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Latent Diversity in Human Concepts

Louis Marti

, Shengyi Wu

, Steven T. Piantadosi

, and Celeste Kidd

University of California, Berkeley, Berkeley, CA

Keywords: concepts, metacognition, individual differences, ordinary meaning

a n o p e n a c c e s s

j o u r n a l

ABSTRACT

Many social and legal conflicts hinge on semantic disagreements. Understanding the origins
and implications of these disagreements necessitates novel methods for identifying and
quantifying variation in semantic cognition between individuals. We collected conceptual
similarity ratings and feature judgements from a variety of words in two domains. We analyzed
this data using a non-parametric clustering scheme, as well as an ecological statistical
estimator, in order to infer the number of different variants of common concepts that exist in
the population. Our results show at least ten to thirty quantifiably different variants of word
meanings exist for even common nouns. Further, people are unaware of this variation, and
exhibit a strong bias to erroneously believe that other people share their semantics. This
highlights conceptual factors that likely interfere with productive political and social discourse.

INTRODUCTION

Even when two individuals use the same word, they do not necessarily agree on its meaning.
Disagreements about meaning are common in debates about terms like “species” (Zachos,
2016), “genes” (Stotz et al., 2004), or “life” (Trifonov, 2011) in biology; “curiosity” (Grossnickle,
2016), “knowledge” (Lehrer, 2018), or “intelligence” (Sternberg, 2005) in psychology; and
“measurement” in physics ( Wigner, 1995). Ernst Mach and Albert Einstein even disagreed
about what constitutes a “fact” (de Waal & ten Hagen, 2020). In contemporary society, social
issues often hinge on the precise meaning of terms like “equity” (Benjamin, 2019), “pornogra-
phy” (Stewart, 1964), “peace” (Leshem & Halperin, 2020), or the “right to bear arms” ( Winkler,
2011). Sometimes these debates are settled by fiat—for example, the U.S. Supreme court
decided that a tomato counted as a vegetable (not a fruit) for tax purposes because the law
should follow the “ordinary meaning” of words rather than their botanical meaning (see
Goldfarb, 2021; Nix v. Hedden, 149 U.S. 304, 1893).

Despite the frequency of such terminological debates, these conflicts have not been char-
acterized using cognitive psychology methods. Multidimensional scaling methods (Shepard,
1962a, 1980; Torgerson, 1952) have been used in psychometrics to study individual differ-
ences in concepts and their relational or geometric structure (Bocci & Vichi, 2011; Carroll
& Chang, 1970; McGee, 1968; Takane et al., 1977; Tucker & Messick, 1963). For example,
Tucker and Messick (1963) used a multidimensional scaling analysis to infer consistent indi-
vidual differences in perceptions and judgements of distance estimates. This approach avoided
the pitfalls of more common methods of using group averages in judgements to draw general
conclusions about a theoretical “average person”—which the authors rightly observe may not
actually resemble any actual participant at all. Instead the authors’ multidimensional scaling

Citation: Marti, L., Wu, S., Piantadosi,
S. T., & Kidd, C. (2023). Latent Diversity
in Human Concepts. Open Mind:
Discoveries in Cognitive Science,
7, 79–92. https://doi.org/10.1162
/opmi_a_00072

DOI:
https://doi.org/10.1162/opmi_a_00072

Supplemental Materials:
https://doi.org/10.1162/opmi_a_00072

Received: 9 August 2021
Accepted: 14 January 2023

Competing Interests: The authors
declare no conflict of interest.

Corresponding Author:
Celeste Kidd
celestekidd@berkeley.edu

Copyright: © 2023
Massachusetts Institute of Technology
Published under a Creative Commons
Attribution 4.0 International
(CC BY 4.0) license

The MIT Press

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Latent Diversity in Human Concepts Marti et al.

