About Me

I am a researcher at MIT (CoCoSci) studying how people perceive, learn, and reason effectively given limited cognitive resources. I did my graduate work with Steve Piantadosi at UC Berkeley. My thesis work was aimed at understanding the mechanisms underlying visual numerosity perception. I've used behavioral experiments and computational models to link the visual information people gather from a scene to their ultimate perceptions of quantities. In one project, I showed that the psychophysics of number, including "subitizing" small quantities and Weber's law for larger quantities, reflects optimal inference under a limited informational capacity. See an explainer here. You can read a précis of my thesis here.

My recent projects explore how people learn geometric patterns, reason about physical systems, and actively seek information in ways that respect their cognitive limitations. One direction I am particularly excited about is modeling and experimentally testing how people reason in complex settings without holding many pieces of information in memory simultaneously.

Interests

Bayesian & information-theoretic modeling
Visual perception
Numerical cognition
Active information-seeking
Concept learning

Education

Postdoctoral Researcher, 2022-Present
MIT, Computational Cognitive Science Lab

PhD in Psychology, 2016-2022
UC Berkeley, Computation and Language Lab

BS in Cognitive Science, 2012-2016
Carnegie Mellon University

Curriculum Vitae

Project Highlights

Human active learning balances informativeness and interpretability

Informativity-interpretability tradeoff illustration — An illustration of the informativity-interpretability tradeoff. Paths show sequences of intermediate representations from uncertainty to certainty. Higher-information queries reach certainty in fewer steps but require more complex intermediate representations.

A serial, foveal accumulator underlies approximate numerical estimation

Example fixation paths — Example fixation paths. Bottom labels show N (dots shown), F (dots foveated), and E (participant estimate).

A unified account of numerosity perception

Probability of numeric responses over time for N=3, 6, 9 with model fits — The probability (y-axis) of numeric responses (x-axis) over presentation times (faceted) for N=3, N=6, and N=9. Bars are shown for the human data and lines are shown for the model predictions.

Spatiotemporal program learning across development and species

Program induction model and participant predictions across timepoints — Left: Illustration of a program induction model predicting how a 2-D sequence unfolds. Right: Predictions at selected timepoints from each population; older children and adults show more structured predictions; younger children and monkeys tend to track the locally linear trend.

Selected Publications

Spatiotemporal program learning in human adults, children, and monkeys

Mills, T., Coates, N., Silva, A.A., Ji, K., Ferrigno, S., Schulz, L.E., Tenenbaum, J.B., Cheyette, S.J.

(Under Review)

Human active learning trades off informativity and interpretability

Cheyette, S.J., Callaway F.L., Bramley N., Nelson, J., Tenenbaum J.B.

(Under Review)

Limited information-processing capacity in vision explains number psychophysics

Cheyette, S. J., Wu, S., & Piantadosi, S. T.

Psychological Review (2024)

Response to difficulty drives variation in IQ test performance

Cheyette, S. J., Wu, S., & Piantadosi, S. T.

Open Mind (2024)

Spatiotemporal pattern learning as probabilistic program synthesis

Mills, T., Tenenbaum, J. B., & Cheyette, S. J.

Neural Information Processing Systems (2024)

A unified account of numerosity perception

Cheyette, S. J. & Piantadosi, S. T.

Nature Human Behaviour (2020)

Recursive sequence generation in monkeys, children, and native Amizonians

Ferrigno, S., Cheyette, S. J., Piantadosi, S. T., & Cantlon, J.

Science Advances (2020)

A primarily serial, foveal accumulator underlies approximate numerical estimation

Cheyette, S. J. & Piantadosi, S. T.

Proceedings of the National Academy of Sciences (2019)

Modeling the N400 ERP component as transient semantic over-activation within a neural network model of word comprehension

Cheyette, S. J., Plaut, D. C.

Cognition (2017)