Memory transmission in small groups and large networks: An empirical study.

https://doi.or g/10.3758/s13423-021-02021-9
BRIEFREPORT
Memorytransmissioninsmallgroupsandlargenetworks:An
empiricalstudy
VaelGates1·JordanW.Suchow2·ThomasL.Griﬃths3
Accepted:26September2021
©ThePsychonomicSociety,Inc.2021
Abstract
When people try to remember information in a group, they often recall less than if they were recalling alone. This finding
is called collaborative inhibition , and has been studied primarily in small groups because of the difficulty of bringing
large groups into the laboratory. To study the dynamics of collaborative inhibition in large groups (Luhmann & Rajaram,
Psychological Science ,26, 1909–1917, 2015 ) constructed an agent-based model that extrapolated from previous laboratory
experiments with small groups. The model predicts that collaborative inhibition should increase with group size. Here, we
evaluate this model by recruiting a large number of participants using crowdsourcing, allowing us to replace the artificial
agents in the model with people to study collaborative memory at larger scales. Our empirical results did not match the model
predictions: there was no evidence for an increase in collaborative inhibition with group size, despite substantial power to
detect such an effect. These findings motivate further empirical work to elucidate the mechanisms of collaborative memory.
Keywords Collaborative memory ·Collaborative inhibition ·Crowdsourcing ·Agent-based modeling
Our memories depend on the people around us and are
reshaped and reformed as we interact. Long-term couples,
for example, learn how to effectively access each other’s
memories, remembering details they would not have
recalled alone (Wegner, Erber, & Raymond, 1991 ), and
groups collectively form memories that define their values
(e.g., Hirst & Echterhoff, 2012 ). However, sometimes our
memories are reshaped in such a way that we remember less
together than we would apart, an effect that psychologists
have been actively investigating.
Imagine a group of people trying to recall a list of
words collaboratively. The group as a whole would recall
some number of words. Next suppose that each individual
had instead tried to recall the list of words alone. Though
the group would likely have recalled a greater number of
words than any individual, the group may have performed
/envelopebackVael Gates
vgates@berkeley.edu
1Department of Psychology, University of California,
Berkeley, Berkeley, CA, USA
2School of Business, Stevens Institute of Technology,
Hoboken, NJ, USA
3Department of Psychology, Princeton University,
Princeton, NJ, USAeven better had they recalled the words individually and
then mechanically combined the list, removing duplicates.
Indeed, this precise comparison is often made in analyses of
the well-established “collaborative memory” task, in which
participants listen to a long list of items (often words)
and then recall as many items as possible, either as a
group or individually. The number of words recalled by
the group is compared to the number of words recalled
by the “nominal group”: the summed list of an equivalent
number of individuals, with duplicate words removed.
In the collaborative memory paradigm, nominal groups
routinely outperform collaborative groups, a finding called
collaborative inhibition . This effect has been replicated
across many studies and variations on the paradigm (see
Marion & Thorley, 2016 ; Rajaram & Pereira-Pasarin, 2010 ,
for reviews).
The leading theory of collaborative inhibition is the
retrieval disruption hypothesis (e.g., Basden, Basden,
Bryner, & Thomas, 1997 ; Rajaram & Pereira-Pasarin,
2010 ). This hypothesis states that when initially listening
to a wordlist, people form idiosyncratic representations
of the words, influenced by factors such as the order of
the words presented and their semantic relations. When
recalling words alone, participants use their idiosyncratic
representations to effectively recall the words. However,
when placed in groups, other participants, who organized/ Published online: 28 October 2021Psychonomic Bulletin and Review (2022) 29:581–588
the words differently, disrupt a participant’s recall, leading
to reduced performance. This hypothesis predicts that whenparticipants are encouraged to organize information in
similar ways, collaborative inhibition will disappear. In
fact, when participants are experts in their domain (Meade,Nokes, & Morrow, 2009) or are exposed to similarly ordered
information (Finlay, Hitch, & Meudell, 2000), inhibition
does not occur.