analysis demonstrated distinct and consistent viewpoints across individuals. Recent implemen-
tations capitalize on the advantages of generative Bayesian statistical inference in order to
characterize individual differences and the importance of specific dimensions (Gelman et al.,
2013; Kruschke, 2010; Okada & Lee, 2016). Prior work has also demonstrated the existence
of individual differences in conceptual judgements (Barsalou, 1987; Hampton & Passanisi,
2016; Koriat & Sorka, 2015), but not quantified the degree of variation for concepts across
the population. Verheyen and Storms (2013) found subgroups of categorizers who emphasize
different attributes (e.g., vagueness or ambiguity) when determining membership. Bush charac-
terized multiple dimensions of feeling adjectives and found individual differences in the per-
ception of feelings (Bush, 1973). Labov (1973) observed that conceptual category boundaries
for cups and bowls could vary across individuals in the two dimensional space of height and
width. Labov found greater disagreement in atypical cases compared to typical exemplars, a
finding which holds in other conceptual domains (McCloskey & Glucksberg, 1978). Differ-
ences in training can result in conceptual variation—for instance, philosophers view “knowl-
edge” differently than others (Starmans & Friedman, 2020).

These data suggest that conceptual variability relates to real world experiences, but does
not tell us how commonly conceptual disagreements occur in semantic cognition. If concep-
tual variability is commonplace, that would suggests the variability is fundamental feature of
our conceptual systems, perhaps an inevitable byproduct of the substantial experiential differ-
ences people accumulate throughout their lives. Indeed, two people may experience the same
event but process it differently due to individual differences in cognition or prior experience,
influencing concept formation. Such a finding would also implicate conceptual misalignment
as an underappreciated explanation for a broad range of disagreements in theoretical and
applied fields.

One challenge for understanding variation in concepts is that there are no complete
accounts of human conceptual representation (see, e.g., Laurence & Margolis, 1999; Murphy,
2004) and therefore people’s representations must be measured indirectly. Here, we ask par-
ticipants about the conceptual representations they attach to words, building on the produc-
tive history of studying concepts via linguistic labels (Lupyan & Thompson-Schill, 2012; Rosch
& Lloyd, 1978). As a quantitative measure, we collected conceptual ratings (Barsalou, 1989;
Landauer & Dumais, 1997; Mikolov et al., 2013; Shepard, 1962a, 1962b, 1980) of both
similarity judgements and features. The similarity task asked people to judge whether, for
example, a penguin is more similar to a chicken or a whale. The feature experiment first freely
elicited features from one set of participants, and then asked a group of participants to rate the
applicability of each of the elicited features to each concept. For example, participants judged
whether a penguin was “majestic”. We note that similarity judgements and features have well-
known limitations, including for example that similarities are sensitive to the respects with
which similarity is computed (Gentner & Markman, 1997; Markman & Gentner, 1993; Medin
et al., 1993; Tversky & Gati, 1978); however, for our purposes of studying variability, it is less
important that features and similarities do not completely characterize people’s conceptual
knowledge. Differences in features and similarities still indicate that there are some underlying
differences.

We gathered these ratings in two domains: common animals and politicians. The animal
domain allows us to characterize diversity for high-frequency nouns which may be most likely
to be shared. We contrast this with politicians, which might vary among individuals with dis-
tinct political beliefs. Prior work for example has found that concepts and language concern-
ing morality differ with political view (Frimer, 2020; Graham et al., 2009). The experiment also
asked participants to make the same similarity ratings and feature judgements multiple times.

OPEN MIND: Discoveries in Cognitive Science

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Latent Diversity in Human Concepts Marti et al.

This allowed us to determine reliability of ratings. We used this to test the possibility that peo-
ple shared the same concepts, but that the concepts were just noisily measured. Our main
results showing multiple concepts in the population therefore reflect statistical evidence of
multiple concepts above and beyond response inconsistencies.

