Quantifying social semantics: An inclusive definition of socialness and ratings for 8388 English words

Abstract

It has been proposed that social experience plays an important role in the grounding of concepts, and socialness has been proffered as a fundamental organisational principle underpinning semantic representation in the human brain. However, the empirical support for these hypotheses is limited by inconsistencies in the way socialness has been defined and measured. To further advance theory, the field must establish a clearer working definition, and research efforts could be facilitated by the availability of an extensive set of socialness ratings for individual concepts. Therefore, in the current work, we employed a novel and inclusive definition to test the extent to which socialness is reliably perceived as a broad construct, and we report socialness norms for over 8000 English words, including nouns, verbs, and adjectives. Our inclusive socialness measure shows good reliability and validity, and our analyses suggest that the socialness ratings capture aspects of word meaning which are distinct to those measured by other pertinent semantic constructs, including concreteness and emotional valence. Finally, in a series of regression analyses, we show for the first time that the socialness of a word's meaning explains unique variance in participant performance on lexical tasks. Our dataset of socialness norms has considerable item overlap with those used in both other lexical/semantic norms and in available behavioural mega-studies. They can help target testable predictions about brain and behaviour derived from multiple representation theories and neurobiological accounts of social semantics.

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Behavior Research Methods
https://doi.org/10.3758/s13428-022-01810-x
Quantifying social semantics: Aninclusive definition ofsocialness
andratings for8388 English words
VeronicaDiveica1 · PennyM.Pexman2 · RichardJ.Binney1
Accepted: 4 February 2022
© The Author(s) 2022
Abstract
It has been proposed that social experience plays an important role in the grounding of concepts, and socialness has been
proffered as a fundamental organisational principle underpinning semantic representation in the human brain. However, the
empirical support for these hypotheses is limited by inconsistencies in the way socialness has been defined and measured. To
further advance theory, the field must establish a clearer working definition, and research efforts could be facilitated by the
availability of an extensive set of socialness ratings for individual concepts. Therefore, in the current work, we employed a
novel and inclusive definition to test the extent to which socialness is reliably perceived as a broad construct, and we report
socialness norms for over 8000 English words, including nouns, verbs, and adjectives. Our inclusive socialness measure
shows good reliability and validity, and our analyses suggest that the socialness ratings capture aspects of word meaning
which are distinct to those measured by other pertinent semantic constructs, including concreteness and emotional valence.
Finally, in a series of regression analyses, we show for the first time that the socialness of a word's meaning explains unique
variance in participant performance on lexical tasks. Our dataset of socialness norms has considerable item overlap with those
used in both other lexical/semantic norms and in available behavioural mega-studies. They can help target testable predictions
about brain and behaviour derived from multiple representation theories and neurobiological accounts of social semantics.
Keywords Word ratings· Lexical decisions· Semantic cognition· Social cognition· Grounded cognition
Introduction
Conceptual knowledge is the foundation of our complex
interactions with the environment, bringing meaning to the
objects, words, and social agents we encounter. A major
challenge for the cognitive sciences is therefore to charac-
terise how meaning is represented in the brain. Of particular
interest has been the issue of how the mental representations
of concepts become connected to their referents, termed the
symbol grounding problem (Harnad, 1990; Searle, 1980).
Within multiple representation accounts of semantic pro-
cessing, concepts are mapped to the world, or grounded,
by being directly represented within the neural systems
underpinning multiple experiential channels such as per-
ception, action, emotion, language and cognition (Borghi
etal., 2018; Kiefer & Harpaintner, 2020). Sensorimotor
systems are particularly important for grounding concrete
concepts such as festival and politician. In contrast, abstract
concepts like romance and democracy cannot, by defini-
tion, be directly experienced through the senses, and may
thus rely to a greater degree on other types of information,
such as affective (Fingerhut & Prinz, 2018; Kousta etal.,
2011), introspective (Shea, 2018) and linguistic experience
(Borghi etal., 2019; Dove, 2018). Further, there is growing
recognition that there are different types of abstract concepts
which depend to varying extents on these manifold sources
of information (Harpaintner etal., 2018; Villani etal., 2019)
and which elicit different patterns of behavioural responses
in lexical-semantic tasks (Muraki etal., 2020).
Recently, there has been a rise in interest concerning the
role that social experience plays in the acquisition and rep-
resentation of concepts. Indeed, there are proposals in which
social interaction and social context are pinpointed as a key
* Veronica Diveica
psuda2@bangor.ac.uk; veronicadiveica@gmail.com
* Richard J. Binney
R.Binney@bangor.ac.uk
1 School ofHuman andBehavioural Sciences, Bangor
University, Gwynedd, WalesLL572AS, UK
2 Department ofPsychology andHotchkiss Brain Institute,
University ofCalgary, Calgary, AB, Canada

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source or mechanism for grounding that may be particu-
larly important for the representation of abstract concepts
(Barsalou, 2020; Borghi etal., 2019). For instance, Barsalou
(2020) proposed that the social environment (e.g., agents,
social interaction, culture) provides one form of grounding,
in addition to that afforded by perceptual modalities, both
of which are distinguished from the body, and the physical
environment. Likewise, Borghi etal. (2019) argued that both
social interactions and linguistic inputs are crucial for the
acquisition of abstract concepts (also see Borghi & Binkof-
ski, 2014). In Pexman etal. (2021), we have reviewed these
theoretical perspectives as well as two parallel sets of empir-
ical literature, which provide some evidence for socialness
being a key principle underpinning semantic representation.
