Contrasting effects of feature-based statistics on the categorisation and basic-level identification of visual objects

Abstract

Conceptual representations are at the heart of our mental lives, involved in every aspect of cognitive functioning. Despite their centrality, a long-standing debate persists as to how the meanings of concepts are represented and processed. Many accounts agree that the meanings of concrete concepts are represented by their individual features, but disagree about the importance of different feature-based variables: some views stress the importance of the information carried by distinctive features in conceptual processing, others the features which are shared over many concepts, and still others the extent to which features co-occur. We suggest that previously disparate theoretical positions and experimental findings can be unified by an account which claims that task demands determine how concepts are processed in addition to the effects of feature distinctiveness and co-occurrence. We tested these predictions in a basic-level naming task which relies on distinctive feature information (Experiment 1) and a domain decision task which relies on shared feature information (Experiment 2). Both used large-scale regression designs with the same visual objects, and mixed-effects models incorporating participant, session, stimulusrelated and feature statistic variables to model the performance. We found that concepts with relatively more distinctive and more highly correlated distinctive relative to shared features facilitated basic-level naming latencies, while concepts with relatively more shared and more highly correlated shared relative to distinctive features speeded domain decisions. These findings demonstrate that the feature statistics of distinctiveness (shared vs. distinctive) and correlational strength, as well as the task demands, determine how concept meaning is processed in the conceptual system.

Object-Specific Semantic Coding in Human Perirhinal Cortex

“…The object-specific RSA effects for semantic-features echoes studies showing the greater involvement of the anterior medial temporal lobes when more fine-grained semantic processes are required for object differentiation, especially for more semantically confusable objects (Tyler et al, 2004Moss et al, 2005;Taylor et al, 2006;Barense et al, 2010). This suggests that, in addition to the perirhinal cortex representing fine-grained semantic information about objects (captured by our semantic feature RDM), the region also becomes increasingly engaged for objects that require the most fine-grained differentiation, i.e., those that are most semantically confusable.…”

“…The feature norms contain lists of features associated with a large range of objects (e.g., has 4 legs, has stripes and lives in Africa are features of a zebra; Table 1) and were collected by presenting participants with a written concept name and asking them to produce properties of the concept. As the features were originally collected from North American English speakers, the data were modified to be relevant to native British English speakers (e.g., concepts like "gopher" were removed, whereas other names were changed; Taylor et al, 2012). Based on the feature norms, each object can be represented by a binary vector indicating whether each feature is associated with the object or not.…”

“…The object-specific semantic feature RDM was based on data from the anglicized McRae feature norms (McRae et al, 2005;Taylor et al, 2012). The feature norms contain lists of features associated with a large range of objects (e.g., has 4 legs, has stripes and lives in Africa are features of a zebra; Table 1) and were collected by presenting participants with a written concept name and asking them to produce properties of the concept.…”

“…One type of representational scheme is provided by models in which the semantic similarity between objects is captured with semantic features (e.g., has legs, made of metal) (Tyler and Moss, 2001;Devereux et al, 2013). Object similarity defined by semantic features captures both category structure (as objects from the same category will have overlapping features) and additionally within-category individuation (as each member of a category will have a unique set of features).…”

Similarity of fMRI Activity Patterns in Left Perirhinal Cortex Reflects Semantic Similarity between Words

“…The difference, therefore, between the two studies is not merely a systematic difference in sensitivity per se. It is plausible that the degree to which this region reflects semantic similarity for words or pictures is dynamically influenced by the exact task performed (Taylor et al, 2012;Mano et al, 2013). Alternatively, we may have missed a similarity effect for pictures belonging to a same semantic cluster because similarity between fMRI response patterns is already high even for random pairs (in contrast to what we found for words).…”