| Title |
The semantic priming project
|
|---|---|
| Published in |
Behavior Research Methods, January 2013
|
| DOI | 10.3758/s13428-012-0304-z |
| Pubmed ID | |
| Authors |
Keith A. Hutchison, David A. Balota, James H. Neely, Michael J. Cortese, Emily R. Cohen-Shikora, Chi-Shing Tse, Melvin J. Yap, Jesse J. Bengson, Dale Niemeyer, Erin Buchanan |
| Abstract |
Speeded naming and lexical decision data for 1,661 target words following related and unrelated primes were collected from 768 subjects across four different universities. These behavioral measures have been integrated with demographic information for each subject and descriptive characteristics for every item. Subjects also completed portions of the Woodcock-Johnson reading battery, three attentional control tasks, and a circadian rhythm measure. These data are available at a user-friendly Internet-based repository ( http://spp.montana.edu ). This Web site includes a search engine designed to generate lists of prime-target pairs with specific characteristics (e.g., length, frequency, associative strength, latent semantic similarity, priming effect in standardized and raw reaction times). We illustrate the types of questions that can be addressed via the Semantic Priming Project. These data represent the largest behavioral database on semantic priming and are available to researchers to aid in selecting stimuli, testing theories, and reducing potential confounds in their studies. |
Mendeley readers
The data shown below were compiled from readership statistics for 180 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 2% |
| Netherlands | 1 | <1% |
| Germany | 1 | <1% |
| Belgium | 1 | <1% |
| United Kingdom | 1 | <1% |
| Unknown | 173 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 40 | 22% |
| Student > Master | 27 | 15% |
| Student > Bachelor | 22 | 12% |
| Researcher | 19 | 11% |
| Student > Doctoral Student | 12 | 7% |
| Other | 31 | 17% |
| Unknown | 29 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Psychology | 80 | 44% |
| Linguistics | 23 | 13% |
| Neuroscience | 12 | 7% |
| Computer Science | 6 | 3% |
| Agricultural and Biological Sciences | 5 | 3% |
| Other | 17 | 9% |
| Unknown | 37 | 21% |
Attention Score in Context
This research output has an Altmetric Attention Score of 7. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 August 2014.
All research outputs
#4,835,823
of 25,373,627 outputs
Outputs from Behavior Research Methods
#673
of 2,525 outputs
Outputs of similar age
#47,354
of 288,068 outputs
Outputs of similar age from Behavior Research Methods
#6
of 29 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,525 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.1. This one has gotten more attention than average, scoring higher than 72% of its peers.
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We're also able to compare this research output to 29 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.