Toddlers Always Get the Last Word: Recency biases in early verbal behavior
Emily Sumner? (esumner@u.rochester.edu)
Erika DeAngelis? (erikadeangelis97@gmail.com)
Mara Hyatt? (hyattm@bc.edu)
Noah Goodman (ngoodman@stanford.edu)
Celeste Kidd?,× (ckidd@bcs.rochester.edu)? Department of Brain & Cognitive Sciences, University of Rochester, Meliora Hall, Rochester, NY 14627-0268
 Department of Psychology, Stanford University, Jordan Hall, Stanford, CA, 94305
× Center for Visual Science, University of Rochester, Meliora Hall, Rochester, NY 14627-0270

Abstract
A popular conception about language development is that comprehension precedes production. Although this is certainly
true during the earliest stages of phonological development,
once a child possesses the basic articulatory skills required forimitation, it need not necessarily be the case. A child could
produce a word without possessing the fully formed lexical
representation through imitation. In some cases, such as inresponse to questions containing fixed choices, this behavior
could be mistaken for a deeper understanding of the words’
semantic content. In this paper, we present evidence that 2to 3-year-old children exhibit a robust recency bias when verbally responding to two-alternative choice questions (i.e., they
select the second, most recently mentioned option), possibly
due to the availability of the second word in phonologicalmemory. We find further evidence of this effect outside ofa laboratory setting in naturalistic conversational contexts in
CHILDES (MacWhinney, 2000), a large corpus of transcribedchild-adult interactions.
Keywords: Decision making; cognitive development; developmental experimentation; language acquisition; learning.

Comprehension Need Not Precede Production
Modern studies of language development commonly use
measurements of children’s early lexical productions as aproxy for their lexical knowledge. This practice seems reasonable given the common view that language comprehension precedes language production. At first glance, this assumption seems justified in light of language comprehension
studies that demonstrate that infants can visually and manually locate the appropriate referents of spoken words before they can actually articulate those words themselves (e.g.,
Bergelson & Swingley, 2012). At such a young age, children
have not overcome many of the rudimentary obstacles required for comprehensible speech production (e.g., they lackthe fine-motor skills required for delicate speech articulation,in addition to teeth). Thus, the ordering of these early language milestones—comprehension before production—must
necessarily be true for children’s earliest lexical acquisitions.
However, once children have the physical and cognitive
fundamentals required for speech production in place, speech
production could theoretically precede comprehension. The
current study examines the degree to which children’s earlyverbal responses are prompted by their knowledge that a response is required (e.g., from high-level knowledge about thediscourse context) more than their desire to communicate asemantically specific message. For example, consider the following exchange between a mother and her toddler from theProvidence corpus in CHILDES (Demuth, Culbertson, & Alter, 2006; MacWhinney, 2000):

MOT:
Would you like some cereal or a bagel?
CHI (2;1): Bagel.
In this exchange, the child responds in a manner consistent
with understanding the word “bagel” (and maybe also “cereal”, since her mother’s question pressed her to weigh thevalue of the two options against each other before responding
with the preferred choice). However, the child could also accomplish the same exchange while understanding far less. In
order to answer her mother’s question, the child only needs
to: (1) recognize that a question was asked, which is easily
accomplished on the basis of prosodic cues, (2) know thatquestions require a verbal response, and (3) know that “or”
marks the onset of a response option. In other words, thechild could simply detect that a response was being requested
and answer with the most readily available response—the lastword of the question.
Prosody is one of the earliest features of language to whichchildren have access, starting during the third-trimester when
they are still in the womb (e.g., Camras et al. 1992, 1998;
Mehler et al. 1988). Further, previous literature has suggested
that children combine their early detection of prosodic cueswith a rudimentary understanding of the linguistic contexts inorder to detect and interpret questions for different types (e.g.,
yes/no questions, wh-questions) (Cruttenden, 1981). Taken
together with the fact that children’s early vocabulary is extremely limited (McMurray, 2007), it is likely that children
pass through a phase of language development in which they
can detect that a speaker is asking a question that requires
some verbal response without fully understanding the question itself. Though the phrase “I don’t know” would be auseful one for toddlers, a quick search in CHILDES reveals
that it is not used until much later in language development.
With this point in mind, consider the following exchange
between a mother and her child from the Brown corpus in
CHILDES (Brown, 1973):

Figure 1: Static-cling stickers used in the experiment, along with verbally presented questions. Children were asked a series ofquestions about each bear—first in one order (e.g., choices in blue on the left) and later in the opposite order (e.g., choices in
red on the right). Children were only shown the stickers after they responded to the question verbally or after two repetitions.
MOT:
Do I want coffee or tea?
CHI (3;1): Tea.

