ArticlePDF Available

Development of Children's Use of External Reminders for Hard-to-Remember Intentions

Authors:

Abstract

The current study explored under what conditions young children would set reminders to aid their memory for delayed intentions. A computerized task requiring participants to carry out delayed intentions under varying levels of cognitive load was presented to 63 children (aged between 6.9 and 13.0 years old). Children of all ages demonstrated metacognitive predictions of their performance that were congruent with task difficulty. Only older children, however, set more reminders when they expected their future memory performance to be poorer. These results suggest that most primary school-aged children possess metacognitive knowledge about their prospective memory limits, but that only older children may be able to exercise the metacognitive control required to translate this knowledge into strategic reminder setting.
Development of Childrens Use of External Reminders for
Hard-to-Remember Intentions
Jonathan Redshaw, Johanna Vandersee,
and Adam Bulley
University of Queensland
Sam J. Gilbert
University College London
This study explored under what conditions young children would set reminders to aid their memory for
delayed intentions. A computerized task requiring participants to carry out delayed intentions under varying
levels of cognitive load was presented to 63 children (aged between 6.9 and 13.0 years old). Children of all
ages demonstrated metacognitive predictions of their performance that were congruent with task difculty.
Only older children, however, set more reminders when they expected their future memory performance to
be poorer. These results suggest that most primary school-aged children possess metacognitive knowledge
about their prospective memory limits, but that only older children may be able to exercise the metacognitive
control required to translate this knowledge into strategic reminder setting.
Prospective memory refers to the cognitive pro-
cesses that enable people to carry out specic tasks
at particular future occasions (Einstein & McDaniel,
1990; McDaniel & Einstein, 2000; Smith & Bayen,
2006). One may, for example, need to remember to
buy milk on the way home from work, to return a
book to the library next week, or to take a pill at
8 a.m. every day. To increase the chance of remem-
bering to carry out these delayed intentions at the
relevant time or location, we often set external
reminders to aid our memory. Writing notes and
lists, leaving items in conspicuous locations, and
creating alarms on computers or smartphones are
all examples of intention ofoading, allowing people
to improve their prospective memory performance
(Gilbert, 2015a, 2015b). This capacity may be under-
pinned by a metacognitive awareness of ones cog-
nitive limitations: once an individual knows that
they may struggle to remember to carry out a task,
they may choose to ofoad their intention to the
external environment by setting up a cue to trigger
future memory retrieval (Risko & Gilbert, 2016). In
other words, intention ofoading can alleviate the
cognitive demands associated with having to
remember a delayed intention using exclusively
internal processes. Although this behavior is highly
adaptive in everyday life (Hall, Johansson, & de
L
eon, 2013; Harris, 1980) and potentially unique to
humans (Redshaw & Bulley, 2018), its developmen-
tal trajectory in children remains surprisingly
unknown. This study aimed to examine when chil-
dren begin to utilize metacognitive evaluations of
their cognitive limits to guide reminder setting.
A wide body of research shows that children can
begin to pass prospective memory tasks from as
early as 2 years onward, with performance continu-
ing to improve throughout childhood and adoles-
cence (e.g., Kvavilashvili, Kyle, & Messer, 2008;
Kvavilashvili, Messer, & Ebdon, 2001; Mahy,
Moses, & Kliegel, 2014a, 2014b; Mattli, Schnitzs-
pahn, Studerus-Germann, Brehmer, & Z
ollig, 2014;
McCauley & Levin, 2004; Redshaw, Henry, & Sud-
dendorf, 2016; Spiess, Meier, & Roebers, 2015, 2016;
Zimmermann & Meier, 2006). Kvavilashvili et al.
(2001), for example, tested prospective memory per-
formance in children aged 4, 5, and 7 years old.
Children were presented with stacks of cards show-
ing pictures of common objects and within these
stacks were several cards depicting different ani-
mals. The experimenter introduced the children to a
puppet character and told them that their task was
to tell the puppet what pictures were on the cards
Jonathan Redshaw and Johanna Vandersee are Co-rst authors.
Sam J. Gilbert was supported by a Royal Society University
Research Fellowship and also received funding from the Eco-
nomic and Social Research Council (ES/N018621/1). We thank
the Queensland Museum staff and patrons for their assistance
with data collection.
Correspondence concerning this article should be addressed to
Jonathan Redshaw, School of Psychology, University of Queens-
land, St Lucia, Qld, 4072, Australia. Electronic mail may be sent
to j.redshaw@uq.edu.au.
©2018 The Authors
Child Development ©2018 Society for Research in Child Development, Inc.
All rights reserved. 0009-3920/2018/xxxx-xxxx
DOI: 10.1111/cdev.13040
Child Development, xxxx 2018, Volume 00, Number 0, Pages 110
(with the premise that the puppet could not see
well). Additionally, children were told that the pup-
pet was afraid of animals, so if they saw any ani-
mal cards, they should hide them from the puppet.
Results showed that prospective memory perfor-
mance (remembering to hide the animal cards)
increased with age, which is indicative of the
broader pattern in the literature.
Previous evidence is mixed on the question of
whether childrens prospective memory perfor-
mance can be aided by the presence of external
cues. Guajardo and Best (2000) studied preschool
childrens performance in tasks that sometimes
included reminders. For example, if the childs
prospective memory instruction was to press a but-
ton whenever they saw an image of a house on the
computer screen, in the external cue condition they
were given a photograph of a house that they
could place near the screen. As expected, 5-year-
old children performed better than 3-year-old chil-
dren. However, the external cues did not improve
performance in either age group. This may have
been because the cues only reminded the children
of the target and not the action they needed to per-
form. Kliegel and J
ager (2007), on the other hand,
gave 2- to 6-year-old children an ongoing task that
required them to name a series of pictures, except
for pictures of apples that were to be placed into a
box instead (the prospective memory task). Even
children as young as 3 were more likely to place
apple pictures into the box when there was an
external reminder of the required action (i.e., the
box was placed in front of them rather than behind
them). Nevertheless, this result only demonstrates
that environmental stimuli can improve childrens
memory for prospective tasks. It remains unknown
whether children will choose to set their own
reminders to improve future performance, and
whether they will be more likely do so under con-
ditions where their unaided performance is likely
to be poorer (thus showing a capacity for strategic
reminder setting). Strategic reminder setting may
become increasingly important as children move
through the primary school years, as they begin to
take on responsibilities that require prospective
memory such as routine household chores (e.g.,
make the bed before leaving the house) and school-
work (e.g., complete mathematics homework before
Thursday).
Gilbert (2015a) conducted several experiments to
investigate adultsuse of external reminders for
delayed intentions. Participants were presented
with a computerized task in which they had to
drag a series of numbered circles in sequence to the
bottom of a box (see Figure 1). At the beginning of
each trial, they were instructed that either one or
three of these circles should be dragged to an alter-
native location (e.g., drag 7 to the right when you
reach it in the sequence). If they wished, partici-
pants could drag target circles toward their speci-
ed location at the beginning of the trial (e.g., drag
7 next to the right side of the box at the beginning
of the trial). This meant that when they eventually
reached the target circle in the sequence, its location
would remind them of the prospective task (analo-
gous to leaving a library book at the front door to
remind yourself to return it). Participants were told
that use of this strategy was voluntary, allowing
the experimenter to investigate whether participants
created these external reminders by choice. Results
showed that performance was better when there
was just one target to remember rather than three,
and that participantsperformance was improved
when they set external reminders. Participants set
reminders on the majority of trials and, most
importantly, were more likely to do so in the more
difcult condition (i.e., trials with three targets). In
a similar study, Gilbert (2015b) observed that par-
ticipantscondence in their unaided prospective
memory capacity, independent of their objective
ability level, predicted their propensity to set remin-
ders. These ndings demonstrate that adults are
strategic in their reminder setting: they use a
metacognitive evaluation of their cognitive limits to
behaviorally compensate for these limits when nec-
essary (Risko & Gilbert, 2016). Here, we aimed to
examine when children begin to engage in such
strategic reminder setting.
