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Will the most informative object stand? Determining the impact of structural context on informativeness judgements created by Ronaldo Vigo, Basawaraj

By: Contributor(s): Material type: TextTextSeries: ; Volume , number ,USA : Taylor & Francis; 2013Content type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
Subject(s): Online resources: Summary: A fundamental unsolved problem in the cognitive sciences concerns why, how, and to what extent humans judge object stimuli as conveying different amounts of information. Central to this problem is how the notion of informativeness is conceptualised by humans in the first place. In this paper, we investigate this question from the standpoint of how the structure of categories of objects influences informativeness judgements about their members. Results from our two experiments show that the structural or relational context surrounding single-object cues from a categorical stimulus largely determines such informativeness judgements. Moreover, we found that object cues elicit absolute magnitude judgements about their associated concept that are not consistent with the prototype interpretation of the concept. We were able to account for over 90% of the variance in the data from our two judgement experiments with a general theory and measure of information referred to as Representational Information.
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A fundamental unsolved problem in the cognitive sciences concerns why, how, and to what extent humans judge object stimuli as conveying different amounts of information. Central to this problem is how the notion of informativeness is conceptualised by humans in the first place. In this paper, we investigate this question from the standpoint of how the structure of categories of objects influences informativeness judgements about their members. Results from our two experiments show that the structural or relational context surrounding single-object cues from a categorical stimulus largely determines such informativeness judgements. Moreover, we found that object cues elicit absolute magnitude judgements about their associated concept that are not consistent with the prototype interpretation of the concept. We were able to account for over 90% of the variance in the data from our two judgement experiments with a general theory and measure of information referred to as Representational Information.

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