Our primary analysis uses a non-parametric Bayesian clustering model in order to infer how
many types of each concept (clusters) were likely to be present in our sample. For example,
how many different concepts of “finch” did people exhibit based on their similarity judge-
ments? This clustering method does not presuppose a fixed number of clusters, but infers a
distribution of what clusters are likely present based on the data by balancing two competing
pressures. First, the model is biased to prefer a small number of clusters since this is a simpler
theory. In the absence of data, the number of clusters should not be “multiplied without neces-
sity” (i.e., Ockham’s Razor). Second, the model prefers clusterings that explain the data. Here,
that means that the inferred clustering should predict responses in the sense that two individ-
uals in the same cluster should give similar responses. This is illustrated in Figure 1, where we
can abstractly imagine possible clusterings (colors) of responses, which are here abstractly
visualized in two dimensions. Clusterings like (A) are too simple; (B) is too complex; (C) is
simple but does not do a good job of explaining the data; intuitively, (D) is a good solution.
In essence, the model stochastically infers the colors in this figure from the responses, provid-
ing us with a statistical estimate of the number of clusters. Specifically, we use a non-
parametric scheme (Anderson, 1991; Gershman & Blei, 2012; Pitman, 1995) which translates
both the pressures for simplicity and fit into probability theory, and then balances—optimally,
in a precise sense–between the two (see Materials and Methods). This inference critically
depends on the reliability of subject responses and only using this model are we able to infer
the number of clusters that likely generated the data.

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Figure 1. Hypothetical clustering of response vectors, here visualized in 2D. The simplest
solution is to put all points into the same cluster (A), but then responses (locations) are not well-
explained by clusters. If each point is in a separate cluster (B) then each point is perfectly predicted
by the cluster, but the solution is complex. A compromise like (D) finds a small number of clusters
that adequately explain the data. The correct clustering (D) will be preferred over alternatives even
with the same number of total clusters which fit the data less well (C).

OPEN MIND: Discoveries in Cognitive Science

Latent Diversity in Human Concepts Marti et al.

We are also interested in the number of clusters present in the population beyond our
experimental sample. To quantify this, we used an estimator from population ecology (Chao
& Chiu, 2016). This model is used in species count estimation (Bunge & Fitzpatrick, 1993),
where one might sample animals, observe how many of each species were collected in the
sample, and estimate the total number of species present in the world (see also Gale &
Sampson, 1995; Good, 1953). We use the most-likely (maximum a posteriori) clustering of
individuals in the Bayesian model in order to estimate the total number of concepts present
in the world, beyond of our sample.

Finally, we asked participants to report what proportion of other people they expected to
agree with them about their similarity judgements. We compared these reports to the observed
agreement levels.

These tools allow us to test a variety of novel hypotheses about variation in human con-
ceptual systems. First, by examining the estimated number of clusters (both in the sample and
the general population), we evaluate how many measurably distinct representations can be
found in the population. This estimate is conservative since it is derived by similarities to a
relatively small number of other nouns; larger and more detailed experiments might reveal
more conceptual variation. Despite this conservativity, our results reveal substantial variation,
with more variation in politicians than animals. Moreover, because our inference relies on a
probabilistic model which incorporates multiple-measurement reliability, these clusters cannot
be due to measurement noise. Finally, the results show that people are generally unaware of
these differences: people expect that others will answer the same way that they do more often
than is true. This lack of awareness suggests that latent variation may underlie disagreement on
broader social and political issues.

MATERIALS AND METHODS

Experiment 1 was run using a custom built web interface on Amazon Mechanical Turk on
8/20/19 through 8/22/19 (animals) and 9/11/19 through 9/12/19 (politicians). Participants were
instructed to “decide which [animal/politician] is more similar to [target concept]” and “asked
to guess how many people out of 100 would agree with you.” All participants were required to
be from the U.S. and have a minimum 95% approval rating from previous tasks. Experiment 2
was run on 04/23/21 through 05/09/21 (animals) and 05/13/21 through 05/17/21 (politicians)
using Prolific and Qualtrics. Participants were all above 18 years old, fluent English speakers,
and physically present in the United States based on pre-screening questions. Responses
were recorded on a secure server and no participants were excluded from data analysis. All
participants were paid at a rate of $10 an hour. This study was approved by the Committee for
Protection of Human Subjects at University of California, Berkeley. Informed consent was
obtained from all subjects. All methods were performed in accordance with relevant guide-
lines and regulations at University of California, Berkeley (CPHS # 2018-12-11675).

