REPORT
Knowing How You Know: Toddlers Reevaluate
Words Learned From an Unreliable Speaker
Isabelle Dautriche1,2, Louise Goupil3, Kenny Smith4, and Hugh Rabagliati4
1Laboratoire de Psychologie Cognitive, Aix-Marseille University, CNRS, Marseille, Frankreich
2Institute of Language, Communication and the Brain, Aix-Marseille University, CNRS, Aix-en-Provence, Frankreich
3School of Psychology, University of East London, London, Vereinigtes Königreich
4University of Edinburgh, Edinburgh, Vereinigtes Königreich
Schlüsselwörter: source monitoring, word learning, selective trust, metacognition
ABSTRAKT
There has been little investigation of the way source monitoring, the ability to track the source
of one’s knowledge, may be involved in lexical acquisition. In two experiments, we tested
whether toddlers (mean age 30 months) can monitor the source of their lexical knowledge
and reevaluate their implicit belief about a word mapping when this source is proven to be
unzuverlässig. Experiment 1 replicated previous research (Koenig & Woodward, 2010): Kinder
displayed better performance in a word learning test when they learned words from a speaker
who has previously revealed themself as reliable (correctly labeling familiar objects) als
opposed to an unreliable labeler (incorrectly labeling familiar objects). Experiment 2 Dann
provided the critical test for source monitoring: children first learned novel words from a
speaker before watching that speaker labeling familiar objects correctly or incorrectly.
Children who were exposed to the reliable speaker were significantly more likely to endorse
the word mappings taught by the speaker than children who were exposed to a speaker who
they later discovered was an unreliable labeler. Daher, young children can reevaluate recently
learned word mappings upon discovering that the source of their knowledge is unreliable.
This suggests that children can monitor the source of their knowledge in order to decide
whether that knowledge is justified, even at an age where they are not credited with the
ability to verbally report how they have come to know what they know.
EINFÜHRUNG
Children learn words through iterative social interactions. Zum Beispiel, in order to determine
the meaning of the word “dog,” English children may repeatedly observe other people using
that word until, across these situations, its referent and meaning became clear. Research on
cross-situational learning suggests that children have a fine-grained sensitivity to patterns of as-
sociation that exist between words and the world, which they can use to gradually update their
knowledge of a word’s meaning (z.B., Colunga & Schmied, 2005; Regier, 2005; Siskind, 1996;
L. Schmied & Yu, 2008; L. B. Schmied, 2000). But much less is known regarding how this updating
process is affected by social factors, such as the reliability of their source. Hier, we ask whether
toddlers between 2 Und 3 years of age can also update their knowledge of how they came
to know a word’s meaning, and use that information when constructing and updating their
lexicon.
Keine offenen Zugänge
Tagebuch
Zitat: Dautriche, ICH., Goupil, L.,
Schmied, K., & Rabagliati, H. (2020).
Knowing How You Know: Toddlers
Reevaluate Words Learned From an
Unreliable Speaker. Open Mind:
Discoveries in Cognitive Science,
5, 1–19. https://doi.org/10.1162/opmi
_a_00038
DOI:
https://doi.org/10.1162/opmi_a_00038
Supplemental Materials:
https://tinyurl.com/y2w8ymmy
Erhalten: 4 Juni 2020
Akzeptiert: 25 November 2020
Konkurrierende Interessen: The authors
declare have declared that no
competing interests exist.
Korrespondierender Autor:
Isabelle Dautriche
isabelle.dautriche@gmail.com
Urheberrechte ©: © 2020
Massachusetts Institute of Technology
Veröffentlicht unter Creative Commons
Namensnennung 4.0 International
(CC BY 4.0) Lizenz
Die MIT-Presse
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Knowing How You Know Dautriche et al.
Being able to reason about the sources of one’s own knowledge is fundamental for do-
mains where learners depend on others to gain knowledge, such as in acquiring a lexicon,
because the continuous flow of social interactions will often require learners to dynamically
update previously acquired knowledge, based upon newly learned properties of their partners.
Zum Beispiel, when learning words, conversational partners may appear to be good models at
Zuerst, but later reveal themselves to lack important knowledge, or be from another community
(perhaps with different naming conventions), or even be untrustworthy. In cases like these it
would thus be important for the learner to reconsider those initially learned words, and poten-
tially expunge them from their lexicon. Sperber et al.
(2010) have argued that humans have
evolved a suite of mechanisms for this “epistemic vigilance,” which let us scrutinize com-
municated information for its veracity through assessing both its content and its source. Eins
may thus predict that these mechanisms would play a crucial role for stabilizing linguistic
forms within a community, because they minimize the risk of being (mis)informed by unreli-
able sources. For children in the process of learning language, tracking how they have come
to know a word’s meaning and scrutinizing informants for their competence with a particular
language may constitute an important mechanism for minimizing “errors” in the lexicon, In
the face of uncertain and occasionally misleading social interactions.
Much research has documented that children, at least in their preschool years, are not
without resources when it comes to selecting informants (Harris, 2015; Poulin-Dubois &
Brosseau-Liard, 2016). Zum Beispiel, von 4 Zu 5 Jahre alt, children prefer to learn from more
reliable speakers (z.B., Corriveau et al., 2009; Koenig et al., 2004; Koenig & Harris, 2005;
Pasquini et al., 2007). In these studies, children first gathered information from and about two
speakers, such as witnessing reliable and unreliable speakers labeling objects differently (z.B.,
calling a ball either “ball” or “dog,” respectively). In subsequent novel word learning tasks,
children preferentially endorsed the label used by the reliable speaker over the one used by
the unreliable speaker, at least by the age of 4. In and of itself, this result could be explained
through fairly simple associative mechanisms, whereby children generalize inaccuracy from
a speaker’s past behavior to a current label. But other studies show that children’s inferences
are based on a more sophisticated understanding, such that they are not merely tracking the
surface accuracy of each speaker but are instead making inferences about the causes of each
speakers’ behavior to inform their reliability judgment (Einav & Robinson, 2011; Nurmsoo &
Robinson, 2009). Zum Beispiel, Nurmsoo and Robinson (2009) show that preschoolers do not
infer that speakers are unreliable if they can explain away inaccurate behavior because of
situational factors (z.B., being blindfolded) rather than epistemic states.
