1. The development of turn-Taking
Pre-schoolers may predict what you will say, but they don’t use those predictions to plan a reply.
Adult conversations involve a rapid exchange of turns. However, children’s turn-taking abilities are slow to develop. In this study, we focused on how these abilities might develop.
Laura Lindsay, Chiara Gambi & Hugh Rabagliati

2. The turn-taking puzzle
Timely turn-taking is typical of adult conversations
Aim for minimal gap
Approx 200ms gap between speakers (Stivers et al., 2009)
HOW?
But production processes take time
600ms for a single content word (Indefrey & Levelt, 2004)
During conversation, adults aim for there to be minimal gap between turns with average turn transitions lasting approximately 200ms. However, production processes are much slower.
Data from Stivers et al., 2009, PNAS

3. The turn-taking puzzle
Prediction
Early Preparation
Content
Timing?
Begin planning a response as soon as possible
400ms
How can you not like birthdays !? Are you not excited at the idea
of eating the …
NO!
To explain this, it’s been proposed that we make predictions about how we think a turn will end. So in this example, listener’s may predict that the speaker’s utterance will end in the word cake.
The listener may then uses this prediction to make a prediction about a turn’s duration – so when they think the speaker will finish speaking. So in this example, the listener anticipates that the speaker will finish speaking in about 400ms. By making timing predictions like this, the listener can anticipate the exact moment they should respond, leading to timely turn-taking.
As well as this, listeners also use content predictions to guide response planning. So in this example, the listener predicts the speaker will finish his utterance with the word cake. BY predicting this, they can begin preparing their response. By doing this, listeners will be ready to speak when the speaker finishes their turn, resulting in minimal gap
Garrod & Pickering, 2015; Levinson, 2016

4. Turn-taking in children
Children slower than adults to respond to turns
However, children are much slower to respond to turns despite the fact they can make predictions about the upcoming content of a sentence. They are also able to anticipate when a speaker switch will occur during a conversation they’re watching.
So, what is children’s difficulty due to?
Data from Casillas, Bobb & Clark, 2016

5. Turn-taking in children
Children slower than adults to respond to turns
How can you not like birthdays !? Are you not excited at the idea
of eating the …
BUT…
They can make predictions about the upcoming content of a sentence (Mani & Huettig, 2012)
What is children’s difficulty with turn-taking due to?
However, children are much slower to respond to turns despite the fact they can make predictions about the upcoming content of a sentence. They are also able to anticipate when a speaker switch will occur during a conversation they’re watching.
Casillas & Frank showed that when watching a video of puppets having a conversation, children’s eye gaze moved to the next speaker just before the current speaker finished speaking, suggesting that they knew when a turn should end. Unclear what the mechanisms supporting this anticipatory eye gaze is
If children can do both these things, then what is their difficulty due to?
So, what is children’s difficulty due to?
They know when speaker switches occur in an observed conversation (Casillas & Frank, 2017)

6. Explanation 1 Explanation 2
How can you not like birthdays !? Are you not excited at the idea
of eating the …
400ms
NO!
Explanation 1
Children observing a conversation predict the timing of turn ends; but can they do so while preparing their own response? (Experiment 1)
NO!
How can you not like birthdays !? Are you not excited at the idea
of eating the …
Explanation 2
One possibility is while that children can predict when a turn will end, they fail to make these fine grained timing predictions when they also have to prepare a response. This means they cannot anticipate the exact moment when they should begin speaking. Experiment one tests this.
Another possibility is that they cannot use predictions about a turn’s content to guide response preparation. So they may wait until the end of the turn before beginning response planning processes, resulting in slower responses. Experiment 2 tests this.
Children predict the content of turns; but can they use these predictions to prepare a response in advance of turn end? (Experiment 2)