For example, property generation and feature ratings studies
found that social semantic content, or socialness, helps dis-
tinguish concrete from abstract concepts (Barsalou & Wie-
mer-Hastings, 2005; Troche etal., 2014; Wiemer-Hastings &
Xu, 2005) and even different sub-types of abstract concepts
(Harpaintner etal., 2018; Villani etal., 2019). In parallel, a
set of neuroimaging studies have found that words high in
socialness are associated with differential patterns of brain
activation during semantic processing (e.g., Arioli etal.,
2021a, b; Binney etal., 2016; Mellem etal., 2016; Rice
etal., 2018; Wang etal., 2019; for another review, also see
Conca etal., 2021). Some authors have argued for a special
status of social concepts over other types of concept, and
have suggested that socialness may even be a fundamental
driver behind the functional organisation of the semantic
system (Lin etal., 2018; Ross & Olson, 2010; Simmons
etal., 2010; Zahn etal., 2007). These studies were all based
on limited word samples, but they provide some evidence
that social words might be a distinct type of concept, in
line with proposals of some multimodal (e.g., Borghi etal.,
2018) and neurobiological models (e.g., Olson etal., 2013)
of conceptual processing.
These theories are nascent and there are many outstand-
ing questions about the nature and extent of the contribu-
tion that socialness makes to semantic representation. One
fundamental question is whether socialness is a behaviour-
ally relevant principle as indexed, for example, by its ability
to account for variance in performance on lexical-semantic
tasks. However, the extant empirical support is limited by
the way socialness has been defined and measured. To our
knowledge, the largest source of openly available socialness
norms was compiled by Troche etal. (2017) and includes
social interaction ratings for 750 English nouns. Another
dataset collected by Binder etal. (2016) includes ratings
for 434 nouns, 62 verbs, and 39 adjectives on four socially
relevant dimensions labelled social, communication, human
and self. Thus, the scale and scope (i.e., the syntactic classes
of words) at which socialness has been explored has been
limited to date. Moreover, socialness as a construct has been
defined variably in terms of behavioural descriptiveness, and
there is no consensus on the criteria that differentiate social
from non-social concepts. The heterogeneity in definitions is
summarised by Pexman etal. (2021); some researchers have
measured socialness as, for example, the degree to which a
word’s meaning refers to relationships between people (Tro-
che etal., 2014, 2017), to social as opposed to individual
contexts (Arioli etal., 2021a), or to the relationship between
self and others (Crutch etal., 2012), and socialness has also
been defined as how well words describe social behaviour
(Zahn etal., 2007). This variability in the operationalisation
of socialness hinders our ability to compare findings across
studies and glean a broader understanding of the contribu-
tion made by socialness to conceptual representation in the
brain, and its behavioural consequences. Thus, we argue that
to further progress theory, the field must first establish a
clearer working definition of socialness.
Moreover, many of these past studies employed social-
ness definitions that emphasise specific aspects of social
experience (Pexman etal., 2021). These narrow definitions
might neglect important aspects of our highly complex inter-
actions with the social environment. Thus, taking a crucial
next step for understanding the construct of socialness,
we aimed to collect ratings using an inclusive definition
designed to capture all manner of features that are deemed
to be socially relevant. This allowed us to test the extent to
which socialness is reliably perceived as a broad construct.
Relatedly, our socialness definition can be equally applied
to a wide range of words, from nouns like those referring to
social roles (e.g., lawyer) or institutions (e.g., government),
to verbs like to befriend, and adjectives like trustworthy.
This broad and inclusive definition can be used as a starting
point for future studies exploring more fine-grained aspects
of the socialness construct.
In summary, the aims of the present study were as fol-
lows: 1) collect socialness ratings for a large set of Eng-
lish words to provide a useful resource for future research
endeavours; 2) use an inclusive definition to assess the extent
to which socialness is reliably perceived as a broad con-
struct; 3) explore to what extent these new socialness rat-
ings capture aspects of word meaning that are distinct from
those measured via other related semantic variables, such
as concreteness and emotional valence, and 4) test whether
socialness is a behaviourally relevant construct.
Method
Participants
Participants were recruited via the online platform Prolific
(https:// www. proli fic. co/). Responders were restricted to
those who self-reported being fluent in English and having

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no language disorders. A total of 605 participants (359 male,
240 female, six unspecified, Mage = 29.44 years, SDage =
10.6) completed the study. Participants completed the rat-
ing task in 34 minutes on average and were compensated
with GBP £4. Following exclusions (see below), the final
sample consisted of 539 participants, with ages ranging
from 18 to 76 years (M = 29.7; SD = 10.67). Of the partici-
pants, 216 (40.07%) were female, 317 (58.81%) male and
six (1.11%) unspecified. English was the first language for
273 (50.65%) participants. Of the remaining 266 (49.35%)
participants, 111 self-reported as being proficient in English,
124 advanced and 31 beginner/intermediate. A total of 185
(34.32%) participants were monolingual, while the remain-
ing 354 (65.68%) reported speaking more than one language.