Critically, 4- to 5-year-olds are not only able to use trustworthiness information for sub-
sequent learning, they can also reevaluate what they have learned about a word when later
discovering that a speaker is unreliable (Luchkina et al., 2020; Schütte et al., 2019; Scofield
& Behrend, 2008). In these experiments, children first learnt novel words from a speaker and
only later discovered that the speaker was either reliable or unreliable (d.h., by witnessing them
labeling known objects correctly or incorrectly). Subsequently, the children showed no recog-
nition of the form-meaning mapping taught by the unreliable speaker, while remembering the
mapping taught by the reliable speaker, which indicates that after initially learning the words,
they subsequently used the speaker reliability information to reevaluate the mappings, provid-
ing evidence that on top of tracking the source of their knowledge, they can also reflect on it
to reevaluate the likely accuracy of their knowledge.
While reasoning about knowledge sources seem to be well in place in the preschool
Jahre, a critical question is whether such a mechanism is in place during the earliest stages
of lexical development, helping children to filter the information communicated to them, In
OPEN MIND: Discoveries in Cognitive Science
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Knowing How You Know Dautriche et al.
order to learn words more rapidly and optimally. Classic work suggests that children younger
than four struggle to explicitly identify the source of their knowledge (Gopnik & Graf, 1988;
Lindsay et al., 1991; O’Neill & Gopnik, 1991; Taylor et al., 1994), but more recent evidence
suggests that even toddlers can evaluate the trustworthiness of a speaker in order to guide
subsequent word learning (Brooker & Poulin-Dubois, 2013; Crivello et al., 2018; Koenig
& Woodward, 2010; Luchkina et al., 2018). Zum Beispiel, in Koenig and Woodward (2010),
24-month-olds interacted with an accurate (labeling familiar objects correctly) or inaccurate
(using wrong labels) speaker who taught them a novel word-object mapping. When a second
speaker requested the target object from them, only children who were previously exposed
to the accurate source showed above chance performance in understanding the novel word,
suggesting that children do not generalize novel words taught by inaccurate speakers to other
speakers. Jedoch, it is still unclear whether these demonstrations of early-developing selec-
tivity in learning are driven by simple associative mechanisms, whereby toddlers would simply
ignore the information delivered by speakers who previously revealed themselves to be unre-
liable, or by higher order social processes whereby children appraise and reason about the
reliability of their sources, and this is the subject of an ongoing debate (Crivello & Poulin-
Dubois, 2019; Heyes, 2017). To date, the only relevant evidence is correlational: toddlers’
selective learning in those tasks is related to their metacognitive and mindreading skills, Aber
not to their associative learning skills (Crivello & Poulin-Dubois, 2019; Kuzyk et al., 2019).
These correlations could potentially support a rich interpretation of infant’s selective learning
behavior, but causal (rather than correlational) evidence is required.
In der vorliegenden Studie, we provided a more direct test of toddlers’ ability to reason about
the source of their linguistic knowledge. Our method differs from prior studies, which focus
on whether children filter out information coming from an unreliable source. Eher, we ask
whether toddlers will reevaluate knowledge learned from a source upon receiving new infor-
mation about her reliability. If they do, this would suggest that, years before being credited
with the ability to verbally report upon the source of their knowledge, young children can still
reason about the source of their knowledge and use this information to reevaluate previously
acquired information.
Experiment 1 aimed to replicate previous findings that 2-year-olds use evaluations of
trustworthiness to guide word learning, and thus learn novel words from a reliable speaker
but not from an unreliable one. To reduce the task demands for these younger children, Wir
used a between-participant design where one group of children is presented with a reliable
speaker and the other group with the unreliable speaker (see Brooker & Poulin-Dubois, 2013;
Koenig & Woodward, 2010) and we tested children’s knowledge of the novel words using
a preferential looking task, using eye-gaze as an implicit correlate of children’s knowledge
(see also Luchkina et al., 2018). In this experiment, children were first exposed to a speaker
that provided either correct labels for familiar objects (z.B., saying “ball” while playing with a
ball; the reliable speaker) or incorrect labels (z.B., saying “dog” while playing with the same
ball; the unreliable speaker) before the speaker taught them two novel labels for two novel
Objekte. The test phase (identical across speaker conditions) was then administered by another
reliable speaker. Following prior work, we only expected children to learn new words from
the reliable speaker.
Experiment 2 then provided the critical test of reevaluation: We used the same procedure,
except that this time children were taught the novel words before being given the chance to
observe the speaker’s reliability. We relied on the result that children presuppose a generally
truthful use of speech (Corriveau et al., 2009;
Jaswal & Neely, 2006) and thus, by default,
should initially have learned the meanings of the novel words in both conditions. Results from
OPEN MIND: Discoveries in Cognitive Science
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Knowing How You Know Dautriche et al.
word learning studies using the same, or similar, teaching procedure support this assumption as
they show that toddlers of this age successfully learn novel words taught on screen by a speaker
they have no experience with (Dautriche et al., 2015; Swingley & Aslin, 2007; Waxman &
Booth, 2002). We thus tested whether children were able to retrospectively reevaluate their
knowledge of the novel word when presented with evidence that the speaker who taught them
the word was unreliable.
EXPERIMENT 1
Method
The preregistration, the data, and the script for their analysis are available here: https://tinyurl
.com/y2w8ymmy. We note below when our analyses departed from the preregistration.
Forty-eight English-speaking children ranging from 24 Monate bis 36 months
Teilnehmer.
took part in this experiment (n = 24 in each condition; reliable condition: M = 29M, 20D,
SD = 121D, 10 boys; unreliable condition: M = 30M, 16D, SD = 102D, 12 boys). Der
sample size was determined based on Koenig and Woodward (2010) who tested 20 Teilnehmer
in each condition in a similar design (albeit with a different measure; Cohen’s d = 0.8). A
power analysis based on this effect suggested that we should at least test 24 children per group
to have a power of 80% Bei der .05 alpha level. Four additional children were replaced because
of fussiness during the experiment resulting in the absence of calibration (n = 1), noise in
the experimental settings requiring the experimenter to play the experimental material twice
(n = 2), or because English was not the dominant language (n = 1). Participants were recruited
in nurseries around Edinburgh (n = 37) and in the lab (n = 11).
