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We ran 4 2x2 preregistered comprehension expts. As predicted, we found (1) a strong knowledge effect in each; and (2) an interaction between knowledge and says-numerals, such that, in partial knowledge, people use what the speaker usually says to temper their interpretations
We provide a simple RSA model, building closely on Goodman & Stuhlmuller’s, whose predictions are a main effect of knowledgeability, and an interaction.
Goodman & Stuhlmuller (G&S, 2016) investigated the effect of *speaker knowledgeability* in scalar implicature of the word "some", by having people make bets on whether all or not all objects had the relevant property in scenarios like:
Omer's claim here is that Ev Fedorenko's localizer relies on the notion of "word".
This is false.
The contrast is between good English (sentences) vs. less English-like (non-words):
The labels for the conditions (sentences, non-words) *don't matter*.
And patients with the syntax deficit (nfvPPA) produce syntax rules with higher frequency than either patients with the lexical deficits (lvPPA and svPPA) or controls. (figs 2B and 2D)
Using these simple metrics, we discovered that patients with the lexical deficits (lvPPA and svPPA) produce words with higher frequency than either patients with the syntax deficit (nfvPPA) or controls. (figs 2A and 2C)
We assumed that the lexical deficit could be measured by lexical frequency. We wanted a comparable syntactic measure: syntactic frequency, using dependency rules automatically extracted from the Stanford Parser’s parses.
The structural accounts, functional/discourse accounts, and processing accounts differ in the answers they provide to a number of general questions about the human capacity of language processing
They and others demonstrate island (unacceptable) super additive interactions for focalizing constructions (e.g., wh-questions) but no interactions for non-focalizing constructions (e.g., relative clauses)
Come see a fascinating discussion about the origins and function of language among Noam Chomsky, Steve Pinker, Ev Fedorenko and Daniel Dor
hosted by Brian Greene
piped.video/watch?v=6LXHtDUX…
If you want evidence for claims, I recommend Fedorenko
There are 3 further experiments, all showing strong effects of construction (declarative vs wh-question or cleft) and verb-frequency, using either a 5-point rating scale or a binary acceptability rating scale.
The Verb-frame Frequency Hypothesis was supported: In an ordinal regression, we found strong effects of construction (declarative rated better than wh-question β=-1.40, Z =-7.04, p<0.001) and verb-frequency (β=0.50, Z =5.89, p<0.001), with no interaction.
To evaluate backgrounded-ness, we ran A&G’s negation test (n=60). The background account predicts a correlation between negation score and the difference in acceptability for the wh-question and declarative versions. We did not find a reliable correlation r=-0.31, p=0.13.
Experiment 1 results n = 120. These results are far from what the syntactic and semantic theories predict. The predicted interactions aren’t close to being there.
Predictions of the four theories:
declarative, bridge / factive / manner: Susan thought / knew / whispered that Anthony liked something.
wh-question, bridge / factive / manner: What did Susan think / know / whisper that Anthony liked?
Veronica Toro Arana is the first ever Puerto Rican Olympic female rower, competing at the Tokyo Olympics:
She was a walk-on lightweight rower at MIT (2016)!
row2k.com/olympics/features/…
She qualified for the quarterfinals in the women's single today. Go Veronica!
People's remarkable general learning ability is part of why we think it's inadequate to hypothesize that exact symbolic number is innate. We know we must be amazing at creating new structured/procedural representations because of everything else people are able to do.
It may be that our biological endowment supports very general structure learning. The fact that the power of human learning has yet to really be understood was the focus of this recent review by Josh Rule, Josh Tenenbaum @MITCoCoSci, and @spiantadocolala.berkeley.edu/papers/r…
None of this is to say that our biology is irrelevant to learning -- after all, we are the only species who learns number like we do. There must be something special about us.
These systems were tied to concrete cultural needs, often economic needs. How do we know that these early symbols weren't number as WE know it? One reason is that some symbols didn't mean numbers as we think about them
... and in creating them, cultures didn't start off with abstract systems that innate logical accounts would naturally predict. They started off with concrete, physical systems. Here's a wonderful example.
Other counting systems only apply to certain kinds of things, which seems unexpected if the abstract, general counting system was innate. "As a rule, they did not count people:"
We also document that there are many diverse forms of counting systems, not just ones based on +1 like you'd expect if that was innate. In particular, languages like Yoruba use *subtraction* in counting.
Our paper highlights some natural predictions of strong numerical nativism which just don't hold. The first is that there are cultures who have NO exact numbers. Here is number elicitation data from the Pirahã, whose quantity words have approximate, context-sensitive meaning
Our hope was to bring cross-cultural phenomena from anthropology into the psychology literature, and evaluate claims that humans are innately endowed with "a little piece of algebra" that defines exact number symbols (here's Leslie, Gelman, & Gallsitel)
This is what we found: in a pre-registered MTurk experiment, we replicated the diff betw Eng / Span redundant color word production n=194 (Eng)/171(Span), and we saw no diff betw Eng/Span redundant number word production n=190(Eng)/164(Span), resulting in interaction
When asked to identify objects having unique shapes and colors among other objects, English speakers often produce redundant color modifiers (“the red circle”) while Spanish speakers produce them less often (“el circulo (rojo)”).
Response to @wmatchin about the "arbitrariness" of the number of macro-regions in the language network (sorry for new thread; twitter doesn't allow a set of tweets as a response)
A house for sale in my neighborhood includes this picture of a bookcase in the house
Who sorts their books by color? It looks cool, but that seems like a poor system for finding a relevant book