Stimuli
The stimuli were 8948 words, including 5569 nouns, 1343
verbs, 2009 adjectives, and 26 other parts of speech (based
on the dominant part-of-speech norms in Brysbaert etal.,
2012) 1. We compiled our stimulus set from two sources:
the Calgary Semantic Decision Project (Pexman etal.,
2017) and Brysbaert etal. (2014)’s dataset of concreteness
ratings. Ratings on emotion dimensions (valence, arousal,
dominance) from Warriner etal. (2013) and on concreteness
from Brysbaert etal. (2014) are available for all of the words
included and the selected words span the entire continuum
of these dimensions. In addition, we specifically selected
these words so that there would be considerable overlap with
behavioural mega-studies and other theoretically important
psycholinguistic dimensions, some of which were used in
analyses reported below, whereas others might be of interest
in future research (e.g., Calgary Semantic Decision Project
(Pexman etal., 2017), the Lancaster Sensorimotor Norms
(Lynott etal., 2020), the Glasgow norms (Scott etal., 2019),
word association norms (De Deyne etal., 2019), word preva-
lence norms (Brysbaert etal., 2018)).
We used 30 of the 8948 words as a set of control items
which were to be presented to every participant and used
during the data cleaning process (see below). These words
were selected based on the ratings received in a pilot study
(N = 36 participants) that was run to obtain an initial assess-
ment of whether participants understand the task instructions
and, in particular, the description of the inclusive socialness
measure, and whether they provide reliable ratings (for a
detailed description, see Section S1 of Supplementary Mate-
rials). Control words were selected to vary in the mean pilot
socialness ratings, as well as in their concreteness (Brysbaert
etal., 2014) and valence ratings (Warriner etal., 2013).
In addition to the 8948 words, we selected 12 practice
words to be rated before the main ratings task so that partici-
pants could become familiar with the task requirements. We
selected practice words that vary in concreteness (Brysbaert
etal., 2014) and valence (Warriner etal., 2013), and that
span the whole range of the social interaction dimension
as measured by Troche etal. (2017) to ensure that partici-
pants practised both items with high and with low socialness
ratings.
We used Qualtrics software (Qualtrics, 2020) to cre-
ate two questionnaires for presentation to participants. To
facilitate efficient Qualtrics processing, we divided the 8918
words into two lists of 4459 words from which each par-
ticipant saw a random subset. These lists were equated for
letter length, frequency (log subtitle frequency; Brysbaert
& New, 2009), concreteness (Brysbaert etal., 2014) and
valence (Warriner etal., 2013) to control for the probability
of selecting words with different characteristics from each
list. The control words were then added to both lists, result-
ing in two questionnaires each with 4489 words.
Procedure
The word stimuli were presented using Qualtrics (2020) and
linked to the Prolific online recruitment platform (www. pr oli
fic. co). Following the consent form, a demographics survey
and instructions, participants rated the 12 practice words,
then proceeded to rate the main set of items. Each partici-
pant rated 370 words randomly selected from one of the two
item lists, plus the 30 control words. The control words were
randomly intermixed with other items. The full instructions
given to participants are presented in Section S2 of sup-
plementary materials. In short, the participants were asked
to rate the degree to which the words’ meaning has social
relevance by describing or referring to the following:
a social characteristic of a person or group of people, a
social behaviour or interaction, a social role, a social
space, a social institution or system, a social value or
ideology, or any other socially relevant concept.
Participants provided their answers using a seven-point
Likert scale presented horizontally below each word. In
addition, there was an “I don’t know the meaning of this
word” option. There were 25 words presented per page. We
collected data until we obtained at least 25 ratings per word.
Data cleaning
In total, we collected 241,575 observations. The data clean-
ing pipeline involved sequentially implementing several
techniques consistent with recommendations for identify-
ing careless or insufficient effort responders (Curran, 2016)
and computer-generated random responding (Dupuis etal.,
1 Note that part-of-speech information was not available for one
word: hip hop.

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2019), as well as other data cleaning procedures used in
previous word norming studies (Brysbaert etal., 2014; Pex-
man etal., 2019; Warriner etal., 2013). First, we removed
data from participants if they completed less than 33% of
the ratings task (n = 0), responded with “I don’t know the
meaning of this word” for more than 25% of items (n = 8)
and provided the same rating for more than 25 words in
a row (n = 17). Next, we examined each participant’s rat-
ings of the 30 control words and generated correlations with
the mean ratings of those words obtained in the pilot study.
We removed data from 36 participants with a correlation
coefficient less than .20. We then computed the correlation
between each participant’s ratings and the mean ratings of
all other participants. We deleted data from five participants
with a correlation coefficient less than .10. Finally, if more
than 15% of raters reported not knowing a particular word,
we removed those words from the analyses reported below.
This led to the exclusion of 560 words.
The final dataset was comprised of 8388 words and
202,841 observations, of which 3542 were “I don’t know the
meaning of this word” responses. Not taking into account
the control words rated by all participants, each word in the
final dataset had 21.92 valid ratings on average (SD = 1.68),
ranging from 15 to 27 ratings. Overall, 7703 (91.83%) words
had at least 20 valid ratings.