There is a long history of research into the devel-
opment of metacognitive knowledge in children
(see Schneider & L
ofer, 2016, for a recent review).
In some of the earliest work on the topic, Kreutzer,
Leonard, Flavell, and Hagen (1975) interviewed
children in kindergarten and Grade 1, 3 and 5
about their knowledge of memory and metamem-
ory (i.e., how certain variables such as study time
and the measure of recall can affect ones perfor-
mance on a memory task). They found that children
of all ages had some knowledge about what makes
certain memory tasks more difcult but that older
children understood more complex inuences on
performance, for example, that retrieval of multiple
items can be affected by relations between the
items. Additionally, results showed that the older
children were able to describe more effective strate-
gies to improve memory. Similar age differences
were found when Annevirta and Vauras (2001)
tested metacognitive knowledge in a longitudinal
2 Redshaw, Vandersee, Bulley, and Gilbert
study of children from 6 to 9 years old. They found
that childrens metacognitive knowledge about mem-
ory, comprehension, and learning increased and
became more stable as they aged.
Nevertheless, it is important to distinguish
between metacognitive knowledge (beliefs and
knowledge about our own minds) and metacogni-
tive control (use of those beliefs and knowledge to
inuence behavior; Dunlosky & Metcalfe, 2008;
Flavell, 2000; Nelson & Narens, 1990). Indeed,
metacognitive knowledge of ones own cognitive
capacities and limits does not necessarily translate
into efcient metacognitive control of action (Nel-
son & Narens, 1990; Schneider, 2008). For instance,
although children around 6 or 7 years of age can
distinguish between easy and hard items to learn
for a memory test, only around age 9 or 10 do they
begin to allocate more study time to hard items
than easy items (see Dufresne & Kobasigawa, 1989;
Lockl & Schneider, 2004; Masur, McIntyre, &
Figure 1. Schematic illustration of the intention ofoading task. Participants are instructed to drag circles numbered 1-10 in ascending
order to the bottom of the box, while also remembering to carry out either one or three alternative actions for specic numbers (A).
The circles are randomly distributed within the box (B), and in some conditions participants have the option of dragging the target cir-
cles to the relevant edge of the box at the beginning of the trial (C). If participants do pursue this option, thenafter dragging non-tar-
get circles to the bottom of the box (D-E)the new location of the target circles will remind them of the required action (F).
Development of Reminder Setting 3
Flavell, 1973). What young children appear to lack,
then, is an ability to proportionately allocate cogni-
tive resources to tasks they have identied as par-
ticularly cognitively taxing. One might therefore
expect a similar pattern in a task like Gilberts
(2015a): Although young children may recognize
the difference between easy-to-remember and hard-
to-remember intentions, they may fail to set remin-
ders strategically until they reach an older age.
The Current Study
We slightly modied Gilberts (2015a) task to
make it more appropriate for school-aged children
while still maintaining the requirement to remem-
ber one or three specic intentions when dragging
10 circles to the bottom of the box. In the rst
phase, children were not able to set reminders,
whereas in the second phase this strategy was vol-
untary. Prior to each phase children predicted how
they would perform when there were one and three
targets to remember. Metacognitive knowledge of
the relative difculty of the 3-target condition
would be evident in cases where children predicted
lower accuracy in this condition than in the 1-target
condition. Metacognitive control and strategic
reminder setting would be evident in cases where
children set more reminders when there were three
targets than when there was only one.
Consistent with previous research on the devel-
opment of metacognitive knowledge and metacog-
nitive control (see Schneider, 2008, for a review),
our sample included primary school children aged
approximately seven through 13 years. Testing chil-
dren much younger than this would have been
impractical, as basic competence on the task
required participants to have an understanding of
left and right, as well as an ability to make
metacognitive predictions on a scale ranging from 0
to 100. Given that children begin to show some
metacognitive awareness of their cognitive limita-
tions during the preschool and early school years
(Neldner, Collier-Baker, & Nielsen, 2015; Redshaw
& Suddendorf, 2016; Schneider, 2008), we expected
that even the youngest children in our sample
would recognize the relative difculty of the 3-tar-
get condition. We did not have any specic predic-
tions regarding the age at which strategic reminder
setting would emerge, although it might be
expected to appear around age 9 or 10 given the
developmental trajectories of metacognitive control
observed in other metamemory tasks (Dufresne &
Kobasigawa, 1989; Lockl & Schneider, 2004; Masur
et al., 1973).
Method
Participants
The sample consisted of 63 children (34 males; 29
females) aged between 6.90 and 12.97 years old
(M=9.86 years, SD =1.70 years), who participated
between April and August 2016. Twenty-one partic-
ipants were aged younger than 9 years, 21 were
aged between 9 and 11 years, and 21 were aged
older than 11 years. All children spoke uent Eng-
lish, and most were of a White middle class back-
ground. Participants were recruited through one of
three methods: 7 were recruited through a
University of Queensland developmental psychol-
ogy database; 35 were recruited at a stall at a local
museum; and 21 were recruited at a local schools
after-school care program. Preliminary analyses
indicated that childrens age did not signicantly
vary with testing location, F(2, 60) =1.4, p=.26,
g2
p=.044; nor did any of the performance measures
(overall accuracy, overall ofoading rate, difference
in ofoading between 1- and 3-target conditions)
differ according to location, F(2, 60) <1.7, p>.20,
g2
p<.06. We therefore collapsed across this variable
in all nal analyses. Ethics approval was obtained
from the University of Queenslands School of Psy-
chology Ethics Committee and verbal or written
consent was obtained from parents before testing.
Materials
The main intention ofoading task (see Figure 1)
was presented on an iPad Air 2 and can be
accessed from the following website: http://sa
mgilbert.net/reminder_development/start.html.
Measures
Intention Ofoading Task
Initial instruction phase. Participants were told
that they would be presented with 10 yellow num-
bered circles inside a box and that they could move
these circles around with their nger. They were
told that their job was to drag the circles in order
from 1 to 10 to the bottom of the box, making each
circle disappear (the experimenter then demon-
strated how to do this). Participants were then told
that, as well as dragging each circle to the bottom
of the box, they would be given specic instructions
to drag 1 or more yellow circles to a different loca-
tion in the box (either to the left, right or top of the
box) instead of the bottom (see Figure 1). No speci-
c instructions were given regarding speed of
4 Redshaw, Vandersee, Bulley, and Gilbert
responding so there was no time pressure to
respond quickly.
Phase 1. In the rst phase, participants were
not able to move the circles out of order. The exper-
imenter rst demonstrated how to complete the
task separately with 1-target and 3-target trials and
the participant practiced these trials immediately
after each demonstration. Six test trials were then
presented, three of which had one target, the
remaining three each having three targets (in a ran-
domized trial order). Target circles were randomly
selected on each trial, with the constraint that Num-
bers 1 and 2 were never used as targets. On 3-tar-
get trials, one target was randomly assigned to
each of the possible locations (top, left, and right)
and instructions were always presented in numeri-
cally ascending order (i.e., possible task instructions
would be drag 4 to the left, drag 5 to the top, drag
9 to the rightor drag 3 to the right, drag 6 to the
left, drag 10 to the top). On 1-target trials, the tar-
get location (top, left, or right) was randomly
selected.
