Learn 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)
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)
In the last several years pharmacogenetics and pharmacogenomics have attracted the interest of the scientific community and of important pharmaceutical groups. What is the consequence for medicine and for the pharmaceutical industry? What has emerged from this investment, and what can we expect for the future? As with many new technologies, pharmacogenetics(More)
Phenomena in a variety of verbal tasks, e.g., masked priming, lexical decision, and word naming, are typically explained in terms of similarity between word-forms. Despite the apparent commonalities between these sets of phenomena, the representations and similarity measures used to account for them are not often related. To show how this gap might be(More)
Encoding information about the order in which words typically appear has been shown to improve the performance of high-dimensional semantic space models. This requires an encoding operation capable of binding together vectors in an order-sensitive way, and efficient enough to scale to large text corpora. Although both circular convolution and random(More)
This multicentre, observational, cross-sectional study was conducted to determine migraine prevalence in a sample of population presenting to their GPs. The study covered all the patients who visited the GPs practice, for any reason, on 5 consecutive days of 2 different weeks. A total of 71,588 patients were interviewed by 902 GPs. The prevalence of(More)
Recent Semantic Space Models (SSMs) are now integrating perceptual information with linguistic statistics into a unified mental space, offering a solution to the criticism that SSMs are disembodied. However, these new models introduce the problem of illusory feature migrations. When the word dog is perceived, its perceptual features should migrate to hyena,(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)
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)