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When New Experience Leads to New Knowledge: A Computational Framework for Formalizing Epistemically Transformative

Joan Danielle K Ongchoco^1,2, Isaac M Davis², Julian Jara-Ettinger^2,3

¹Department of Psychology, The University of British Columbia.

Open Mind : Discoveries in Cognitive Science

|November 15, 2024

Summary

New experiences can be transformative, teaching agents what they are like. A computational framework using partially observable Markov Decision Processes (POMDPs) distinguishes these from non-transformative learning, offering insights into cognitive science.

Keywords:

computational framework epistemic change learning transformative experience

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Area of Science:

Cognitive Science
Computational Neuroscience
Philosophy of Mind

Background:

Learning can occur through new experiences or by expanding existing knowledge via inference and imagination.
Distinguishing between genuinely novel learning and incremental knowledge acquisition is crucial for understanding cognitive processes.

Purpose of the Study:

To present a computational framework for formalizing the distinction between epistemically transformative and non-transformative experiences.
To identify a measurable "signature" of epistemically transformative experiences.
To explore how prior knowledge impacts learning in new environments.

Main Methods:

Utilizing partially observable Markov Decision Processes (POMDPs) to model agent learning.
Developing a computational framework to formalize epistemic change.
Conducting a synthetic experiment inspired by "Flatland" with agents transitioning between 2D and 3D environments.

Main Results:

Epistemically transformative experiences render prior knowledge obsolete, akin to "learning from scratch."
Non-transformative experiences allow for facilitated learning by leveraging existing knowledge.
A measurable signature was identified for transformative experiences.

Conclusions:

The computational framework successfully formalizes the distinction between transformative and non-transformative learning.
This work provides a novel method for evaluating philosophical concepts of epistemic change using computational tools.
The findings have implications for understanding how agents adapt and learn in novel or expanded environments.