Clustering Methods

Responses were clustered using a non-parametric, Bayesian clustering model, a “Chinese res-
taurant process.” If x = hx1, x2, …, xki denotes the number of subjects in each cluster (for a
given word), and n denotes the total number of subjects, this model assigns x, a partition
on individuals, a prior of

OPEN MIND: Discoveries in Cognitive Science

Γ θð Þ_θk
ð
Γ n þ θ

Þ _

Γ xið

Þ;

i¼1

(1)

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Latent Diversity in Human Concepts Marti et al.

where we use θ = 1, to characterize how strongly the model prefers fewer clusters. Within
each cluster, we use a Beta-Binomial likelihood where subjects assigned the same cluster
are assumed to generate the same latent vector of answer probabilities, with each cluster’s
probability vector marginalized out. Thus, if aij and bij are the number of each type of response
to question j in cluster i, and q the number of questions, then the marginal likelihood of the
responses is,

(cid:3)
(cid:2)
B aij þ (cid:2); bij þ (cid:2)
Þ
B (cid:2); (cid:2)ð

;

i¼1

j¼1

(2)

where B(a, b) = Γ(a) · Γ(b) / Γ(a + b). Here, (cid:2) characterizes the noise level assumed by the
likelihood. We set the single likelihood parameter (cid:2) = 0.16 such that two samples from a
Bernoulli with parameter p (cid:2) Beta((cid:2), (cid:2)) agreed with each other with probability 0.88,
which is the proportion of time subjects’ second and first responses agreed (analysis of the
dependence of the results to the assumed (cid:2) is in Supplementary Materials).

Inference was run using a Gibbs sampler, using both the prior (Eq. 1) above and a uniform
prior over clusters. All runs used the same likelihood (Eq. 2). The sampler was initialized with a
configuration where each individual started in the same cluster. This sampling method requires
iterations of burn-in before it converges to a stable set posterior distribution. We assessed con-
vergence using multiple runs and ensured that chains arrived at the same solution. Figure 7 in
Supplementary Materials shows the convergence of three chains for each concept over 500
iterations (one iteration is a Gibbs sweep through the whole population). We discarded the first
100 samples from each run as burn-in.

Ecological Estimator

Finally, we use an ecological estimator from Chao and Chiu (2016), extending a previous esti-
mator (Colwell & Coddington, 1994), in order to approximate the total number of concepts in
the population. This estimator uses the total number of observed clusters (concepts) and the
total number of sampled individuals in order to estimate how many concepts were likely
unobserved. The method is a relative of Good-Turing estimation (Good, 1953), and also
depends on the number of clusters containing a single person, among additional terms. For
this we use our maximum a posteriori Bayesian clustering. Let fi denote the number of clusters
containing i individuals, then the estimator ^S Chao1 is given by,

8
>>>< >>>:

^SChao1 ¼

ð
Sobs þ n − 1
n
ð
Sobs þ n − 1
n

;

f 2
1
2f2
f1 f1 − 1
Þ
ð
2

f2 > 0

;

f2 ¼ 0:

(3)

Here, Sobs denotes the number of observed clusters and n is the number of participants sam-
pled. The estimator we used (Chao & Chiu, 2016) adjusts ^SChao1 to yield ^SiChao1,
Þ
^SiChao1 ¼ ^SChao1 þ n − 3
n

Þ
ð
_ max f1 − n − 3
Þ
n − 1
ð

f2f3
2f4

f3
4f4

(cid:5)
:

(4)

; 0

(cid:4)

EXPERIMENT 1

We recruited 1,799 participants on Amazon Mechanical Turk. Half were asked to make
similarity judgements about animals (finch, robin, chicken, eagle, ostrich, penguin, salmon,
seal, dolphin, whale) and the other half to make judgements about U.S. politicians (Abraham

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Latent Diversity in Human Concepts Marti et al.

Lincoln, Barack Obama, Bernie Sanders, Donald Trump, Elizabeth Warren, George W. Bush,
Hillary Clinton, Joe Biden, Richard Nixon, Ronald Reagan). Each participant was randomly
assigned to a single target from one domain (e.g., “finch”), presented with 36 unique pairs
of other objects in the domain (drawing from the 10 objects in each domain), and asked which
was more similar to the target. Thus, participants responded to queries such as “Which is more
similar to a finch, a whale or a penguin?” Each trial was shown twice (for a total of 72 trials) in
order to measure response reliability (calculated as the percentage of trial-pairs with identical
responses) and detect trial-by-trial idiosyncratic features of stimuli. To quantify metacognitive
awareness of diversity, participants were also simultaneously asked on every trial to guess how
many people out of 100 would agree with their response.

