Human sleep/wake cycles follow a stable circadian rhythm associated with hormonal, emotional, and cognitive changes. Changes of this cycle are implicated in many mental health concerns. In fact, the bidirectional relation between major depressive disorder and sleep has been well-documented. Despite a clear link between sleep disturbances and subsequent disturbances in mood, it is difficult to determine from self-reported data which specific changes of the sleep/wake cycle play the most important role in this association. Here we observe marked changes of activity cycles in millions of twitter posts of 688 subjects who explicitly stated in unequivocal terms that they had received a  diagnosis of depression as compared to the activity cycles of a large control group (n = 8791). Rather than a phase-shift, as reported in other work, we find significant changes of activity levels in the evening and before dawn. Compared to the control group, depressed subjects were significantly more active from 7 PM to midnight and less active from 3 to 6 AM. Content analysis of tweets revealed a steady rise in rumination and emotional content from midnight to dawn among depressed individuals. These results suggest that diagnosis and treatment of depression may focus on modifying the timing of activity, reducing rumination, and decreasing social media use at specific hours of the day. It is furthermore associated with a reported 800,000 suicides on an annual basis, mostly among young adults2. Depression is significantly under-reported, under-diagnosed, and under-treated, in part due to its heterogeneous nature which involves subjective and culturally shaped experiences such as motivation, mood, and well-being3. Furthermore, in spite of its prevalence, the dynamics of its onset and development remain poorly understood6, limiting the development of treatment options8.Depression is one of the most important global public health challenges. It is the single largest contributor to disability and disease, affecting 4% of the world’s population, causing 11% of all years lived with disability globally9, humans experience circadian rhythms involving hormonal, behavioral, and cognitive changes that lead to stable sleep-wake cycles, even when individuals are disconnected from natural daylight11 or travel across time zones. Unsurprisingly, a stable daily activity cycle is important to maintain physical and mental health13. In fact, disturbances of the human circadian rhythm are strongly associated with mood disorders22 such as depression and anxiety, bipolar, and borderline personality disorder. The severity of depression has been linked to the magnitude of the sleep-wake cycle disturbance23 while reports of sleep disturbances can be used as an early warning signal of recurrent depression24 and predict risk of poor outcomes in treatments for depression25. As a result, interventions targeting sleep are now considered an essential component of efforts to improve depression treatment outcomes27. This is also emphasized by the central position of sleep-related symptoms in disorder networks28.Like most mammals30 indicate that the association between sleep and mood disorders is not necessarily modulated by the amount of sleep per se31, but by its specific timing and pattern. In particular, questions have arisen with respect to whether phase and/or magnitude changes of the sleep-wake cycle account for the association between sleep and risk for depression32.Although the connection between sleep-wake cycle disturbances and depression has been firmly established, it is not clear which specific disturbances or changes are most strongly implicated in the onset and remission of depression. Reports of the effectiveness of sleep deprivation therapy33. These limitations can be mitigated by the post hoc analysis of alternative sources of information such as microblogs, diaries, mobile phone34, and social media activity. The latter in particular serve as a daily cognitive and behavioral diary to billions of individuals. In fact, activity levels in on-line platforms, e.g. using Digg35, Foursquare36, Twitter37, Wikipedia editing behavior38, and YouTube35, have already proven to be a useful resource to estimate circadian cycles.Observations of daily activity levels of individuals require continuous monitoring of a large number of subjects throughout numerous circadian cycles to establish sufficient statistical power while avoiding observer bias. However, most studies establishing circadian rhythm disturbances in mental disorders suffer from small sample sizes39. We find that the activity levels of depressed individuals, like those of a random sample, fluctuate reliably according to a well-defined circadian rhythm as was shown previously40. Our results extend these findings by showing no evidence of a significant phase-shift, but rather that activity levels for the depressed individuals differ significantly in the early evening and early morning hours, which is when we also see increased indications of emotionality and self-reflection. These findings point towards targeted interventions that focus on the reduction of rumination at specific times of the day.Here, we use large-scale, longitudinal, social media activity