Data analysis overview
Data pre-processing, analysis and visualisation was accom-
plished using RStudio version 3.6.1 (RStudio Team, 2020).
We first computed descriptive statistics for the socialness
ratings and assessed their reliability. Then, to begin to
explore the nature of the information captured by the social-
ness dimension and characterize its relationship with other
pertinent psycholinguistic constructs, we computed the zero-
order correlations between the mean socialness ratings and
a variety of lexical and semantic properties of the words.
Next, we conducted a series of hierarchical regression analy-
ses to examine whether the socialness measure is related
to behaviour in lexical tasks, using behavioural responses
from the English Lexicon Project (ELP) lexical decision task
(LDT; Balota etal., 2007) and the English Crowdsourcing
Project (ECP) word knowledge task (Mandera etal., 2020).
The LDT outcome variables quantify the speed and accuracy
with which participants could distinguish between words
and non-word letter strings. The ECP RT outcome variable
measures the speed with which participants could recognize
a word as known to them, while the percentage of partici-
pants reporting not knowing a word (henceforth proportion
unknown) is a measure of word prevalence. We selected
these tasks because they require only a fairly shallow level
of semantic access (Muraki etal., 2020) and thus provide
a conservative test of the relationship between this meas-
ure and lexical semantic processing. In addition, in both of
these tasks, all word stimuli received the same behavioural
response (“word” in the ELP LDT, or “I know that word” in
the ECP) unlike, for instance, semantic decision tasks (e.g.,
Pexman etal., 2017) which involve different responses for
different types of words. All predictor variables were mean-
centred and we used reaction times standardized as z-scores
because these reduce the influence of individual differences
on overall processing speed (Faust etal., 1999).
Results
Descriptive statistics
The raw data and resulting socialness ratings are provided
on the Open Science Framework (OSF) project page (avail-
able at: https:// osf. io/ 2dqnj/). The socialness ratings have
a unimodal distribution with a mean of 3.63 (SD = 1.24)
Fig. 1 Distribution of socialness ratings. A Histogram of socialness
ratings for 8388 words; the dotted line represents the mean. B Ker-
nel density plot of ratings as a function of syntactic class. C Standard
deviation of ratings plotted against their respective mean rating, along
with a loess line (in green) that highlights the functional relationship

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(Fig.1a). More descriptive statistics for the mean ratings
are provided in Table1 and the distribution of ratings as a
function of part of speech is depicted in Fig.1b. The rat-
ings have an average standard deviation of 1.85 (SD = 0.35)
and participants provided more consistent responses at the
extremes of the scale (Fig.1c). Examples of words at the
extremes of the socialness dimension are given in Table2.
Words like friendship, people and sociable received high
socialness values, while words like avalanche, millimeter
and hemoglobin received low socialness ratings, suggesting
good face validity.
Reliability andvalidity
We first examined the reliability of the ratings by computing
the one-way intra-class correlation coefficient (ICC) of all
ratings using variances estimated via a random effects model
with a global intercept and a random intercept per word
(Brysbaert, 2019; Stevens & Brysbaert, 2016). We found
an ICC of 0.9, which indicates good reliability of the mean
socialness ratings. We further computed the split-half reli-
ability for the 30 control words which were the only items in
our dataset rated by all participants. We found a mean Spear-
man–Brown corrected split-half reliability of 0.998 (SD =
0.16) across 100 random splits, suggesting high reliability
for the control items.
We then examined the validity of the ratings by comput-
ing the correlations between the ratings observed here and
the mean ratings collected in the pilot study (n = 60 words),
as well as two previous related sets of social interaction
norms collected by Binder etal. (2016) (n = 258 words),
and Troche etal. (2017) (n = 450 words). The current social-
ness ratings were strongly and positively correlated with the
ratings collected in the pilot study (r = 0.97) and with the
previous social interaction ratings collected by Binder etal.
(2016) (r = 0.76) and Troche etal. (2017) (r = 0.76), sug-
gesting good validity.
Correlations withlexical andsemantic properties
We examined the correlations between the socialness ratings
and various lexical and semantic properties of the words.
We included lexical dimensions in our analysis as previ-
ous work has shown that semantic content is not independ-
ent of the linguistic properties of words (Lewis & Frank,
2016; Reilly etal., 2012, 2017; Strik-Lievers etal., 2021).
