It would be cool to develop the idea that the humors are actually closer to the Big Two. The ancients were right, and it only took us 2000 years to show it mathematically. Pretty funny result
One thing that bothers me about a lot of personality psychology is that it is just stirring around in some variance of abstract words, without getting at any dynamics involved.
However, one thing that is interesting about language models is that they can sort-of kind-of bypass that by asking them to write stories. For instance if I ask GPT on OpenAI's playground to...
> Write a detailed, concrete vignette of a person acting in an abusive way.
Then it writes the following story:
---
Jane was exhausted from a long day at work and the last thing she wanted to do was make dinner. When she walked in the door, her husband, John, was sitting on the couch with a cold beer in hand.
John looked up at her and said, "What took you so long? You know I'm hungry. Shouldn't you have been home earlier to make dinner for me?"
Jane was taken aback by his tone and the fact that he hadn't offered to help her with dinner. She tried to explain that she had been working late, but he wouldn't listen. Instead, he started to raise his voice and tell her that she was lazy and irresponsible.
He then stood up and started to walk towards her, getting closer and closer with each step. Jane was terrified and tried to back away, but he kept coming closer, until he was right in front of her. She could feel his breath on her face and he was shouting at her that she was worthless and he couldn't believe he had married her.
At this point, Jane was shaking and crying, and she was too scared to move or speak. John eventually backed away and left the room, leaving Jane in a state of shock and fear.
---
This sort of thing seems like it has the potential to reveal information about personality-descriptive words that is not available when just treating them abstractly.
To an extent, I think the Lexical Hypothesis solves this! The idea is that all of this information is packed into the word "abusive" and when we do factor analysis on a word list that includes abusive we are getting all of the information in that story.
Now, it still requires humans to correctly describe the factors and map it back on to these stories/concepts, which I think psychology is incredibly poor at. They don't even recognize the GFP as obviously more than a statistical artifact, and likely involved in the evolution of our species.
In fact, abusive is one of the words that loads most on the GFP (good example, tail). I interpret this as saying it is the trait that is most selected against by culture. And, very likely, we have been evolving against being abusive for a very long time. This is one reason I'd like to do more multi-lingual work, as it would be interesting to know if, say, Chinese implies a different ranking of values. It would be really hard to say for certain as there are so many moving parts, but I'm open to there being some slight differences in personality structure across the world.
I decided that I wanted to see if there was anything to my point about asking the language models to write detailed vignettes, so I wrote up a program to do so and did a PCA on embeddings of those vignettes. So far I've generated 27 vignettes, though I will probably generate more later.
Unfortunately, though perhaps unsurprisingly, these embeddings were totally dominated by the situation rather than by the behavior. So PC1 was basically the freetime vs work factor, and PC2 was basically the family vs school factor. I have some ideas for how to avoid this problem, but it makes things somewhat difficult. Will provide updates later.
Onto your comment:
"To an extent, I think the Lexical Hypothesis solves this! The idea is that all of this information is packed into the word "abusive" and when we do factor analysis on a word list that includes abusive we are getting all of the information in that story."
😅 I think I'm not as big a fan of the lexical hypothesis as you are. I worry that one erases a lot of information by combining the abstract words with another layer of abstraction from the factor analysis, hence why I tend to want to "dig down" underneath the lexical abstraction.
Update 2: I created something to control for environment (basically embedded the start of the story that contained the environment and then using regression to subtract that before the PCA.)
I created 175 vignettes and extracted 29 principal components (based on a parallel analysis). I then did some varimax stuff to align the components to the vignettes. Also I asked the language models to summarize the vignettes to single sentences to make it easier to read/share quickly without having to read a ton of random blocks of text.
Here's my attempt at interpreting the resulting factors:
Factor 0: Job interview vs party
Factor 1: Meeting
Factor 2: Introverted vs extraverted
Factor 3: Work vs lecture
Factor 4: Competence
Factor 5: Unsure
Factor 6: Healthcare
Factor 7: ?Social influence on other's moods?
Factor 8: Restaurant
Factor 9: Unsure
Factor 10: Shopping
Factor 11: Charity
Factor 12: Only 2 primary loadings so unsure
Factor 13: Unsure
Factor 14: Animal/homeless shelters vs celebrations
Factor 15: Social tension
Factor 16: Possibility of taking sides in a conflict
Factor 17: Studying hard vs ???
Factor 18: Romance vs CEOs encouraging workers to be productive (surely this is a rotation failure...)
Factor 19: Happy atmosphere
Factor 20: Exciting things (e.g. sports, concerts, but also anger)
Factor 21: Unsure
Factor 22: ?Making friends vs making progress??
Factor 23: Something to do with rules and conflict
The big two look like Galen's 4 "humors": sanguine, phlegmatic, melancholy, choleric
Proof that there is always a paper written, no matter how obscure the subject. Two personality researchers argue that Galen's model is a close match to Extroversion and Neuroticism: https://www.sciencedirect.com/science/article/abs/pii/019188699190111N
It would be cool to develop the idea that the humors are actually closer to the Big Two. The ancients were right, and it only took us 2000 years to show it mathematically. Pretty funny result
Totally unrelated to your post, but:
One thing that bothers me about a lot of personality psychology is that it is just stirring around in some variance of abstract words, without getting at any dynamics involved.