Psychologists have historically studied collaborative
memory in small-scale experiments of dyads or triads inthe lab. Bringing groups of participants into the lab tosimultaneously recall information—often lists of words—is logistically complex, slow, and expensive. However, in
today’s world of online communities and internet forums,
information is being recalled and exchanged with muchlarger groups than pairs or triads, and many of the findings
from this small-scale work may not be applicable to our
daily lives. As people form ever-larger social networks,understanding how information is recalled among large
groups of people becomes increasingly important.
To address this lack of understanding of large groups,
Luhmann and Rajaram (2015) developed an agent-based
model to predict what performance might be like at larger
group sizes, based on known factors from empirical small-
scale experiments. Computational “agents” follow algorithmsfor memory storage and retrieval, and can participate in
simulated experiments interacting with other agents (details
of the model appear in the Supplementary Material). This
agent-based model predicts that collaborative inhibition will
increase as group size increases from 1 to 4 participants,
as has been suggested by the experimental literature(Basden, Basden, & Henry, 2000; Thorley & Dewhurst,
2007). The model then predicts that collaborative inhibition
will continue to increase with group size, until nominal
recall reaches ceiling performance as the disruption ofidiosyncratic recall strategies is compensated for by sheer
group size (see Fig. 1a).
1
Previously, it was a logistical impossibility to experi-
mentally study collaborative memory at this kind of scale.However, using web-based crowdsourcing tools such as
Amazon Mechanical Turk it is possible to recruit and orga-nize hundreds of online participants into real-time interac-tive chatrooms. Here we use this new approach to translate
the agent-based model of Luhmann and Rajaram ( 2015)i n t o
an empirical experiment, testing its predictions about the
1We present model predictions using identical parameter settings to
Luhmann and Rajaram ( 2015)( α=0.2,β=0.05 and γ=
0.75) except we use 60 recalled words rather than 40, because pilot
studies indicated that participants were nearing ceiling performancewhen recalling fewer words (an analysis of the results of this changeappears in the Supplementary Material ). Note also that when recalling
60 words, the model prediction of increased collaborative inhibitionwith group size appears robustly across parameter settings (see
Supplementary Material ).effect of group size on collaborative inhibition. When we
simulate the model with parameter settings correspondingto our experimental task, it predicts nominal ceiling perfor-
mance around a group size of 16. Our experiments thus used
group sizes of 2 through 16, meaning that the model predictscollaborative inhibition should increase with group size.
More precisely, it predicts an interaction between group size
and recall method (collaborative or nominal), with the effectof recall method increasing with group size.
Experiment1:CollaborativeInhibition
inSmallandLargeGroups
Methods
All experiments were IRB-approved by University of Cali-
fornia, Berkeley, Committee for Protection of Human Sub-
jects/Office for Protection of Human Subjects, Protocol ID:
2015-12-8227, Protocol Title: Culture-on-a-chip Comput-ing: Testing Evolutionary Hypotheses through Large-Scale
Behavioral Simulations.
Participants A total of 1,138 participants were recruited
through Amazon Mechanical Turk. Participants would
occasionally repeat the task, as they could choose tocomplete the task again on Amazon Mechanical Turkdespite written advisement against this. A total of 134
participants repeated the experiment more than once (14.6%
of participants), and 30.3% of the data was generated bythese participants.
2
Participants were excluded from the experiment if they
did not complete a pre-experiment arithmetic task andthey did not contribute words in the main experiment.
Participants waited in a virtual waiting room until the group
size was met; experiments listed with less than the full groupsize contained excluded participants. There were two recallconditions in the experiment: collaborative and nominal.
