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On Pulling the Strings or Having Your Strings Pulled: The Relation Between Personal and

Linguistic Agency

Almog Simchon1,2*, Britt Hadar3, and Michael Gilead1,4*

1Department of Psychology, Ben-Gurion University of the Negev, Israel

2School of Psychological Science, University of Bristol, UK

3Princeton School of Public and International Affairs, Princeton University, NJ, USA

4The School of Psychological Sciences, Tel Aviv University, Israel

*almogsi@post.bgu.ac.il; michaelgil@tauex.tau.ac.il

Keywords: agency, language, sense of control, depression

Acknowledgments

We thank Roey Gafter, Yoav Kessler, Yoav Bar Anan, Ruvi Dar, Si Berrebi, Noa Bassel, Or

Kronenblum, Almog Bowman, Ofir Neomi Aharon, Chaya Leibman, Yuval Gaiger, Bore’ Olam.

This research was supported by the United states - Israel Binational Science Foundation grant no.

2015258 to M.G; Israel Science Foundation grant no. 1113/18 to M.G and by the Ministry of

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Science & Technology, Israel. Author contributions: Conceptualization, A.S., M.G.; Data

curation, A.S., M.G.; Formal analysis, A.S.; Funding acquisition, M.G., A.S.; Investigation, A.S.,

B.H. and M.G.; Methodology, A.S. and M.G; Software, A.S.; Supervision, M.G.; Validation,

A.S..; Visualization, A.S.; Writing, A.S., B.H. and M.G. All sharable data and code are available

at https://osf.io/nwsx3/?view_only=21a12e5779194993a71bcd6296852bf2 and will be made

public upon acceptance. Study 2 was pre-registered at

https://aspredicted.org/blind.php?x=py37ws; Study 3a was pre-registered at

https://aspredicted.org/blind.php?x=y2ad75 and Study 3b was preregistered at

https://aspredicted.org/blind.php?x=z8s7en.

Declaration of Conflicting Interests

Authors declare no competing interests

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Abstract

We examined whether and how linguistic agency and personal agency are intertwined.

We conducted ten studies in which we used experimental, questionnaire, and big-data methods in

several samples (online participants, university students and social media users) to provide

evidence that: (i) participants’ sense of personal agency affects their use of agentive language (ii)

subtle manipulations of linguistic agency (i.e., whether task instructions are written in an

agentive form) affect participants’ sense of personal agency; (iii) the language of individuals

who suffer from a diminished sense of personal agency (due to depression) is less agentive; (iv)

increased personal agency (operationalized as one’s social media “influence”) is associated with

more agentive language; and (v) the association between linguistic and personal agency does not

reflect a stable characteristic of individuals. Together, these findings advance our understanding

of the nuanced relation between language and thought.

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On Pulling the Strings or Having Your Strings Pulled: The Relation Between Personal and

Linguistic Agency

“Never use the passive voice.”

― George Orwell, 1946

The relation between language and thought has long fascinated both scholars and artists.

One of the most prominent voices in this discussion was George Orwell, whose novel 1984

(1949) famously depicts a dystopia in which a totalitarian regime controls the public by

mandating a language designed to restrict thought. Orwell further explored this theme in his

1946 essay “Politics and the English Language,” wherein he proposed that certain linguistic

structures such as the use of the passive voice may facilitate oppressive ideologies: passive

sentences (e.g., Her arms were clasped tightly around Winston) and active sentences (e.g., She

clasped her arms tightly around Winston) supposedly describe the same activity, however,

whereas the active voice highlights the subject of the sentence, the passive voice diminishes or

eliminates it (Andre, 1973; Huttenlocher et al., 1968); Orwell argued that the use of non-agentive

language undercuts the self-agency ascribed to the individual, and may be used to disempower

people. Interestingly, despite his abovementioned objection to the passive voice, he revised

earlier versions of the novel 1984 to include numerous passive constructions—supposedly, to

convey the feeling of a world wherein individuals have no power and no control over their lives

(Kies, 1992).

Aside from being an effective literary device, scientists have suggested that the use of

passive language, and more generally non-agentive language, may indeed affect people’s

construal of the world (Henley et al., 1995; Yamamoto, 2006)—echoing Orwell’s intuition.

Empirical research provided evidence to support this view by showing that when reading about

legal offenses that were described in more (vs. less) agentive terms, people attributed more

blame and increased financial liability to the plaintiff (Fausey & Boroditsky, 2010). Furthermore,

it was shown that speakers of languages that differ in their agentive marking also differed in their

memory for observed accidental events, such that speakers of English remembered the causers of

accidents better than speakers of Spanish and Japanese (Fausey et al., 2010; Fausey &

Boroditsky, 2011). Finally, sexist men were shown to be prone to associate images of sexualized

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women with descriptions of non-agentive language, (e.g., grab [her], rather than [she] grabs;

Cikara et al., 2011).

Thus, there is compelling evidence that people attribute less agency to others when they

are described in non-agentive language. However, could it be the case that, as suggested in the

writings of Orwell, the implications of language use relate to people’s construal of themselves

and their place in the world? In other words, can subtle syntactic variations in linguistic agency

reflect - or even generate - variations in people’s sense of personal agency?

Personal agency can be most simply defined as “the human capacity to act” (Ahearn,

1999, p. 12). Thus, a person who objectively lacks the power to act (e.g., because they live in an

oppressive regime) could have a reduced sense of agency. In other cases, there may be no clear

barriers for a person to act in line with their intentions, but the person may nonetheless believe

that they cannot affect the world (e.g., because they suffer from depression). Finally, the sense of

agency can be compromised even when one’s actions caused their intended consequences—but

the individual construed the outcome as stemming from a causal chain wherein they were

unimportant (e.g., because the action was not the result of their free will; because some other

force such as nature, god or fate was responsible).

Thus, when discussed in psychological (rather than linguistic) terms, the sense of

personal agency is a broad construct that encompasses several nuanced, intercorrelated

constructs such as sense of control (SoC; Ross & Mirowsky, 2013; Rothbaum et al., 1982),

feelings of (social) power (Galinsky et al., 2003; Keltner et al., 2003), Sense of Agency (SoA;

David et al., 2008), self-efficacy (Bandura, 1982), locus of control (Rotter, 1966) and belief in

free will (Feldman, 2017). Research conclusively shows that such manifestations of

psychological agency contribute to well-being and are a protective factor against

psychopathology (Chorpita & Barlow, 1998; Keeton et al., 2008; Lachman & Weaver, 1998;

Mirowsky & Ross, 1990; Shapiro et al., 1996; Smith & Hofmann, 2016).