7. Experiment 1
Tested if children (and adults) predict turn timing based on their predictions about content.
Participants played an interactive maze game
4 blocks
36 trials per block
24 target
12 filler
Participants played an interactive ipad-based game where they helped peter pan chase captain hook around a maze.
Crossroads in the maze were associated with different cartoon characters. When peter pan reached these crossroads, peter pan asked about which character he should go past in order to catch captain hook.
MENTION SPOKEN RESPONSE WE ARE MEASURING!!!!
Dependent measure:
Gap duration
24 Adults
30 5yo (1 excluded, N=29)
47 3yo (13 excluded, N=34)
Can you help me? Should we go past Mickey Mouse
Press this button to go back to the maze
Captain Hook disappears, and is replaced with a pirate accessory

8. Experiment 1 Manipulated: Expected Unexpected
Expected Content (Question Type)
1/3 of the time, Peter Pan asked about the incorrect character
2/3 of the time, Peter Pan asked about the correct character
Expected
Should we go past Boots?
Unexpected
Should we go past Boots?
Our critical prediction for this experiment was about an interaction of two factors.
So we manipulated the expected content of Peter Pan’s question. One third of the time, peter pan would ask to go past the wrong character. However, two thirds of the time, peter pan would ask to go past the right character. This meant that participants expected peter pan to be correct most of the time.

9. Experiment 1 Manipulated: Expected Content (Question Type)
1/3 of the time, Peter Pan asked about the incorrect character
2/3 of the time, Peter Pan asked about the correct character
Expected Timing (Relative Length)
Half of the time, character names have the same length
The other half, character names have different length (mean length difference = 429ms)
We also manipulated the expected timing of a turn

10. Same Length, Unexpected
Should we go past Boots?
Skye
Boots
Same Length, Expected
Same Length, Unexpected
Should we go past Boots?
If participants predict timing based on content predictions, they should be much slower to respond in this condition
Boots
Skye
Skye
Should we go past Po?
Fireman Sam Po
Different Length, Expected
Should we go past Po?
Fireman Sam Po
Different Length, Unexpected
When character names have the same length participants should anticipate that they are going to hear a short word at the end of the question, regardless of whether the question is expected or unexpected. However, crucially, when the character name lengths are different, when the question is unexpected, they should be expecting to hear a long word, so when they hear a short word, po, they shouldn’t be ready to respond, resulting in a delay
Half of the time, the 2 characters shown had the same length name. If participants make timing predictions, then in both these conditions, they should expect to hear a short name and ready to begin speaking.
The other half of the time, the 2 characters had substantially different length names. If speakers predict timing, then they should have an issue with this condition because they are expecting to hear a long name but instead hear a short name. Thus, they shouldn’t be ready to begin speaking meaning response times will be slower for this condition.
Should we go past Po?
Should we go past Boots?
Boots
Boots Po Po
Skye
Skye
Should we go past Po?
Should we go past Belle?
Curious George
Fireman Sam
Belle
Fireman Sam Po
Belle

11. Neither adults nor children make fine-grained timing predictions
Results
No interaction between Question Type and Relative Length
t=.68
t=.81
t=.27
IN fact, we found no evidence supporting this prediction suggesting that children and adults were not making fine-grained timing predictions.
Mention what the graphs are showingg!!!
Response times for Expected questions on the left, unexpected questions on the right. The black bars show response times for when the 2 character names had the same length name and greay bars for when the character name lenghts were different. Red bars show where we’d expec
Neither adults nor children make fine-grained timing predictions
* Gaps < 2 sec; Linear mixed-effects models with maximal random structure; |t| > 2 means p<.05

12. Explanation 1 Explanation 2
How can you not like birthdays !? Are you not excited at the idea
of eating the …
400ms
NO!
Explanation 1
Children observing a conversation predict the timing of turn ends; but can they do so while preparing their own response?
NO!
How can you not like birthdays !? Are you not excited at the idea
of eating the …
Explanation 2
Pause here and link it back to theories
One possibility is while that children can predict when a turn will end, they fail to make these fine grained timing predictions when they also have to prepare a response. This means they cannot anticipate the exact moment when they should begin speaking. Experiment one tests this.
Another possibility is that they cannot use predictions about a turn’s content to guide response preparation. So they may wait until the end of the turn before beginning response planning processes, resulting in slower responses. Experiment 2 tests this.
Children predict the content of turns; but can they use these predictions to prepare a response in advance of turn end? (Experiment 2)

