Object Categorization Processing Differs According to Category Level: Comparing Visual Information Between the Basic and Superordinate Levels

Object category levels comprise a crucial concept in the field of object recognition. Specifically, categorization performance differs according to the category level of the target object. This study involved experiments with two types of stimulus sequences (i.e., forward condition: presenting the target name before the line-drawing stimulus; and reverse condition: presenting the target name after the line-drawing stimulus) for both basic- and superordinate-level categorizations. Adult participants were assigned to each level and asked to judge whether briefly presented stimuli included the same object and target name. Here, we investigated how the category level altered the categorization process. We conducted path analyses using a multivariate multiple regression model, and set our variables to investigate whether the predictors affected the categorization process between two types of stimulus sequence. Dependent variables included the measures of performance (i.e., reaction time, accuracy) for each categorization task. The predictors included dimensions and shapes of the line-drawings, such as primary and local shape information, shape complexity, subject estimation, and other shape variables related to object recognition. Results showed that the categorization process differed according to shape properties between conditions only for basic-level categorizations. For the forward condition, the bottom-up processing of primary visual information depended on matches with stored representations for the basic-level category. For the reverse condition at the basic-level category, decisions depended on subjective ratings in terms of object-representation accessibility. Finally, superordinate-level decisions depended on higher levels of visual information in terms of complexity, regardless of the condition. Thus, the given category level altered the processing of visual information for object recognition in relation to shape properties. This indicates that decision processing for object recognition is flexible depending on the criteria of the processed objects (e.g., category levels).