The mother seemed to be asking this question aloud of herself in a rhetorical manner, but the child answered her question directly. Though it is not possible to ascertain the reason for the child’s unexpected response, one possibility isthat the child recognized the mother’s utterance as a question
and knew that questions are typically requests for a verbal response. The child may simply have responded with “tea” because he recognized it as a possible response. Further,“tea”
would have been more easily accessible than other possible
responses (e.g., “coffee”) due to its temporal recency.
It is also important to consider that children often use
words even before they have a complete, adult-like semantic understanding of those words. Research has shown thatchildren may view words as prototypical, not necessarily definitional (e.g., Keil, 1992). Additionally, 2- and 3-year-olds
sometimes acquire multi-word phrases (e.g.,“want to play”)
in advance of the meanings of the individual words that they
contain (e.g., Bannard & Matthews, 2008).
The current paper examines whether children exhibit systematic recency biases in their early verbal behavior, firstthrough a behavioral study and then with a corpus analysis
using CHILDES. In both studies, we present evidence thatchildren exhibit recency biases when responding to questions
verbally. We argue that this strategy could be very useful inenabling toddlers to engage in verbal exchanges even before
they possess fully developed semantic representations for thewords that they are using.

Experimental Data
Methods
Participants Twenty-four children (mean=24.29, age
range=21.2 - 27.0 months) were tested for this study. The
children were recruited from the database of the Rochester
Baby Lab, which includes volunteer families from the greater
Rochester, NY area. All participants had normal vision andhearing, according to parental report. They were also fromhome environments where they were exposed to at least 90%
English. Families were compensated $10 and a child-sizedt-shirt for their participation in the study. An additional 6
children were tested in the study, but were excluded fromthe final analysis because of failure to complete at least
half of the total number of trials in the study (mean=24.84,
range=21.1 - 26.0).
Procedure Children were run in a quiet testing room at the
Rochester Baby Lab at the University of Rochester. Upon arrival at the lab, the researcher running the study began playing with them in the waiting room to give the child time tobecome comfortable with the researcher and the lab space.
A second researcher described the study to the parent or
guardian and obtained the appropriate consent paperwork before the experiment began. Children were tested without theirparents present in order to prevent parental influence on children’s responses. Parents remotely monitored their children(without the children’s knowledge) throughout the study via
a webcam in the testing room. The testing sessions wererecorded so that coding could be completed by two coders
after the testing sessions were over.
The testing room contained a small, child-sized table witha 17”x14” double-sided whiteboard easel. Each child par-

Table 1: Scale for rating verbal skills.
Rating1
2
3
4

Criteria
Utterances are always a single word or phrase; no word-sequence combinations (sentences), even simple ones;
may have some memorized phrases (e.g., “go bye-bye”); estimated mean length of utterance (MLU) = 1.0 - 1.5
Utterances contain some short (mostly 2-word) word-sequence combinations (e.g., “baby cry”); no longer or complex word-sequence combinations; estimated MLU = 1.5 - 2
Utterances contain a combination of short and some longer word-sequence combinations (e.g., “Malachai wantcookie”), but with lots of agrammaticality and a moderate level of complexity; estimated MLU = 2 - 3
Utterances contain many full sentences that are fairly grammatical, with many novel word-sequence combinations;estimated MLU = 3+