Despite the importance of personal agency, and despite the intriguing suggestions of a

possible link between linguistic agency and manifestations of psychological agency, previous

research has not investigated how the agentivity of language may affect and reflect individuals’

sense of personal agency and control over their lives. Therefore, in the current research, we

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present a detailed analysis of the relation between linguistic and psychological agency. In two

experimental studies, we examined the effect of state-level changes in sense of control on

linguistic agency (Study 1) and of manipulations of linguistic agency on sense of control (Study

2); we then examined the relation between linguistic and personal agency in ecological settings,

relying on big-data analyses (Study 3). Finally, we investigated whether any link between

agentive language and agentic traits is stable at the trait-level (Study 4).

Study 1: Does manipulating personal agency affect linguistic agency?

Method

As noted above, the sense of personal agency is dependent on having the power to pursue

one’s goals effectively. In light of this, a first, straight-forward prediction is that when

individuals lose the power to affect their surroundings, this will be reflected in diminished

linguistic agentivity. Therefore, in Study 1 we examined the effect of a well-established

experimental manipulation of sense of power (Dubois et al., 2015; Galinsky et al., 2003, 2006;

Hadar et al., 2020; Schmid et al., 2015) on participants’ use of passive voice.

We performed a re-analysis of data from a study of Kasprzyk and Calin-Jageman (2014)

who conducted a replication study of (Galinsky et al., 2006), wherein power was primed using an

essay writing task. The data repository contained samples collected online from MTurk and

Prolific. Participants were asked to write about incidents where they had power over others (high

power) vs. incidents where other people had power over them (low power). In this study, as well

in subsequent studies, we quantified non-agentive language as the use of passive voice. Previous

studies estimate that 70% of the passive voice use comes in the agentless form (Jespersen, 2004;

Svartvik, 1985), serving as an adequate measure for non-agentive language.

Participants

In the mTurk study, 469 responses were collected (182 men; 269 women; 56 did

not respond). The average age in the sample was 34.91 (SD = 12.46). Ten observations had

missing text and therefore excluded from the analysis. In the Prolific study, there were 416

collected responses (151 men; 184 women; 81 did not respond). Due to technical problems, only

331 participants had a valid age response. The average age was 27.88 (SD = 9.91). Forty

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observations had missing text and therefore excluded from the analysis. The study was

conducted under the Departmental IRB guidelines.

Procedure

In the Study by Kasprzyk and Calin-Jageman (2014), participants were assigned to one of

the two conditions: high power or low power, and were requested to write about an incident

where they had power over other individuals or vice versa.

Measures

To measure passive language in the dataset, we used spaCy (Honnibal & Johnson, 2015),

a popular and powerful natural language processing tool that, among other features, allows users

to tag text containing passive voice in a context-dependent manner (https://spacy.io). The passive

voice measure was calculated using the ‘spaCyr’ package (Benoit & Matsuo, 2018) as the

number of passive voice auxiliary verbs in each text (see supplementary code). These include

both was passives (e.g., “the ball was kicked”) and got passives (e.g., “the ball got kicked”).

Results

We combined the data gathered from mTurk and Prolific to a unified dataset of n = 835.

For a passive voice histogram, see Figure S1. We fitted a negative binomial count model,

predicting the number of passive auxiliary verbs by power condition (high vs. low), keeping text

length and text source (mTurk/Prolific) as covariates. We found that the low power condition

was associated with a 34% increase in passive voice, IRR = 1.34, p = .002, 95% CI [1.11, 1.61]

see Figure 1 and Table S1 in Supplementary Material (SM). We did not find an effect for text

source IRR = 0.95, p = .60, 95% CI [0.79, 1.15].

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Figure 1. Cross bar of passive voice use by condition in Study 1. X axis represents the groups, Y

axis represents the predicted number of passive auxiliary verbs. Bold points denote marginalized

group means; gray rectangles represent 95% confidence intervals. Low power condition is

associated with 34% increase in passive voice.

We find greater use of non-agentive language when participants describe incidents

wherein other people had control over them, vs. incidents where they were the ones with control

over others. These findings provide initial evidence for the link between personal and linguistic

agency, and suggest that reductions in sense of personal agency are reflected in reductions in

linguistic agency. However, the question remains whether linguistic agency can affect

psychological agency. In Study 2, we conducted experiments designed to explore this hypothesis.

Study 2: Does manipulating linguistic agency affect sense of personal agency?

In the current set of studies, we intended to test the hypothesis that manipulations of

linguistic agency will affect participants’ sense of personal agency. To do that, we developed a

novel paradigm, we term “Bogus BCI,” specifically designed to tap into the effect of agentive

language on personal sense of control.

Study 2a

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Method

Participants attended a bogus-pipeline-like (Jones & Sigall, 1971) “Brain-Computer

Interface” experiment in which they were connected to an EEG system. They were requested to

affect a visual stimulus by mentally simulating the desired outcome. In actuality, the odds for a

true change in the stimulus were fixed and independent from the participants’ mental work

(probability of 0.7 for a change). For each stimulus (e.g., light bulb, doorknob) the task

instructions were presented either in an agentive form (e.g., “Tsarikh LeHadlik et Ha’Or,” which

means “[You] need to turn on the light”) or in an agentless form (e.g., “Tsarikh SheHa’Or

I’dalek,” which can be translated as “[The] light needs to get turned on”). After each trial, the

participants rated how much they felt in control of the outcome. We expected the type of

instructions to affect the subjective control such that the agentless form would lead to a

decreased sense of control (see pre-registration1).

Participants

We recruited 55 participants (8 men; 47 women) from the Ben-Gurion participants pool,

all native speakers of Hebrew. Participants were students at Ben-Gurion University and were

given credit for their participation. The mean age was 22.9 (SD = 1.48). One subject did not

complete the experiment due to technical reasons, and seven more subjects had a keyboard

malfunction resulting in failed data collection. The final sample size consisted of 47 participants.

According to our pre-registration program, for a medium hypothesized effect-size (Cohen's d =

0.35, alpha = 0.05, 1-beta = 0.8) we sought to have data for 64 participants. Aiming to meet this

goal, data collection continued until the end of the academic year. Participants signed informed

consent before their participation. Due to the deceptive nature of the experiment, participants

were given an option to be debriefed only after the full study had finished running. The study

was conducted under the Departmental IRB guidelines.