We coded each participant’s responses to a single word as a binary vector, corresponding
to theforced-choice similarity rating between every other pair of items. In modeling, we
assumed that individual vectors were sampled from a collection of latent clusters that specified
an average response vector. We used a nonparametric Bayesian technique called a Chinese
Restaurant Process (Anderson, 1991; Gershman & Blei, 2012; Pitman, 1995), to model a
posterior distribution on the number of clusters for each word independently, assuming a
reliability given by the overall average reliability. We note this clustering model works in
the space of response vectors, not in the lower-dimensional psychological space itself; thus,
our approach does not explicitly model correlations that may exist between items, but also
does not require us to make assumptions about the dimensionality or metric properties of
the latent space. This technique permits us to find a distribution over the number of clusters
present in the population, taking into account both the reliability of individual responses and
uncertainty about the latent response vector characterizing each cluster (e.g., what each
participant’s “finch” cluster corresponds to in terms of similarities). The model builds in a prior
preference for fewer clusters but we also present results with no such prior. The maximum a
posteriori clusterings found in sampling were additionally put through a species-count estima-
tor which estimates the true number of clusters present in the global population, beyond our
finite sample size (Chao & Chiu, 2016). This estimator uses sampled individuals which are
observed to fall into a distribution of species and estimates the total number of species (here,
clusters) in the population at large.

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Experiment 1 Results

The overall subject reliability was 88% (see Figure 6 in Supplemental Materials), indicating
subjects are both not responding with random guesses, nor are they responding with ad
hoc responses that vary from trial to trial. Subject responses likely reflect stable aspects of
how they conceptualize these concepts throughout the context of the experiment. On the
other hand, the average intersubject reliability across all concepts was 50% (ranging from
33% to 62% with no significant differences between animals or politicians), meaning two peo-
ple picked at random are just as likely to disagree as agree for a typical concept judgement.
Intersubject reliability was 50% for both the first and second judgements. We kept the first
judgement for analysis.

Figure 2 shows a visualization of participants’ similarity judgements using distributed sto-
chastic neighbor embedding (t-SNE) (van der Maaten & Hinton, 2008). This technique places
individual participants’ response vectors in a 2D plane such that nearby participants give sim-
ilar response vectors. The closer two points are together, the more closely their concepts align;
however, these scales are relative and cannot easily be compared across plots. Points in this
plot have been colored according to the maximum a posteriori assignment of participants to

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Latent Diversity in Human Concepts Marti et al.

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Figure 2. Distances between participants’ conceptual representations of each target concept using distributed stochastic neighbor embed-
ding. In this visualization, the distances between two points approximate the distance between their full rating vectors. Each plot is on the same
scale. Additionally, each data-point is colored with the cluster they were assigned to in our clustering analysis, showing that the t-SNE clus-
tering finds similar groupings.

clusters according to the clustering model, which was run independently from t-SNE, and thus
convergence serves as a check on both methods. This figure illustrates that two independent
methods provide convergent characterizations of how people are distributed in the space
since each color (generated according to the clustering model) tends to be in a single spatial
position (generated by t-SNE). Note that the color assignments do not perfectly match spatial
arrangements, likely due to t-SNE dimensionality reduction and different trade-offs being
applied to edge-case participants by our algorithm and t-SNE.

To understand the number of concepts in the population, we first look at the posterior
distribution over the number of clusters inferred. Figure 3 shows the estimated number of
conceptual kinds (y axis) for each semantic domain (subplot), as a function of the number
of participants included (x axis). This figure shows that as our sample size increases from 10
to 100 individuals per concept, the number of estimated concepts reaches 9 to 19 for politi-
cians and 5 to 13 for animals. The maximum a posteriori clustering (in purple) and the eco-
logical estimator (in blue) are in the range of 5–50 latent concepts in the population, and are

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Latent Diversity in Human Concepts Marti et al.