The lexical variables included letter length, orthographic
Levenshtein distance (Yarkoni etal., 2008), phonological
Levenshtein distance and frequency (log subtitle frequency;
Brysbaert & New, 2009). To examine the proposed rela-
tionship between socialness and abstractness (Borghi etal.,
2019), we included the following semantic variables that
index sensorimotor experience: concreteness (the degree
to which the word’s referent can be experienced through
one of the five senses ; Brysbaert etal., 2014), imageabil-
ity (the ease with which the word arouses a mental image ;
Cortese & Fugett, 2004; Schock etal., 2012), body–object
interaction (BOI; the ease with which a human body can
physically interact with a word’s referent; Pexman etal.,
2019), and sensory experience ratings (the degree of sen-
sory experience evoked; Juhasz & Yap, 2012). To assess the
generalizability of the association between socialness and
Table 1 Descriptive statistics for socialness ratings for 8388 words
Descriptive statistic Value
Mean 3.63
Median 3.57
Standard Deviation 1.24
Minimum 1.05
Maximum 7.00
1st Quartile 2.62
3rd Quartile 4.58
Skewness 0.19
Kurtosis – 0.80
Table 2 List of words at the extremes of the socialness dimension
Highest-rated words Rating Lowest-rated words Rating
friendship 7.00 eucalyptus 1.05
socialize 7.00 horizontal 1.09
relationship 6.96 crocodile 1.09
people 6.90 sulfur 1.10
romance 6.78 sleeve 1.17
marriage 6.76 turbo 1.18
socialism 6.75 cranberry 1.18
political 6.73 dragonfly 1.18
family 6.72 hemoglobin 1.20
teamwork 6.72 shark 1.21
boyfriend 6.68 sunflower 1.21
friend 6.68 sandpaper 1.22
sociable 6.68 millimeter 1.22
sisterhood 6.67 avalanche 1.22
mother 6.67 spinach 1.22
democracy 6.65 airspeed 1.23
togetherness 6.65 button 1.23
sister 6.65 redwood 1.23
festival 6.64 pistachio 1.24
stepfather 6.64 birch 1.25
humankind 6.62 haystack 1.25
meeting 6.62 toothpaste 1.26
parental 6.62 paprika 1.27
befriend 6.61 cellophane 1.28
chatty 6.61 magnolia 1.28

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affective information reported in previous studies (Troche
etal., 2014, 2017; Villani etal., 2019), we included in our
analysis valence extremity (the degree to which the word
evokes positive/negative feelings; this was measured as the
absolute difference between the valence rating and the neu-
tral point of the original valence scale by Warriner etal.,
2013), arousal (the degree to which the word evokes feel-
ings of arousal as opposed to calm; Warriner etal., 2013),
and dominance (the degree to which the word evokes feel-
ings of being controlled as opposed to in control; Warriner
etal., 2013). Finally, to assess the relationship between the
socialness ratings and linguistic experience, the semantic
variables included semantic diversity (the extent to which
a word appears in semantically diverse contexts; Hoffman
etal., 2013), rating-based age of acquisition (AoA) (Kuper-
man etal., 2012), and a test-based AoA measure derived
from (Dale & O’Rourke, 1981) and updated by (Brysbaert
& Biemiller, 2017).
These correlations revealed several interesting relation-
ships that provide insight as to the nature of the word social-
ness measure (Fig.2; see Fig.S1 for scatterplots). Socialness
was negatively correlated with concreteness (r = – 0.32),
imageability (r = – 0.18), and BOI (r = – 0.17), which sug-
gests that words with less social relevance are associated
with more embodied sensorimotor information. In contrast,
socialness ratings were positively correlated with valence
extremity (r = 0.22) and arousal (r = 0.22), suggesting that
social words tend to have more affective information.
Relationships withperformance onlexical tasks
Next, we examined whether the socialness ratings are related
to lexical-semantic processing using behavioural responses
from the ELP LDT (Balota etal., 2007) and the ECP word
knowledge task (Mandera etal., 2020). We conducted a
series of item-wise hierarchical regression analyses in which
we included other lexical and semantic predictors (that are
typically related to behaviour in lexical tasks) in order to
isolate the unique relationships of socialness to standardized
reaction times (RTs), ELP error rates and ECP proportion
unknown. In the first step, we entered the control predic-
tors letter length, frequency (Brysbaert & New, 2009) and
Fig. 2 Correlations between mean socialness ratings and lexical-
semantic dimensions. Only correlations significant at p < .01 are
shown. The strength and direction of the correlation coefficients are
indicated by the colour and the numerical values. For each variable of
interest, the numbers of items in common with our socialness ratings
are as follows: length, concreteness, valence, arousal, and dominance:
8388; log subtitle frequency: 8160; OLD and PLD: 8027; rating-
based AoA: 8348; test-based AoA: 7321; imageability: 2680; BOI:
4038; SER: 2645. SER = sensory experience rating; BOI = body-
object interaction; AoA = age of acquisition; PLD = phonologic Lev-
enshtein distance; OLD = orthographic Levenshtein distance

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rating-based AoA (Kuperman etal., 2012). In the second
step, we entered the semantic predictors: socialness, con-
creteness (Brysbaert etal., 2014), valence extremity (War-
riner etal., 2013) and semantic diversity (Hoffman etal.,
2013). We selected these other semantic predictors on the
basis of multidimensional theories (e.g., Borghi etal., 2019)
that highlight the simultaneous contribution of semantic var-
iables derived from multiple sources, including linguistic
(semantic diversity), sensorimotor (concreteness) and affec-
tive experience (valence extremity).
