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Related Experiment Videos

Tracing the identity of objects.

Lance J Rips1, Sergey Blok, George Newman

  • 1Psychology Department, Northwestern University, Evanston, IL 60208, USA. rips@northwestern.edu

Psychological Review
|February 16, 2006
PubMed
Summary
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People judge object identity using a causal continuer model. This model accurately predicts judgments of object identity across various transformations and disassemblies.

Area of Science:

  • Cognitive Science
  • Philosophy of Mind
  • Psychology of Perception

Background:

  • Understanding object identity over time is a fundamental cognitive challenge.
  • Previous theories, like Nozick's (1981), provide a basis for object persistence.
  • Judgments of object identity are crucial for navigating the physical world.

Purpose of the Study:

  • To propose and test a causal continuer model for object identity judgments.
  • To quantitatively and qualitatively assess the model's predictive accuracy.
  • To investigate how people determine if an object at time t(1) is the same as at time t(0).

Main Methods:

  • Development of a causal continuer model based on proximity and closeness of contenders.
  • Quantitative testing of the model using human judgments of object identity (Experiments 1 and 2).

Related Experiment Videos

  • Qualitative evaluation of the model's predictions across radical disassembly and ordinary transformations (Experiments 1-3).

    • Main Results:

    • The causal continuer model accurately predicted human judgments of object identity.
    • The model demonstrated strong predictive power for identity across significant object transformations.
    • Both quantitative and qualitative predictions of the model were validated by experimental data.

    Conclusions:

    • The causal continuer model provides a robust framework for understanding object identity judgments.
    • Causal closeness and the principle of the closest continuer are key factors in object persistence perception.
    • This model offers a parsimonious explanation for object identity across diverse scenarios.