Children were either tested in their nursery or in the lab. Sie
Verfahren, Design and Material.
sat on a small chair in front of a laptop with the experimenter sitting next to them. The experi-
menter greeted the child before introducing them to a game (the experiment). The accuracy of
the speaker was not mentioned during the experiment. The experimenter avoided responding
to any-task relevant comments the child might have said. The experiment was composed of
three phases (siehe Abbildung 1):
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1. Speaker exposure phase. Participants saw a video of a native English female speaker
playing with five objects and labeling them. Each object was taken out of a box individually,
labeled three times and put back into the box. Half of the participants heard the speaker using
the correct label for the object she was playing with (the reliable condition; z.B., “This is a ball!
Look, a ball! This ball is really nice” while playing with a ball) and the other half heard the
speaker using an incorrect label (the unreliable condition; z.B., “This is a dog! Look, a dog!
This dog is really nice” while playing with the ball). The same five objects were used across
the two conditions: a tiger puppet, a banana, a ball, a shoe, and glasses. In the unreliable
condition, the speaker used labels that referred to objects that did not appear anywhere else
in the experiment (d.h., “flower,” “car,” “dog,” “book,” “star”).
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2. Teaching phase. Nächste, participants watched two videos, each teaching them one novel
word. Each video was about 30 s long and showed the speaker seen during Phase 1. In each
video the speaker showed a novel object and labeled it five times using one of of two novel
Wörter (“danu” or “modi”).
3. Testing phase. The test phase assessed children’s learning and generalization of the
Namen. We used a second reliable speaker for these trials, to minimize the possibility that
OPEN MIND: Discoveries in Cognitive Science
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Knowing How You Know Dautriche et al.
Figur 1. Design of Experiment 1. The experiment consisted of three phases: the speaker exposure
phase where a speaker was labeling familiar objects, the teaching phase where the speaker was
teaching two novel words (“danu” and “modi”), and the testing phase, which included included a
short video of a second reliable speaker and a succession of 16 Versuche: eight test trials (as pictured,
with the two novel objects on the screen) and eight familiar trials (with two known objects on the
screen). The critical difference between the conditions happened during the speaker exposure face:
In the reliable condition, the speaker used the correct word to label the object she was playing with
(z.B., calling a ball “ball”), however in the unreliable condition, the speaker used the wrong label
(z.B., calling a ball “dog”).
children would treat the unreliable labels as situationally defined (see Koenig & Woodward,
2010): das ist, relevant for communicating with the unreliable speaker, and thus recogniz-
able if tested with that speaker.1 We chose a male speaker to maximize children’s ability
to differentiate the voice of the novel speaker during the test phase from the voice of the
female speaker seen in Phase 1 Und 2. To ensure that children would recognize the novel
speaker as reliable, participants first saw a short 15-s video of the speaker. The speaker greeted
the child and placed two familiar objects in front of him, a shoe and a banana. He then
asked the child whether (S)he knew where the banana is (“Do you see the banana?”) and then
picked the banana after a brief delay (“Here it is! Here is the banana!”).
After watching the video, participants were tested using a preferential looking proce-
dure, in which they saw two objects on screen and heard the novel speaker name one of the
Objekte. We chose looking time as a measure to minimize task demands, which has been suc-
cessfully used in word learning paradigms at this age range (z.B., Messenger & Fischer, 2018;
Naigles, 1990; Yuan & Fischer, 2009). Participants were administrated a total of 16 Versuche: eight
familiar word trials and eight novel word trials, four per novel word. Each trial started with the
simultaneous presentation of two pictures on the right and left sides of the screen (the baseline
Zeitraum). Two s later, the test sentences started, Zum Beispiel, “Look at the [target]! Do you see
Die [target]?” The target word was pronounced twice in each trial. The trial ended 4 s after the
first target word onset. Targets appeared eight times on the right side of the screen and eight
times on the left side of the screen across the testing phase, and the target was not on the same
side on more than two consecutive trials. Wichtig, the audio stimuli was recorded by the
reliable male speaker.
Exposure phase. The five labels used during the exposure phase in the reliable
Materials.
condition (“tiger,” “banana,” “ball,” “shoe,” “glasses”) and in the unreliable condition (“flower,”
“car,” “dog,” “book,” “star”) were chosen such that they are all likely to be known by children
1 Note that this may be overly cautious depending on the theory of mind abilities of children of this age.
OPEN MIND: Discoveries in Cognitive Science
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Knowing How You Know Dautriche et al.
of that age range. According to Wordbank (Frank et al., 2017), 87% of British 24-month-olds
understand the words used in the reliable condition and 94% in the unreliable condition.
Based on parental report (on the 35 questionnaires that could be collected, 17 from children
assigned to the reliable condition and 18 from children assigned to the unreliable condition),
participants in the reliable condition knew on average 97% (SD = 6.6%) of the 5 words used
in the exposure phase and participants in the unreliable condition knew on average 95%
(SD = 8.4%) of the 10 words or object labels used in the exposure phase. There was no
difference of vocabulary knowledge across these two groups, T(35) = 0.94, p = .34.
Novel word trials. The novel word trials featured the two novel objects on the screen.
The novel objects were the two unfamiliar animals introduced during the teaching phase. Eins
was a plush of Kirby, a character in Nintendo games, a round pink creature with an oversized
Kopf. The other looked like a rat with bunny ears and a trunk. At the end of the experiment,
parents who came into the lab were asked whether their child was familiar with either animal;
all parents said no. The novel words were both bisyllabic and did not have any phonological
neighbors in children’s lexicon (“danu” and “modi”).