Phase 2. At the end of Phase 1, participants
were informed that there would be a slight change
in the next phase, in that they could now move any
of the circles around the box at any time. The
reminder strategy was then explained to partici-
pants. Participants were told that they could drag
the target circles toward the instructed location
(left, right, or top of the box) at the beginning of
each trial, and that when they reached this number
in the sequence its location would remind them of
the target instruction. The participants were told
that using this strategy was entirely optional and
they were again reminded of this during the prac-
tice trials. The experimenter demonstrated how to
complete the task using reminders with one target,
immediately followed by how to complete the task
using reminders with three targets. The participant
then practiced one trial of each condition, before six
test trials were presented in the same manner as
Phase 1.
Metacognitive Judgment Scale
After completing the practice trials in each phase
(and immediately before completing the test trials),
participants were presented with a computerized
scale asking them how well they thought they
would perform on the task. Specically, partici-
pants were asked how many special circles they
thought they would get right, separately for trials
with one target and three targets. The experimenter
demonstrated that children could drag the cursor
on the scale from none of them(left endpoint) to
all of them(right endpoint), with a number inside
the cursor ranging from 0% to 100% depending
where the cursor was located on the scale.
Procedure
Participants completed the primary task as out-
lined above, before being thanked for their time
and compensated with a small prize. Total testing
time was approximately 1525 min. Some (mostly
older) participants completed measures of executive
function after the main task, but most children did
not complete these measures due to loss of motiva-
tion or time constraints. We therefore did not
include these data in the analyses.
Results
Predicted and Actual Accuracy
Predicted accuracy (as measured by the metacog-
nitive judement scale) was analyzed with a repeated-
measures analysis of covariance (ANCOVA) includ-
ing within-subject factors of Phase (1 vs. 2) and
Targets (1 vs. 3), along with age as a continuous
covariate allowing us to examine whether perfor-
mance across conditions changed linearly as children
got older. The age covariate was mean centered so
that its inclusion did not alter the evaluation of
within-subject factors (Delaney & Maxwell, 1981). As
seen in Figure 2, this analysis revealed signicant
effects of phase, F(1, 61) =8.4, p=.005, g2
p=.12, tar-
gets, F(1, 61) =89.3, p<.001, g2
p=.59, and age, F(1,
61) =8.9, p=.004, g2
p=.13: children predicted bet-
ter performance for Phase 2 (M=.76, SD =.19) ver-
sus Phase 1 (M=.70, SD =.20), and for 1-target
(M=.90, SD =.18) versus 3-target trials (M=.56,
SD =.27); furthermore, predicted accuracy increased
with age. There were no other signicant effects, F(1,
61) <3.5, p>.06, g2
p<.055.
Actual accuracy (proportion of target circles
dragged to their instructed location) was analyzed
in a similar manner. Again seen in Figure 2, this
analysis revealed signicant main effects of phase,
F(1, 61) =31.7, p<.001, g2
p=.34, targets, F(1, 61) =
53.1, p<. 001, g2
p=.47, and age, F(1, 61) =18.9,
p<.001, g2
p=.24, along with an Age 9Targets
interaction, F(1, 61) =4.9, p=.031, g2
p=.07. Accu-
racy was higher in Phase 2 (M=.86, SD =.12) ver-
sus Phase 1 (M=.74, SD =.15) and for 1-target
(M=.90, SD =.11) versus 3-target trials (M=.70,
SD =.18). Older children were more accurate
than younger children, particularly in the 3-target
Development of Reminder Setting 5
condition (the 1-target condition was close to ceil-
ing), hence the Age 9Targets interaction. There
were no other signicant effects, F(1, 61) <2.7,
p>.11, g2
p<.042.
Additionally, each participantsdiscrepancy
between predicted and actual accuracy in each con-
dition was entered into a similar analysis. This
showed a main effect of targets, F(1, 61) =14.8,
p<.001, g2
p=.20, but no other signicant effects,
F(1, 61) <3.2, p>.07, g2
p<.052. Children were
undercondent in their predictions for 3-target tri-
als (M=.14, SD =.25) but not 1-target trials
(M=.01, SD =.21). There although no signicant
age effects. Thus, although older children both pre-
dicted better performance and did indeed perform
better (as shown in the two analyses above), the
discrepancy between predicted and actual perfor-
mance did not change with age, F(1, 61) =0.21,
Figure 2. Predicted and actual accuracy measures in the two phases of the task. Error bars indicate 95% CI for the within-subject com-
parison between 1-target and 3-target conditions, based on Loftus and Massons (1994) method and scaled so that nonoverlapping error
bars indicate a signicant difference between means (Hollands & Jamasz, 2010).
6 Redshaw, Vandersee, Bulley, and Gilbert
p=.65, g2
p=.003. This lack of an age effect appears
inconsistent with previous results suggesting that
metacognitive knowledge increases during the early
school years (e.g., Annevirta & Vauras, 2001; Kreut-
zer et al., 1975). The children in our task, however,
were able to practice the main task prior to predict-
ing performance, which may have allowed the
younger children to more accurately calibrate their
predictions.
Intention Ofoading in Phase 2
Ofoading proportion was operationalized (as in
Gilbert, 2015a, 2015b) as the difference between the
proportion of target circles moved before their turn
in the numerical sequence, minus the proportion of
nontarget circles moved before their turn. The ratio-
nale for this measure is that participants occasion-
ally move circles before their turn in the sequence
simply due to picking up the wrong circle by acci-
dent. This would not constitute ofoading. By sub-
tracting the likelihood of moving a nontarget circle
before its turn in the sequence (M=.04, SD =.04)
from the equivalent number for target circles
(M=.70, SD =.30) we can obtain a measure of
ofoading behavior that is selectively directed
toward target circles, corrected for any general ten-
dency to accidentally select the wrong circle. This
measure was entered into an ANCOVA including
the within-subject factor of targets (1, 3) along with
age as a continuous, mean-centered, covariate. This
showed a signicant effect of targets, F(1, 61) =
14.7, p<.001, g2
p=.19: children were more likely
to set reminders for 3-target (M=.74, SD =.28)
than 1-target trials (M=.56, SD =.40). The main
effect of age was not signicant, F(1, 61) =0.2,
p=.66, g2
p=.003, but there was a signicant Tar-
gets 9Age interaction, F(1, 61) =8.8, p=.004,
g2
p=.13: the tendency to set more reminders for
the 3-target than 1-target trials increased with age.
These ndings are illustrated in Figure 3. In order
to visualize the results, participants were divided
into three age groups: below 9 years, 911 years,
and older than 11 years (N=21 in each group).
The youngest age group set a similar number of
reminders in 1-target and 3-target trials, F(1, 20) =
0.005, p=.95, g2
p<.001. In contrast, 911 year olds
set signicantly more reminders for 3-target trials, F
(1, 20) =5.7, p=.027, g2
p=.22, and this difference
in reminder setting between the two conditions was
highly signicant in the oldest age group, F(1,
20) =15.4, p<.001, g2
p=.43.