ticipant was invited to sit on one side of the table, opposite
the researcher on the other side. The researcher explainedthat they were going to play a sticker game together. The
researcher introduced the child to a bear character (named either Rori, a polar bear, or Quinn, a grizzly bear) whose image
was printed on a static-cling sticker and placed on the easel
in front of the child. The researcher explained that the childwas going to help Rori/Quinn make some choices and that foreach answer, the child would get a “sticker” (i.e., static-cling)
to place on the board (Figure 1).
The researcher then asked the child a set of questions about
items for the bear character (e.g., “Is Rori’s shirt red or yellow?”). The questions were asked verbally, in the absence ofany visual cues as to the question’s meaning. The static-cling
stickers were kept inside a book that the researcher held behind the easel so that children could not peek and see whatsticker options were available. When children gave an answer, they were given a static-cling sticker that depicted theirchoice to stick on the board. If the child did not make achoice after the question was asked the first time, the question
was repeated up to two times. If the child still did not makea choice verbally, the researcher visually presented the twostatic-cling stickers that corresponded to the choices in thequestion and repeated the question up to two more times. The
two static-cling stickers were presented in a randomized leftright configuration, each equidistant from the child. Children
either responded to this final question verbally or by pointing,
and were given the appropriate static-cling sticker to place on
the board. This design allowed them to get a sticker to placeon the board for each trial, regardless of their ability to makea verbal choice.
Once the child ran through the first set of choice questions,the child was asked to make the same choices for a different
bear character (either polar bear Rori or grizzly bear Quinn,
depending upon who they were introduced to in the first partof the experiment). Children were asked the same set of questions for the new bear character with the choices presented in
the opposite order. For example, if they were asked if Rori’s
shirt was “red or yellow” during the first part of the experiment, they were asked if Quinn’s shirt was “yellow or red”
during the second part (Figure 1). The bear characters and order of the question sets was randomized across participants.

If children appeared restless midway through the experiment,
they were offered a brief free-play break.
Stimuli The stimuli consisted of two sets of 20 questions,each containing two choices. The second set of questions
was identical to the first except that choices were presented in
the opposite order. Question choices varied in terms of theircommonality and frequency in child-directed speech. Somewords were highly familiar to most 2-year-olds (e.g., red, cat)and some were likely to be unfamiliar (e.g., ganache, khaki).
Questions also varied in terms of where the choices appeared
within them. Some questions contained choices at the end(e.g., “Does Rori like to eat apples or bananas?”) and somecontained choices earlier (e.g., “Should Rori bring a backpack
or a lunchbox to school”).
Coding Two researchers coded each child’s responses fromthe video recording of the testing session. Additionally, thechild’s verbal skills were ranked on a scale of 1 to 4 independently by two researchers who observed the participant
in the waiting room in advance of the study, according to apredetermined set of rating criteria (detailed in Table 1). The
two coders assigned the same verbal-skill rating to children
70.83% of the time and differed by one 29.17% of the time.
When coders differed by one, the two verbal-skill rating values for that child were averaged. Child participants had a
median verbal-skill rating of 2 (mean=2.1, range=1-4).
Analysis Our primary analysis examined the proportion of
second-choice responses children made when verbally responding to the questions. This analysis allowed us to determine if children have a bias to respond with the most easily accessible choice option—a recency bias. We used a
Wilcoxon signed-rank test in order to compare this proportion
to the proportion we would expect by chance—mu=0.5. We
also compared the proportion of second-choice responses thatchildren made verbally to those that they made non-verbally.
If, in fact, children exhibit a recency bias because recent linguistic material is more readily available in their phonological loop, we would expect to see an effect only for verballyanswered questions.
We also used a generalized linear mixed model with random intercepts for items and subjects in order to evaluate the influence of whether the response was verbal—

Table 2: Generalized linear mixed model results.

Proportion of Second Choices

1.00

FactorIntercept
VerbalResponse
scale(AgeMo)
scale(log(Freq))
Trial
VerbalSkill
EndChoiceLoc

0.75

0.50

Coef.

-0.30
2.18
-0.31
-0.10
0.00
-0.04
0.31

SE
0.44
0.26
0.15
0.14
0.01
0.15
0.29

z
-0.69
8.22
-2.07
-0.74
0.12
-0.26
1.07

p
0.49
<2e-16 ***<0.04 *
0.46
0.90
0.80
0.28

tion of second choices was significantly higher for verbal thannon-verbal responses (W = 384.5, p<0.0001).2 Further, 22 of
the 24 subjects showed significantly more second-choice responses in an analysis of individual subjects (Figure 3).

