Presentation Outline

• Quick overview of the project
• Specific description of stimuli selection
• Project progress
• Questions

Semantic Priming

• Semantic priming has a rich history in cognitive psychology
• Semantic priming occurs when response latencies are facilitated (faster) for related word-pairs than unrelated word-pairs
• Usually measured with the lexical decision or naming task
• The Semantic Priming Project (Hutchison et al., 2013) provided priming values for 1661 English word-pairs
• https://psa007.psysciacc.org/

Previous Datasets

• Recent increase in publication of linguistic norms has lead to the availability of these norms for many variables
• Multi-linguistic overlap is poor

SPAML Goals

• Outcomes:

• Create an online framework for data collection, modeling after the success of the Small World of Words (De Deyne et al., 2019)
• Produce a large, multi-linguistic semantic priming dataset complete with other cognitive variables
• Provide computational packages for researchers to explore and use the datasets, modeled after LexOPS (Taylor et al., 2020)

The Stimuli

• How do you create stimuli for a priming study?
• Similarity: shared meaning between concepts
• Defined by face value DOG-CAT versus DOG-SPOON
• Number of shared features using feature production norms
• Association strength using free association norms
• Co-occurrence using computational models and text corpora

Text Corpora

• Where do you get large, open text corpora that are comparable for calculating similarity?
• The Open Subtitles Corpus (http://opus.nlpl.eu/OpenSubtitles-v2018.php) provides linguistic data for 50+ languages
• Subtitles have shown to be critically useful datasets for word frequency calculation (New et al., 2007; Brysbaert & New, 2009; Keuleers et al., 2010; Cuetos et al., 2012; Van Heuven et al., 2014; Mandera et al., 2015; and more)
• The corpora are freely available to download for use in linguistic projects

Selection Procedure

• First, we decided to select nouns, verbs, adjectives, and adverbs for potential inclusion in the study
• Therefore, we needed to be able to use part of speech tagging on our potential languages
• udpipe is a language package in R that provides part of speech tagging in many languages
##     token  upos
## 1    This   DET
## 2 package  NOUN
## 3      is   AUX
## 5       ! PUNCT

Selection Procedure

• Each language corpus with an available udpipe model was examined for corpus size and the Wikipedia corpus for that language was added to small corpus languages (Afrikaans, Hindi, Armenian, Tamil, Urdu)
• All stop words and numbers were removed
• All words with less than three characters were removed
• Using word frequency, the top 10,000 words in each language were selected
• Forty-four languages in total are included!

Similarity Calculation

• Similarity was calculated by using a FastText model based on the language subtitles and/or Wikipedia combination
• The subs2vec project was used for initial calculations (van Paridon & Thompson, 2021)
• Vector space model of 300 dimensions with 5 count window size
• Other ongoing word is comparing different dimensions and window sizes for models
• Cosine is a distance measure of vector similarity, similar to correlation
• Top five cosine values for each word were calculated from the FastText model

Cross Referencing

• We then used translation to convert each language’s stimuli back to English
• These data were merged together to create a dataset of possible stimuli across all languages
• 1208416 number of pairs were found across the forty-four languages with an average overlap of 3.23% (2.70 to 70.27)
• The pairs were sorted by language overlap to final selection

Cross Referencing

• 1000 pairs were selected:
• Each word was only used once
• Words were not different forms of the same word (RUNNING - RUN)
• Limit use of proper names

Cross Referencing

• The overlap between languages is still difficult:
• Mean overlap: 28%
• Average words for translation: 710

Thoughts on Selection

• Multi-linguistic research is often in a few selected languages
• Even with advances in published datasets, cross referencing is difficult
• However, by focusing on the available data, we can be guided by the language, rather than selecting English words and simply translating

SPAM-L Update

• The network just got its first shot at the registered report!
• There are nearly 800 comments - whew!
• We will put together the final draft for you to check your information and provide small comments.
• Missed out? Make sure you are getting Canvas notifications so you can join on the second pass of the paper.

SPAM-L Update

• We are going to apply for the ZPID call for projects.
• Thankfully, we’ve written the registered report.
• Their timeline works with our goals.
• This support would allow us to cover more languages!

SPAM-L Update

• We have almost 150 people on the project (and an email box I’ve been ignoring)
• About 60 filled out the information for the registered report
• Languages covered: English, Arabic, Dutch, German, Greek, Italian, Polish, Portuguese, Romanian, Russian, Serbian, Spanish
• We hope to have you involved!

SPAM-L Update

• Timeline:
• Revise the registered report (now, couple weeks)
• Send to network for final thoughts
• ZPID application would be submitted first week of December if we pass the first round
• Submit the registered report to Nature Human Behavior at the same time
• What’s next?
• Translation of final instructions and words
• Programming of the final procedure
• Labs can work on their IRBs
• Then magic! :)