However, one thing that is interesting about language models is that they can sort-of kind-of bypass that by asking them to write stories. For instance if I ask GPT on OpenAI's playground to...
> Write a detailed, concrete vignette of a person acting in an abusive way.
Then it writes the following story:
---
Jane was exhausted from a long day at work and the last thing she wanted to do was make dinner. When she walked in the door, her husband, John, was sitting on the couch with a cold beer in hand.
John looked up at her and said, "What took you so long? You know I'm hungry. Shouldn't you have been home earlier to make dinner for me?"
Jane was taken aback by his tone and the fact that he hadn't offered to help her with dinner. She tried to explain that she had been working late, but he wouldn't listen. Instead, he started to raise his voice and tell her that she was lazy and irresponsible.
He then stood up and started to walk towards her, getting closer and closer with each step. Jane was terrified and tried to back away, but he kept coming closer, until he was right in front of her. She could feel his breath on her face and he was shouting at her that she was worthless and he couldn't believe he had married her.
At this point, Jane was shaking and crying, and she was too scared to move or speak. John eventually backed away and left the room, leaving Jane in a state of shock and fear.
---
This sort of thing seems like it has the potential to reveal information about personality-descriptive words that is not available when just treating them abstractly.
To an extent, I think the Lexical Hypothesis solves this! The idea is that all of this information is packed into the word "abusive" and when we do factor analysis on a word list that includes abusive we are getting all of the information in that story.
Now, it still requires humans to correctly describe the factors and map it back on to these stories/concepts, which I think psychology is incredibly poor at. They don't even recognize the GFP as obviously more than a statistical artifact, and likely involved in the evolution of our species.
In fact, abusive is one of the words that loads most on the GFP (good example, tail). I interpret this as saying it is the trait that is most selected against by culture. And, very likely, we have been evolving against being abusive for a very long time. This is one reason I'd like to do more multi-lingual work, as it would be interesting to know if, say, Chinese implies a different ranking of values. It would be really hard to say for certain as there are so many moving parts, but I'm open to there being some slight differences in personality structure across the world.
So just a quick update:
I decided that I wanted to see if there was anything to my point about asking the language models to write detailed vignettes, so I wrote up a program to do so and did a PCA on embeddings of those vignettes. So far I've generated 27 vignettes, though I will probably generate more later.
Unfortunately, though perhaps unsurprisingly, these embeddings were totally dominated by the situation rather than by the behavior. So PC1 was basically the freetime vs work factor, and PC2 was basically the family vs school factor. I have some ideas for how to avoid this problem, but it makes things somewhat difficult. Will provide updates later.
Onto your comment:
"To an extent, I think the Lexical Hypothesis solves this! The idea is that all of this information is packed into the word "abusive" and when we do factor analysis on a word list that includes abusive we are getting all of the information in that story."
😅 I think I'm not as big a fan of the lexical hypothesis as you are. I worry that one erases a lot of information by combining the abstract words with another layer of abstraction from the factor analysis, hence why I tend to want to "dig down" underneath the lexical abstraction.
Update 2: I created something to control for environment (basically embedded the start of the story that contained the environment and then using regression to subtract that before the PCA.)
Here's with 16 dimensions: https://pastebin.com/22VHe2Us
Some more investigations with my method:
I created 175 vignettes and extracted 29 principal components (based on a parallel analysis). I then did some varimax stuff to align the components to the vignettes. Also I asked the language models to summarize the vignettes to single sentences to make it easier to read/share quickly without having to read a ton of random blocks of text.
Here's my attempt at interpreting the resulting factors:
Factor 0: Job interview vs party
Factor 1: Meeting
Factor 2: Introverted vs extraverted
Factor 3: Work vs lecture
Factor 4: Competence
Factor 5: Unsure
Factor 6: Healthcare
Factor 7: ?Social influence on other's moods?
Factor 8: Restaurant
Factor 9: Unsure
Factor 10: Shopping
Factor 11: Charity
Factor 12: Only 2 primary loadings so unsure
Factor 13: Unsure
Factor 14: Animal/homeless shelters vs celebrations
Factor 15: Social tension
Factor 16: Possibility of taking sides in a conflict
Factor 17: Studying hard vs ???
Factor 18: Romance vs CEOs encouraging workers to be productive (surely this is a rotation failure...)
Factor 19: Happy atmosphere
Factor 20: Exciting things (e.g. sports, concerts, but also anger)
Factor 21: Unsure
Factor 22: ?Making friends vs making progress??
Factor 23: Something to do with rules and conflict
Factor 24: Beach/summer/???
Factor 25: ?Homework? vs ???
Factor 26: ???
Factor 27: Horny
Factor 28: ???Game???
Here's the summary stats I made the interpretation based off: https://pastebin.com/vBSbMHCK