Rationality Enhancement Conference Paper 2019

How do people learn how to plan?

The mouselab mdp paradigm

How does the brain learn how to plan? We reverse-engineer people's underlying learning mechanisms by combining rational process models of cognitive plasticity with recently developed empirical methods that allow us to trace the temporal evolution of people's planning strategies. We find that our Learned Value of Computation model (LVOC) accurately captures people's average learning curve. However, there were also substantial individual differences in metacognitive learning that are best understood in terms of multiple different learning mechanisms-including strategy selection learning. Furthermore, we observed that LVOC could not fully capture people's ability to adaptively decide when to stop planning. We successfully extended the LVOC model to address these discrepancies. Our models broadly capture people's ability to improve their decision mechanisms and represent a significant step towards reverse-engineering how the brain learns increasingly effective cognitive strategies through its interaction with the environment.

Author(s): Yash Raj Jain and Sanit Gupta and Vasundhara Rakesh and Peter Dayan and Frederick Callaway and Falk Lieder
Year: 2019
Month: September
Project(s):
Bibtex Type: Conference Paper (conference)
Event Name: 2019 Conference on Cognitive Computational Neuroscience
State: Published
Electronic Archiving: grant_archive
Language: English
Links:
Attachments:

BibTex

@conference{Jain2019CCN,
  title = {How do people learn how to plan?},
  abstract = {How does the brain learn how to plan? We reverse-engineer people's underlying learning mechanisms by combining rational process models of cognitive plasticity with recently developed empirical methods that allow us to trace the temporal evolution of people's planning strategies. We find that our Learned Value of Computation model (LVOC) accurately captures people's average learning curve. However, there were also substantial individual differences in metacognitive learning that are best understood in terms of multiple different learning mechanisms-including strategy selection learning. Furthermore, we observed that LVOC could not fully capture people's ability to adaptively decide when to stop planning. We successfully extended the LVOC model to address these discrepancies. Our models broadly capture people's ability to improve their decision mechanisms and represent a significant step towards reverse-engineering how the brain learns increasingly effective cognitive strategies through its interaction with the environment.},
  month = sep,
  year = {2019},
  slug = {jain2019ccn},
  author = {Jain, Yash Raj and Gupta, Sanit and Rakesh, Vasundhara and Dayan, Peter and Callaway, Frederick and Lieder, Falk},
  month_numeric = {9}
}