Sixteen participants were removed from the collaborative
experiments for a total of 561 participants. Nominal groupswere matched; thus 561 participants participated in the
2Participants participated an average of 1.22 times. The mean
proportion of repeaters across group sizes (both nominal and
collaborative) was as follows: group size of 2: 0.33 ±0.35 (SD), group
size of 3: 0.39 ±0.31, group size of 4: 0.27 ±0.21, group size of
8: 0.27 ±0.20, and group size of 16: 0.27 ±0.09. The participants
who repeated the nominal experiments did not show improvement over
time, despite having seen the same wordlists: the correlation betweennumber of repetitions and number of words recalled was r=− 0.03
(91 data points). Participants did improve across repetitions in thecollaborative experiments ( r=0.47, 89 data points). However, the
proportion of repeaters within experiments was anti-correlated with
group size: the correlation between experiments of group sizes 2, 3, 4,
8, and 16 and the proportion of repeaters was r=− 0.086.582 Psychon Bull Rev (2022) 29:581–588
Fig.1 Effect of group size on collaborative inhibition. (a) Model pre-
dictions, produced using code from Luhmann and Rajaram (2015). (b)
Experiment 1behavioral results. The mean proportion of words (±
SE) recalled in the nominal (red/grey) and collaborative (blue/black)conditions are shown; collaborative inhibition (yellow/light-grey) is
the subtraction of these two values. Note that the horizontal axis islogarithmic. (c) Experiment 2behavioral results
nominal experiments. The average ( ±SD) number of
participants in collaborative and nominal experiments was
15.2±0.7 for groups of size 16, 7.6 ±0.7 for groups of
size 8, 4.0 ±0.0 for groups of size 4, 3.0 ±0.0 for groups of
size 3, and 2.0 ±0.0 for groups of size 2. (In group sizes of
4, 3, and 2, groups were only included if they contained the
maximum number of participants. In the nominal condition,
participants completed the experiments individually andthen were only later matched to the appropriate group sizes,so all participants were included.) Participants were paid
$1.00 for completing the task, plus bonuses for time spent
waiting for other participants to arrive in a chatroom tobegin the experiment ($5.00/hour).
Procedure Participants were presented with a wordlist: 60
unrelated words each selected from a different category
from Overschelde, Rawson, and Dunlosky ( 2004). (Exam-
ple words include “diamond,” “hour,” and “uncle.” The 60words were presented in a random order for each partic-ipant.) Each word was presented for two seconds. Afterseeing the list, participants completed an arithmetic filler
task for 30 seconds before advancing to the recall task.
Participants were placed in chatrooms alone or with otherparticipants, and were encouraged to type as many of the
words they had seen as possible. Participants were free
to submit words at any time. Participants were not toldhow many other participants were in the chatroom: their
responses appeared in blue font, and responses from all
others appeared in black. They saw all previous wordsentered and were not permitted to submit any word thathad already been submitted (by anyone in the group). This
choice—that any words already present on the group list
were not redisplayed—was made to encourage participantsto read others’ submitted words, and because it more closely
matched the lab-based version of the collaborative memory
paradigm, in which verbal recall creates social pressure tonot repeat words. There was no time limit for the recall task.
Groups contained 2, 3, 4, 8, or 16 participants. For each
recall method (nominal or collaborative), 48 groups of 2were analyzed; 32 groups of 3 were analyzed; 24 groups of4 were analyzed, and 12 groups of 8 and 16 were analyzed.The sample sizes for group sizes 2, 3, and 4 were consistent
with and generally larger than those in previous experiments
(Marion & Thorley, 2016). An unbalanced design was used
to ensure a similar number of participants for each group
size. This was a 2 ×5 design, crossing recall method by
group size. In the nominal recall condition, participants
recalled words alone (and were not informed their recalllists would later be pooled with other participants’). In the
collaborative recall condition, groups of participants were
placed in chatrooms and recalled together. Recalled wordsthat had not been on the original lists were marked as
incorrect and not included.
Participants completed a post-experiment questionnaire,
in which we asked about participants’ engagement in thetask (1–10), the perceived difficulty of the task (1–10), and
had a textbox for any other comments.
