For Experiment 2

Use tab Expt 2 with training of the excel file.
Run the numbers with Training and NoTraining combined. Then run the numbers excluding NoTraining. See which one gives you better verb variation and a stronger effect. Do the better instructions do anything to our values?

Trial

2_agent_gab_gauge
2: experiment 2
agent: manipulating the name of the agent
morph: morphological overlap

gab: First 2 letters = agent (Gabe), last letter = recipient (boy)
gauge: the object transferred

Codes:

Ga = Gabe
Ha = Hannah
Si = Simon
Pa = Patty
B = boy
W= woman

Trial Type

For right now we are only interested in “agent” trials agent: manipulating the name of the agent Patti/Gabe/Hannah
morph: morphological overlap

Codes for Phonetic Matches

PhonMatch_Intended_Agent
PhonMatch_actual_Agent
PhonMatch_Intended_theme
PhonMatch_actual_theme

0=none
1= initial phonetic match

Total #Disfluencies

Column Name = TotalNumDisfluencies. The number of different disfluencies participants produced in their sentences.

Loc Disfluencies

Currently contains 6 columns:
For all of these columns except 'other' a 1 indicates the presence of a disfluency in this location.

In the case of word restarts, the last noun that the person chooses is considered the real head. For example in: Gabe gave the boy a fence / a cage / gate@wr. We count this as being before the second NP, even though we have 3 attempts at the 2nd noun phrase.

In the case of word lengthenings, we assume that the reason the word is extended is because of upcoming problems. Therefore in: Gabe gave@ln the boy a gate. We count this as a disfluency after the verb and before the first noun phrase.

Column Name	Explanation	Examples
NP-SUBJdisf	Receives a 1 for all disfluencies before the first noun.	Yes: Um@fp Gabe gave the woman a pan . Yes: Gabe was going to oo sorry Gabe@sr gave the boy a harp. No: There is a boy wearing um@fp blue shorts. Hannah passes him the pan .
VERBdisf	Receives a 1 for all disfluencies between the first noun and the verb. Do NOT count verb restarts here.	Yes: Gabe / pushes over the shovel.
POSTVB-NP1disf	Receives a 1 if there is a disfluency between the verb and the first noun phrase.	Yes: Patty handed@ln / the pan to a woman. Yes: Gabe pushes / the rainbow. YES: Gabe gave a pot / a pan@wr to the woman.
POSTVB-NP2disf	Receives a 1 if there is a disfluency between the first noun and the second noun.	Yes: Gabe gave a heart / to a woman . Yes: Patty passed a boy@ln a globe .
Other	Count of disfluencies that occur in other parts of the sentence. (Receives numbers > 0)

Examples:
Hannah gave / the child a uh@fp / pipe.
Receives a 1 for POSTVB-NP1disf, and a 1 for POSTVB-NP2. Other = 0. Total Disfluencies = 3.

Gabe@ln punched /// a snack // that was sitting on the table. Receives a 1 for VERBdisf, a 1 for POSTVB-NP1disf, and a 1 for Other. Total Disfluencies = 3.

Ha- um@fp Patty@sr placed a ladle@ln on a table. Receives a 1 for SUBJdisf, a 1 for POSTVB-NP2, and a 1 for Other. Total Disfluencies = 3.

Subject, verb, object, recipient

Subject & Subject_intended
Object & Object_ intended : for any analysis of the affect of the object on the sentence... make sure that Object & Object_intended are the same
Recipient
Verb = The verb phase. You will find things like “has passed” or “ is passing” in this column. VerbStem = The first active transitive verb in the sentence in singular past tense.

Use the VerbStem column for any and all analysis.

Exclude

if != 0 then exclude

DO

0 = Prepositional Phrase
1 = Double object
2 = Other (passive, incomplete, etc)

Strong Prime

1 = strong
0 = weak
control = filler

Version

No training = our original 14 subjects who learned the names of the pictures but didn't have beautiful instructional videos to watch that helped prime for pass and hand. Hopefully there is less verb variation in these people.
These people got trained on object and people names using the same methods as the no training people, but also got an instructional video with subtle primes for pass and hand.