My research is focused on human learning, specifically how people learn the structure of their world. My work draws heavily from statistics and machine learning, particularly reinforcement learning, non-parametric Bayesian methods and neural networks as inspiration for computational descriptions of learning. I am deeply interested in how people do what they do and how we can describe behavior in multiple ways, either in mechanistic terms of interacting groups of cells or more broadly through the language of statistics.
- Franklin NT, Frank MJ (submitted). Generalizing to generalize: humans flexibly switch between compositional and conjunctive structures during reinforcement learning. bioRxiv [paper, code]
- Schulz E*, Franklin NT*, Gershman SJ (submitted). Finding structure in multi-armed bandits. bioRxiv [paper, code]
- Franklin NT, Norman KA, Ranganath C, Zacks JM, Gershman SJ (in press). Structured event memory: a neuro-symbolic model of event cognition. Psychological Review [paper, code (model), code (vae)]
- Franklin NT, Frank MJ (2018). Compositional clustering in task structure learning. PLOS Comp Bio [paper, code]
- Franklin NT, Frank MJ (2015). A cholinergic feedback circuit to regulate striatal population uncertainty and optimize reinforcement learning. eLife [paper, code]
- Teslovich T, Mulder M, Franklin NT, Ruberry EJ, Millner A, Somerville LH, Simen P, Durston S, Casey BJ (2014). Adolescents let sufficient evidence accumulate before making a decision when large incentives are at stake. Developmental Science
- Casey BJ, Somerville LH, Gotlib IH, Ayduk O, Franklin NT, Askren MK, Jonides J, Berman MG, Wilson NL, Teslovich T, Glover G, Zayas V, Mischel W, & Shoda Y (2011) Behavioral and neural correlates of delay of gratification 40 years later. PNAS