Familiar word trials. The familiar word trials featured two familiar objects not used during
Phase 1 (orange, butterfly, spoon, duck, cat, boat, hat, fish). Pictures were yoked in pairs (d.h.,
the orange always appeared with the butterfly) and each pair appeared twice during the test
Phase (one time for each referent). The familiar words were chosen to be likely known by
children of that age range. According to Wordbank (Frank et al., 2017), 89% of British 24-
month-old children understand the familiar words we used during the test phase.
Trials for which we have more than 35% of trackloss
Criteria for Trial and Participant Exclusion.
were rejected. Note that we first preregistered a more stringent criteria of 25%, yet given the
difficulty in recruiting, we decided to keep trials with 35% of missing data instead of 25%.
This change was amended in the original preregistration while data collection was ongoing.
Keeping the original criteria does not change the pattern of results, although some of the effects
reported below were only marginally significant as more participants were excluded with the
25% Kriterien, thus reducing statistical power. Participants that provided fewer than two test
trials were excluded, as were participants that were not attentive during Phase 1 and Phase 2
(not looking at the screen). Note that in the present sample none of the children were excluded
based on these criteria. Children received on average 12.47 Versuche (6.00 novel word trials) nach
applying the criteria for trial rejection.
Measurement and Analysis. We measured the time course of children’s gaze toward the target
picture (excluding looks away from the screen). Gaze position on each trial was recorded via
an eye-tracker (SMI) with a 33-ms sample rate. We inspected the time course of eye move-
ments from the onset of the first occurrence of the target word (“Look at the [target]”) until
the end of the trial (4 S). We assessed familiar and novel word comprehension as a prefer-
ence for the matching object similarly to previous studies using the same teaching and testing
phases (Dautriche et al., 2015; Dautriche et al., 2018) and following other research demonstrat-
ing a preference for the matching object during novel word comprehension in this age range
(z.B., Messenger & Fischer, 2018; Yuan & Fischer, 2009). Since we did not expect any learning
difference between the specific novel words being tested (“danu” or “modi”), we compared
participants’ behavior across conditions (reliable vs. unzuverlässig) collapsing looking behavior
for all test trials. We note that toddlers could display above-chance performance either because
they learned both words, or because they learned only one word and inferred the other word
during the test phase by relying on mutual exclusivity (z.B., Diesendruck & Markson, 2001;
Golinkoff et al., 1992; Graham et al., 1998; Halberda, 2003; Markman, 1989; Xu et al.,
OPEN MIND: Discoveries in Cognitive Science
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Knowing How You Know Dautriche et al.
2005). While our data do not disambiguate between these two possibilities, it does not under-
mine our conclusions as any observed difference between the two experimental conditions
would reflect differences in word learning.
To test our hypothesis that children take into account speaker’s reliability when learning
novel words we conducted two statistical tests. Erste, we ran a cluster-based permutation anal-
ysis (Maris & Oostenveld, 2007) as used previously in eyetracking studies (Dautriche et al.,
2015; Ferguson et al., 2018; Hahn et al., 2015) on the proportion of target looks (down-
sampled in bins of 50 ms excluding away looks) across the whole trial duration. This type
of analysis, originally developed for EEG data, does not require an a priori choice of a win-
dow of analysis (that could differ across reliability conditions, Alter, and word knowledge) Und
preserves the information available in the time-series. The cluster-based permutation analysis
proceeds in two phases. Erste, we define clusters in the data: Temporally adjacent time-points
that show statistically significant effects. For each time point, we compute a paired two-tailed
t test comparing fixations across conditions (reliable vs. unzuverlässig). All fixation proportions
were transformed via the arcsin square function to better fit the assumptions of the t test. Ad-
jacent time points with a t value greater than a predefined threshold (t = 2, as in Dautriche
et al., 2015) are grouped together into clusters. We then define the size of each cluster as
the sum of the t values from each of its constituent time points. Zweite, for each cluster, Wir
assess the probability of observing a cluster of that size by chance through permutation. To
do this we conduct 1,000 simulations where we randomly shuffle the relevant experimental
Bedingungen (d.h., reliable vs. unzuverlässig) für jeden Teilnehmer, while holding constant all other
aspects of the data’s structure. For each permuted data set, we then identify clusters using ex-
actly the same procedure as above, and reserve the largest of these clusters, eventually creating
a distribution of largest clusters that were generated under this null (permuted) Hypothese. Wir
then compare the clusters from the real (unpermuted) data to this null distribution. A real clus-
ter shows a significant effect of condition if it is greater than 95% of the simulated clusters,
implying a p value less than .05. Note that if no cluster is found in the original data set (NEIN
adjacent time points have a t value greater than the predefined threshold), the second step
involving data permutation cannot be run (as the statistics calculated gives the likelihood of
a cluster arising by chance, would such a cluster exists) and thus we simply report the result
of the first step (d.h., no cluster found). To test word recognition specifically, we conduct two
zusätzlich (not-preregistered) cluster-based analyses comparing the proportion of target looks
in each condition to the chance level. Because pairs of picture stimuli may not be equally
interesting to children, it is standard practice to compare the proportion of target looks in a
postnaming time window to the proportion of target looks in a prenaming, or baseline, Zeit
window for each trial (Bergelson & Aslin, 2017; Swingley & Aslin, 2007). Daher, the chance
level is determined from the average proportion of target looks before hearing any audio mate-
rial (the baseline period corresponding to the first 2 s of the trial) across all participants in both
Bedingungen. Baseline target preference (see also Figure 2C and D) Ist 0.5 for known words (SE =
0.01) Und 0.47 for novel words (SE = 0.01; significantly below the theoretical chance level
von 0.5 according a one-tailed t test: t = −2.37, p = .02). The cluster-based permutation anal-
ysis proceeds similarly as the between-condition comparison except that at each time point
we computed a one-tailed t test2 comparing the proportion of target look to chance (0.50 für
familiar words and 0.47 for novel words).
2 Note that we used one-tailed t tests because our hypothesis was directional as we expected a higher-than-
chance looking proportion when the word was recognized. Using two-tailed t test did not change the pattern
of results; insbesondere, no cluster below the chance level passed the permutation test.