There were 6926 items for which we had values for all
variables of interest in the analysis predicting LDT per-
formance. Descriptive statistics and zero-order correla-
tions between all variables of interest from this dataset are
reported in Supplementary TableS1. The statistical results
are reported in Table3 and the standardized coefficients are
illustrated in Fig.3a. In this analysis, the control variables
were all significant predictors of LDT latencies – RTs were
faster for words that are shorter, more frequent and acquired
earlier. There was significant improvement in model fit with
the addition of the semantic variables, which collectively
accounted for a further 0.61% of variance in LDT laten-
cies. Of the semantic variables, only socialness and seman-
tic diversity were significant predictors, with faster RTs for
words with increased social relevance and for those encoun-
tered in more semantically diverse contexts. A similar pat-
tern of results was observed when predicting LDT error
rates. The control variables were all significant predictors,
with fewer errors for words that are longer, more frequent
and acquired earlier. There was significant improvement
in model fit with the inclusion of the semantic variables,
which accounted for an additional 0.56% of variance in LDT
error rates. Socialness and semantic diversity were the only
significant semantic predictors – error rates were lower for
words with increased socialness and for those that are more
semantically diverse.
There were 7010 items for which we had values for all
variables of interest in the analysis predicting performance
in the ECP word knowledge task. Descriptive statistics and
zero-order correlations between all variables of interest from
this dataset are reported in Supplementary TableS2. The
statistical results are reported in Table4 and the standardized
coefficients and illustrated in Fig.3b. In this analysis, the
control variables were all significant predictors of response
latencies – RTs were faster for words that are shorter, more
frequent and acquired earlier. There was significant improve-
ment in model fit with the addition of the semantic variables,
which accounted for a further 0.78% of variance in recogni-
tion RTs. All semantic variables were significant predictors,
with faster RTs for words with increased socialness, con-
creteness and valence extremity and for those encountered
in more semantically diverse contexts. The control variables
were all significant predictors of the proportion of people
reporting not knowing a word, with words that are longer,
more frequent and acquired earlier being more prevalent.
There was significant improvement in model fit with the
inclusion of the semantic variables, which accounted for an
additional 0.83% of variance in ECP proportion unknown.
Valence and semantic diversity were the only significant
semantic predictors – words that are more valenced and
encountered in more semantically diverse contexts were
reported as known by more people.
Table 3 Regression coefficients from item-level analyses predicting lexical decision task latencies and error rates (N = 6926)
Note. b represents unstandardized regression weights. SE represents the standard error of the regression weights. sr2 represents the semi-partial
correlation squared. zRTs standardized reaction times. *p < .05; **p < .01; ***p < .001
Predictor zRTs Error rates
b SE t p sr2R2∆R2b SE t p sr2R2∆R2
Step 1 0.51 0.21
 Intercept – 0.25 0.003 – 94.49 *** 0.06 0.001 70.97 ***
 Length 0.05 0.001 35.6 *** 0.09 – 0.01 < .001 – 22.57 *** 0.058
 Frequency – 0.15 0.005 – 29.99 *** 0.064 – 0.03 0.002 – 19 *** 0.041
 Age of Acquisition 0.04 0.001 26.91 *** 0.051 0.01 < .001 22.99 *** 0.06
Step 2 0.52 0.006 0.22 0.006
 Intercept – 0.25 0.003 – 95.06 *** 0.06 0.001 71.21 ***
 Length 0.05 0.001 35.75 *** 0.089 – 0.01 < .001 – 21.5 *** 0.052
 Frequency – 0.13 0.005 – 23.9 *** 0.04 – 0.03 0.002 – 14.7 *** 0.024
 Age of Acquisition 0.04 0.001 25.78 *** 0.046 0.01 0.001 22.31 *** 0.056
 Socialness – 0.01 0.002 – 4.73 *** 0.002 – 0.003 0.001 – 3.57 *** 0.001
 Concreteness < .001 0.004 0.02 0.984 0 0.002 0.001 1.7 0.088 0
 Valence Extremity 0.01 0.004 1.83 0.067 0 – 0.001 0.001 – 0.64 0.525 0
 Semantic Diversity – 0.07 0.01 – 6.77 *** 0.003 – 0.01 0.003 – 3.54 *** 0.001

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Discussion
Although some contemporary accounts (e.g., Barsalou,
2020;Borghi etal., 2019 ; Kiefer & Harpaintner, 2020) prof-
fer a role for socialness in the organization and grounding
of conceptual knowledge, many key questions remain about
the nature of its contribution and its neural underpinnings.
With the aim of facilitating future endeavours, in the pre-
sent work we sought to 1) collect socialness norms for a
large set of words; 2) test the extent to which socialness is
reliably perceived as a broad construct; 3) explore to what
extent socialness captures a distinct aspect of word meaning
compared to those measured by other lexical and semantic
variables, and 4) assess whether socialness can account for
variance in behavioural responses in lexical tasks. To this
end, we compiled the largest set of socialness norms avail-
able to date by collecting ratings for a set of 8388 English
words, including nouns, verbs and adjectives. The social-
ness ratings show high reliability, and this suggests that the
construct is meaningful to participants even at the broad
and inclusive level of description provided. Moreover, the
validity of the socialness construct was confirmed by a
strong correlation with ratings on two other social semantic
dimensions (Binder etal., 2016; Troche etal., 2017), despite
the distinct definitions employed. However, our socialness
measure shared around 58% of its variance with each of
these other ratings, possibly reflecting differences in par-
ticipant characteristics or perhaps methodological choices
such as our more inclusive definition which might capture
some additional aspects of social experience. Subsequent
research will be needed to more thoroughly explore the pre-
cise aspects of our interactions with the social environment
that are captured by this inclusive socialness measure, such
as those measured by more restricted definitions (for exam-
ples, see Pexman etal., 2021).