Materials

Stimuli

1 https://aspredicted.org/blind.php?x=py37ws

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The stimuli consisted of 41 pairs of pictures (e.g, a closed door and an opened door) and

41 pairs of sentences (agentive/agentless). The sentences were distributed into two lists, such that

each participant was presented with one form of a given stimulus (agentive/agentless) throughout

the experiment (i.e., Participant 1 was assigned to List A, and was presented with the agentive

form of item a and the agentless form of item b throughout the experiment; Participant 2 was

assigned to List B, and was presented with the agentive form of item b and the agentless form of

item a throughout the experiment). The lists were constructed as such that there would be a

minimal difference in verb frequencies (the agentive form always took the infinitive of the verb).

Linguistic stimuli were both displayed on the center of the screen in Droid Sans Hebrew font size

18, and were played auditory through headphones.

Electroencephalogram (EEG) recordings

EEG was recorded using a BioSemi Active Two system (Biosemi, Inc., Amsterdam, The

Netherlands). Although EEG recordings were never saved (and therefore, never analyzed) it was

a crucial part of the deception to make participants believe they were connected to a working

EEG system. Hence, participants were connected to six electrodes (CMS/DRL on left hand, left

mastoid, right and left temples, and Fpz). As part of the manipulation, they were requested to

clench their jaws and make eye saccades in order to show how such artifacts affect the EEG

signal, and were requested to avoid doing so during the experiment.

Procedure

The task was programmed with OpenSesame (Mathôt et al., 2012), and consisted of one

practice block and three experimental blocks. The practice block consisted of ten trials followed

by the three experimental blocks, comprising 31 trials each. Trials began with a 200 ms blank

screen, followed by the linguistic stimulus presented and played for 3000 ms (e.g., “[You] need

to turn on the light”). Next, a picture appeared in the center of the screen (e.g., a light bulb), and

the participants were requested to make a mental effort in order for the action to take place. The

stimulus appeared on the screen for 6000 ms, followed by a blank screen for 100 ms. Feedback

was given both visually and auditory: in case of “success” (probability of 0.7), another picture

would appear depicting the intended action (e.g., turned-on light bulb) for 1000ms alongside a

sound signifying success (a sine sound of 300 Hz); otherwise, the unchanged picture would

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appear again with a sound signifying failure (a sine sound of 150 Hz). At the end of each trial,

the participant was asked to rate how much they felt they controlled the result on a Likert scale

from 1 (very little) to 6 (very much) (up to 5000 ms to respond). For an illustration of the

procedure, see Figure 2.

After the participants had finished the experiment, they answered a modified version of

the personal mastery scale (Pearlin & Schooler, 1978), desirability of control scale (Burger &

Cooper, 1979), brief free will scale (Paulhus & Carey, 2011; Zhao et al., 2014) and eight items

(5-8,13-16) of the vividness of visual imagery scale (Marks, 1973), all translated to Hebrew. In

addition, they wrote a short paragraph about “something that happened to them this year.” As

pre-registered, the questionnaires and free text were collected for exploratory purposes, though

eventually were not analyzed in the context of the current study.

All data, experiment files, R scripts, and analyses can be found on the Open Science

Framework (https://osf.io/nwsx3/?view_only=21a12e5779194993a71bcd6296852bf2).

Figure 2. The Bogus BCI paradigm and procedure. In this example trial, participants are

requested to mentally simulate the lightbulb lighting up (either in an agentive or agentless

instruction). In ~30% of the trials, the lightbulb does not light up, and in ~70%, it does. At the

end of each trial, participants report their state-level sense of control.

Results

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In the pre-registration, we committed to do an ANOVA; however, we decided to deviate

from the pre-registered plan and conduct a more suitable linear-mixed model, to account for

stimuli variation, as recommended in the psycholinguistics field (Locker et al., 2007). We fitted a

linear mixed model using the `lmerTest` R package (Kuznetsova et al., 2017). The model was

fitted in a maximal way, with agency and success as fixed factor (including their interaction),

and with the interaction term as a random effect by subject, and with linguistic list as random

effect by stimuli. We were interested in the specific contrast between the agentive condition and

the agentless condition. The rest of the results are found in Supplementary Table S2.

We extracted marginalized means with the `emmeans` R package (Lenth et al., 2018)

and tested the hypothesis that agentive instructions would lead to a greater sense of control. The

contrast was in the hypothesized direction, but did not reach statistical significance, t(30.5) =

1.272, p = 0.213, Cohen’s d = 0.23, see Figure 3.

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Figure 3. Estimated marginal means per condition by study (A – feedback before, B – feedback

after, and C – no feedback). X axis represents groups, Y axis represents the estimated marginal

means. Error bars denote the contrast standard error.

One possible reason for our inability to find a significant effect is the current study was

relatively underpowered (post-hoc power of 0.46). Additionally, we saw that much of the

variance in participants’ responses was driven by the feedback they receive (i.e., supposed

success or failure) and may have drowned out the effect of interest; namely, on trials where they

“succeeded”, they believed that the outcome was their own making, and when they failed, they

believed it was not. Therefore, in experiment 2b we changed the design such that the response

will take place prior to the feedback.

Study 2b

Method

The methods of Study 2b remained as in Study 2a, except for changes described below.

Additionally, Study 2b (and 2c) ran in a double-blind fashion, i.e., the experimenters were not

aware of the deceptive manipulation.

Participants

The target sample size was the same as study 2a. We recruited 77 participants (15 men;

62 women) from the Ben-Gurion participants pool, all native speakers of Hebrew. Participants

were students at Ben-Gurion University and were given credit for their participation. The mean

age was 23.1 (SD = 0.99). Twelve subjects had a keyboard malfunction resulting in failed data

collection. The final sample size consisted of 65 participants. The study was conducted under the

Departmental IRB guidelines.

Procedure

The procedure was identical to that of Study 2a with the difference that the response had

now preceded the feedback (see Figure 4). Additionally, the response question changed to a

rating on a Likert scale from one to six of the participants’ confidence that the change will occur

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(“Do you think you turned on the light?” / “Do you think that the light got turned on?”). Both

forms are valid and prevalent in Hebrew.

Figure 4. The Bogus BCI paradigm and procedure of study 2b. In this example trial, participants

are requested to mentally simulate the lightbulb lighting up (either in an agentive or agentless

instruction). Participants report their state-level sense of control. At the end of each trial, they

receive feedback: in ~30% of the trials the lightbulb does not light up, and in ~70% it does.

Results

We fitted a linear mixed model with agency as a fixed effect, and as a random effect by

subject. Additionally, we added the linguistic list as random effect by stimuli. The extracted

marginalized means were again in the hypothesized direction, however, again, not significant,

t(24.7) = 0.75, p = 0.459, Cohen’s d = 0.15, see Figure 3.

