Connecting human and machine learning via probabilistic models of cognition

Research output: Contribution to journalConference articlepeer-review

3 Scopus citations

Abstract

Human performance defines the standard that machine learning systems aspire to in many areas, including learning language. This suggests that studying human cognition may be a good way to develop better learning algorithms, as well as providing basic insights into how the human mind works. However, in order for ideas to flow easily from cognitive science to computer science and vice versa, we need a common framework for describing human and machine learning. I will summarize recent work exploring the hypothesis that probabilistic models of cognition, which view learning as a form of statistical inference, provide such a framework, including results that illustrate how novel ideas from statistics can inform cognitive science. Specifically, I will talk about how probabilistic models can be used to identify the assumptions of learners, learn at different levels of abstraction, and link the inductive biases of individuals to cultural universals.

Original languageEnglish (US)
Pages (from-to)9-12
Number of pages4
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
StatePublished - Nov 26 2009
Externally publishedYes
Event10th Annual Conference of the International Speech Communication Association, INTERSPEECH 2009 - Brighton, United Kingdom
Duration: Sep 6 2009Sep 10 2009

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Signal Processing
  • Software
  • Sensory Systems

Keywords

  • Human learning
  • Machine learning
  • Probabilistic models

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