Our preliminary analyses provide some important ini-
tial insights into the nature of the socialness dimension.
First, while low socialness words tend to be concrete,
Fig. 3 Standardized coefficient weights and 95% CIs for the second
step of the hierarchical regression analyses predicting task outcome
variables. A Standardized beta coefficients for LDT RTs (blue) and
errors (red). B Standardized beta coefficients for ECP Word Knowl-
edge Task RTs (blue) and the proportion of people reporting not
knowing a word (red)

Behavior Research Methods
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high socialness words span the entire concreteness con-
tinuum, from concrete concepts like mother, to more
abstract ones like political. In line with previous reports
of a negative association between a social interaction
measure and modality-specific perceptual ratings (Tro-
che etal., 2017), we found that words high in socialness
tend to be more abstract and to rely less on sensorimotor
information. However, the present findings further suggest
that socialness does not relate to concreteness in a simple
linear fashion. Although theories of conceptual represen-
tation have proposed that social concepts are a sub-type
of abstract concepts (Borghi etal., 2019; Kiefer & Har-
paintner, 2020), this finding highlights the need to better
understand the contribution made by socialness beyond
this extreme of the concreteness dimension. Second, we
found that words with increased socialness tend to be more
valenced and arousing. This is in line with findings that
social and affective dimensions reduce to the same latent
factor of a multidimensional semantic space (Troche etal.,
2014, 2017; Villani etal., 2019). Importantly, while the
socialness ratings are significantly correlated with all the
lexical and semantic variables explored here, the associ-
ated effect sizes are modest and suggest that the socialness
measure captures a distinct aspect of word meaning. This
is consistent with fMRI studies which found that the effect
of socialness on brain activation during lexical-semantic
processing is independent from that of a number of key
semantic variables, namely imageability, concreteness,
and valence, and suggest that socialness makes a unique
contribution to semantic representation (Lin etal., 2018;
Wang etal., 2019).
Using regression analyses, we have demonstrated for
the first time that socialness of word meaning is related to
performance in lexical tasks. This is true even at the broad
and inclusive level of description provided. Specifically,
we found a facilitatory effect on behavioural performance
in lexical decision and word knowledge tasks – increased
socialness was associated with faster decision latencies in
both tasks and with better accuracy in the LDT. Importantly,
this was true after controlling for other semantic variables
known to influence lexical-semantic processing, namely con-
creteness, valence and semantic diversity. Further, this was
true even in lexical tasks that involve only shallow semantic
processing, where there is a limited pool of variance to be
explained by semantic predictors. This unique contribution
of the socialness measure suggests that it captures important
information about semantic representation and processing
and is in line with previous research on semantic richness
effects. Semantic richness refers to the phenomenon whereby
responses to words that are associated with relatively more
semantic information tend to be facilitated in lexical and
semantic tasks by virtue of their richer representations that
allow faster and more accurate retrieval of meaning (for a
review, see Pexman, 2012). As such, increased socialness
might enrich a word’s conceptual representation and, con-
sequently, facilitate lexical decisions via stronger feedback
from semantic to orthographic representations (Hino etal.,
2002; Hino & Lupker, 1996). Furthermore, our results sug-
gest that socialness contributes to processing alongside
other meaning dimensions derived from multiple experien-
tial channels including linguistic (i.e., semantic diversity),
sensorimotor (i.e., concreteness) and affective experience
Table 4 Regression coefficients from item-level analyses predicting ECP word knowledge task latencies and proportion unknown (N = 7010)
Note. b represents unstandardized regression weights. SE represents the standard error of the regression weights. sr2 represents the semi-partial
correlation squared. zRTs standardized reaction times. *p < .05; **p < .01; ***p < .001
Predictor zRTs Proportion unknown
b SE t p sr2R2∆R2b SE t p sr2R2∆R2
Step 1 0.4 0.23
 Intercept – 0.53 0.001 – 495.33 *** 0.013 < .001 69.57 ***
 Length 0.01 0.001 19.37 *** 0.032 – 0.002 < .001 – 22.24 *** 0.055
 Frequency – 0.06 0.002 – 27.65 *** 0.065 – 0.007 < .001 – 19.99 *** 0.044
 Age of Acquisition 0.01 0.001 25.28 *** 0.054 0.002 < .001 24.02 *** 0.064
Step2 0.41 0.008 0.23 0.008
 Intercept – 0.53 0.001 – 498.44 *** 0.013 < .001 69.93 ***
 Length 0.01 0.001 20.21 *** 0.034 – 0.002 < .001 – 21.67 *** 0.051
 Frequency – 0.05 0.002 – 22.07 *** 0.041 – 0.006 < .001 – 15.74 *** 0.027
 Age of Acquisition 0.01 0.001 22.5 *** 0.043 0.002 < .001 22.38 *** 0.055
 Socialness – 0.003 0.001 – 3.6 *** 0.001 < .001 < .001 – 0.31 0.754 0
 Concreteness – 0.003 0.001 – 2.04 * < .001 < .001 < .001 1.46 0.145 0
 Valence Extremity – 0.01 0.001 – 6.09 *** 0.003 – 0.001 < .001 – 3.52 *** 0.001
 Semantic Diversity – 0.02 0.004 – 6.01 *** 0.003 – 0.004 0.001 – 5.89 *** 0.004

Behavior Research Methods
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(i.e., valence). This is consistent with theories claiming that
conceptual representation is multidimensional in nature and
that social experience may be one of the underlying semantic
dimensions (e.g., Borghi etal., 2019).