The current design was meant to overcome the effect of success in Study 2a, however, we

realized it could be the case that for a given trial, success in the preceding trial may affect the

rating in the current trial. Therefore, we added a variable of lagged success (i.e., success in the

preceding trial) and fitted the same type of model as in Study 2a, with lagged success instead of

success. The success in the preceding trial indeed significantly affected participants' sense of

control, F(1,66.54) = 12.01, p < .001, Partial η2 = 0.15 (see full results in Table S3).

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Study 2b was designed to deal with the caveats of Study 2a, however, in post-hoc

analysis, we discovered that the effect of the feedback still exists. Thus, in Study 2c we

completely removed the feedback from the experimental trials in order to deal with the effect of

(lagged) feedback.

Study 2c

Method

Study 2c was a replication of study 2b, with one major change: we eliminated the effect

of feedback. Feedback was given in the practice trials and was not given in the experimental

trials. In addition, the practice block has doubled in length, from 10 trials to 20 trials.

Participants

We recruited 74 participants (20 men; 54 women) from the Ben-Gurion participants pool,

all native speakers of Hebrew. Participants were students at Ben-Gurion University and were

given course credit for their participation. The mean age was 23.8 (SD = 1.54). Data of one

subject was not recorded due to a technical malfunction. The final sample size consisted of 73

participants. The study was conducted under the Departmental IRB guidelines.

Results

We fitted a linear mixed model with agency as a fixed effect, and as a random effect by

subjects. We also added the linguistic list variation as random effect by stimuli. The extracted

marginalized means were, again, in the hypothesized direction, t(30.3) = 1.705, p =.098, Cohen’s

d = 0.31, see Figure 3.

In all the three variations, the contrast was in the hypothesized direction. While in all

three studies the effect size was of a comparable magnitude, individually -- they failed to attain

significance. To examine the robustness of the effect across studies, we applied two approaches.

One way to examine the effect across experiments is to collate the data from the

individual subjects, treat the experiment as a between-participants factor, and treat the mean

control levels (in the agentive vs. non-agentive condition) as a within-participants variable. The

results of this analysis showed a significant effect for the Agency condition, F(1, 169) = 11.36, p

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< .001, partial η2 = 0.06, and no other effects (see Table S4 for full results). Such an analysis is

potentially less controlled because it does not account for the effect of stimuli; thus, we

conducted a random-effects meta-analysis of the Agency condition contrast. Given that all of the

studies had similar designs, and given that we report the findings from all of the studies we

conducted, including non-significant findings (i.e., there is no “file drawer effect” to worry

about) such a meta-analysis provides a cogent way to estimate the population effect. We

extracted the unstandardized estimates of mean difference (i.e., unstandardized effect size) and

standard errors of the three studies and calculated the overall summary difference,

unstandardized difference = 0.05, z = 2.08, p = .037.

In Studies 2a-c, we introduced a novel paradigm called “Bogus BCI.” In these three

studies with an overall of 185 participants, we tested the pre-registered hypothesis that task

instructions given in the agentive form would be associated with a greater sense of control than

those framed in a non-agentive form. Each study by its own provided weak evidence to support

our hypothesis. However, the effect was reliable across the three studies. Taken together, the

current set of studies supports the hypothesis that linguistic agency can affect individuals’ sense

of control.

In Studies 1-2, we used experimental designs to show that changes in one’s sense of

personal agency affect the use of agentive language (Study 1), and that agentive language affects

one’s sense of agency (Study 2). However, as is often the case in psychological research, these

results came from somewhat artificial experimental models, whose relevance to real-world

phenomena may be questioned. In light of this, in Study 3, we turned to investigate the relation

between personal and linguistic agency in ecological settings, by relying on big-data analyses.

Study 3: Does the relation between personal and linguistic agency translate to

consequential real-world contexts?

To see whether we can observe the relation between linguistic and personal agency in the

real world, we sought to examine states wherein one’s sense of personal agency is diminished

(i.e., when sense of control is lost because of a depressive episode; Studies 3a-3b) and states

wherein one’s sense of agency is enhanced (i.e., when one is in possession of social power in the

form of a social media following).

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Study 3a

Depression is a debilitating mental illness characterized by recurring episodes of low

mood, anhedonia, low self-esteem, and hopelessness (for an exhaustive list see DSM-V;

American Psychiatric Association, 2013). According to the Learned Helplessness Model of

Depression (Seligman, 1974), depression arises when a person forms the belief that they have no

control over the negative outcomes in their lives. Indeed, previous research has shown that

individuals suffering from depression report having a lower sense of efficacy (Maddux & Meier,

1995), lower sense of control (Lachman & Weaver, 1998), and enhanced external locus of

control (Wiersma et al., 2011). In light of this, in the current study, we leveraged large datasets

of online discussion forums to investigate the hypothesis that people who suffer from a

depressive episode increase their use of non-agentive language.

Method

People who suffer from depressive episodes often seek solace in online communities

wherein they find support and empathy. In light of this, in the current study, we examined

whether people who are in the midst of a depressive episode also express less agentivity in their

language. We utilized large datasets from the community network Reddit to test the pre-

registered2 hypothesis that the online communities of people dealing with depression would

exhibit more passive voice in their messages than a random sample of other popular

communities.

Data collection

We collected 10,000 messages from the depression3 subreddit (i.e., Reddit community),

and 100 messages from 100 randomly selected subreddits (sampled from a list of 1000 popular

subreddit, see SI). The messages ranged in their time between July 2020 and November 2019.

The study was conducted under the Departmental IRB guidelines.

Preprocessing

2 https://aspredicted.org/blind.php?x=y2ad75

3 https://www.reddit.com/r/depression/

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Our sample size consisted of 10,000 messages from the depression subreddit and 9901

messages from the randomized control sample. Preprocessing included the removal of links,

emoticons, messages tagged as ‘removed’ or ‘deleted,’ and empty text messages. In addition, any

posts by users who posted more than one message or cross-posted in both conditions were

removed. The final sample size consisted of 8,690 messages (5,703 from the depression

condition). Passive voice was extracted the same way as in Study 1.

Results

Upon inspection of the data, it was evident that despite the lengthy nature of the message,

passive voice was heavily zero-inflated, such that using a linear model on normalized counts was

the wrong analytical choice. In light of this, we deviated from the pre-registered plan and

analyzed the data using a count model; given this substantial deviation, we pre-registered a

replication study (Study 3b).