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Estimated number of concepts (y axis) depending on the number of people sampled (x axis). Boxes show the median 50%
Figure 3.
quantiles of the number of unique concepts. Purple data points are the number of clusters for the maximum a posteriori clustering. Orange
data points are the number of clusters for the MAP clustering with a uniform prior. Blue data points show the ecological estimator using the
MAP clustering.

higher for politicians than for animals. We find similar ranges even if we use a prior which is
uniform on the clusterings (orange).

We note that the number of inferred concepts is not necessarily monotonically increasing in
the number subjects, since additional subjects may shape the geometry of the space (e.g., pro-
viding evidence that two separate clusters are actually one wider cluster). In addition, most of
the latent diversity can be found in small numbers of subjects—even distinct clusters can be
found when examining 50 individuals. The point at which each subplot levels off is due to a
combination of the reliability of individual responses, the number of items we sampled
(sampling less results in fewer concepts), and the true number of concepts in the population.
However, limited reliability and a finite number of items mean that our analysis is likely to
under-estimate the number of clusters.

OPEN MIND: Discoveries in Cognitive Science

Latent Diversity in Human Concepts Marti et al.

Left: Probabilities that only a single conceptual representation for each word exists, with log-axis, showing near zero probability
Figure 4.
for all words, especially politicians. Middle: Probability that two random individuals will share the same table (i.e., concept), showing low
rates of agreement for politicians and slightly higher for animals. Right: Predicted answers (y axis) vs. actual answers (x axis), showing people
tend to over-estimate others’ rate of agreement compared to the truth (line y = x).

Figure 4 shows the probability that the population contains only one concept for each
word, according to the clustering model. Because samples rarely contained a single cluster,
we used a normal approximation to compute this probability, using the mean and standard
deviation of number of samples according to the posterior distribution on clustering. Political
words are far less likely to have a single meaning than animal words, matching the patterns in
the number of clusters in Figure 3. Generally, this provides strong statistical support to the idea
that there are multiple meanings in the population for these terms, despite the fact that these
multiple concepts all have the same word. However, if the distribution of participants to mean-
ings tends to be heavily skewed (e.g., most participants have the same meaning), then this
diversity might be relatively inconsequential. Figure 4 shows the probability that two randomly
chosen individuals will have the same concept in this analysis, which is a relatively robust
statistic since it depends largely on the frequency of the most common concepts for each word
rather than the tails of the distribution. This agreement probability averages to around 14–70%
for animals and 13–33% for politicians. This indicates that most individuals one encounters
will tend to have a measurably different conceptual representation. Again, this is likely to over-
estimate true rate of agreement since we only tested a small number of questions.

Most importantly, our results show that people are generally not aware of these differences.
Figure 4 shows the agreement rate on responses (x axis) compared to people’s predicted esti-
mates of agreement (y axis). If people understood the population’s variation in responses, the
trials shown in this plot would all fall along the y = x line. Instead, this figure shows that for
most of the range of actual agreement (e.g., (cid:2)0%–80%) people tend to consistently believe
that about 2/3 of participants will agree with them, no matter what true proportion actually do.
This is true even for the lowest agreement responses: most participants believe their response is

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Latent Diversity in Human Concepts Marti et al.