data to study the daily activity cycles of hundreds of individuals who stated in unequivocal terms that they had received a  diagnosis of depression, using an similar sample inclusion criterion as Coppersmith, Dredze & Harman39. A team of 3 raters independently evaluated each ‘diagnosis tweet’ to determine whether it pertained to an explicit, unequivocal statement of an actual diagnosis, removing self-diagnoses, retweets, quotes, or jokes. In other words, we excluded individuals who “self-diagnosed” with depression. This second step was taken to remove false-positives from the cohort, which has been proven to increase performance in classification tasks41. We also mapped references to a time of diagnosis, e.g. “today”, “last week”, “2 months ago”, or “in 2014” to a likely diagnosis time interval , supports the validity of our inclusion criteria for the “Depressed” cohort. We found 688 individuals that explicitly stated their  depression diagnosis and whom we assigned to the “Depressed” cohort, or D cohort for short. We downloaded the past tweets of these aforementioned individuals to obtain a longitudinal timeline.For our analysis, we define 2 disjoint cohorts of Twitter users: “Depressed” and “Random”. In our “Depressed” cohort we only include individuals with a  diagnosis of depression, which they report on Twitter explicitly , similar to the approach of Coppersmith, Dredze & Harmanval see “”. This mD cohort. However, a highly accurate sex classifier45 (Macro-F1: 0.915) applied to the Twitter profiles of our D cohort , is also in line with clinical studies47, specifically we find a decreasing number of individuals per age-group as the age of the group increases in our D cohort.Neither the reported diagnosis nor the Twitter profiles of the sampled individuals provide demographic information with respect to our ort see “”, shows RS cohort for short, as a control group by taking a random sample of 8791 Twitter users. To compensate for possible changes of user behavior in the social media platform over time, we sample these individuals such that the distribution of their account creation month matches that of the individuals in the D cohort  that can be characterized by its median and 95% confidence interval, denoted by D and RS cohort.Naturally, differences can arise in the level of activity between both individuals and cohorts in general. Since we are not looking to make inferences about the total amount of tweets nor the average number of tweets per cohort, but rather the relative differences of hourly activity patterns between the two cohorts, we account for this variation by calculating hourly activity levels for 10,000 re-samples of the individuals in the The resulting time series mentclass2pt{minimmentclass2pt{minimD and RS cohorts, we find periodic changes in activity levels throughout the day, resulting in a well-defined circadian rhythm of activity levels. We find that both cohorts experience a valley in activity levels from roughly 10PM to 6AM, a time that is traditionally reserved for sleep. Activity levels quickly recover from a low point at 6AM as people wake up and become active during the morning hours. This is followed by a first peak at noon, after which activity plateaus for 6 h from noon to 6 PM. This is followed by a slight ramp up of activity peak around 9 PM, after which activity levels drop again.For both the Tweets can be posted at any time of year, hence seasonal changes in daylight times or Daylight Savings Time could affect our observations. However, we find that daylight times changes throughout the year do not account for our pattern of results  as the expected number of times that the token is used per tweet  selected a set of 76 tokens , this pattern is indicative that wakefulness at that time is associated with negative psychological states. The increase in usage of both Positive and Negative Affect may be indicative of higher levels of emotionality.More specifically, we see a drop in positive affect and an increase in Rigid Thinking and Questioning from midnight to 3AM. This is followed by an increase in the use of tokens associated with Personal Pronouns and Negative Affect from 4AM to 6AM. Token use across all categories peaks from 5AM to 6AM, the early morning hours, which indicates higher levels of “rumination” and “self-reflection” among individuals in the D cohort than the RS cohort. In addition, mean prevalence values per token category can vary considerably, i.e. some categories of tokens occur more frequently than others throughout the day and hourly as shown in Table The time series in Fig. D and RS cohorts, but rather significant differences in the magnitude of activity levels at specific times. As shown in Fig. D cohort is significantly less active from 3 AM to 6 AM and significantly more active from 7 PM to midnight than the RS cohort.Comparing hourly Twitter activity levels for two cohorts of respectively “Depressed” and “Random” individuals, we find significant differences in the activity patterns of depressed Twitter users vs. a random sample. Unlike previous studies, we observe no phase-shift between the circadian rhythms of the 49. Since this peak is more pronounced for the D cohort, it raises the interesting