We fitted a negative binomial generalized linear model predicting the number of passive

auxiliary verbs by group (depression vs. control) and keeping word count as a covariate. We

found that the language in the depression forum (vs. control forums) was associated with a 19%

increase in passive voice use IRR = 1.19, p < .001, 95% CI [1.12, 1.27], see Figure 5 and Table

S6 in SM.

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Figure 5. Panels A and B show the predicted number of passive auxiliary verbs by subreddit

group (Studies 3a-b). Panel C shows the predicted followers count as a function of the number of

passive auxiliary verbs in the tweet (Study 3c). Solid points denote marginalized means; gray

rectangles represent 95% confidence intervals.

In line with our pre-registered prediction, these results suggest that people who suffer

from depression use passive voice to a greater extent. However, in this study, we substantially

deviated from the original analysis plan. Therefore, we ran a pre-registered replication of Study

3a in which we collected older data, one year prior.

Study 3b

Method

This study was an exact replication of Study 3a, with the exception that the collected data

were posted between July 2019 and November 2016. The original sample size consisted of 9,999

messages from the depression subreddit, and 10,001 messages from the control sample. After

preprocessing, the final sample size consisted of 9,685 (6,325 from the depression condition).

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The replication was pre-registered4 as well. The study was conducted under the Departmental

IRB guidelines.

Results

In line with our pre-registered hypothesis and the results of Study 3a, we found that the

language in the depression forum was associated with 38% increase in passive voice use, IRR =

1.38, p < .001, 95% CI [1.29, 1.47], see Figure 5 and Table S5 in SM.

In the current study, we found and replicated that non-agentive language was much more

prevalent (up to 38% more) in depression-related communities vs. a random sample of Reddit

communities. These findings suggest that real-life situations that involve a diminished sense of

control and agency are strongly related to diminished linguistic agency.

The findings described in Studies 3a-b are extremely robust and pertain to a domain of

great clinical importance. However, a limitation of these studies (and of Study 1) is that it is

likely that depressed participants were explicitly discussing their sense of diminished agency,

and it is perhaps possible that only a discussion of this specific topic affects linguistic agency.

Thus, in Study 3c, we turned to examine linguistic agency in individuals that are less likely to

discuss their levels of personal agency, thereby examining the generality of the effect.

Study 3c

Method

One of the most fundamental features of one’s control and influence is their position in

the social hierarchy (e.g., Fiske, 1991; Magee & Galinsky, 2008). The attainment of social power

can be determined by the extent of admiration and respect granted by others (e.g., Anderson et

al., 2006; Fragale et al., 2011). Some initial evidence for a link between social power and

agentive language use comes from ethnographic research by Duranti (1994), who studied the

local language of a western Samoan village, a language that can explicitly mark an agent in its

grammar. He discovered that agentive language use corresponded with the speaker’s social

4 https://aspredicted.org/blind.php?x=z8s7en

21

position. That is, speakers with a higher status within the village hierarchy (thereby having

higher control over the community’s decisions) tended to use more agentive language.

Here we generalize the findings from the western Samoan village to a population of

millions and ask whether naturally occurring social power in social networks (i.e., numbers of

followers) relates to agentive language use. We utilized big data from Twitter (26.4M messages)

to examine this relationship. We hypothesized that the number of followers on social media

would be negatively associated with the use of passive voice.

Data collection

We used the SCI lab twitter database at Ben-Gurion University (e.g., Simchon et al.,

2020). Tweets were collected from across the United States, including all 50 states and the

District of Columbia. We extracted tweets between April 2019 and June 2019. The study was

conducted under the Departmental IRB guidelines.

Preprocessing

Our sample size consisted of 26,473,715 tweets, all were in the English language, and all

were original (i.e., retweets were filtered out). Text cleaning included the removal of links, tags,

and emoticons before any linguistic analysis. Passive voice was extracted the same way as in

Study 1.

Results

To examine the relation between passive voice on social media status, we first aggregated

our twitter sample by users, to avoid dependencies in the model. For each user in the sample, we

calculated the average percent of passive voice use and average twitter followers (a user may

gain followers over the course of the sampling duration); the number of followers was rounded

to the nearest integer. Our final dataset consisted of 2,726,733 unique users.

We fitted a negative binomial generalized linear model predicting twitter followers by

their corresponding average passive voice use and average tweet length. We found that every

addition in passive auxiliary verbs was associated with a decrease of 39% in followers count

(IRR = 0.61, p < .001, 95% CI = 0.59, 0.61), see Figure 3 and Table S7 in SM.

22

Study 3c joins the results of Studies 3a-b in showing that linguistic and personal agency are

correlated in natural language use. Across millions of users, greater passive voice use was

associated with fewer followers (i.e., lower status on social media) such that each instance of

passive voice is related to a decrease of 39% in followership. This finding showcases how

syntactic style is related to social disposition.

The results of Study 3c are extremely robust, and (given the immense power of social

media) demonstrate a novel finding of social (and economic) importance; however, the

correlational design does not allow us to arbitrate whether people who use more active language

accrue a greater following or whether increased sense of control leads to more agentive language

use. The results of Study 1 and 3a-3b are consistent with the latter interpretation; nonetheless,

further research using non-correlational designs will be required to address this question.

Another issue that remains to be addressed is whether the association observed in Study 3

reflects a stable (trait-level) or state-level phenomenon. For example, a person may feel

chronically disempowered in their daily lives but may feel empowered in the virtual world—

whenever they address a large group of interested followers. Likewise, a person who suffers

from a depressive episode may lose their sense of agency, but regain it once their mood

stabilizes. Thus, in Study 4, we sought to investigate whether the link between personal and

linguistic agency is a stable characteristic of individuals.

Study 4: Do people who use more agentive language have a higher sense of personal

agency?

We conducted three studies aimed to investigate whether the association between

agentive language and personal agency goes beyond the state-level and may generalize to stable

traits. Studies 4a and 4b were conducted in English and used free text to gauge passive voice

tendency. Study 4c was conducted in Hebrew and used a specialized task to quantify passive

voice use.

Study 4a

23

Method

In the current study, we explored individual differences in the relation between trait-level

sense of control and agentive language use. Namely, to see whether people who use more

agentive language in a free text task, report having a higher trait-level sense of control and

internal locus of control. Additionally, work from the clinical field suggests that Obsessive-

Compulsive Disorder (OCD) is associated with a reduced sense of agency (e.g., Belayachi &

Van der Linden, 2010) and could be manifested in the language of individuals who are affected

by it (Oren et al., 2016). Therefore, a secondary goal of the current study was to explore the

possible relationship between passive voice and Obsessive-Compulsive tendencies.