13. Results
The longer the character name, the faster the participant’s response
r(45)=-.65
r(45)=-.77
r(45)=-.44
Children and adults began preparing their response at the final word.
BUT
Are they using their predictions about how an utterance will end to aid response planning?
However, children and adults were faster to respond when the character name was long. SO they were faster to respond when peter pan asked about fireman sam as opposed to po.
This suggests that children and adults begin preparing their response before the end of the turn. However, it’s possible that they were using their predictions about how a turn will end to guide response planning. Experiment 2 tests this question by manipulating whether critical information appears early or late in a question.
* Gaps < 2 sec; by-item correlations; all ps < .01

14. Experiment 2
Tested if children (and adults) are able to begin preparing their responses once the content of a question becomes predictable.
Participants played an interactive maze game
4 blocks
24 trials per block
Oh look! Is Doc Mcstuffins hiding the parrot?
Experiment 2 tested whether children and adults use predictions to guide response planning.
Animal hides behind a cartoon character
48 Adults
50 5yo (2 excluded, N=48)
70 3yo (12 excluded, N=58)

15. Experiment 2 Manipulated: Early Late
Position critical info mentioned in question (within-participants)
Cartoon character mentioned early in question
Is Doc McStuffins hiding the parrot?
Cartoon character mentioned late in question
Is the parrot behind Doc McStuffins?
Early
Late
Is Doc McStuffins hiding the parrot?
Is the parrot behind Doc Mcstuffins?
We manipulated the position the character name – the critical information – would appear in a question. Half of the time the character name appeared early and the other half of the time it appeared late.

16. Experiment 2 Manipulated:
Position critical info mentioned in question (within-participants)
Cartoon character mentioned early in question
Is Doc McStuffins hiding the parrot?
Cartoon character mentioned late in question
Is the parrot behind Doc McStuffins?
Maze Structure (between-participants)
The number of animals Peter Pan is searching for
1 animal (a parrot or a tiger)
2 animals (a parrot and a tiger)
We crossed this with a manipulation of order of mention

17. One Animal, Early One Animal, Late Two Animal, Early Two Animal, Late
Is Doc McStuffins hiding the parrot?
Is the parrot behind Doc Mcstuffins?
The Gruffalo
The Gruffalo
Doc McStuffins
Doc McStuffins
Two Animal, Early
Two Animal, Late
Is Doc McStuffins hiding the parrot?
If participants use predictions to prepare a response, they should be fastest to respond in this condition
Is the parrot behind Doc Mcstuffins?
Half of the participants played a two-animal maze where Peter Pan chased two animals.
The other half played a single-character maze where peter pan only chased one animal.
Crucially, only the one-animal early condition allows for early response preparation. So, if participants use predictions about how a turn will end to guide response preparation, they should be faster to respond in this condition.
The Gruffalo
The Gruffalo
Doc McStuffins
Doc McStuffins

18. Results
Only adults showed the predicted interaction between order of mention and maze structure
-51.19ms, t=-2.57
t=-1.12
t=-.46
One animal mazes on left, two animal maze on right. Black bar = critical info appears early, grey bars when critical info appears late.
However, only adults showed the predicted interaction – they were faster to response when the character name appeared early in the question in the single-animal mazes.
This suggests that adults use predictions about upcoming content to guide response planning but children don’t.
MENTION WHAT THE GRAPH SHOWS
Adults can use predictions about upcoming content to aid response preparation.
* Gaps < 2 sec; Linear mixed-effects models with maximal random structure; |t| > 2 means p<.05

19. Why do children find turn-taking difficult?
Content Predictions
Response Preparation
Based on this analyses, children’s turn-taking difficulties are due to an inability to combine their predictions about content with response preparation. But why might this be the case?
The developmental hurdle is learning to combine these 2 skills?

20. Results n.s. n.s. No higher order interaction
Puzzled that there was no higher order interaction. So despite the fact adults show this interaction between order of mention and maze structure, it’s not the case they are different from children. This result puzzled us so we decided to look at our data in a different way.

