Divergent Association Task code

The DAT measures creativity in under 4 minutes. It involves thinking of 10 unrelated words. Creative people choose words with greater semantic distances between them.

See our manuscript in PNAS titled Naming unrelated words predicts creativity for more details.

Online version

Try the task and see your score at datcreativity.com.

Installation

1. Clone this code:

   • git clone https://github.com/jayolson/divergent-association-task.git

2. Install Python 3 and pip.

3. Download the dependencies and model:

   • make install on Unix-like systems

   • or else:

     • pip3 install --user numpy scipy

     • Download and extract glove.840B.300d.zip from https://nlp.stanford.edu/projects/glove/

4. Try it:

   • python3 examples.py

Examples

See examples.py:

import dat

# GloVe model from https://nlp.stanford.edu/projects/glove/
model = dat.Model("glove.840B.300d.txt", "words.txt")

# Compound words are translated into words found in the model
print(model.validate("cul de sac")) # cul-de-sac

# Compute the cosine distance between 2 words (0 to 2)
print(model.distance("cat", "dog")) # 0.1983
print(model.distance("cat", "thimble")) # 0.8787

# Compute the DAT score between 2 words (average cosine distance * 100)
print(model.dat(["cat", "dog"], 2)) # 19.83
print(model.dat(["cat", "thimble"], 2)) # 87.87

# Word examples (Figure 1 in paper)
low = ["arm", "eyes", "feet", "hand", "head", "leg", "body"]
average = ["bag", "bee", "burger", "feast", "office", "shoes", "tree"]
high = ["hippo", "jumper", "machinery", "prickle", "tickets", "tomato", "violin"]

# Compute the DAT score (transformed average cosine distance of first 7 valid words)
print(model.dat(low)) # 50
print(model.dat(average)) # 78
print(model.dat(high)) # 95

Data

For open data (8,900 participants from 98 countries), see https://osf.io/kbeq6/.

Credits

By Jay Olson at Harvard University.

The dictionary (words.txt) is based on Hunspell by László Németh.

License

MIT