Header logo is

Measuring How People Learn How to Plan


Conference Paper


The human mind has an unparalleled ability to acquire complex cognitive skills, discover new strategies, and refine its ways of thinking and decision-making; these phenomena are collectively known as cognitive plasticity. One important manifestation of cognitive plasticity is learning to make better – more far-sighted – decisions via planning. A serious obstacle to studying how people learn how to plan is that cognitive plasticity is even more difficult to observe than cognitive strategies are. To address this problem, we develop a computational microscope for measuring cognitive plasticity and validate it on simulated and empirical data. Our approach employs a process tracing paradigm recording signatures of human planning and how they change over time. We then invert a generative model of the recorded changes to infer the underlying cognitive plasticity. Our computational microscope measures cognitive plasticity significantly more accurately than simpler approaches, and it correctly detected the effect of an external manipulation known to promote cognitive plasticity. We illustrate how computational microscopes can be used to gain new insights into the time course of metacognitive learning and to test theories of cognitive development and hypotheses about the nature of cognitive plasticity. Future work will leverage our computational microscope to reverse-engineer the learning mechanisms enabling people to acquire complex cognitive skills such as planning and problem solving.

Author(s): Yash Raj Jain and Frederick Callaway and Falk Lieder
Pages: 357--361
Year: 2019
Month: July

Department(s): Rationality Enhancement
Bibtex Type: Conference Paper (conference)
Paper Type: Abstract

Event Name: RLDM 2019

Language: English
State: Published
URL: http://rldm.org/papers/extendedabstracts.pdf


  title = {Measuring How People Learn How to Plan},
  author = {Jain, Yash Raj and Callaway, Frederick and Lieder, Falk},
  pages = {357--361},
  month = jul,
  year = {2019},
  url = {http://rldm.org/papers/extendedabstracts.pdf},
  month_numeric = {7}