Metacognitive Knowledge Versus Metacognitive Control
Age did not correlate signicantly with metacog-
nitive knowledge, operationalized as the predicted
difference in accuracy for 1-target versus 3-target
trials (r=.19, p=.13). It did, however, correlate
with metacognitive control, as operationalized by
Figure 3. Use of reminders in Phase 2. While younger children set a similar number of reminders for 1-target and 3-target trials, older
children were much more likely to set reminders in the more demanding 3-target condition. Error bars indicate 95% CIs for the compar-
ison between 1-target and 3-target conditions, calculated in the same manner as Figure 2.
Development of Reminder Setting 7
differential use of reminders for these conditions
(r=.36, p=.004; Figure 3). Supporting the pro-
posed dissociation between these two measures,
these two correlations are signicantly different
from each other (z=2.8, p=.004; results were simi-
lar when the knowledge measure was based on
either of the phases alone, rather than collapsed
across the two). The correlation between metacogni-
tive knowledge and metacognitive control, as
dened above, was not signicant (r=.19, p=.13).
Nor was there a signicant correlation between
metacognitive knowledge and task performance
(i.e., the difference in accuracy between 1-target
and 3-target trials; r=.07, p=.57). However, there
was a signicant negative correlation between
metacognitive control and task performance (r=
.30, p=.018): A larger difference in ofoading
between the two conditions was associated with a
smaller difference in accuracy. One interpretation of
this would be that selective use of reminders for
the more difcult condition helped to improve
accuracy toward the level seen in the easier condi-
tion, hence reducing the difference between the
two. Alternatively (or in addition), this pattern
could reect increasingly selective ofoading in
older children, who also had a smaller performance
difference between conditions due to a ceiling effect
in the 1-target condition.
Discussion
This study was the rst to investigate the develop-
ment of metacognitive evaluations and use of exter-
nal reminders for remembering delayed intentions.
Although the present paradigm involved only a
brief retention interval between encoding intentions
and acting on them, results were consistent with
previous research investigating prospective memory
over longer intervals, showing that performance
increases throughout childhood and degrades
under high cognitive demand (Kvavilashvili et al.,
2008; Leigh & Marcovitch, 2014; Mattli et al., 2014;
Ward, Shum, McKinlay, Baker-Tweney, & Wallace,
2005; Zimmermann & Meier, 2006). Only the older
children in our sample, however, engaged in strate-
gic reminder setting, in that they set more remin-
ders under conditions of high demand. There are
several possible explanations for this observed
developmental trajectory.
Arst possibility is that the younger children
did not set more reminders in 3-target trials simply
because they were less willing to change strategies
after completing the task without reminder setting
in Phase 1. This explanation can be ruled out, how-
ever, by noting that the overall proportion of
reminders set did not vary with age, and that even
the younger children set reminders for more than
half of the targets (see Figure 3). It is unlikely that
they would do this if they were unwilling to
change strategies between phases.
A second potential explanation is that the
younger children were overcondent in their abili-
ties (especially in the 3-target condition) and, there-
fore, did not think that reminders would be useful
to them. Inconsistent with this interpretation, how-
ever, the metacognitive judgment scores revealed
that younger children were actually less condent
than older children when predicting their perfor-
mance; and children across all ages were appropri-
ately more condent in the 1-target trials than in
the 3-target trials. There was no signicant inu-
ence of age on the discrepancy between predicted
and actual performance. Thus, consistent with
broader metacognition literature (e.g., Balcomb &
Gerken, 2008; Neldner et al., 2015; Redshaw & Sud-
dendorf, 2016), even the youngest children in our
sample appeared to possess insight into the relative
difculty of the 3-target trials. These results also
converge with those of Kvavilashvili and Ford
(2014), who found that children as young as ve
can be highly accurate in their metacognitive
knowledge of prospective memory abilities, despite
showing overcondence when asked about retro-
spective memory.
One remaining explanation for the results is that
the younger children, unlike the older children,
lacked sufcient metacognitive control to translate
their metacognitive insight into increased reminder
setting in the 3-target condition (see Balcomb &
Gerken, 2008; Dunlosky & Connor, 1997; Nelson &
Narens, 1990; Schneider, 2008). Both younger and
older children in our sample were aware of the
relative difculty of the 3-target condition (as indi-
cated by their metacognitive evaluations), suggest-
ing they possessed similar metacognitive knowledge
of their cognitive limitations. Only older children,
however, utilized these evaluations to inform strate-
gic behavior, such that they exibly chose an
appropriate strategy based on the cognitive
demand of each trial. This interpretation is consis-
tent with a wide body of literature on the develop-
ment of metamemory, which indicates that only
around age 9 or 10 do children begin to translate
their well-established knowledge of memory limita-
tions into efcient memorization behavior (e.g.,
Dufresne & Kobasigawa, 1989; Lockl & Schneider,
2004; Masur et al., 1973). One implication of the
8 Redshaw, Vandersee, Bulley, and Gilbert
developmental lag between metacognitive knowl-
edge and metacognitive control is that simply mak-
ing young primary school-age children aware of
cognitive limitations cannot be assumed to lead to
the use of compensatory strategies (see Cherkaoui
& Gilbert, 2017, for a related nding in individuals
with autism spectrum conditions).
The capacity to translate metacognitive evalua-
tions of ones cognitive limits into strategic action
underpins many complex human behaviors, from
recording information that is likely to be forgotten,
to setting up contingency plans just in case the
future does not turn out as one expects. The present
ndings point to a period of emergence for one
instantiation of this important capacity during the
early school years.
References
Annevirta, T., & Vauras, M. (2001). Metacognitive knowl-
edge in primary grades: A longitudinal study. European
Journal of Psychology of Education,16, 257282. https://
doi.org/10.1007/BF03173029
Balcomb, F. K., & Gerken, L. (2008). Three-year-old chil-
dren can access their own memory to guide responses on
a visual matching task. Developmental Science,11, 750
760. https://doi.org/10.1111/j.1467-7687.2008.00725.x
Cherkaoui, M., & Gilbert, S. J. (2017). Strategic use of
reminders in an intention ofoading task: Do indi-
viduals with autism spectrum conditions compensate
for memory difculties? Neuropsychologia,97, 140151.
https://doi.org/10.1016/j.neuropsychologia.2017.02.008
Delaney, H. D., & Maxwell, S. E. (1981). On using analy-
sis of covariance in repeated measures designs. Multi-
variate Behavioral Research,16, 105123. https://doi.org/
10.1207/s15327906mbr1601_6
Dufresne, A., & Kobasigawa, A. (1989). Childrens spon-
taneous allocation of study time: Differential and suf-
cient aspects. Journal of Experimental Child Psychology,
47, 274296. https://doi.org/10.1016/0022-0965(89)90033-7
Dunlosky, J., & Connor, L. T. (1997). Age differences in
the allocation of study time account for age differences
in memory performance. Memory & Cognition,25, 691
700. https://doi.org/10.3758/BF03211311
Dunlosky, J., & Metcalfe, J. (2008). Metacognition. Thou-
sand Oaks, CA: Sage.
Einstein, G. O., & McDaniel, M. A. (1990). Normal aging
and prospective memory. Journal of Experimental Psy-
chology: Learning, Memory, and Cognition,16, 717726.
https://doi.org/10.1037/0278-7393.16.4.717
Flavell, J. H. (2000). Development of childrens knowl-
edge about the mental world. International Journal of
Behavioral Development,24,1523. https://doi.org/10.