0.25

0.00
VerbalNonverbal

Response Type

Figure 2: Proportion of second choices made for verbal responses (red) and nonverbal responses (blue). Dotted line
indicates chance. Error bars represent 95% confidence intervals.

along with other factors (age, verbal skills, choice familiarity/frequency, and whether or not the choices occurred at theend of the question)—on children’s likelihood of responding with the second choice. Age (in months) was scaled before it was entered into the regression analysis. Frequencyof the choice words—which was estimated using the Google
Books NGram corpus for English (Michel et al., 2010)1 —waslogged and then scaled before it was entered.

Results
Recency bias in verbal responses Participants responded
verbally 78.7% of the time. Across all responses (verbal
and nonverbal), we found that children were more likely topick the second choice during this task. On average, participants chose the second option 69.93% of the time. A
Wilcoxon signed-rank test showed that this was significantly
above chance (V=803.5, p<0.0001). A comparison of verbal
to nonverbal responses suggests that this bias is limited to theverbal domain (Figure 2).
For verbal responses only, participants chose the second
choice 88.9% of the time—a value that a Wilcoxon signedrank test confirmed to be significantly above chance (V=231,
p<0.0001). For nonverbal responses, participants chose thesecond choice only 51.6% of the time, which was not significantly different from chance (V=102, p = 0.79). The propor1 Though children’s familiarity with words is more traditionallyestimated with child-specific resources such as the MacArthur-Bates
Communicative Development Inventories (Fenson et al., 2007), we
opted instead to use the much larger Google Books NGram corpus
because the low-frequency words in our stimuli could not be accurately estimated with smaller corpora.

Effects of verbal response-type and age A generalized
linear mixed model with random intercepts for items and subjects revealed that verbal responses and age were significant
predictors of second-choice responses (Table 2). Consistent
with the results reported above, verbal responses generated
more second-choice responses (β=2.18, z=8.22, p<0.0001).
Older children also made fewer second-choice responses than
younger children, as revealed by the significantly negative coefficient for age in the regression results (β=-0.31, z=-2.074,
p<0.04). No other factors—verbal skills, choice familiarity/frequency, and choice position in the question—reachedsignificance.
More repetitions for choice-medial questions Though
there was no effect of the location of the choices on thelikelihood of second-choice responses in the mixed-model
analysis, the question structure did impact how many repetitions were necessary before children answered the questions. Fewer repetitions were required when choices were at
the end of the sentence (mean=0.499) than when the choices
were embedded earlier in the question (mean=0.979). This
result suggests that children struggled more with comprehension and response formulation when the choices were earlier
in the question rather than at the end.

CHILDES Data
We performed an analysis using CHILDES (MacWhinney,
2000), a large corpus of transcribed caretaker-child interactions, in order to test whether the second-choice bias we observed existed in more naturalistic conversational contexts involving children and adults. We also sought to determine until what age the observed bias persisted, following up on thesignificant coefficient for age in the mixed-model analysis.
The mixed-model results suggested that children make fewer
second-choice responses as they age, which is consistent with
the idea that the behavior is a heuristic which is convenient in
2 In this within-subject analysis, three participants responded verbally 100% of the time so they were excluded from this particularanalysis.

Proportion of Second Choices

1.00
0.75
0.50
0.25
0.00
21

22

23

24

25

Age (months)

26

27

Figure 3: Each dot represents an individual’s proportion of second choices during trials in which they responded verbally. The
error bars depict 95% confidence intervals. The dotted line represents chance. Twenty-two of the twenty-four participants
exhibited significantly more second-choice responses than would be predicted by chance.
the earliest stages of language acquisition that becomes unnecessary as the child ages and gains more language knowledge. Presumably, children must reach an age and point inlanguage development at which they no longer depend on
simple heuristics for responding to questions. Though recency biases have been attested in adults, so have primacy biases and other serial position effects (e.g., Deese & Kaufman,
1957; Greene, 1986; Murdock, 1962; Neath & Knoedler,
1994). We expected that, if such questions occurred in naturalistic contexts in CHILDES, the strength of the bias should
decrease with age.