21. Distribution of Gap Durations in Children
Children’s response distributions different to adults response distributions – we see they have a wider curve
If you look at the response time data from corpora work, we see that children’s response times have a fat right tail
And if you look at our own response time distributions, children show this fat right tail
Data from Casillas, Bobb & Clark, 2016

22. Maze Structure
One Animal
Two Animal
Distribution of children’s response time data different from adult data

23. Distributional Analyses (Work in Progress)
Number of Trials:
Adults – 4522
Children – 4650
Mu (Mean)
Tau (rate of exponential)
See Staub et al, 2010
Sigma (Standard Deviation)
Based on this, we conducted a distributional analyses where we fitted an ex-gaussian distribution to our response time data. By doing this, we are able to take into account all of the response time data (whereas in previous analysis we used only trials where gap duration was less than 2000ms) An ex gaussian distribution consists of a normal distribution and an exponential distribution.
Normal distribution consists of 2 parameters – the mean (or mu) and standtard deviation (Sigma). An exponential dist consists of 1 parameter, tau which is the rate of the exponential. In psychological literature, mu has been thought of as representing basic functioning mechanisms and tau as interference effects.
We hierarchically modelled these parameters by condition accounting for random subject and item variance using a bayesian algorithm. (mention that I have not been at the forefront of this?)
Mu ~ Order of Mention * Maze Structure * Age + (1|Subject) + (1|Item)
Sigma ~ Order of Mention * Maze Structure * Age + (1|Subject) + (1|Item)
Tau ~ Order of Mention * Maze Structure * Age + (1|Subject) + (1|Item)
Jointly estimated using a Bayesian Algorithm

24. Distributional Analyses (Work in Progress) Experiment 2
Reliable interaction between maze structure and order of mention on tau in children only * *
Order of Mention
n.s
Reliable interaction between maze structure and order of mention on mu in adults and children
Make sure to mention that in mu, this is DIFFERENT to normal distribution analyses. Really stress this point.
However, the distributional analyses for exp2 tells a different story from the non-distributional analyses. Both children AND adults show the expected interaction – they were faster to respond in the early, one-animal condition.
REMEMBER this is normalised response times. Therefore, a more negative value means a faster response time.
In the children’s data, it looks like they are faster than adults, but this is because tau affects mu? However, as there is no significant higher order interaction between children and adults, this is not something to worry about.
WHY MU IS MORE NEGATIVE
I think this was about why mu was more negative (i.e., faster) for children overall compared to adults (not specifically in the early one-animal condition). The reason being that mu and tau are not completely independent of one another, and there is a tendency for larger tau (as seen in children vs. adults across all conditions) to go together with smaller mu. Not a problem though - although there is a bit of a trade-off between tau and mu it clearly cannot account for our findings. This is because the interaction on mu is driven by one animal early while the interaction on tau is driven by two animal late. * *
One Animal
Two Animal
One Animal
Two Animal
Maze Structure

25. To sum up…
How can you not like birthdays !? Are you not excited at the idea
of eating the …
400ms
NO!
Neither adults nor children make fine grained timing predictions when they also have to prepare a response
NO!
How can you not like birthdays !? Are you not excited at the idea
of eating the …
When we take into account any interference effects on response times, we find that both children and adults use predictions to aid response planning processes (stress that it’s the case when answering yes no questions)
NOTE: more course grained timing predictions (e.g. 1.5 seconds) may occur, but the point is that they are not as detailed as our predictions about content.
Questions about Exp1 Design – if someone says that the unexpected condition may have just thrown people off and that’s why we didn’t find an interaction…
When accounting for response time distributions, both children and adults use predictions to aid response planning processes

26. Matching Data to Theory
By the age of 3, children have the cognitive architecture necessary to:
Predict upcoming content of an utterance
Prepare a response before the speaker’s turn ends
Combine these 2 skills
Levinson’s Turn-Taking Model
Levinson, 2016
Early-emerging system for prediction and preparation
Important role for predictions about turn ends
Neither children nor adults predict turn ends by making fine-grained timing predictions

27. Thank you!
Chiara Gambi
Hugh Rabagliati