OPEN MIND: Discoveries in Cognitive Science
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Figur 2. Results of Experiment 1. A and B. Proportion of looks toward the target picture during
the familiar word trials (A) and the novel word trials (B), time-locked to the beginning of the first
target word for children in the reliable condition (in red) and for children in the unreliable condition
(in blue). The ribbon surrounding each curve represents the standard error of the mean obtained at
each time bin for each word condition. The horizontal dashed lines represent the chance level: Die
average proportion of target looks during the baseline period. C and D. Overall proportion of looks
toward the target in the reliable (Rot), in the unreliable (Blau) Bedingungen, and during the baseline
Zeitraum (black) for familiar word trials (C) and novel word trials (D). Individual points represent
individual participant means. The error bars represent standard errors of the mean.
Zweite, we compared the overall proportion of target looks, averaged across the whole
trial duration (4 S), between the two conditions. We modeled the proportion of target looks
in a mixed-model analysis (Bates & Sarkar, 2004) in R using the following model: PropTar-
getLook ~Condition + (1 | Participant). We added this more standard analysis
to our preregistered cluster-based analysis as recommended by previous research (Delle Luche
et al., 2015). We also analyzed how the proportion of target looks differed from chance where
chance was the average proportion of looks during the baseline period for both the reliable
and the unreliable condition. This was used as the intercept in the mixed-model analysis.
Ergebnisse
The cluster-based analysis revealed no difference in target-looking fixations between speaker
conditions on the familiar word trials (no time window found in the between-condition cluster
Analyse; Figure 2A): Both groups of children fixated the target above chance (in the reliable
condition: aus 550 ms to 4,000 MS, P < .001; in the unreliable condition: from 600 ms to
4,000 ms, p < .001). In the novel word trials, however, children in the reliable group looked
OPEN MIND: Discoveries in Cognitive Science
8
Knowing How You Know Dautriche et al.
significantly more toward the target object than children in the unreliable group (from 1,250
ms to 1,600 ms after target word onset, p = .04 and from 1,850 ms to 2,550 ms p = .01 after
target word onset; Figure 2B) with only children in the reliable group looking toward the target
above-baseline preference (from 1,250 ms to 1,750 ms, p = .01; no time window found by
the cluster analysis for the unreliable group).
The mixed-effect analysis conducted on the overall target looking time also show no
difference between conditions for the familiar word trials (β = 0.02, t = 0.80, p = .43), with
children in both conditions looking to the target object significantly above their baseline prefer-
ence (reliable: M = 0.70, SE = 0.02, β = 0.19, t = 9.1, p < .001; unreliable: M = 0.70, SE =
0.02, β = 0.20, t = 10, p < .001). Yet, critically, for the novel word trials, children from the
reliable group (M = 0.54, SE = 0.02) looked significantly more toward the target than chil-
dren in the unreliable group (M = 0.44, SE = 0.03, β = 0.10, t = 3.38, p < .001, Cohen’s
d = 0.78). While the reliable group looked at the target object significantly above their base-
line preference (β = 0.08, t = 3.16, p < .001, Cohen’s d = 0.75), the unreliable group did not
(β = −0.02, t = −0.98, p = .33).3
Discussion
Toddlers selectively learned words depending on the speaker’s past accuracy: they learned
novel words when taught by a speaker who previously labeled known objects correctly but
not when the speaker used incorrect labels. Of course, it could also be that children in the
unreliable condition performed at chance because of general confusion arising from observing
labels being used incorrectly. However, children responded with high accuracy on familiar
words in both conditions (i.e., even in the unreliable condition), suggesting that this was not
the case.
This result thus replicates the general findings of selective trust research that a speaker’s ac-
curacy modulates the word learning behavior of children (e.g., Corriveau et al., 2009; Koenig
et al., 2004; Koenig & Harris, 2005; Pasquini et al., 2007) using an implicit measure of word
recognition (see also Luchkina et al., 2018), in toddlers (see also Brooker & Poulin-Dubois,
2013; Luchkina et al., 2018).
However, the results of Experiment 1 do not directly speak to whether 2- to 3-year-
old children are already equipped with a suite of cognitive processes for epistemic vigilance
(Sperber et al., 2010). The existence of learning selectivity does not necessarily entail that
children can evaluate the trustworthiness of the speaker to decide whether a word should
be learned or not, as it could emerge through simple associative mechanisms: Children may
ignore new information conveyed by an unreliable speaker, because this speaker is more as-
sociated with being incorrect in general. Even 14-month-olds selectively avoid learning from
3 It is to be noted that the comparison of prenaming and postnaming proportion of target looks may suffer from
a temporal confound. One can imagine for instance that as more time passes, children become more bored with
the objects overall, or shift their attention from familiar object to novel objects (e.g., Hunter & Ames, 1988). To
deal with such a potential confound, we conducted a third analysis whereby, given paired pictures A and B, we
calculated the fixation to picture A relative to B when A was the target, minus the fixation to A when A was the
distractor (for each participant). A difference score above 0 would evidence word understanding. According to
this approach participants learned the novel words in the reliable condition (difference score significantly above
0; t = 2.18, p = .03) but not in the unreliable condition (difference score not different from 0; t = −0.75; p = .45)
and there was a significant difference between conditions, F(1, 41) = −2.04, p = .04. We note that using this
measure requires that we have valid trials when A is a target and when A is a distractor. In the present case it
was true for only 42 out of 48 participants, thus resulting in a loss of power compared to the pre- vs. postnaming
analysis. Yet, despite reduced statistical power, this analysis replicates our main findings while dealing with the
potential temporal confound of our baseline analysis.
OPEN MIND: Discoveries in Cognitive Science
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inaccurate/unconventional models (Buttelmann et al., 2013; Chow et al., 2008) pointing to the
idea that such a strategy is a robust (and sensible) component of cultural learning, but crucially
does not require learners to make inferences on others’ and their own knowledge states.
The next experiment provides the critical test for source monitoring in toddlers: Can
children reflect on how they come to know the meaning of a word in order to update their
lexicon? In particular, we tested whether children can reevaluate a word mapping they recently
learned when later discovering that the speaker that taught them the word (the source of their
knowledge) is unreliable.