Methods
We limited our analysis to CHILDES transcript files that involved only two participants—one adult and one child—andthat included the age of the child. This way, we could insure that questions we extracted from adult speech were most
likely directed to a child of a known age (as opposed to asibling or other adult). From this predefined subset, we extracted 534 two-alternative choice questions. The questions

were posed to children ranging in age from 0.74 to 4.54 yearsof age (mean = 2.51). We then coded children’s responses tothe question for whether they chose the first option, the second option, or neither option (e.g., by failing to respond, or
responding with an irrelevant verbal response).

Results
Out of all of the two-choice questions adults asked children
in CHILDES, children responded with the first or second option 58.17% of the time. The proportion of first and secondchoice responses they gave binned by age appears in Figure
4. As evident from the plot, binned responses from 2-yearolds are significantly more biased towards the second-choice
option, as compared to chance. This bias is no longer significant in the 3- and 4-year-olds, who are equally likely to selectthe first and second choice. Figure 5 shows the raw data (displayed as circles at the top and bottom of the plot to represent
the 1’s and 0’s encoding whether each response was for thesecond-choice option), as well as a LOESS curve fitted to thedata. The smoothed fit to the data suggests that young chil-

1.00

Proportion of Second Choices

Proportion of Second Choices

1.00

0.75

0.50

0.25

0.00

0.75

0.50

0.25

0.00
2

3

Binned Age (years)

4

Figure 4: Proportion of second-choice responses in
CHILDES binned by age of the children in years.

1.5

2.0

2.5

3.0

Age (years)

3.5

4.0

Figure 5: A smoothed LOESS curve fitted to the raw datafrom CHILDES (empty dots).

dren exhibit a strong second-choice bias, but that choices areat chance starting at between 2.5 and 3 years of age. The datasuggests that recency biases persist until around the third yearof life.

Conclusion and Discussion
In both our experimental data and CHILDES analysis, wefound that young children exhibit a strong recency bias
when responding to questions that present two choices. The
CHILDES analysis confirms that this effect exists outside ofa laboratory setting, and that it persists until around the thirdyear of life.
We often view language production as an indicator of achild’s comprehension and language knowledge. Our findings have demonstrated that this is not always the case. Our
results show how children may begin engaging in conversation even without a complete semantic understanding of what
they are saying (e.g., Skinner, 1957). These findings also haveobvious and direct implications for the field of developmental psychology, especially work with children younger than
3 years of age. Researchers of language development shouldbe especially careful in how they phrase questions, and ensure that questions that pose two-alternatives are appropriately counterbalanced. More importantly, however, production measures in language acquisition research may strongly
reflect factors relating to memory and processing rather thantrue knowledge of language.
Though we have demonstrated here that this bias is robust across young children, we have not yet tested the causal
mechanism underlying it. While we mentioned the possibility that children simply lack sufficient language knowledge
in order to make a choice based on a genuine value comparison, this bias could be considerably more general. It could bethat this bias appears in the face of a cognitive overload generally (either because of limited understanding of the words,
or because of some other factor that limits processing speed
and capacity, such as exhaustion). Limited cognitive processing resources (e.g., memory) could mean that adults could
show this same bias under high cognitive load (when sleep
deprived or choosing between long, low-frequency options).
The second option is likely to be far more accessible in thesecircumstances due to its availability in the phonological loop
(e.g., Baddeley, Gathercole, & Papagno, 1998).

Acknowledgments
We thank R. Malachai Goodman for the inspiration and unsolicited pilot data for these experiments; Steve Piantadosi andShirlene Wade for assistance with analyses; Holly Palmeri,
Claire Hart, Anetaijia Porter-Monroe, Ashley Rizzieri, Emily
Roemer, and Kelsey Spear for recruiting and scheduling participants; Carla Macias, Eric Partridge, and Madeline Pelz forcoding; the University of Rochester for funding; and members of the Kidd Lab for helpful feedback and discussions.

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