Participants

We recruited 200 English-speaking participants from Amazon Mechanical Turk services

(MTurk; M age = 36.64, SD = 11.67; 106 women); responses were collected using Qualtrics.

Participants who failed attention tests, or were suspected to be bots (duplicated IP address,

nonsensical text) were excluded from the analysis. Overall, 181 participants remained in the

sample. All participants signed informed consent before the study and were compensated with

2.8$ for their participation. The study was conducted under the Departmental IRB guidelines.

Measures

Sense of Control Scale (Lachman & Weaver, 1998). The SoC scale lists 12 statements

regarding two prime factors, Mastery, and Constraints. Each statement was followed by a 7-point

Likert scale ranging from Strongly Agree to Strongly Disagree. SoC score was calculated as the

sum of mastery items and the sum of reversed constraints items

Locus of Control Scale (Rotter, 1966). The LoC scale consists of 29 items of forced

choice between two opposing statements. For our purposes, we used 27 items out of the 29. The

LoC score is bounded between internal LoC (corresponds to high control) and external LoC

(corresponds to low control).

Obsessive-Compulsive Inventory-Revised (Foa et al., 2002). The OCI-R consists of 18

items. Participants were instructed to indicate the level of distress or bothersome they felt in the

24

past month regarding the different experiences portrayed in the items. Each statement was

followed by a 5-point Likert scale ranging from Not at All to Extremely.

Passive Voice Measure. Passive voice was extracted the same way as in Study 1 and

Study 3.

Procedure

Participants began by filling out the free text task, in which they were instructed to write

about something that happened to them in the past year. Word limit was set to 300 (minimum)

and 600 (maximum). It was then followed by SoC and LoC questionnaires (in that order).

Results

In Figure S2 we report the histogram of passive voice use. Sense of Control (SoC; M =

60.89, SD = 15.48; high scores represent high control), Locus of Control (LoC; M = 9.34, SD =

4.67; high scores represent external locus of control) and Obsessive-Compulsive Inventory-

Revised (OCI-R; M = 2.19, SD = 12.16; high scores mean more OC symptoms), all showed high

reliability, SoC Cronbach's alpha = .94, 95% CI [.93,.95]; LoC Cronbach's alpha = .82, 95% CI

[.78,.86]; OCI-R Cronbach's alpha = .93, 95% CI [.92,.95] and correlated with each other (r

SoC,LoC = -.67; r SoC,OCI-R = -.34; r OCI-R,LoC = .27; ps < .001).

We fitted three negative binomial generalized linear models predicting the number of

passive auxiliary verbs by SoC/LoC/OCI-R and word count as a covariate. Neither SoC (IRR =

0.99, p = .258) nor LoC (IRR = 1.01, p = .402) were found to be significant predictors of non-

agentive language. Contrary to our prediction, OC tendencies were negatively associated with

passive voice use (IRR = 0.98, p = .024, 95% CI [.97,.99]), see Tables S8-S10 in SM.

In Study 4a, did not find any evidence to suggest that passive voice is significantly

associated with trait-level measures of personal agency. Although OC tendencies were indeed

correlated with a lower sense of personal agency, higher OC tendencies were associated with the

use of more agentive language, contrary to earlier proposals.

Study 4b

25

Method

Study 4b was meant to make sure that the lack of relation between linguistic and personal

agency is not the result of a type-II error. Study 4b was a direct replication of Study 4a with

minor changes. In the current study, we did not administer OCI-R, and we replaced the LoC

scale with a shortened version (Brief Locus of Control Scale, B-LoC; Sapp & Harrod, 1993).

Participants

We recruited 210 English-speaking participants from Amazon Mechanical Turk services

(MTurk; M age = 34.26, SD = 9.97; 123 men, one non-binary). After exclusions, 191 participants

remained in the sample. All participants signed informed consent before the study and were

compensated with 2.8$ for their participation. The study was conducted under the Departmental

IRB guidelines.

Results

In Figure S3 we report the histogram of passive voice use. Sense of Control (SoC; M =

61.54, SD = 13.74; high scores represent high control), Locus of Control (LoC; M = 18.03, SD =

1.15; high scores represent internal locus of control), all showed high reliability, SoC Cronbach's

alpha = .93, 95% CI [.93,.94]; LoC Cronbach's alpha = .90, 95% CI [.87,.92] and correlated with

each other, r (189) = .59, p < .001.

We fitted two negative binomial generalized linear models predicting the number of

passive auxiliary verbs by SoC/LoC and word count as a covariate. Again, neither SoC (IRR =

0.99, p = .297) nor LoC (IRR = 1.00, p = .953) were found to be significant predictors of non-

agentive language, see Table S11-S12 in SM.

In Study 4b, we replicated the results of Study 4a and again did not find an association

between non-agentive language and trait-level control measures obtained from self-reports.

However, it could be that the lack of effect stems from the low signal-to-noise ratio in the

analysis of free text, or some specific aspect of the English language that obfuscates any effect.

We thus addressed these possibilities in Study 4c.

Study 4c

26

Method

In Study 4c, we used a different method to assess preference for agentive vs. agentless

linguistic forms and applied it in a different language, Hebrew. We created a task in which

participants were asked to describe a scenario by constructing a sentence from a pool of words in

a drag-and-drop fashion. By doing so, we significantly reduced the variability associated with

free text and were still able to assess agentive language production. We were interested in

whether a tendency to describe events in an agentive form is associated with trait-level control,

measured by alternative yet equivalent measures of SoC.

Participants

We recruited 101 participants (23 men ;77 women; 1 non-binary) from the Ben-Gurion

participants pool, all native speakers of Hebrew. Participants were students at Ben-Gurion

University and were given credit for their participation. The mean age was 23.63 (SD = 1.45).

The study was conducted under the Departmental IRB guidelines.

Measures

Personal Mastery Scale (Pearlin & Schooler, 1978). The Mastery scale consists of 7

statements; each statement was followed by a 7-point Likert scale ranging from Strongly Agree

to Strongly Disagree. Mastery score was calculated as the sum of the items.

Desirability of Control (Burger & Cooper, 1979). The Desirability of Control (DoC)

scale consists of 20 statements; each statement was followed by a 7-point Likert scale ranging

from Strongly Agree to Strongly Disagree. DoC score was calculated as the average of the items.

Belief in Free Will (Paulhus & Carey, 2011; Zhao et al., 2014). We adapted a shortened

version of Belief in Free Will scale that consisted of 6 items, ranked on a 7-point Likert scale

ranging from Strongly Agree to Strongly Disagree. Belief in Free Will score was calculated as

the average of the items.