in the majority even when essentially 0% of other participants agree with them. This is unlikely
to be due to a failure to engage this aspect of the task because participants do reliably increase
their estimates on the highest agreement items (e.g., (cid:2)80%–100%), which results in a reliable
rank-order correlation overall (Spearman’s ρ = 0.45, p < 0.001). The increase, though, is not well-calibrated to the population variation. Moreover, these patterns likely reflect meta- cognitive limitations (Goldman, 2006; Gopnik & Meltzoff, 1997; Wimmer & Perner, 1983) rather than differences in effort or motivation because these trials were interspersed with the main task, which had very high within-subject reliability. EXPERIMENT 2 Experiment 2 consisted of two parts: feature elicitation and feature rating. In feature elicitation, we recruited 16 registered users on Prolific. Half of the participants were asked to list 10 single-word adjective features for each of the 10 animals in Experiment 1. The other half were asked to list 10 single-word adjective features for each of the 10 U.S. politicians in Experiment 1. We kept all features that were mentioned more than once after removing non-adjectives, inappropriate words, and typos, as well as combining synonyms. Then, 1,000 registered users on Prolific were asked to rate either 105 animal features or 105 politician features from the feature elicitation experiments. Each participant was randomly assigned to rate features of two animals (e.g., “dolphin” and “whale”) or two U.S. politicians (e.g., “George W. Bush” and “Hillary Clinton”). Participants were asked questions such as “Is a finch smart?” and responded by clicking either the “Yes” or “No” button on the screen. Each question was asked twice to measure response reliability. Thus, each participant saw 420 question trials. Experiment 2 Results Clustering participants based on their feature ratings serves as a conceptual replication of Experiment 1. In the feature rating experiment, participant reliability was high with an average reliability of 86%. Similar to Experiment 1, subject responses likely reflect stable aspects of subjects’ conceptual representations. The number of concepts found was 6 to 16 for politicians 6 to 11 for animals, compared to 9 to 19 for politicians and 5 to 13 for animals in Experiment 1 (see Figure 10 in Supplementary Materials). Likewise, the ecological estimator results in 6 to 66 latent concepts in the population, compared to 6 to 50 in Experiment 1. Comparing the number of concepts for each word between experiments also results in high agreement, in both the MAP clustering (cosine similarity = 0.92) and ecological estimator (cosine similarity = 0.67). Such similar findings, despite a very different paradigm, provides convergent support for conceptual diversity. Figure 5 shows agreement for a sample of features and concepts. Many features show near universal agreement among participants, but many also show large disagreement among participants. For example, most participants agreed that seals are not feathered but are slippery while disagreeing as to whether they are graceful. Likewise, most participants agreed that Trump is not humble and is rich, but there is high disagreement as to whether he is interesting. These sorts of disagreements likely reflect the different conceptual representations possessed by our participants, especially given the convergence between these findings and the similarity experiment. We note, however, that the results here could be due to differences between participants in the meaning of the features (e.g., what they think “interesting” refers to), though several theories of concepts (e.g., conceptual role theories, classical theories) have the meaning of “Trump” critically dependent on underlying features or related terms. OPEN MIND: Discoveries in Cognitive Science 88 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u o p m i / l a r t i c e - p d f / d o i / i . / / 1 0 1 1 6 2 o p m _ a _ 0 0 0 7 2 2 0 7 4 9 3 3 o p m _ a _ 0 0 0 7 2 p d / . i f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Latent Diversity in Human Concepts Marti et al. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u o p m i / l a r t i c e - p d f / d o i / Figure 5. A sampling of feature responses for 3 animals and 3 politicians (y axis). The x axis plots the mean percentage of “yes” responses for a given feature. Features in the center show high disagreement among participants and are the primary features responsible for differing conceptual representations among participants. DISCUSSION i . / / 1 0 1 1 6 2 o p m _ a _ 0 0 0 7 2 2 0 7 4 9 3 3 o p m _ a _ 0 0 0 7 2 p d . / i f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 We report statistical evidence of more than one variant of concepts in the population. In fact, we find that most people the average language user meets will not share their same concept. These results are unexpected in part because the measures we used are coarse. If one could gather an arbitrary amount of data, one might expect to find small differences between people: one interlocutor might have specific memories that make their representation idiosyncratic, perhaps different from anyone else. However, our experimental approach was based on judg- ing similarities and features—not an exhaustive inventory of each person’s memories or associations—and we were nonetheless able to statistically justify measurably distinct repre- sentations, even for common nouns. If differences can be detected with