The ability of the semantic dimensions to explain vari-
ance in behavioural responses varied depending on the
requirements of the task. While socialness and semantic
diversity had a facilitatory effect on RTs in both tasks, con-
creteness and valence contributed to the word knowledge
task, but not to the LDT. This is in line with research sug-
gesting that conceptual representations are not stable across
time and contexts; instead, the aspects of a word’s concep-
tual representation retrieved at any one point depend on the
specific task/context (Pexman, 2020; Yee & Thompson-
Schill, 2016). Our pattern of findings may be explained by
the fact that LDT only requires the retrieval of some indica-
tion that a word has meaning, such as that indexed by its
association with a multiplicity of meanings (i.e., semantic
diversity). In comparison, the word recognition task might
require access to additional features of a word’s meaning,
like those that tap into the richness of associated sensori-
motor (i.e., concreteness) and emotional experience (i.e.,
valence extremity). It might also suggest that socialness
does not contribute additional semantic features to enrich
a word’s conceptual representation, but is more indicative
of the general relevance or salience of its meaning. This
might be consistent with our finding that the socialness of a
word does not account for variance in the number of people
who know its meaning. Relatedly, it has been observed that
social stimuli are preferentially processed during free view-
ing of complex naturalistic scenes, to the extent that social-
ness competes with the physical saliency of stimuli (End
& Gamer, 2017, 2019). However, future research is needed
to better understand the nature of the contribution made by
socialness to the semantic richness of concepts (see Muraki
etal., 2019 for an example of how to approach examining
the factor structure of semantic richness). Moreover, it is
important to highlight that, while the words we encounter
are typically embedded in rich linguistic contexts (e.g., sen-
tences) that shape our understanding of individual words, the
socialness ratings were generated based on words presented
in isolation. Future research should address this limitation
by moving away from single word processing and consider-
ing the lexical-semantic properties of connected text/speech.
Conclusions
In the present study, we compiled the largest set of openly
available socialness norms to date. We used an inclusive defi-
nition, found that it produced reliable ratings and, thereby,
showed that socialness has meaning as a broad construct. An
important avenue for future research is identifying the specific
aspects of social experience that are most related to concep-
tual processing to refine our working definition of socialness.
Further, our explorations suggest that socialness captures an
aspect of word meaning that is distinct to those measured by
other key semantic variables and notably, an aspect of meaning
that is behaviourally relevant. Our study also provides some
initial insights into the information captured by the socialness
measure, but subsequent work will be needed on this mat-
ter, as well as its role and behavioural consequences across
the lifespan, including during acquisition, retrieval and when
the semantic system is impaired. Thus, the socialness norms
described here will enable future research into the organiza-
tion and grounding of conceptual knowledge, and can help
target testable predictions about brain and behaviour that can
be derived from multiple representation theories (e.g., Borghi
etal., 2019) and neurobiological accounts of social semantics
(for an extensive discussion, see Pexman etal., 2021; also Bin-
ney etal., 2016; Binney & Ramsey, 2020; Diveica etal., 2021).
Supplementary Information The online version contains supplemen-
tary material available at https:// doi. org/ 10. 3758/ s13428- 022- 01810-x.
Open practices statement The datasets generated and/or analysed in
the current study and the analysis scripts are available via the Open Sci-
ence Framework (OSF) project: https:// osf. io/ 2dqnj/. Given its explora-
tory nature, the study was not pre-registered.
Author’s contribution The authors wish it to be known that PMP and
RJB contributed equally to this article. Veronica Diveica: Conceptual-
ization, Methodology, Formal Analysis, Investigation, Visualization,
Writing - Original Draft, Writing – Review and Editing. Penny Pex-
man: Conceptualization, Methodology; Writing – Review and Editing.
Richard Binney: Conceptualization, Methodology; Writing – Review
and Editing; Supervision.
Funding This work was supported by the Economic and Social
Research Council (ESRC) Wales Doctoral Training Partnership in the
form of a PhD studentship [ES/P00069X/1], a joint award from UK
Research and Innovation (UKRI) and Mitacs under the UK-Canada
Globalink Doctoral Exchange Scheme [NE/T014180/1] (both awarded
to VD and RJB; PhD student: VD), the Professor Beatrice Edgell Post-
graduate Grant from the British Psychological Society Welsh Branch
(awarded to VD) and a Social Sciences and Humanities Research Coun-
cil (SSHRC) of Canada Insight Grant (awarded to PMP).
Declarations
Competing interests The authors declare no potential conflicts of inter-
est.
Ethics approval This study was performed in line with the principles
of the Declaration of Helsinki. Ethics approval was granted by Bangor
University School of Psychology Ethics Board (Approval Number:
2017-16108).
Consent to participate Informed consent was obtained from all indi-
vidual participants included in the study.

Behavior Research Methods
1 3
Open Access This article is licensed under a Creative Commons Attri-
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tion, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source,
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