EXPERIMENT 2
Method
Fifty-one English-speaking children, ranging from 24 months 21 days to 36
Participants.
months took part in this experiment (n = 25 in the reliable condition and n = 26 in the
unreliable condition; reliable condition: M = 29M, 19D, SD = 93D, 10 boys; unreliable
condition: M = 31M, 7D, SD = 87D, 17 boys). As in Experiment 1, we aimed to test at least
24 children in each group. 7 additional children were replaced because of fussiness during
the experiment resulting in too many missing trials (n = 6; see exclusion criteria), technical
issues (n = 1). Participants were recruited in nurseries around Edinburgh (n = 18) and in the
lab (n = 33).
The procedure and material were the same as in Experiment 1.
Procedure, Design, and Material.
The experiment was also composed of the three phases described in Experiment 1 but their
order was different (see Figure 3): Participants were first exposed to the teaching phase, then
to the speaker exposure phase before going into the test phase. Critically they first learned
novel words before discovering whether the speaker that taught them these novel words was
accurate or inaccurate.
Same as in Experiment 1. Based on parental
Materials, Criteria for Trial and Participant Exclusion.
report (on the 43 questionnaires that could be collected, 19 from children assigned to the reli-
able condition and 24 from children assigned to the unreliable condition), participants in the
Figure 3. Design of Experiment 2. Experiment 2 consisted of the same three Experimental phases
presented for Experiment 1, except that, critically, this time participants were first exposed to the
teaching phase, where a speaker was teaching two novel words (“danu” and “modi”), before watch-
ing the video of the speaker exposure phase that revealed the (un)reliability of the speaker. The
testing phase concluded the experiment and was identical to the one of Experiment 1.
OPEN MIND: Discoveries in Cognitive Science
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reliable condition knew on average 97% (SD = 7.4%) of the five words used in the exposure
phase and participants in the unreliable condition knew on average 97% (SD = 7.01%) of the
10 words or object labels used in the exposure phase. There was no difference in vocabulary
knowledge on these word lists across groups, t(37) = 0.08, p = .94. Children received on
average 11.39 trials (5.21 novel word trials) after applying the criteria for trial rejection.
Same as in Experiment 1. In the cluster-based analysis comparing
Measurement and Analysis.
target looking behavior to chance, we define chance as the average proportion of looks toward
the target during the baseline period as in Experiment 1 (familiar words: M = 0.51, SE =
0.02; novel words: M = 0.48, SE = 0.01; see also Figure 4). The baseline target preference
was marginally different from 0.5 for the novel words according to one sample t test (t =
−1.81, p = .07). In addition, we conducted another cluster-based permutation test comparing
children’s target-fixation behavior between Experiments 1 and 2 for each condition (reliable;
unreliable). Note that the mixed-effect model for novel words on the overall proportion of target
looks came out to be singular. We, however, present the estimates of this model given that a
Bayesian method forcing the random-effects variance-covariance matrix away from singularity
gave similar results (see script online).
Results
The findings of Experiment 2 were very similar to those of Experiment 1, even though chil-
dren received evidence about speaker reliability after they had been taught the novel words
(Figure 4).
Our cluster-based analysis revealed that the manipulation of reliability again did not af-
fect children’s behavior on the familiar trials (pmin = .31; see Figure 4C), and children looked to
the target object at above-baseline preference levels (in the reliable condition: from 750 ms to
4,000 ms, p < .001; in the unreliable condition: from 600 ms to 4,000 ms, p < .001). Second,
and critically, speaker reliability did affect children’s behavior regarding the newly learned
words. As in Experiment 1, there was a significant difference in performance between the re-
liable and the unreliable conditions during the novel word trials (from 2,750 ms to 3,350 ms
after target word onset, p = .01) with children in the reliable group looking reliably more to-
ward the target object than children in the unreliable group. As in Experiment 1, children in
the reliable group looked toward the target above-baseline levels (from 1,900 ms to 2,100 ms,
p = .04) but the unreliable group did not (pmin = .08). In addition, there was no difference
between experiments in processing of the novel words: children’s fixation behavior in the re-
liable condition was comparable across Experiments 1 and 2 (no cluster found), similarly for
children in the unreliable condition (no cluster found).
Our mixed-effect analysis revealed a preference for the target in the unreliable condition
(M = 0.67, SE = 0.02) compared to the reliable condition (M = 0.67, SE = 0.02) during
familiar trials (β = 0.05, t = 2.27, p = .01, see Figure 4D). Yet both children in the reliable
group and in the unreliable group looked toward the target above-baseline levels (reliable:
β = 0.15, t = 7.54, p < .001; unreliable: β = 0.21, t = 10.93, p < .001). Importantly, for
the novel words, children in the reliable condition (M = 54, SE = 0.03) looked significantly
more toward the target than children in the unreliable condition (M = 0.47, SE = 0.02,
β = 0.07, t = 2.39, p = .01, Cohen’s d = 0.51). The reliable group showed target looks
significantly above baseline preference (β = 0.05, t = 2.01, p = .04, Cohen’s d = 0.57) but not
OPEN MIND: Discoveries in Cognitive Science
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Figure 4. Results of Experiment 2. A and B. Proportion of looks toward the target picture during
the familiar word trials (A) and the novel word trials (B), time-locked to the beginning of the first
target word until the end of the trial for children in the reliable condition (in blue) and for children
in the unreliable condition (in red). The ribbon surrounding each curve represents the standard
error of the mean obtained at each time bin for each word condition. The horizontal dashed
lines represent the chance level: the average proportion of target looks during the baseline period.