these methods, it indi- cates that there is substantial variation in the population for lexical meanings. This variation exists despite the fact that people use the same word for each concept, and people are rela- tively unaware that others will tend to give differing similarity judgements. However, our results do not support the notion that every single use of a concept is distinct or entirely idiosyncratic (Casasanto & Lupyan, 2015): subjects did group into clusters and did provide highly reliable responses across trials. We emphasize, though, that studies with more items, or items that focus more on corner cases, might find greater diversity than reported here. Future studies should examine the sensitivity of these results to target word and feature OPEN MIND: Discoveries in Cognitive Science 89 Latent Diversity in Human Concepts Marti et al. selection, with specific attention given to highly unrelated comparisons (e.g., is a train more similar to a dolphin or a slime mold). Moreover, the subject pool in our experiment was relatively homogeneous, and future studies of cultural differences may point to more diversity in word usage based on diversity of experience (Clark, 1998). Indeed, while our method allows us to quantify conceptual diversity, it does not pinpoint what specific representational differences drive this diversity. These differences may indeed go deep with respect to theories and interrelations between the concepts studied and others (Gelman & Legare, 2011; Medin & Rips, 2005; Murphy & Medin, 1985). In general, theories of word learning and conceptual development will need to work out how human language users acquire distinct representations for shared words. In turn, theories of communication and language use (e.g., Grice, 1989; Wilson & Sperber, 2004) will need to address both differences in word referents, and lack of awareness of those differences. People’s general obliviousness to variation has important implications for productive discourse struc- ture, and has been studied by psychologists in more general forms such as the false consensus effect (Marks & Miller, 1987) and egocentric bias (Ross & Sicoly, 1979). Fundamental misun- derstandings may originate with individuals using the same word for distinct conceptual rep- resentations or under different contexts. Indeed, such differences in word meanings might underlie many classic philosophical questions (Piantadosi, 2015). Generally, our results may help to explain why “talking past each other” appears to be common in social and political debates: the common ground of even the most basic word meanings is only imperfectly shared. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u o p m i / l a r t i c e - p d f / d o i / i / / . 1 0 1 1 6 2 o p m _ a _ 0 0 0 7 2 2 0 7 4 9 3 3 o p m _ a _ 0 0 0 7 2 p d / . i f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 STATEMENT OF RELEVANCE We demonstrate that conceptual variability is a common part of human conceptual systems, one that likely arises from experiential differences. Our results document substantial disagree- ment between people for word meanings, even for common concepts. These results suggest that fundamental conceptual differences in political and social discourse underlie many semantic disagreements. DATA AND MATERIALS AVAILABILITY A l l d a t a a n d c o d e c a n b e f o u n d a t h t t p s : / / o s f . i o / b f w c e / ? v i e w o n l y =aaf1b62123ce4a31938e6a5b03e140cc. ACKNOWLEDGMENTS The authors thank the Kidd Lab and the Computation and Language Lab for feedback. CK and SP received funding from DARPA (Machine Common Sense TA1, BAA number HR001119S0005) and NSF (Division of Research on Learning, Grant 2000759). CK received funding from Human Frontier Science Program (RGP0018/2016), Berkeley Center for New Media, The Jacobs Foundation, and Google Faculty Research Awards in Human-Computer Interaction. AUTHOR CONTRIBUTIONS Louis Marti: Conceptualization; Formal analysis; Investigation; Methodology; Software; Visu- alization; Writing—Original draft; Writing—Review & editing. Shengyi Wu: Investigation; Visualization; Writing—Review & editing. Steven T. Piantadosi: Conceptualization; Funding acquisition; Methodology; Software; Supervision; Visualization; Writing—Review & editing. Celeste Kidd: Conceptualization; Funding acquisition; Methodology; Supervision; Writing— Review & editing. OPEN MIND: Discoveries in Cognitive Science 90 Latent Diversity in Human Concepts Marti et al. REFERENCES Anderson, J. R. (1991). The adaptive nature of human categoriza- tion. Psychological Review, 98(3), 409–429. https://doi.org/10 .1037/0033-295X.98.3.409 Barsalou, L. W. (1987). The instability of graded structure: Impli- cations for the nature of concepts. In U. Neisser (Ed.), Con- cepts and conceptual development: Ecological and intellectual factors in categorization (pp. 101–140). Cambridge University Press. Barsalou, L. W. (1989). Intraconcept similarity and its implications for interconcept similarity. In S. Vosniadou & A. Ortony (Eds.), Similarity and analogical reasoning (pp. 76–121). 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Springer. https://doi.org/10.1007/978-3-319-44966-1 OPEN MIND: Discoveries in Cognitive Science 92 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u o p m i / l a r t i c e - p d f / d o i / i . / / 1 0 1 1 6 2 o p m _ a _ 0 0 0 7 2 2 0 7 4 9 3 3 o p m _ a _ 0 0 0 7 2 p d . / i f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 REPORT image

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