Results
The dependent variable in these analyses was the number ofwords recalled for each group; nominal participants’ recalllists (with redundant words removed) were added together
according to the appropriate group size. The collaborative
inhibition effect is most reliably observed in triads (Rajaram& Pereira-Pasarin, 2010), and we replicated this effect in
our behavioral data at group size 3: t(62)=2.34,p=
0.022, effect size d=0.60, independent two-sample t-test
(Fig. 1b, Table 1).
3Collaborative inhibition in the literature
is frequently but not always observed in pairs (Rajaram &Pereira-Pasarin, 2010), but we did not observe this effect
in this group size (p =0.44,d=0.16). There are only
two studies known to the authors examining tetrads (Thorley
3α=0.05 for all planned comparisons to follow. Unlike previous
studies, we investigate the collaborative inhibition effect at multiple
group sizes, while previous studies used α=0.05 at a single group
size, justifying its use here.583 Psychon Bull Rev (2022) 29:581–588
Table1 Two-sample t-test results for each group size in Experiment 1
t p d M 95% CI
Group size 2 t(94)=0.78 0.44 0.16 2.33 [−3.53, 8.19]
Group size 3 t(62)=2.34 0.022 0.60 8.69 [1.40, 15.98]
Group size 4 t(46)=− 0.42 0.67 0.13 −1.58 [−8.94, 5.77]
Group size 8 t(22)=0.25 0.80 0.11 0.83 [−5.74, 7.41]
Group size 16 t(22)=2.17 0.041 0.93 3.33 [0.29, 6.38]
Alongside t-test and effect size (Cohen’s d) statistics we report the
mean difference (M) in words recalled between the nominal and
collaborative groups and associated confidence intervals (CI)
& Dewhurst, 2007;B a s d e ne ta l . ,2000). Collaborative
inhibition effects were observed in these studies, but we
did not observe this effect at group size of 4 ( p=0.67,
d=0.13).
Given previous observations of the collaborative inhibi-
tion effect for small group sizes, Luhmann and Rajaram(2015) extrapolated and hypothesized that the effect ofcollaborative inhibition would increase with group size
(Fig. 1a). There was no significant collaborative inhibition
effect at group size of 8 (p =0.80,d=0.11). However,
we did find a collaborative inhibition effect at group size 16(p=0.041, d=0.93, note that effect size is especially
large due to small variance in word recall because of ceiling
effects).
Overall, using a between-participants two-way unbal-
anced
ANOV A , we did not observe a statistically significant
main effect of recall method, F(1,246) =2.03, p=
0.16,η2=0.0045: nominal and collaborative groups did
not recall significantly different numbers of words whenresults from all groups were combined (Fig. 1b). We did
observe an expected main effect of group size, F(4,246) =
49.08, p< 0.0001, η
2=0.44, in that larger group sizes
increased word recall. Critically, there was no interactioneffect between recall method and group size, F(4,246) =
1.15,p=0.33,η
2=0.010. This was the key prediction of
the model proposed by Luhmann and Rajaram ( 2015).
We conducted a power analysis to determine whether
our sample sizes were sufficient to detect effects as large
as those predicted by the model. We generated 1000
simulations of the entire experiment using the parametersettings from Luhmann and Rajaram ( 2015) with 60 words.
In each simulation, the model was run a number of timesmatching the sample sizes used in the experiment,
4and we
conducted an ANOV A testing for a main effect of recall,
a main effect of group size, and an interaction effect. In1000/1000 simulations, the model had p< 1e-40 for
all these effects. We conclude that if our participants had
4Sample sizes: 48 groups for group size 2, 36 groups for group size 3,
24 groups for group size 4, and 12 groups for group sizes 8 and 16.behaved like the model predicted, we would have hadenough power to detect these effects.