1080/016502500383421
Gilbert, S. J. (2015a). Strategic use of reminders: Inuence
of both domain-general and task-specic metacognitive
condence, independent of objective memory ability.
Consciousness and Cognition,33, 245260. https://doi.
org/10.1016/j.concog.2015.01.006
Gilbert, S. J. (2015b). Strategic ofoading of delayed inten-
tions into the external environment. Quarterly Journal of
Experimental Psychology,68, 971992. https://doi.org/
10.1080/17470218.2014.972963
Guajardo, N. R., & Best, D. L. (2000). Do preschoolers
remember what to do? Incentive and external cues in
prospective memory. Cognitive Development,15,7597.
https://doi.org/10.1016/S0885-2014(00)00016-2
Hall, L., Johansson, P., & de L
eon, D. (2013). Recompos-
ing the will: Distributed motivation and computer
mediated extrospection. In A. Clark, J. Kiverstein, & T.
Vierkant (Eds.), Decomposing the will (pp. 298324).
Oxford, UK: Oxford University Press. https://doi.org/
10.1093/acprof:oso/9780199746996.001.0001
Harris, J. E. (1980). Memory aids people use: Two inter-
view studies. Memory & Cognition,8,3138. https://
doi.org/10.3758/BF03197549
Hollands, J. G., & Jamasz, J. (2010). Revisiting condence
intervals for repeated measures designs. Psychonomic
Bulletin & Review,17, 135138. https://doi.org/10.
3758/PBR.17.1.135
Kliegel, M., & J
ager, T. (2007). The effects of age and
cue-action reminders on event-based prospective
memory performance in preschoolers. Cognitive Devel-
opment,22,3346. https://doi.org/10.1016/j.cogdev.
2006.08.003
Kreutzer, M. A., Leonard, C., Flavell, J. H., & Hagen, J.
W. (1975). An interview study of childrens knowledge
about memory. Monographs of the Society for Research in
Child Development,40(1), 160. https://doi.org/10.
2307/1165955
Kvavilashbili, L., & Ford, R. M. (2014). Metamemory pre-
diction accuracy for simple prospective and retrospec-
tive memory tasks in 5-year-old children. Journal of
Experimental Child Psychology,127,6581. https://doi.
org/10.0.3.248/j.jecp.2014.01.014
Kvavilashvili, L., Kyle, F., & Messer, D. (2008). The devel-
opment of prospective memory in children: Method-
ological issues, empirical ndings, and future
directions. In M. Kliegel, M. A. McDaniel, & G. O. Ein-
stein (Eds.), Prospective memory: Cognitive, neuroscience,
developmental, and applied perspectives (pp. 115140).
Oxford, UK: Taylor & Francis.
Kvavilashvili, L., Messer, D. J., & Ebdon, P. (2001).
Prospective memory in children: The effects of age and
task interruption. Developmental Psychology,37, 418430.
https://doi.org/10.1037/0012-1649.37.3.418
Leigh, J., & Marcovitch, S. (2014). The cognitive cost of
event-based prospective memory in children. Journal of
Experimental Child Psychology,127,2435. https://doi.
org/10.1016/j.jecp.2014.02.010
Lockl, K., & Schneider, W. (2004). The effects of incen-
tives and instructions of childrens allocation of study
time. European Journal of Developmental Psychology,1,
153169. https://doi.org/10.1080/17405620444000085
Development of Reminder Setting 9
Loftus, G. R., & Masson, M. E. J. (1994). Using condence
intervals in within-subject designs. Psychonomic Bulletin
&Review,1, 476490. https://doi.org/10.3758/BF03210951
Mahy, C. E. V., Moses, L. J., & Kliegel, M. (2014a). The
development of prospective memory in children: An
executive framework. Developmental Review,34, 305
326. https://doi.org/10.1016/j.dr.2014.08.001
Mahy, C. E. V., Moses, L. J., & Kliegel, M. (2014b). The
impact of age, ongoing task difculty, and cue salience
on preschoolersprospective memory performance: The
role of executive function. Journal of Experimental Child
Psychology,127,5264. https://doi.org/10.1016/j.jecp.
2014.01.006
Masur, E. F., McIntyre, C. W., & Flavell, J. H. (1973).
Developmental changes in apportionment of study time
among items in a multitrial free recall task. Journal of
Experimental Child Psychology,15, 237246. https://doi.
org/10.1016/0022-0965(73)90145-8
Mattli, F., Schnitzspahn, K. M., Studerus-Germann, A.,
Brehmer, Y., & Z
ollig, J. (2014). Prospective memory
across the lifespan: Investigating the contribution of ret-
rospective and prospective processes. Aging, Neuropsy-
chology, and Cognition,21, 515543. https://doi.org/10.
1080/13825585.2013.837860
McCauley, S. R., & Levin, H. S. (2004). Prospective mem-
ory in pediatric traumatic brain injury: A preliminary
study. Developmental Neuropsychology,25,520. https://
doi.org/10.1080/87565641.2004.9651919
McDaniel, M. A., & Einstein, G. O. (2000). Strategic and
automatic processes in prospective memory retrieval: A
multiprocess framework. Applied Cognitive Psychology,
14, S127S144. https://doi.org/10.1002/acp.775
Neldner, K., Collier-Baker, E., & Nielsen, M. (2015).
Chimpanzees (Pan troglodytes) and human children
(Homo sapiens) know when they are ignorant about the
location of food. Animal Cognition,18, 683699.
https://doi.org/10.1007/s10071-015-0836-6
Nelson, T. O., & Narens, L. (1990). Metamemory: A theo-
retical framework and new ndings. Psychology of
Learning and Motivation,26, 125173. https://doi.org/
10.1016/S0079-7421(08)60053-5
Redshaw, J., & Bulley, A. (2018). Future-thinking in ani-
mals: Capacities and limits. In G. Oettingen, A. T.
Sevincer, & P. M. Gollwitzer (Eds.), The psychology of
thinking about the future (pp. 3151). New York, NY:
Guilford.
Redshaw, J., Henry, J. D., & Suddendorf, T. (2016). Disen-
tangling the effect of event-based cues on childrens
time-based prospective memory performance. Journal of
Experimental Child Psychology,150, 130140. https://doi.
org/10.1016/j.jecp.2016.05.008
Redshaw, J., & Suddendorf, T. (2016). Childrens and
apespreparatory responses to two mutually exclusive
possibilities. Current Biology,26, 17581762. https://doi.
org/10.1016/j.cub.2016.04.062
Risko, E. F., & Gilbert, S. J. (2016). Cognitive ofoading.
Trends in Cognitive Sciences,20, 676688. https://doi.
org/10.1016/j.tics.2016.07.002
Schneider, W. (2008). The development of metacognitive
knowledge in children and adolescents: Major trends and
implications for education. Mind, Brain, and Education,2,
114121. https://doi.org/10.1111/j.1751-228X.2008.00041.x
Schneider, W., & L
ofer, E. (2016). The development of
metacognitive knowledge in children and adolescents.
In J. Dunlosky & S. K. Tauber (Eds.), The Oxford hand-
book of metamemory (pp. 491518). Oxford, UK: Oxford
University Press.
Smith, R. E., & Bayen, U. J. (2006). The source of adult
age differences in event-based prospective memory: A
multinomial modeling approach. Journal of Experi-
mental Psychology: Learning, Memory, and Cognition,32,
623635. https://doi.org/10.1037/0278-7393.32.3.623
Spiess, M. A., Meier, B., & Roebers, C. M. (2015). Prospec-
tive memory, executive functions, and metacognition
are already differentiated in young elementary school
children: Evidence from latent factor modeling. Swiss
Journal of Psychology,74, 229241. https://doi.org/10.