possibility that social media use is partly involved in altering the circadian activity levels of the D cohort. As such, in this case, we are not merely observing a change in circadian rhythms, but the differential effects of social media.The latter difference corresponds to an interesting daily peak of highest activity at 9 PM which occurs in both cohorts. This peak of activity levels may correspond to a period of time after dinner and before bedtime which individuals use for recreational social media useD and RS cohort shows that the D cohort more frequently use tokens that relate to “self-reflection” and “rumination” than the RS cohort. Furthermore, Fig. D cohort who are awake between 3 AM and 6 AM show higher levels of “rumination” and “self-reflection”. Moreover, the simultaneous rise in both Positive and Negative Affect tokens at this time may be indicative of a higher level of emotionality in the D cohort. Since the D cohort is less active at a given time, we note that our content analysis only captures the activity of individuals that are still awake.Comparing tweet content at specific times between the The fact that we find more frequent usage of the majority of tokens across all six categories suggests that depressed individuals, when awake, post messages that contain higher levels of so-called “depressogenic” language. Therefore, by analyzing the corresponding patterns around the chosen tokens we could gain insight into differences in broader language use between depressed individuals and the general population. These difference could be leveraged in the development of treatments as well as early intervention, in particular in the context of cognitive-behavioral therapy (CBT) which involves a significant cognitive and linguistic component. Our current analysis first and foremost aims to establish the difference in activity levels and associated language use between the described cohorts, hence we leave this analysis for future work.D cohort explicitly stated that they received a clinical diagnosis of depression, we have no ground-truth verification of the veracity of these statements, nor do we have specific information about the time of the diagnosis for all individuals in the D cohort. Based on the time indications in some the expressions, e.g.  “yesterday” or “last week”, we were able to determine an interval in which the diagnosis occurred for only 93 individuals in the D cohort (out of 688). However, We caution that although we verified that the individuals in our D cohort will contain a mix of individuals with recent or past diagnoses, cases that have either been resolved or remain unresolved, or co-morbid disorders. Likewise, our RS cohort may contain depressed individuals who did not explicitly state a diagnosis, but suffered from depression in the past or the present. Even though our D cohort is likely to contain a significantly greater number of individuals that suffer from depression than our RS cohort, both cohorts may be heterogeneous to some degree. Such heterogeneity would decrease the observed differences between the groups and increase error, thus tending to reduce statistical power, not increase it. As a result, it would not call into question the validity of our results. We furthermore carefully bootstrapped all activity estimators to estimate the sensitivity of our results to sample heterogeneity.It is nevertheless inevitable that our 50. The value of social media data can be augmented further with other sources of mental health information, such as electronic health records, which can be cross-validated to social media text with the appropriate use of machine learning and AI algorithms. Third, studies of the online patterns of activity of depressed individuals may inspire new opportunities for intervention, for example with CBT for insomnia, delivered efficiently over the internet51. Finally, social media, regardless of its utility as data sources for social science55, has become an important factor in the social lives of billions of individuals. The analysis of social media and related mobile communication data might therefore shed light on how or whether these platforms affect public health at a global scale56.Our findings illustrate how social media data can be leveraged to investigate the longitudinal and individual effects of mental disorders on circadian sleep/wake cycles, complementing insights obtained through traditional methods. Based on our results, social media data offers distinct opportunities for this field of research. First, it allows for the construction of large cohorts to be analyzed with higher statistical power with respect to changes in circadian rhythms, and second, the fact that the tweets are analyzed ex post hoc ensures that the results are not influenced by the Hawthorne effect57 for tweets that were posted between Jan 1st 2013 and Jan 1st 2019, and contained the terms: diagnos* and depress* (* indicates “any following characters”). We found 4,002 such tweets posted by 3,324 unique Twitter users. Three of the co-authors manually and independently rated the content of each such tweet in terms of whether it did in fact contain a valid, unequivocal, and explicit statement of a  depression diagnosis, removing matches resulting from jokes, quotes, self-diagnoses, or non-self-referential statements, e.g. “a friend just told me...”