C and D. Overall proportion of looks toward the target in the reliable (red), in the unreliable (blue)
conditions and during the baseline period (black) for familiar word trials (C) and novel word tri-
als (D). Individual points represent individual participant means. The error bars represent standard
errors of the mean.
the unreliable group (β = −0.02, t = −0.69, p = .49).4 Similarly to the cluster-based analysis,
our mixed-effect analysis revealed no difference between children’s fixation behavior across
experiments for novel words: There was no main effect of experiment, χ2(2) = 0.88, p = .64,
nor interaction between conditions and experiments, χ2(1) = 0.05, p = .82.5
4 The mixed model used for the novel word analysis suffered from singularity. Using a Grubb test (Grubbs,
1950), we removed two outliers (one in the unreliable condition and one in the baseline; no other participant
qualified as an outlier), this fixed the model’s singularity but did not change the pattern of results. Children
in the reliable group looked significantly more toward the target than children in the unreliable group (β =
0.06, t = 2.87, p < .01) and above baseline levels (β = 0.04, t = 2.33, p = .02) contrary to the unreliable group
(β = −0.02, t = −1.25, p = .21).
5 Based on likelihood ratio tests on the following mixed-model PropTargetLook ~Condition * Ex-
periment + (1 | Participant).
OPEN MIND: Discoveries in Cognitive Science
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Discussion
Toddlers reevaluated a word mapping when they discovered that the source of their knowledge
was unreliable. We suggest that the most likely explanation of this is that children continuously
tracked the reliability of the source of their knowledge and used that information to reevaluate
past information provided by this source. Such an interpretation relies on the assumption that
toddlers successfully learned the novel word prior to reevaluating that knowledge when the
speaker was subsequently shown to be unreliable. There is good reason to believe this is actu-
ally the case. First, studies using a similar teaching procedure show that children successfully
learn novel words offered by a speaker they have no experience with (e.g., Dautriche et al.,
2015; Swingley & Aslin, 2007; Waxman & Booth, 2002). Second, language has been high-
lighted as a domain in which children are thought to be particularly credulous simply because
the risk of being misled while acquiring language is low. In normal circumstances speakers
presumably do not have much interest to transmitting inaccurate lexical entries to children
(except perhaps for a few teasing situations, a point we come back to later in discussing infer-
ences about speaker motivations) if they want those children to share a common vocabulary
with them (see also Sperber et al., 2010). Of course, this does not mean that children are
fully credulous while building their vocabulary: 3- to 4-year-olds display better learning per-
formance when a speaker is knowledgeable (Sabbagh & Baldwin, 2001) and as previous work
(Koenig & Woodward, 2010) and our Experiment 1 have shown, children do not learn words
from an unreliable speaker. Yet in the absence of any information about the speaker, children of
this age range seem to generally accept the testimony of others as evidenced by their successful
performance in word learning studies.
An unexpected observation from Experiment 2 is that the timing of the effect between
the reliable and unreliable group appeared late, after participants heard the target word for
the second time, while in Experiment 1 the effect emerged early, soon after participants first
heard the target word. This may be because Experiment 2 presents a more challenging task
for children, as it imposes a delay between the teaching phase and the testing phase of novel
words. In such case the retrieval of the target object may be more effortful, and thus the effect
delayed compared to Experiment 1. Whatever the exact nature of this process, the timing of
word recognition in the two experiments may not be directly comparable. At any rate, our
results show that children take speaker reliability into account independently of whether this
reliability information comes before or after their word learning experience.
GENERAL DISCUSSION
These experiments show that young children can reevaluate a word mapping that they recently
learned, after subsequently discovering that the source of this knowledge was unreliable. This
suggests that children are able to monitor the source of their knowledge and use that source
monitoring for word learning from early in life, long before developing the ability to verbalize
that information (see Gopnik & Graf, 1988; O’Neill & Gopnik, 1991; Taylor et al., 1994).
Monitoring the Source of One’s Knowledge in the Wild
One immediate question is whether these experimental results generalize outside this labo-
ratory context, such that young learners tag their lexical knowledge with information about
how this knowledge has been acquired in real-world settings. Critically, in the experiments we
reported, the word-mapping revision process depends on children’s capacity to encode the
source of the word mapping (who taught them the word). This memory may be quite vivid in the
OPEN MIND: Discoveries in Cognitive Science
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present experimental context because children learned the words and witnessed the speaker’s
reliability in a short sequence. More ecologically valid learning settings, however, would in-
clude multiple speakers, multiple words, and nonsequential learning events, all of which may
make it harder for learners to track the source of their lexical knowledge. While the develop-
ment of more sophisticated source-tracking abilities is an open question, there are two reasons
to believe that children may be able to track the source of their lexical knowledge in the wild.
First, multiple pieces of evidence suggest that word learning extends beyond label-referent as-
sociations: Children and adults track information about the contexts in which a word occurs.
For instance, they can keep track of the semantic and contextual relations between words
(e.g., that “shoe” is often uttered with “foot” and more frequently in a dressing-event than in a
eating-event: Arias-Trejo & Plunkett, 2013; Bergelson & Aslin, 2017; Dautriche & Chemla,
2014; Perfetti & Hart, 2002; Wojcik & Saffran, 2013). And second, while young children were
long thought to have very poor episodic memory, recent research suggest that these abilities
had been underestimated by previous research (e.g., Király et al., 2018). Such an episodic trace
may not only help the word learning process but also be an integral part of lexical entries, and
as such be a naturally available source of information to learners.
In the present set of experiments we used the speaker’s identity as a convenient way to test
whether children monitor and reflect upon the source of their lexical knowledge. Yet, speaker
identity is clearly not the only type of source that children will need to monitor in order to learn
words. For instance, word meanings can be taught directly by someone else, as in the present
case, but they can also be inferred (as through mutual exclusivity inferences), or observed
indirectly (as when learning from overheard speech). Thus, an important open question is
whether children are also able to monitor these other sources of quality and reliability in terms
of their lexical knowledge. Since young learners depend on communication with others to
gain lexical knowledge, keeping track of who taught them what may be more critical, and more
easily available, than monitoring which knowledge came directly versus from an inference. But
monitoring of these latter processes may still be important, as children do rely on inferential
processes, like judgments of mutual exclusivity or contrast (e.g., Clark, 1990; Markman, 1989),
in order to acquire many words that are not ostensively taught. For instance, by 12 months,
infants presented with a familiar and a novel object tend to choose the novel object when they
hear “look at the dax” (e.g., Bion et al., 2013; Diesendruck & Markson, 2001; Golinkoff et al.,
1992; Graham et al., 1998; Halberda, 2003; Xu et al., 2005). Inferential processes provide
children with a powerful tool to learn a word within a single instance (Carey & Bartlett, 1978),
yet it is unknown whether children recognize the inferential nature of a lexical entry acquired
through such processes and weight it differently from a lexical entry acquired ostensively.
