Modeling infant object perception as program induction

Jan-Philipp Fränken, Christopher G Lucas, Neil R Bramley, Steven T Piantadosi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Infants expect physical objects to be rigid and persist through space and time and in spite of occlusion. Developmentists frequently attribute these expectations toa “core system” for object recognition. However, it is unclear if this move is necessary. If object representations emerge reliably from general inductive learning mechanisms exposed to small amounts of environment data, it could be that infants simply induce these assumptions very early. Here, we demonstrate that a domain general learning system, previously used to model concept learning and language learning, can also induce models of these distinctive “core” properties of objects after exposure to a small number of examples. Across eight micro-worlds inspired by experiments from the developmental literature, our model generates concepts that capture core object properties, including rigidity and object persistence. Our findings suggest infant object perception may rely on a general cognitive process that creates models to maximize the likelihood of observations.
Original languageEnglish
Title of host publicationProceedings of the Computational Cognitive Neuroscience Society Meeting 2023
Publication statusAccepted/In press - 31 May 2023
EventConference on Cognitive Computational Neuroscience 2023 - Oxford, United Kingdom
Duration: 24 Aug 202327 Aug 2023
https://2023.ccneuro.org/index.php

Conference

ConferenceConference on Cognitive Computational Neuroscience 2023
Abbreviated titleCCN 2023
Country/TerritoryUnited Kingdom
CityOxford
Period24/08/2327/08/23
Internet address

Keywords / Materials (for Non-textual outputs)

  • core knowledge
  • perception
  • vector quantization
  • program induction
  • Bayes

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