. In other words, we excluded individuals who “self-diagnosed” with depression. We thus obtained a list of 1,211 individuals that explicitly expressed that they received a  diagnosis of depression.In our “Depressed” cohort we only include individuals with a  diagnosis of depression, which they report on Twitter explicitly . To obtain the widest possible set of tweets that could contain such a statement, we searched the Twitter search Application Programming Interface (API) and the IUNI Observatory on Social Media (OSoMe)Next, for each individual who posted a ‘diagnosis’ tweet, we harvest their public profile information and a timeline of their past tweets  from Twitter’s API. The post dates and times of these tweets range from 2008-10-22 01:23:03+0000 to 2018-09-12 12:48:04+00:00. For every individual tweet, we retrieve a unique identifier, the content of the tweet, and the coordinated universal time (UTC) at which it was posted. In total, we find 2,928,720 tweets across the timelines of these 1,211 individuals.Since we want to compare the timing of circadian activity levels between groups of individuals, we have to localize their respective time zones. We use the self-reported location of the Twitter user’s profile page to determine the time zone of that specific user. Using this approach, we are able to determine a time zone for 690 individuals, for which we obtained a total of 1,686,182 tweets.Subsequently, we remove tweets that are not posted in English according to the tweet’s language label supplied by Twitter, as well as tweets that match our initial “diagnostic” search criteria so our analysis is not biased by linguistic variables and our sample selection criterion. We also remove all retweets since they are by definition not written by the same individual. We thus retain 1,042,838 tweets that are posted by 688 Twitter users. We will refer to these individuals as the “Depressed” cohort.2 weeks ago”. Not all TODs refer to an exact date on which the diagnosis took place, but rather an interval such as “a couple of weeks ago”. Therefore, we translate these statements to intervals in which the diagnosis most likely happened based on the magnitude and units of time stated by the individual. For example, if an individual stated “2 weeks ago”, this was interpreted as an interval that spans the entire week that took place two weeks before the date of the statement.For each of the tweets that express a diagnosis, we determine whether their content provides an indication of the Time of Diagnosis (TOD), e.g.  “I was diagnosed with depression Furthermore, if an individual posted several diagnosis tweets with a TOD indication, we use the intersection of all determined intervals, since the diagnosis is most likely to have occurred during this time. If this intersection does not exists, we do not quantify a TOD for this user. In addition, when a tweet text states “I have been recently diagnosed with depression”, we assume that the diagnosis was given in the last three months. We refer the reader to Section After we obtained the “Depressed” cohort, we consecutively build a second cohort that consists of random Twitter users as a comparison to the “Depressed” cohort, which we will refer to as the “Random” cohort. Since habits and behavior in platform usage may have changed over time, we construct our random sample in such a way that the distribution of the account creation dates for these individuals (when they started using Twitter) match those of the previously established “Depressed” cohort.First, we select random tweets from OSoMe. The individuals who posted these tweets were taken as the seed set for the random sample. From this seed set, we then select all Twitter users that have specified their location and that are not in our “Depressed” cohort. We then sample these remaining individuals based on their creation date according to the creation date distribution of the “Depressed” cohort. After these steps, we collect the timelines of the 9,121 individuals we select and obtain 24,992,122 tweets for our random sample data set. These tweets’ post dates range from 2007-08-24 20:36:05+00:00 to 2019-02-08 12:51:43+00:00.The vast majority of tweets from both cohorts are posted in an overlapping time period of nearly a full decade (2008 to 2018), however there exists a slight variation in the dates of the earliest and latest tweets between the two cohorts. This variation pertains to a minority of tweets and does not affect our results since we are comparing 24 h circadian pattern. The difference in timing of the first observed tweets between the two cohorts is a consequence of the fact that the Twitter API returns the most recent 3,200 tweets for each individual. Since individual activity levels can vary over time, this may lead to slight variations in how far back in time the earliest tweets of a given cohort can be retrieved. The difference in dates of the last tweets recorded for the “Depressed” and “Random” cohorts therefore results from the latter data being retrieved after the former.After applying the same filtering steps as we used to