Most tellingly, previous work suggests that identifying inference as a source of knowledge is
particularly difficult for young children (O’Neill & Gopnik, 1991). It is thus open to question
whether source monitoring extends beyond a speaker identity and applies to the many paths
that a piece of knowledge can originate from.
A related question concerns the types of evaluations that toddlers can apply to the sources
that they monitor. In the present set of experiments, as in previous studies (e.g., Corriveau et al.,
2009; Koenig et al., 2004; Koenig & Harris, 2005; Koenig & Woodward, 2010; Luchkina
et al., 2018), we have described our participants as being sensitive to the reliability of the
speaker, defined as their tendency to conform to the conventions of a supposedly shared native
language. But a speaker can be unreliable for many reasons: they could make a simple mistake,
or be ignorant, or deceptive, or just joking, or maybe even from a different speech community.
This highlights that an important aspect of source monitoring must be to infer why a source
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behaves in the way that they do. Such inferences are particularly important in the context of
word learning, because the lexicon is a conventional system, in which two speakers can use
two different labels for the same meaning, and both can be correct (cf. the difference between
soda and pop, or pants versus trousers). Thus, children must learn to distinguish between the
different motivations that sources may have for using words in seemingly unreliable ways.
Research with preschoolers suggests that 5- to 6-year-old children put an increasing emphasis
on speaker’s intention (e.g., being helpful or misleading) over the actual outcome they observe
(e.g., Liu et al., 2013; Mascaro & Sperber, 2009; Vanderbilt et al., 2011), an ability that
develops slowly over the preschool years (e.g., Vanderbilt et al., 2011) together with trait-
reasoning development (e.g., Heyman & Gelman, 2000; Liu et al., 2007) and/or theory
of mind development (Koenig & Harris, 2005). Yet, it is an open question whether younger
children, whose sensitivity to inaccurate labeling develops early on (Koenig & Echols, 2003),
can appraise the intention of unreliable speakers.
Implications for Models of Word Learning
Our data indicate that children are not only able to monitor the source of their knowledge but
they are able to use this information to update their lexicon. While children are skilled asso-
ciative learners when acquiring the meaning of words (e.g., L. B. Smith, 2000), these results
are difficult to explain under theories that rely on simple associative learning mechanisms,
without important alterations that may no longer be in the spirit of associative learning. For
instance, one could imagine that a lexical entry is associated to the valence of its source (e.g.,
Sumner et al., 2014). If the source has a negative valence (because it is atypical or inaccurate)
this could affect the word as well. Yet it is unclear how children would retrospectively update
a lexical entry as a function of its valence without involving some form of source monitoring.
Rather, we suggest that revising a word meaning in this particular context is based on chil-
dren’s self-reassessment of their knowledge. Word-mapping revision in this context implies
that children are able to infer the correctness of the word mapping they have formed based on
the informant’s accuracy. As such, this suggests that children are able to reflect on how they
have come to know the meaning of a word, and use that information when constructing and
updating their lexicon.
These results extend previous research suggesting that children can actively modulate
their learning, not only by monitoring what they know and are interested in (e.g., Begus &
Southgate, 2018; Goupil & Kouider, 2019; Lucca & Wilbourn, 2018), but also by monitoring
how they know it, as we propose here. Yet, word learning models mainly focus on inferring
word meaning based on observations (Siskind, 1996; Xu & Tenenbaum, 2007): if a learner
hears “blicket” frequently while observing Dalmatians then they would infer that “blicket”
means Dalmatian and not tree or dog. Much evidence, including the current study, suggests
that learners are not only statistical accumulators but also display sensitivity to their own or
others’ epistemic states to build their lexicon.
CONCLUSION
In sum, our work shows that 2- to 3-year-old children can use a speaker’s accuracy to reevaluate
a word’s meaning that was previously taught by that speaker. While pre-schoolers can reflect
on how they have come to know the meaning of a word to guide word learning (Luchkina
et al., 2020; Schütte et al., 2019; Scofield & Behrend, 2008), there was no evidence that
younger children could monitor the source of their knowledge and use it to update their implicit
beliefs. The present result suggests that these younger children track how they came to know
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the meaning of a word and use that information to update their lexicon. This research provides
an important first indication that some form of self-reflective mechanism may play a key role
in regulating knowledge-acquisition processes in the construction of the lexicon.
ACKNOWLEDGMENTS
Many thanks to Jenny Chim, Rachel Kindellan, and Rebekah Oakley for data collection and
to the actors of our stimuli, Jon Carr and Emma Healey. The research leading to these results
received funding from the ESRC under the Future Research Leaders schemes (ES/N017404/1
and ES/N005635/1).
FUNDING INFORMATION
ID, Economic and Social Research Council (http://dx.doi.org/10.13039/501100000269), Award
ID: ES/N017404/1. HR, Economic and Social Research Council (http://dx.doi.org/10.13039
/501100000269), Award ID: ES/N005635/1.
AUTHOR CONTRIBUTIONS
ID: Conceptualization: Lead; Formal analysis: Lead; Methodology: Lead; Visualization: Lead;
Writing - Original Draft: Equal; Writing - Review & Editing: Equal. LG: Formal analysis: Sup-
porting; Writing - Original: Equal; Writing - Review & Editing: Equal. KS: Conceptualization:
Supporting; Formal analysis: Supporting; Methodology: Supporting; Supervision: Equal; Visu-
alization: Supporting; Writing - Original Draft: Equal; Writing - Review & Editing: Equal. HR:
Conceptualization: Supporting; Formal analysis: Supporting; Methodology: Supporting; Super-
vision: Equal; Visualization: Supporting; Writing - Original Draft: Equal; Writing - Review &
Editing: Equal.
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