Procedure

Participants were presented with 13 pictures of objects (e.g., a cracked egg, broken

smartphone, etc.) accompanied by a sentence providing laconic context (e.g., “Dana held an

27

egg,” “Gill had an iPhone”). The participants were requested to drag-and-drop words from a pool

of words (presented in a randomized order) to create a sentence that best describes the situation.

Importantly, the words in the pool were carefully chosen to allow for both an agentive

description (e.g., “Dana cracked the egg”) or an agentless description (e.g., “The egg got

cracked,” or a special non-agentive form of Hebrew “[they] cracked the egg”). Next, the

participants were requested to take a modified version of the personal mastery scale (Pearlin &

Schooler, 1978), desirability of control scale (Burger & Cooper, 1979), brief free will scale

(Paulhus & Carey, 2011; Zhao et al., 2014), and lastly, write a short paragraph about something

that happened to them last year.

Results

To estimate the agentive language tendency, we coded the restricted-text responses to

agentive (1) and non-agentive (0). Our key dependent variable was the sum of agentive

responses, such that 0 represents no agentive responses, and 13 is the maximum agentive

responses. We correlated the agentive score with our three measures of control: Mastery (high

scores represent high mastery; Cronbach’s alpha = .69, 95% CI [.60,.78]), Desirability of Control

(DoC; high scores represent high desire for control; Cronbach’s alpha = .82, 95% CI [.78,.87])

and Belief in Free Will (high scores represent high belief in free will; Cronbach’s alpha = .62,

95% CI [.52,.74]). In line with Studies 4a and 4b, none of the trait-level measures of control were

correlated with a preference for agentive language. See Table 1.

Table 1. Means, standard deviations, and correlations with confidence intervals. n = 101.

Variable M SD 1 2 3

1. Agency 3.58 2.10

2. Mastery 36.82 5.15 .00†

[-.19, .20]

28

3. DoC 4.57 0.55 -.09‡ .34**

[-.28, .11] [.15, .50]

4. Free Will 4.95 0.82 .00† .49** .36**

[-.19, .20] [.32, .62] [.17, .52]

Note. M and SD are used to represent mean and standard deviation, respectively. Values in

square brackets indicate the 95% confidence interval for each correlation. * indicates p < .05. **

indicates p < .01. † indicates BF01 = 4.37. ‡ indicates BF01 = 2.96

Similarly to Studies 4a-b, we found no evidence that participants’ preference for agentive

language was associated with their trait-level sense of control. Together, Studies 4a-c converge

to highlight the boundary conditions of the effects observed in Studies 1-3. Namely, they indicate

that the observed relation between personal and linguistic agency is a contextual, state-level

phenomenon, and does not seem to reflect a stable, trait-level characteristic of individuals.

General Discussion

In the current work, we examined the relation between language and thought by

investigating whether and how linguistic and personal agency are intertwined. In Study 1 we saw

that manipulation of participants’ sense of personal agency (specifically, of sense of power)

affects their use of agentive language; in Study 2 we saw that manipulation of linguistic agency

(i.e., whether task instructions are written in agentive language) affects participants’ sense of

personal agency (specifically, their sense of control); in Study 3 we used big data analyses to

show that the language of individuals who suffer from a diminished sense personal agency (due

to depression) is less agentive and that increased personal agency (operationalized as one’s social

media “influence”) is associated with more agentive language. Lastly, in study 4, we did not find

evidence for a trait-level association between linguistic and personal agency. Together, these

findings advance our understanding of the nuanced relation between language and thought, in

showing that state- (but not trait-) level variation in agentive language use both reflects and

affects individuals’ sense of personal agency.

29

Our research joins ever-increasing attempts to understand how language and thought are

linked. Much of the work into this question focused on investigating differences between

languages and speakers at the level of semantics (e.g., Pederson et al., 1998; Boroditsky, 2011;

Frank et al., 2008; Roberson et al., 2005). Somewhat consistent with the so-called “Whorfian

view” (Hunt & Agnoli, 1991), this line of research has shown that the availability of words in a

given language - or the choice of words by a given speaker - can significantly affect people’s

construal of the world. Whereas the effects of language on thought at the level of semantics have

been widely demonstrated, much less research has examined the effects of syntactic variations,

namely variations in how the words are arranged within a sentence.

Our results suggest that despite the fact that passive and active sentences are semantically

similar (in fact, it has sometimes been argued that they have an identical “deep structure”

(Chomsky, 1965), linguistic agency reflects and can even affect one’s sense of personal agency.

The results of the experimental studies allow us to find evidence for a causal relation wherein

agentive language use is not only affected by psychological agency (Study 1) but also affects

one’s sense of personal agency (Study 2). Namely, when participants read task descriptions in a

non-agentive form, they attributed less control to themselves. Despite the arbitrariness of this

extremely subtle rephrasing, participants (falsely) believed that they had greater control over

occurrences merely as a result of a simple syntactic alternation.

Manipulations that increase people’s sense of control are potentially important, given that

people’s construal of themselves as being capable of controlling their environment is related to

their sense of well-being and mood (Chorpita & Barlow, 1998; Lachman & Weaver, 1998;

Shapiro et al., 1996). However, a limitation of our lab-based studies is that the observed effects

were relatively small, casting doubt on the relevance of these findings to daily life. Therefore, in

Study 3, we stepped outside of the laboratory and examined the relation between linguistic and

psychological agency in highly consequential domains. Specifically, we saw that online

communities of people struggling with depression use up to 38% more passive voice than a

random sample of online communities, suggesting that lower levels of linguistic agency may be

a marker of deteriorated mental health. Furthermore, we observed that passive voice is

negatively associated with gaining followers on Twitter, such that every passive auxiliary verb in

a tweet is associated with 39% reduction in followership, directly linking linguistic agency with

30

social power. Notably, these results show that subtle syntactic variation in agency is related to

matters of both clinical and social\economic importance.

While we observed a robust association between contextual changes in levels of

psychological agency and agentive language use, in Study 4, we provide evidence to suggest that

this relation reflects state-level variability that does not extend to the trait-level. The current

research sets the stage for future research that examines culture-level variations in linguistic and

psychological agency. The degree to which cultures differ in their prevailing beliefs about one’s

sense of control has important societal consequences including economic development (Groves,

2005; Kaufmann et al., 1995; Urban, 2015; Wuepper & Lybbert, 2017) and upward mobility

(Schnitzlein & Stephani, 2016). Future research could adopt a cross-cultural perspective to

examine whether cultures whose language prefers agentivity are more likely to adopt agentic

beliefs (e.g., Chen, 2013; Feldmann, 2019), thereby further elucidating the consequences of

(supposedly arbitrary) linguistic features on human life.