Experiment2:PreregisteredReplication
withaModiﬁedTask
Because the main finding from Experiment 1was a null
effect for the predicted interaction, and there were method-
ological concerns (e.g. repeat participants), we conducteda preregistered replication (Experiment 2). In Experi-
ment 1, we used a “free-flowing” collaborative memory
procedure, but another common collaborative memory pro-cedure is “turn-taking” (see Marion & Thorley, 2016;
Rajaram & Pereira-Pasarin, 2010, for reviews); Luhmann
and Rajaram (2015) used a turn-taking procedure. In Exper-iment 2, we sought to increase our study’s similarity to
Luhmann and Rajaram (2015) and to lab-based turn-takingparadigms, while reducing methodological concerns thatmay have suppressed memory interference. To this end, thetwo largest changes in our replication were using a turn-
taking rather than a free-flowing procedure, and having
submitted words be read aloud to the other participants toincrease similitude to the lab-based experiment.
In lab-based free-flowing experiments, participants recall
as many words as they’d like, whenever they would liketo, and interact organically with each other. However,
in lab-based turn-taking experiments, participants do not
interact with each other in a naturalistic way; on each turn,participants say a single word, then wait silently for thenext participant to say a single word, while the experimenter
records words and monitors turn-taking (Basden et al.,
1997;B a s d e ne ta l . , 2000; Thorley & Dewhurst, 2007;
for a writing version of this procedure, see Wright &
Klumpp, 2004). We sought to replicate this lab-based turn-
taking paradigm in our online paradigm, not only to better
approximate Luhmann and Rajaram ( 2015), but also to
facilitate comparison of our online collaborative memorystudy to a traditional lab-based study with similarly reducednaturalistic interaction.
This experiment was preregistered (planned methods and
analyses) with the Open Science Framework (https://osf.io/7ht3g).
Methods
Participants A total of 1,135 participants were recruited
through Amazon Mechanical Turk. Participants were
not permitted to repeat the task. As in Experiment 1,
participants were excluded from the experiment if they did
not complete the pre-experiment arithmetic task and they
did not contribute words in the main experiment. Again as
in Experiment 1, participants waited in a virtual waiting584 Psychon Bull Rev (2022) 29:581–588
room until the group size was met; experiments listed with
less than the full group size contained excluded participants.575 participants were recruited and 36 participants were
excluded from the collaborative conditions, for a total of 539
participants. The average ( ±SD) number of participants in
the collaborative conditions was 2.0 ±0.0 for groups of
size 2, 2.9 ±0.3 for groups of size 3, 3.7 ±0.5 for groups
of size 4, 7.3 ±0.6 for groups of size 8, and 14.5 ±1.4
for groups of size 16. 560 participants were recruited and
23 participants were excluded from the nominal conditions,
for a total of 537 participants. The average ( ±SD) number
of participants in the nominal conditions was 2.0 ±0.0
for groups of size 2, 3.0 ±0.2 for groups of size 3, 3.9
±0.3 for groups of size 4, 6.8 ±0.8 for groups of size
8, and 14.3 ±1.2 for groups of size 16. (Across both the
collaborative and nominal conditions, groups with less than
two participants were re-run.) Participants were paid $1.00
for completing the task, plus bonuses for time spent waitingfor other participants to arrive in a chatroom to begin the
experiment ($5.00/hour) and bonuses for the number of
correct words they recalled and participants who transmittedwords to them recalled ($.015/word).
Procedure Participants observed 60 words, randomly
selected for each group of participants across all conditions,
drawn from a list of 200 words from Overschelde et al.
(2004). (Example words include “adverb,” “pine,” and “dol-lar.” The 60 words were presented in a random order foreach participant.) Each group had a different wordlist pre-
sented, and within each group, the order of presented words
in the wordlist was selected randomly for each participant.Each word was presented for two seconds. After seeing
the list, participants completed a 30-second-long arithmetic
filler task before advancing to the recall task. Participantswere placed in chatrooms alone (the nominal recall con-
dition) or with other participants (the collaborative recall
condition), and were encouraged to type as many of thewords they had seen as possible.