1024/1421-0185/a000165
Spiess, M. A., Meier, B., & Roebers, C. M. (2016). Devel-
opment and longitudinal relationships between
childrens executive functions, prospective memory,
and metacognition. Cognitive Development,38,99113.
https://doi.org/10.1016/j.cogdev.2016.02.003
Ward, H., Shum, D., McKinlay, L., Baker-Tweney, S., &
Wallace, G. (2005). Development of prospective mem-
ory: Tasks based on the prefrontal-lobe model. Child
Neuropsychology,11, 527549. https://doi.org/10.1080/
09297040490920186
Zimmermann, T. D., & Meier, B. (2006). The rise and dec-
line of prospective memory performance across the lifes-
pan. Quarterly Journal of Experimental Psychology,59,
20402046. https://doi.org/10.1080/17470210600917835
10 Redshaw, Vandersee, Bulley, and Gilbert
... When and how do children develop the ability to supplement their brain-based cognitive processes with external reminders? Redshaw et al. (2018) investigated this question in children aged approximately 7-13 years, using an intention offloading task similar to that of Gilbert (2015a) administered with a touchscreen tablet computer. As in Gilbert (2015b), the task was performed in two phases, first using unaided memory and second with the option to set reminders. ...
... These findings show that under the right circumstances, selective offloading strategies may be observed in very young children (see also Armitage et al., 2020;Armitage & Redshaw, 2021). However, the scenario studied by Redshaw et al. (2018) could have presented a particular challenge for young children. Some potential reasons for this might be the need to remember intentions prospectively, rather than a directly cued retrospective memory test, and the intermixing of the two difficulty levels on a trial-by-trial basis. ...
... On the other hand, an individual might offload sub-optimally because they fail to use metacognitive beliefs at all. This appears to be the case in the youngest children studied by Redshaw et al. (2018), who knew that they were more likely to forget when they had more items to remember, but did not use this knowledge to inform their reminder-setting strategy. ...
Article
Full-text available
How do we remember delayed intentions? Three decades of research into prospective memory have provided insight into the cognitive and neural mechanisms involved in this form of memory. However, we depend on more than just our brains to remember intentions. We also use external props and tools such as calendars and diaries, strategically placed objects, and technologies such as smartphone alerts. This is known as ‘intention offloading’. Despite the progress in our understanding of brain-based prospective memory, we know much less about the role of intention offloading in individuals’ ability to fulfil delayed intentions. Here, we review recent research into intention offloading, with a particular focus on how individuals decide between storing intentions in internal memory versus external reminders. We also review studies investigating how intention offloading changes across the lifespan and how it relates to underlying brain mechanisms. We conclude that intention offloading is highly effective, experimentally tractable, and guided by metacognitive processes. Individuals have systematic biases in their offloading strategies that are stable over time. Evidence also suggests that individual differences and developmental changes in offloading strategies are driven at least in part by metacognitive processes. Therefore, metacognitive interventions could play an important role in promoting individuals’ adaptive use of cognitive tools.
... When children were provided with a means to devise their own cognitive offloading strategies, children aged 10 and 11 years devised solutions on their own; however, few younger children were able to do so (Bulley et al., 2020). In addition, children aged 9-11 years old could mark targets more often as the number of targets increased, whereas children aged younger than nine years demonstrated no difference in offloading behavior in terms of the number of targets (Redshaw et al., 2018). Studies regarding study time allocation have yielded similar results. ...
... Children in the middle and upper elementary grades can allocate learning time on the basis of the difficulty of word pairs (Koriat et al., 2009). The perception of difficulty emerges early in childhood, and the ability to use external tools to compensate for limited cognition continues to develop throughout elementary school (Bulley et al., 2020), which is consistent with the results of Study 1. Children older than nine years of age use cognitive offloading more frequently than children at age below 9 in a high-cognitive-demand task condition (Redshaw et al., 2018). The aforementioned findings suggest that children in middle childhood can apply item difficulty cues for offloading. ...
... In conclusion, the present research expanded on previous research related to the development of cognitive offloading during difficult tasks (Armitage et al., 2020;Redshaw et al., 2018) by exploring the effects of item difficulty and value on cognitive offloading and demonstrating the developmental characteristics and mechanisms of cognitive offloading during cue utilization. ...
Article
Full-text available
The storage of information in external tools (e.g., notebook, cellphone) has become increasingly common. Some researchers have defined this behavior as cognitive offloading, which is a type of learning strategy. Studies have indicated that as age increases, children become increasingly capable of calibrating their learning strategies according to the difficulty of learning items. The value of items is also essential in people’s daily learning. However, how children apply both cues of item difficulty and item value for cognitive offloading to regulate their learning process remains unclear. In three studies, we investigated children’s offloading of learning items by manipulating these items’ difficulty and value (Study 1), value alone with difficulty being unvaried (Study 2), and difficulty and value with an emphasis on value (Study 3). The results indicate that children aged 11 years used difficulty cues alone for cognitive offloading when both difficulty and value cues were presented. However, when difficulty was controlled and value was emphasized, the 11-year-old children adopted cognitive offloading strategies based on value cues. The three studies revealed the conditions under which children in middle childhood apply cues of the item value, which are goal-driven cues, for cognitive offloading and provided methods for encouraging children to simultaneously apply item difficulty cues, which are data-driven cues, and item value cues.
... When and how do children develop the ability to supplement their brain-based cognitive processes with external reminders? Redshaw et al. (2018) investigated this question in children aged approximately 7-13 years, using an intention offloading task similar to Gilbert (2015a) administered with a touchscreen tablet computer. As in Gilbert (2015b), the task was performed in two phases, first using unaided memory and second with the option to set reminders. ...
... These findings show that under the right circumstances, selective offloading strategies may be observed in very young children (see also Armitage et al., 2020;Armitage & Redshaw, 2021). However, the scenario studied by Redshaw et al. (2018) could have presented a particular challenge for young children. Some potential reasons for this might be the need to remember intentions prospectively, rather than a directly cued retrospective memory test, and the intermixing of the two difficulty levels on a trial-by-trial basis. ...
Preprint
How do we remember delayed intentions? Three decades of research into prospective memory have provided insight into the cognitive and neural mechanisms involved in this form of memory. However, we depend on more than just our brains to remember intentions. We also use external props and tools such as calendars and diaries, strategically-placed objects, and technologies such as smartphone alerts. This is known as ‘intention offloading’. Despite the progress in our understanding of brain-based prospective memory, we know much less about the role of intention offloading in individuals’ ability to fulfil delayed intentions. Here, we review recent research into intention offloading, with a particular focus on how individuals decide between storing intentions in internal memory versus external reminders. We also review studies investigating how intention offloading changes across the lifespan and how it relates to underlying brain mechanisms. We conclude that intention offloading is highly effective, experimentally tractable, and guided by metacognitive processes. Individuals have systematic biases in their offloading strategies which are stable over time. Evidence also suggests that individual differences and developmental changes in offloading strategies are driven at least in part by metacognitive processes. Therefore, metacognitive interventions could play an important role in promoting individuals’ adaptive use of cognitive tools.
