Processing spatial relations with different apertures of attention

Neuropsychological studies suggest the existence of lateralized networks that represent categorical and coordinate types of spatial information. In addition, studies with neural networks have shown that they encode more effectively categorical spatial judgments or coordinate spatial judgments, if their input is based, respectively, on units with relatively small, nonoverlapping receptive fields, as opposed to units with relatively large, overlapping receptive fields. These findings leave open the question of whether interactive processes between spatial detectors and types of spatial relations can be modulated by spatial attention. We hypothesized that spreading the attention window to encompass an area that includes two objects promotes coordinate spatial relations, based on coarse coding by large, overlapping, receptive fields. In contrast, narrowing attention to encompass an area that includes only one of the objects benefits categorical spatial relations, by effectively parsing space. By use of a cueing procedure, the spatial attention window was manipulated to select regions of differing areas. As predicted, when the attention window was large, coordinate spatial transformations were noticed faster than categorical transformations; in contrast, when the attention window was relatively smaller, categorical spatial transformations were noticed faster than coordinate transformations. Another novel finding was that coordinate changes were noticed faster when cueing an area that included both objects as well as the empty space between them than when simultaneously cueing both areas including the objects while leaving the gap between them uncued.     

Graph‐Theoretic Properties of Networks Based on Word Association Norms: Implications for Models of Lexical Semantic Memory

We compared the ability of three different contextual models of lexical semantic memory (BEAGLE, Latent Semantic Analysis, and the Topic model) and of a simple associative model (POC) to predict the properties of semantic networks derived from word association norms. None of the semantic models were able to accurately predict all of the network properties. All three contextual models over‐predicted clustering in the norms, whereas the associative model under‐predicted clustering. Only a hybrid model that assumed that some of the responses were based on a contextual model and others on an associative network (POC) successfully predicted all of the network properties and predicted a word's top five associates as well as or better than the better of the two constituent models. The results suggest that participants switch between a contextual representation and an associative network when generating free associations. We discuss the role that each of these representations may play in lexical semantic memory. Concordant with recent multicomponent theories of semantic memory, the associative network may encode coordinate relations between concepts (e.g., the relation between pea and bean, or between sparrow and robin), and contextual representations may be used to process information about more abstract concepts.