As in Experiment 1, participants were not permitted
to submit any word that had previously been submitted,to increase similarity to the lab-based version of thecollaborative memory paradigm in which verbal recall
creates social pressure to not repeat words. Moreover,
words that were submitted by others in the chatroom, andnot by the participant, were read aloud to the participant,
again to increase similarity to the lab-based task. Words
were read aloud by a text-to-speech computer algorithmthat automatically converted participant text during theexperiment. To ensure that participants could understand
the computerized speech and that their audio was working,
before the task began participants had to correctly type inan isolated phrase read aloud by this computerized voice. In
the nominal conditions, no words were read aloud, since noother participants were present in the chatroom. There was
no time limit for the recall task.
To increase similarity to the specific methodology
of Luhmann and Rajaram ( 2015), participants in the
collaborative conditions were not permitted to submitwords whenever they wished (as was true in the nominal
condition). Rather, participants took turns to submit
words and did so in random order, following Luhmannand Rajaram (2015). (Both turn-based and free-flowing
procedures are commonly used in the collaborative memory
paradigm, as described in Marion & Thorley, 2016; Rajaram
& Pereira-Pasarin, 2010). Participants took part in “rounds,”
in which each participant had the option to submit one word
during their five-second turn, and turn order was determined
randomly within each round. Participants could submit aword that had not already been submitted, press a “Pass”
button, wait for their 5-second turn to elapse on each turn,
or press “I can’t recall any more.” When participants exitedthe study by clicking the latter button, they were no longer
included in the turn order for the remaining participants.
As in Experiment 1, groups contained 2, 3, 4, 8, or 16 par-
ticipants. For each recall method (nominal or collaborative),
48 groups of 2 were analyzed; 32 groups of 3 were ana-
lyzed; 24 groups of 4 were analyzed, and 12 groups of 8 and
16 were analyzed. The sample sizes analyzed were on parwith and generally larger than those in other collaborative
memory experiments (Marion & Thorley, 2016).
Participants completed a post-experiment questionnaire,
in
which we asked about participants’ engagement in the
task (1–10), the perceived difficulty of the task (1–10), and
had a textbox for any other comments.
Results
Recall that in this experiment, we would observe a col-laborative inhibition effect if the nominal groups recalledmore words than the collaborative groups. For all group
sizes in Experiment 2, we observed that the nominal groups
did indeed recall more words than the collaborative groups
in absolute number, but this effect was small (Fig. 1c).
When results from all groups were combined, nominal andcollaborative groups recalled significantly different num-bers of words: in a between-participants two-way unbal-
anced
ANOV A , we observed a main effect of recall method,
F(1,246) =6.47,p=0.012, η2=0.013. We ran indepen-
dent 2-sample t-tests for group sizes 2, 3, 4, 8, and 16, with
α=0.05 for all planned comparisons shown (Table 2).
To summarize, we did not observe a statistically
significant collaborative inhibition effect for group size 2
(p=0.10, effect size d=0.34). We saw marginally
significant effects at group size 3 (p =0.056, d=0.50) and
group size 4 (p =0.056, d=0.58) (Fig. 1c). Consistent
with previous work showing collaborative inhibition at585 Psychon Bull Rev (2022) 29:581–588
Table2 Two-sample t-test results for each group size in Experiment 2
t pdM 9 5 % C I
Group size 2 t(94)=1.64 0.10 0.34 3.85 [−0.76, 8.47]
Group size 3 t(62)=1.95 0.056 0.50 5.84 [−0.054, 11.74]
Group size 4 t(46)=1.96 0.056 0.58 7.54 [−0.035, 15.12]
Group size 8 t(22)=0.086 0.93 0.037 0.42 [−9.17, 10.00]
Group size 16 t(22)=1.76 0.093 0.75 3.67 [−0.48, 7.81]
Alongside t-test and effect size (Cohen’s d) statistics we report the
mean difference (M) in words recalled between the nominal and
collaborative groups and associated confidence intervals (CI)
small group sizes, the effect sizes for group sizes of 3 and 4
were similar to the adjusted estimate of overall mean effectsize (d =0.56) for collaborative inhibition (calculated in a
meta-analysis by Marion and Thorley ( 2016), using studies
of group sizes 2–4 and compensating for publication bias)and the effect size for group size 3 was similar to that
in Experiment 1(d=0.60). There were no statistically
significant differences at group size 8 (p =0.93,d=
0.037), nor at group size 16 (p =0.093, but note the
large effect size of d=0.75 due to small variance in the
proportion of words recalled because of ceiling effects). The
number of words recalled did increase with group size, andin a between-participants two-way unbalanced
ANOV A ,w e
observed the expected main effect of group size, F(4,246)
=56.38, p< 0.0001, η2=0.47. However, once again we
found no interaction effect between recall method and group
size,F(4,246) =0.47,p=0.76,η2=0.0039, contrary to
the prediction of an increase in collaborative inhibition with
group size from the model of Luhmann and Rajaram ( 2015).