... Our findings demonstrate that, when provided with a cognitive offloading strategy, around one-third of even 4-and 5-year-old children choose to use it selectively, in line with task demands. Such calibration implies a degree of metacognitive insight and control [4,19,[51][52][53], given that children must differentiate between situations where offloading will and will not benefit performance (a previous study with a more complicated task only found selective offloading in children aged around 9 years and older) [52]. Nonetheless, when participants had to devise their own cognitive offloading strategy in experiment 2, we found very little evidence for selective offloading. ...
... Our findings demonstrate that, when provided with a cognitive offloading strategy, around one-third of even 4-and 5-year-old children choose to use it selectively, in line with task demands. Such calibration implies a degree of metacognitive insight and control [4,19,[51][52][53], given that children must differentiate between situations where offloading will and will not benefit performance (a previous study with a more complicated task only found selective offloading in children aged around 9 years and older) [52]. Nonetheless, when participants had to devise their own cognitive offloading strategy in experiment 2, we found very little evidence for selective offloading. ...
Article
Metacognition plays an essential role in adults’ cognitive offloading decisions. Despite possessing basic metacognitive capacities, however, preschool-aged children often fail to offload effectively. Here, we introduced 3- to 5-year-olds to a novel search task in which they were unlikely to perform optimally across trials without setting external reminders about the location of a target. Children watched as an experimenter first hid a target in one of three identical opaque containers. The containers were then shuffled out of view before children had to guess where the target was hidden. In the test phase, children could perform perfectly by simply placing a marker in a transparent jar attached to the target container prior to shuffling, and then later selecting the marked container. Children of all ages used this strategy above chance levels if they had seen it demonstrated to them, but only the 4- and 5-year-olds independently devised this external strategy to improve their future performance. These results suggest that, when necessary for optimal performance, even 4- and 5-year-olds can use metacognitive knowledge about their own future uncertainty to deploy effective external solutions. This article is protected by copyright. All rights reserved
... This implies that executing prospective intention at the right time while engaging in background tasks, is demanding for children. In older children and adolescents, executive function, future thinking, and meta-cognition have been suggested to be important for PM success Cottini et al., 2021;Ford et al., 2012;Kretschmer et al., 2014;Redshaw et al., 2018). In addition to those executive functions, an executive framework of PM development suggests internal monitoring of intention and external monitoring of environment are also important for PM . ...
Article
Full-text available
To remember the prospective intention successfully, going back and forth between the background task and the intention, i.e., the dynamics of these multiple processes can be critical. An executive function like task switching has been associated with the success of prospective memory (PM) in children, but the neural mechanism of PM in children has not been investigated. The purpose of this study was to reveal the dynamic functional connectivity underlying the success of PM in children. Healthy 108 children, aged 7 to 15, were engaged in a single trial PM task, with a 30-min delay. Temporal variabilities in their resting-state functional connectivity were analyzed, using sliding windows with seed regions of interest ROIs of the PM network. About 70% of children successfully remembered the intention; they showed greater dynamics in neural connectivity between the right dorsolateral prefrontal cortex (DLPFC) and intraparietal sulcus, and between the right DLPFC and insula as compared to children with PM failure. Everyday activities and the usual attention to ongoing processes can be associated with alertness in the right frontoparietal network and internal-state monitoring in the insula network, and those dynamics might be associated with one-time event PM success in children.
... Recent research on the psychological mechanisms of cognitive offloading has revealed that the flexible use of external memory aids depends on reliable metacognitive insight [14,[225][226][227][228]. To strategically adopt a clock-watching strategy requires a degree of awareness about the inaccuracy of one's internal time perception, and a level of awareness that clock monitoring will be required to successfully fulfil one's intention. ...
Article
Full-text available
The capacity for subjective time in humans encompasses the perception of time’s unfolding from moment to moment, as well as the ability to traverse larger temporal expanses of past- and future-oriented thought via mental time travel. Disruption in time perception can result in maladaptive outcomes—from the innocuous lapse in timing that leads to a burnt piece of toast, to the grievous miscalculation that produces a traffic accident—while disruption to mental time travel can impact core functions from planning appointments to making long-term decisions. Mounting evidence suggests that disturbances to both time perception and mental time travel are prominent in dementia syndromes. Given that such disruptions can have severe consequences for independent functioning in everyday life, here we aim to provide a comprehensive exposition of subjective timing dysfunction in dementia, with a view to informing the management of such disturbances. We consider the neurocognitive mechanisms underpinning changes to both time perception and mental time travel across different dementia disorders. Moreover, we explicate the functional implications of altered subjective timing by reference to two key and representative adaptive capacities: prospective memory and intertemporal decision-making. Overall, our review sheds light on the transdiagnostic implications of subjective timing disturbances in dementia and highlights the high variability in performance across clinical syndromes and functional domains.
Book
This Element describes the main theories that guide contemporary research in cognitive development along with research discoveries in several important cognitive abilities: attention, language, social cognition, memory, metacognition and executive function, and problem solving and reasoning. Biological and social contributions are considered side-by-side, and cultural contributions are highlighted. As children participate in social interactions and learn to use cultural symbols and tools to organize and support their thinking, the behaviors and understandings of the social community and the culture more broadly become an integral part of children's thoughts and actions. Culture, the natural ecological setting or habitat of human beings, plays a significant role by providing support and direction for cognitive development. Without the capacity to learn socially, human cognition would be markedly different from what it is today.
Article
Metamemory is a component of metacognition that includes both the knowledge of factors that affect memory (i.e. declarative metamemory) and knowledge and application of factors in one's own learning and recall performance (i.e. procedural metamemory). The current paper aims to provide a comprehensive review of studies examining metamemory ability development from preschool into adolescence in order to improve the understanding of metamemory, its developmental course, and the available assessment methods. We examined the developmental trajectory of procedural and declarative metamemory abilities for both typically developing children and clinical populations. We found procedural metamemory abilities emerge around 4 to 5 years old, and significantly improve across childhood and into adulthood, although less is known about metamemory development across adolescence in typically developing children. Additionally, metamemory abilities vary significantly based on clinical pathology, although relatively fewer studies have examined these abilities in children with neurodevelopmental disorders or other neurologic conditions, such as acquired brain injury. The methods of metamemory assessment varied significantly across studies as well, indicating a need for a standardized metamemory measure, which would have high utility for clinical care.
Article
Full-text available
Metacognition describes the process of monitoring one's own mental states, often for the purpose of cognitive control. Previous research has investigated how metacognitive signals are generated (metacognitive monitoring), for example when people (both f/m) judge their confidence in their decisions and memories. Research has also investigated how metacognitive signals are used to influence behavior (metacognitive control), for example setting a reminder (i.e. cognitive offloading) for something you are not confident you will remember. However, the mapping between metacognitive monitoring and metacognitive control needs further study on a neural level. We used fMRI to investigate a delayed-intentions task with a reminder element, allowing human participants to use their metacognitive insight to engage metacognitive control. Using multivariate pattern analysis, we found that we could separately decode both monitoring and control, and, to a lesser extent, cross-classify between them. Therefore, brain patterns associated with monitoring and control are partially, but not fully, overlapping.SIGNIFICANCE STATEMENT:Models of metacognition commonly distinguish between monitoring (how metacognition is formed) and control (how metacognition is used for behavioural regulation). Research into these facets of metacognition has often happened in isolation. Here, we provide a study which directly investigates the mapping between metacognitive monitoring and metacognitive control at a neural level. We applied multivariate pattern analysis to fMRI data from a novel task in which participants separately rated their confidence (metacognitive monitoring) and how much they would like to use a reminder (metacognitive control). We find support for the notion that the two aspects of metacognition overlap partially but not fully. We argue that future research should focus on how different metacognitive signals are selected for control.