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Supplementary Materials

For full code and analysis see OSF repository:

https://osf.io/nwsx3/?view_only=21a12e5779194993a71bcd6296852bf2

40

Figure S1. Histogram of passive auxiliary verbs in Study 1.

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Figure S2. Histogram of passive auxiliary verbs in Study 4a.

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Figure S3. Histogram of passive auxiliary verbs om Study 4b.

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Table S1. Negative Binomial Generalized Linear Model predicting passive auxiliary verbs as a

function of power condition (high vs. low) in Study 1.

Count Passive

Predictors Incidence Rate Ratios std. Error CI Statistic p

(Intercept) 0.23 0.11 0.19 – 0.29 -13.00 <0.001

Condition [Low Power] 1.34 0.09 1.11 – 1.61 3.09 0.002

Group [prolific] 0.95 0.10 0.79 – 1.15 -0.52 0.602

Word Count 1.01 0.00 1.01 – 1.01 14.80 <0.001

Observations 835

44

Table S2. Fixed-effects ANOVA table of Study 2a.

term Sum Sq Mean Sq Num DF Den DF F p

Partial η2

[90% CI]

Agency 1.65 1.65 1 30.48 1.62 .213 0.05 [0.00,0.22]

Success 89.31 89.31 1 45.84 87.4 <.001 0.66 [0.52,0.75]

Agency * Success 0.26 0.26 1 313.35 0.25 .617 0.00 [0.00,0.01]

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Table S3. Fixed-effects ANOVA table of Study 2b.

term

Sum

Sq

Mean

Sq NumDF

Den

DF F p

Partial η2

[90% CI]

Agency 1.37 1.37 1 36.22 1.11 .298 0.03 [0.00,0.17]

Lagged Success 14.78 14.78 1 66.54 12.01 <.001 0.15 [0.04,0.29]

Agency * Lagged

Success 1.41 1.41 1 80.78 1.15 .287 0.01 [0.00,0.08]

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Table S4. ANOVA table of Study 2.

term MSE NumDF Den DF F p

Partial η2

[90% CI]

Study 1.13 2 169 23.25 <.001 0.22 [0.13,0.30]

Agency 0.04 1 169 11.36 <.001 0.06 [0.02,0.13]

Study * Agency 0.04 2 169 1.11 .331 0.01 [0.00,0.05]

.

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Table S5. Negative Binomial Generalized Linear Model predicting passive auxiliary verbs as a

function of online community (depression vs. random sample of popular communities), Study

3a.

Count Passive

Predictors Incidence Rate Ratios std. Error CI Statistic p

(Intercept) 0.41 0.03 0.39 – 0.43 -31.64 <0.001

Depression 1.19 0.03 1.12 – 1.27 5.38 <0.001

Word Count 1.00 0.00 1.00 – 1.00 73.25 <0.001

Observations 8690

48

Table S6. Negative Binomial Generalized Linear Model predicting passive auxiliary verbs as a

function of online community (depression vs. random sample of popular communities), Study

3b.

Count Passive

Predictors Incidence Rate Ratios std. Error CI Statistic p

(Intercept) 0.35 0.03 0.33 – 0.37 -36.08 <0.001

Depression 1.38 0.03 1.29 – 1.47 9.87 <0.001

Word Count 1.00 0.00 1.00 – 1.00 76.79 <0.001

Observations 9685

49

Table S7. Negative Binomial Generalized Linear Model predicting number of followers as a

function of number of passive auxiliary verbs in tweet, Study 3c.

Count Followers

Predictors Incidence Rate Ratios std. Error CI Statistic p

(Intercept) 1066.14 0.00 1060.87 – 1071.45 2753.85 <0.001

Passive 0.61 0.01 0.60 – 0.61 -69.13 <0.001

Word Count 1.04 0.00 1.04 – 1.04 185.50 <0.001

Observations 2726733

50

Table S8. Negative Binomial Generalized Linear Model predicting number of passive auxiliary

verbs in free text as a function of SoC score, Study 4a.

Count Passive

Predictors Incidence Rate Ratios std. Error CI Statistic p

(Intercept) 1.01 0.46 0.40 – 2.52 0.01 0.990

Sense of Control 0.99 0.00 0.99 – 1.00 -1.13 0.258

Word Count 1.00 0.00 1.00 – 1.00 3.14 0.002

Observations 181

51

Table S9. Negative Binomial Generalized Linear Model predicting number of passive auxiliary

verbs in free text as a function of LoC score, Study 4a.

Count Passive

Predictors Incidence Rate Ratios std. Error CI Statistic p

(Intercept) 0.62 0.36 0.31 – 1.21 -1.35 0.176

Locus of Control 1.01 0.02 0.98 – 1.04 0.84 0.402

Word Count 1.00 0.00 1.00 – 1.00 3.26 0.001

Observations 181

52

Table S10. Negative Binomial Generalized Linear Model predicting number of passive auxiliary

verbs in free text as a function of OCI-R score, Study 4a.

Count Passive

Predictors Incidence Rate Ratios std. Error CI Statistic p

(Intercept) 1.06 0.37 0.51 – 2.21 0.17 0.864

OCI-R 0.99 0.01 0.97 – 1.00 -2.26 0.024

Word Count 1.00 0.00 1.00 – 1.00 3.31 0.001

Observations 181

53

Table S11. Negative Binomial Generalized Linear Model predicting number of passive auxiliary

verbs in free text as a function of SoC score, Study 4b.

Count Passive

Predictors Incidence Rate Ratios std. Error CI Statistic p

(Intercept) 0.79 0.48 0.30 – 2.03 -0.49 0.622

Sense of Control 0.99 0.01 0.98 – 1.01 -1.04 0.297

Word Count 1.00 0.00 1.00 – 1.01 2.28 0.022

Observations 191

54

Table S12. Negative Binomial Generalized Linear Model predicting number of passive auxiliary

verbs in free text as a function of B-LoC score, Study 4b.

Count Passive

Predictors Incidence Rate Ratios std. Error CI Statistic p

(Intercept) 0.53 1.28 0.05 – 6.17 -0.50 0.615

Brief Locus of Control 1.00 0.07 0.88 – 1.15 0.06 0.953

Word Count 1.00 0.00 1.00 – 1.01 2.11 0.035

Observations 191