Analyzingthemodel
The results of our experiments are not consistent withthe increase in collaborative inhibition with group sizepredicted by Luhmann and Rajaram (2015). However, onecould argue that if we had altered the parameters ofthe model, we would have seen patterns that were more
compatible with our empirical results. To address this,
we conducted two additional analyses showing that theempirical results do not match model predictions across a
range of parameter settings.
Our first analysis asked what predictions would be
possible with the model, given the constraint of tryingto match the empirical results as closely as possible.
We thus did a grid search over the model’s parameters,minimizing the squared error between the mean nominaland collaborative results for each group size.
Even for the best-fitting model, the model predictions
do not qualitatively match the empirical results forExperiment 1(Fig. 2) or Experiment 2(Fig. 3). Specifically,
the model is unable to predict a collaborative inhibitioneffect at small group sizes (as has been shown in theliterature and our experiments) even with parameters
optimized for reducing the discrepancy between the model
and empirical results.
Our second analysis asked a different question: how
often would we see model trends that fit the empirical
data if we used a broad array of parameter settings? One
way to operationalize the differences between the modelpredictions and empirical results is to check whether there
is a smaller or larger collaborative inhibition effect for
groups sizes 2, 3, and 4 compared to group sizes 8 and16. The model predicts a smaller collaborative inhibition
effect for group sizes 2, 3, and 4. In our empirical data,
we observe a larger collaborative inhibition effect for groupsizes 2, 3, and 4. This is one of the most qualitativelysalient differences between the model and our results. In
theSupplementary Material we present a detailed analysis
showing that the model rarely predicts such an effect, and
only does so in the presence of unrealistically strong ceiling
effects.
Fig. 2 Best-fit model predictions and behavioral results for Experi-
ment 1. (a) Model predictions with the best-fitting parameters ( α=
0.10,β=0.07,γ=0.55, number of rounds =23), 1000 simulations.(Compare to the Luhmann and Rajaram ( 2015) model parameters of
α=0.20,β=0.05,γ=0.75, number of rounds =20.) (b)
Behavioral results for Experiment 1586 Psychon Bull Rev (2022) 29:581–588
Fig. 3 Best-fit model predictions and behavioral results for Experi-
ment 2. (a) Model predictions with the best-fitting parameters ( α=
0.12,β=0.03,γ=0.39, number of rounds =22), 1000 simulations.(Compare to the Luhmann and Rajaram ( 2015) model parameters of
α=0.20,β=0.05,γ=0.75, number of rounds =20.) (b)
Behavioral results for Experiment 2
Generaldiscussion
The critical prediction that Luhmann and Rajaram ( 2015)
made is that collaborative inhibition—an effect found
robustly at smaller group sizes—would be even greater
with group sizes 8 and 16. Under their model, researchers
would expect to see an interaction effect between recallmethod (collaborative or nominal) and group size, reflecting
increased collaborative inhibition as group size increases.
We did not observe this interaction effect in our data,and believe we had enough power to detect this effect
had participants behaved as the model predicted. We now
consider possible interpretations of these results.