Article
Ninety‐seven children aged 4–11 (49 males, 48 females, mostly White) were given the opportunity to improve their problem‐solving performance by devising and implementing a novel cognitive offloading strategy. Across two phases, they searched for hidden rewards using maps that were either aligned or misaligned with the search space. In the second phase, maps were presented on rotatable turntables, thus allowing children to manually align all maps and alleviate mental rotation demand. From age six onwards, children showed strong evidence of both mentally rotating misaligned maps in phase 1 and manually aligning them in phase 2. Older children used this form of cognitive offloading more frequently, which substantially improved performance and eliminated the individual differences observed in phase 1.
Chapter
Full-text available
The previous two decades have seen much theoretical and empirical research into the future thinking capacities of non-human animals. Here we critically review the evidence across six domains: (1) navigation and route planning, (2) intertemporal choice and delayed gratification, (3) preparing for future threats, (4) acquiring and constructing tools to solve future problems, (5) acquiring, saving and exchanging tokens for future rewards, and (6) acting with future desires in mind. In each domain we show that animals are capable of considerably more sophisticated future-oriented behavior than was once thought possible. Explanations for these behaviors remain contentious, yet in some cases it may be most parsimonious to attribute animals with mental representations that go beyond the here-and-now. Nevertheless, we also make the case that animals may not be able to represent future representations as future representations – an overarching capacity that allows humans to reflect on their own natural future thinking limits and act to compensate for these limits. Throughout our analysis we make specific suggestions for how future research can continue to make progress on this and other important questions in the field.
Article
Full-text available
Animal brains have evolved to predict outcomes of events in the immediate environment [1–5]. Adult humans are particularly adept at dealing with environmental uncertainty, being able to mentally represent multiple, even mutually exclusive versions of the future and prepare accordingly. This capacity is fundamental to many complex future-oriented behaviors [6, 7], yet little is known about when it develops in children [8] and whether it is shared with non-human animals [9]. Here we show that children become able to insightfully prepare for two mutually exclusive versions of an undetermined future event during the middle preschool years, whereas we find no evidence for such a capacity in a sample of chimpanzees and orangutans. We gave 90 preschool children and 8 great apes the opportunity to catch an item dropped into a forked tube with two bottom openings. Children’s performance improved linearly across age groups (2, 2.5, 3, 3.5, and 4 years), with none of the youngest group but most of the oldest group spontaneously covering both openings the first time they prepared to catch the item. The apes performed like 2-year-olds on the first trial, with none of them covering both openings. Some apes and 2-year-olds eventually passed the task, but only in a manner consistent with trial-and-error learning. Our results reveal the developmental trajectory of a critical cognitive ability that allows humans to prepare for future uncertainty, and they also raise the possibility that this ability is not shared with other hominids.
Article
Full-text available
This study investigated the empirical differentiation of prospective memory, executive functions, and metacognition and their structural relationships in 119 elementary school children (M = 95 months, SD = 4.8 months). These cognitive abilities share many characteristics on the theoretical level and are all highly relevant in many everyday contexts when intentions must be executed. Nevertheless, their empirical relationships have not been examined on the latent level, although an empirical approach would contribute to our knowledge concerning the differentiation of cognitive abilities during childhood. We administered a computerized event-based prospectivememory task, three executive function tasks (updating, inhibition, shifting), and a metacognitive control task in the context of spelling. Confirmatory factor analysis revealed that the three cognitive abilities are already empirically differentiable in young elementary school children. At the same time, prospective memory and executive functions were found to be strongly related, and there was also a close link between prospective memory and metacognitive control. Furthermore, executive functions and metacognitive control were marginally significantly related. The findings are discussed within a framework of developmental differentiation and conceptual similarities and differences.
Article
Full-text available
Twenty children at each of grades K, 1, 3, and 5 were interviewed in order to sample their knowledge concerning various memory or memory-related phenomena (metamemory). In the present study, metamemory referred primarily to the child's verbalizable knowledge of how certain classes of variables act and interact with one another to affect the quality of an individual's performance on a retrieval problem. Examples of such variables include the relations among the items to be learned, how much study time is allotted, and how exactly they must be reproduced on the recall test. The results of this exploratory study tentatively suggest that children of grades K and 1 can often articulate the intuition that decay from shortterm memory (STM) can be very rapid; there are apt to be savings in the relearning of previously learned but subsequently forgotten information; retrieval performance is affected by amount of prior study time, by properties of individual items (e. g., familiarity), and especially by the number of items to be retrieved. Younger as well as older subjects also showed a striking tendency to think of external mnemonic resources, such as written records and other people, when proposing solutions to various preparation-for-retrieval and retrieval problems. Children of grades 3 and (especially) 5 appear considerably more planful and self-aware in their approach to both kinds of problems and also command a wider variety of solution procedures. Specific acquisitions include a more differentiated concept of self and others as mnemonic organisms and a better understanding of how relations among items (as contrasted with their individual properties) can variously facilitate or interfere with retrieval.
Article
Previous studies have found that individuals with autism spectrum conditions (ASC) can have difficulty remembering to execute delayed intentions. However, in these studies participants were prevented from setting external reminders, whereas the use of such reminders in everyday life is commonplace (e.g. calendars, to-do lists, smartphone alerts). In the present study, 28 participants with ASC and 24 matched neurotypicals performed a task requiring them to remember delayed intentions. In the first phase participants were required to use unaided memory, whereas in the second they had the option to offload their intentions by setting reminders if they wished. Performance of the ASC group was significantly poorer than the neurotypical group in phase 1, and metacognitive evaluations of memory abilities mirrored this. Nevertheless, in the second phase, the ASC group failed to compensate for impaired performance: if anything they set fewer reminders than the neurotypical group. These results indicate that intact explicit metacognitive judgements cannot be assumed to lead directly to the use of compensatory strategies.
Article
If you have ever tilted your head to perceive a rotated image, or programmed a smartphone to remind you of an upcoming appointment, you have engaged in cognitive offloading: the use of physical action to alter the information processing requirements of a task so as to reduce cognitive demand. Despite the ubiquity of this type of behavior, it has only recently become the target of systematic investigation in and of itself. We review research from several domains that focuses on two main questions: (i) what mechanisms trigger cognitive offloading, and (ii) what are the cognitive consequences of this behavior? We offer a novel metacognitive framework that integrates results from diverse domains and suggests avenues for future research.
Chapter
This chapter focuses on research program, providing a description of a theoretical framework that has evolved out of metamemory research, followed by a few remarks about the methodology. Research in metamemory is initiated by the paradoxical findings that people can accurately predict their subsequent likelihood of recognizing nonrecallable items and that they can quickly and accurately decide-on the basis of no more than a cursory search through memory-that they will not retrieve particular sought after items. Those findings lead to develop a methodology based on psychophysical methods that are used to empirically investigate people's feeling of knowing. The results of the experiments convinced that for dealing with only a part of a complex metacognitive system and to account adequately for feeling-of-knowing phenomena, a larger perspective was needed. This eventuated in the present theoretical framework that emphasizes the role of control and monitoring processes. The embedding of the feeling of knowing in a richer framework helped to dissipate the paradoxical nature of the feeling of knowing. The chapter discusses that today there are many capable, active investigators and a wealth of solid empirical findings.