Halberda, J., Pietroski, P., and Lidz, J. (2018) Different Determiners, Different Algorithms: Two Majority Quantifiers in Cantonese Bias Distinct Verification Strategies. 31st Annual CUNY Conference, UC Davis. 

Knowlton, T., Halberda, J., Pietroski, P., and Lidz, J. (2017) Sentences, Centers, and Sets: Set Selection and the Meanings of More and Most. CDS 10th Biennial Meeting, Portland, OR. 

Knowlton, T., Halberda, J., Pietroski, P., and Lidz, J. (2017) Distinguishing First- from Second-order Specifications of Each, Every, and All. The Seventh MACSIM, Georgetown. 

Knowlton, T., Halberda, J., Pietroski, P., and Lidz, J. (2017) Set Selection and Storage Reflect Differences in Quantifier Meanings. McDonnell Network Plenary Workshop on “The Ontogenetic Origins of Combinatorial Thought”, UCSD. 

Perkins, L., Knowlton, T., Hirzel, M., Dudley, R., Williams, A., and Lidz, J. (2017) Linguistic and Conceptual Structure in Verb Learning. McDonnell Network Plenary Workshop on “The Ontogenetic Origins of Combinatorial Thought”, UCSD. 

Knowlton, T. and Omaki, A. (2016) The Parser's Dilemma: Memory vs. Grammatical Constraints in Sentence Processing. PURA poster session, Johns Hopkins. 

Contact Info

  • Email

    tzknowlt@umd.edu
  • Address

    1413H Marie Mount Hall
    7814 Regents Drive
    College Park, MD 20742