One possibility is that our online task differed from
laboratory studies in ways that had a meaningful impact
on the results. First, we note that the online task
produced results consistent with those from the laboratory:we observed collaborative inhibition at group size 3 in
Experiment 1and had marginally significant evidence for
an effect at group sizes 3 and 4 ( p=0.56) as well as
a main effect of collaborative inhibition across groups inExperiment 2, and the size of these effects corresponds to
those suggested by meta-analysis of laboratory experiments
(Marion & Thorley, 2016). Second, we note that all of
the factors relevant to the agent-based model—and to
the broader theory of retrieval disruption—are present in
our online tasks, which closely mimic the structure ofthe model. If there are critical features of the laboratory
setting that are missing from our task, they are equally
absent from the model. Third, we note that though ouronline experiments are not as naturalistic as lab-based
free-flowing experiments, in our preregistered replication
(Experiment 2), we used a turn-taking procedure which was
very similar to the lab-based turn-taking methodology. Inlab-based turn-taking procedures, interactions are restricted:
participants do not experience interactions like multiple
voices and interruptions, irregular overlapping timings, ornormal social cues and organic camaraderie, just as in
our Experiment 2. We thus believe that Experiment 2
especially can be fairly compared to lab-based collaborative
experiments. Finally, our online tasks—in which peopleinteracted in chatrooms—are increasingly representative of
the contexts in which people interact with large groups,
making them a reasonable object of study in themselves.
Another possibility is that while retrieval disruption is
the dominant force in smaller groups, additional factorscome into play at larger group sizes. For example, itmight be the case that intense competition as group size
increases drives greater recall. Alternatively, at larger group
sizes, groups may need to adopt different representationalstrategies that are less prone to disruption. To determinewhat these different mechanisms would be, we will need
controlled experiments aimed at developing additional
cognitive, social, or motivational hypotheses explaining theunexpected lack of consistent collaborative inhibition at
large group sizes.
Participant reports hint at what might make large groups
distinct. In post-Experiment 1questionnaires, for group
size 16, participants reported time pressure and competitive
stress from many people racing to submit responses atonce. In smaller groups, the number of responses wasless overwhelming and more cooperative. In Experiment 2,
participants took timed turns rather than experiencingfree-flowing recall, but in larger groups people had tocomparatively wait much longer for their turns compared
to in smaller groups. These are examples of factors that are
comparatively minor in small group collaborative recall, butwere qualitatively salient in large group collaborative recall.
In an increasingly interconnected world, it is important
to understand how information is shared and rememberedby interacting groups of people. Methods like agent-basedmodeling provide a starting point for developing hypotheses
about groups that are larger than we can normally study
in the laboratory. However, crowdsourcing and computer-587 Psychon Bull Rev (2022) 29:581–588
controlled recruitment offer new tools that we can use
to evaluate these hypotheses and extend the scope of ourempirical results. Our findings raise the possibility that
there may be additional factors that influence collaborative
recall in large group sizes, illustrating the importance oftranslating agent-based models based on lab studies into
online studies that can improve our understanding of the
interactive behavior of large groups of people.
SupplementaryInformation The online version contains supplemen-
tary material available at https://doi.org/10.3758/s13423-021-02021-9.
Acknowledgments Preliminary results from Experiment 1were
presented at the Annual Conference of the Cognitive Science Society
and appear in the Proceedings of that conference (Gates, Suchow, &Griffiths, 2017). This work was funded in part by NSF Grant 1456709
to T.L.G., DARPA Cooperative Agreement D17AC00004 to T.L.G
and J.W.S., and N.I.H. U.C. Berkeley Neuroscience Training ProgramGrant T32NS095939 to V .G.
AuthorNote Materials and data on Open Science Framework: https://
osf.io/txe9a .
OpenPracticesStatement The data and materials for all experiments
a r ea v a i l a b l ea t https://osf.io/txe9a and Experiment 2was preregis-
tered.
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