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We contrasted the predictive power of three measures of semantic richness-number of features (NFs), contextual dispersion (CD), and a novel measure of number of semantic neighbors (NSN)-for a large set of concrete and abstract concepts on lexical decision and naming tasks. NSN (but not NF) facilitated processing for abstract concepts, while NF (but not NSN)(More)
How do people understand the everyday, yet intricate, behaviors that unfold around them? In the present research, we explored this by presenting viewers with self-paced slideshows of everyday activities and recording looking times, subjective segmentation (breakpoints) into action units, and slide-to-slide physical change. A detailed comparison of the joint(More)
Recent research has challenged the notion that word frequency is the organizing principle underlying lexical access, pointing instead to the number of contexts that a word occurs in (Adelman, Brown, & Quesada, 2006). Counting contexts gives a better quantitative fit to human lexical decision and naming data than counting raw occurrences of words. However,(More)
Given that human memory is fallible, it is likely adaptive for people to preferentially encode, retain, and retrieve important items better than insignificant ones. Using a dynamic decision-making paradigm with a response deadline, we find that humans demonstrate a bias to better remember 1) items with positive rather than negative value, and 2) items with(More)
Circular convolution and random permutation have each been proposed as neurally plausible binding operators capable of encoding sequential information in semantic memory. We perform several controlled comparisons of circular convolution and random permutation as means of encoding paired associates as well as encoding sequential information. Random(More)
Semantic space models of lexical semantics learn vector representations for words by observing statistical redundancies in a text corpus. A word's meaning is represented as a point in a high-dimensional semantic space. However, these spatial models have difficulty simulating human free association data due to the constraints placed upon them by metric(More)
The diversity of ways in which toponyms are specified often results in mismatches between queries and the place names contained in gazetteers. Search terms that include unofficial variants of official place names, unanticipated transliterations, and typos are frequently similar but not identical to the place names contained in the gazetteer. String(More)
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.(More)
Human ratings of valence, arousal, and dominance are frequently used to study the cognitive mechanisms of emotional attention, word recognition, and numerous other phenomena in which emotions are hypothesized to play an important role. Collecting such norms from human raters is expensive and time consuming. As a result, affective norms are available for(More)