Abstract
Accumulating evidence suggests that category representations are based on features. Distinguishing features are considered to define categories, because of all-or-none responses for objects in different categories; however it is unclear how distinguishing features actually classify objects at various category levels. The present study included 75 animals within three classes (mammal, bird, fish), along with 195 verbal features. Healthy adults participated in memory-based feature-animal matching verification tests. Analyses included a hierarchical clustering analysis, Support Vector Machine, and Independent component analysis to specify features effective for classifications. Quantitative and qualitative comparisons for significant features were conducted between super-ordinate and sub-ordinate levels. The number of significant features was larger for super-ordinate than sub-ordinate levels. Qualitatively, the proportion of biological features was larger than cultural/affective features in both the levels, while the proportion of affective features increased at the sub-ordinate level. To summarize, the two types of features differentially function to establish category representations.
Highlights
IntroductionOur environment is full of natural and artificial objects, and we classify and deal with these objects (e.g., avoid dangerous animals) during our daily lives
Our environment is full of natural and artificial objects, and we classify and deal with these objects during our daily lives
The 75 animals from the mammal, bird, and fish categories, and 195 features were used in the memory-based feature-animal matching verification test
Summary
Our environment is full of natural and artificial objects, and we classify and deal with these objects (e.g., avoid dangerous animals) during our daily lives. Throughout our development, we do not necessarily learn how to classify objects based on instructions from other people; instead, we learn to recognize that dogs and cats, for instance, are classified into the same class. This type of observation suggests that semantic knowledge is constructed with a non-supervised learning mechanism, likely the result of using cue information from overlapping properties across objects (Sloutsky, 2003). Several neuro-cognitive models have been proposed for feature-based object representations (Caramazza and Mahon, 2003) Among these models, distributed memory models have been widely supported (Tyler and Moss, 2001). Many models share this feature-based concept, including the parallel-distributed model (Rumelhart et al, 1986; Farah and McClelland, 1991; Rogers and McClelland, 2004), the feature model (Damasio, 1990), the relevance-based model (Sartori and Lombardi, 2004; Mechelli et al, 2006), and the distributed-plus-hub model (Patterson et al, 2007)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