construct the “Depressed” cohort, our “Random” cohort consists of 8,498,574 tweets that are posted by 8791 Twitter users.45, a deep learning system for demographic inference  that was trained on massive Twitter data set using profile images, screen names, names, and biographies. For both the gender-  and age-dimensions , we assign an individual we found to a group if the corresponding Twitter account scores above 0.8 on the given label and on the ‘non-org’-label.To further quantify our obtained cohorts, we derive the demographic information of all obtained individuals using the M3 systemTo control for the effects of daylight cycles for each of the individuals in our cohorts, we retrieve the user-defined locations of each tweet to derive the time of dawn, sunrise, sunset, and dusk at the location that it was posted from. The inclusion of these times may indicate whether the observed differences in circadian rhythms can arise from differences in the respective daylight cycles that individuals in the “Depressed” and “Random” cohorts experience at their location. Figure For each individual in our cohorts, we determine the circadian rhythm as follows. We localize all creation dates of that individual’s tweets using their respective time zone which was determined from the user-defined location in their Twitter profile. We use these localized post times to determine in which hour of the individual’s local time the tweet was posted. Using this information, we can then produce circadian rhythms for the individuals by binning their tweets based on the localized hour in which they were posted. We normalize the counts of tweets for any given hour to a percentage by dividing the hourly count by the total number of tweets over all 24 h in the day. Hence, for uniform activity levels throughout the day, each hour would have 100% / 24 In the comparison of the activity levels of the “Depressed” and “Random” cohorts, we also check whether these time series display a phase-shift. We do this by calculating the cross-correlation of the two circadian time series shifted − 12 to + 12 h (wrapped window). The maximum correlation coefficient between the two time series is 0.9929 at a lag of 0 h, which implies the absence of a phase-shift between the time series of our two cohorts  in Table https://hotemoji.com/emoji-meanings.html, but only including emoji’s with obvious positive and negative valence, which are also indicated by an asterisk (*) in Table To focus our analysis on tokens that are consistently used throughout the day , we determine the 250 most used tokens for each separate hour. Of these tokens, 187 tokens occur in the top 250 tokens for Supplementary Information.


Diagnostic and Statistical Manual of Mental Disorders, many researchers find this definition too constraining and not accounting for the complexity and many aspects of trauma. This touched off a quest for a broader more accommodating trauma concept, and a dimensional view of trauma with PTSD as its extreme manifestation has been suggested. The dimensional view also has its detractors arguing that “conceptual bracket creep” may undermine the category’s utility. Both categorical and dimensional views mostly rely on trauma’s clinical phenomenology and lack a unified theoretical basis. In an attempt to reconcile this contradiction, a hybrid categorical–dimensional model of trauma based on the general theory of stress has been recently proposed  as defined in the proposed . Herein, Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition  of the following ways…” . This criterion establishes the severity threshold in search of a categorical boundary  abuse  abuse . Anotheruse . In the following sections, I explore the PSR/TSR difference in the PPF to further elucidate the functional basis of this distinction.The main feature distinguishing TSR from PSR was identified as a breakdown of self-regulatory functions. Neurological markers of such breakdown were hypothesized to associate with a malfunction of the default mode network  and clinprior or prior belief (or prediction) and is thought of as a probability distribution function representing prior learning. Bayesian statistics establish a rule for updating the prior according to the likelihood that S does, indeed, follow E. The updated prior is called posterior (posterior belief). Thus, the model continuously updates itself through posterior learning. Prior beliefs are understood here in a broad sense, where they can reside at any level of information processing from unconscious expectancy to the declarative abstract thought. When sensory input does not match the prior, a prediction error (PE) is generated. Prediction error’s function is to update the brain’s generative model, which happens through resolving/suppressing the PE. This can happen in two ways through (a) posterior learning by adjusting the model’s prior to match the input or (b) active inference by adjusting the organism’s properties and/or behavior so that it controls the sensory input (“samples the environment”) in a way that matches the model’s prior. Through iterative cycles of perception–action, the brain directs the organism’s behavior to selectively seek and gate sensory information to fulfill its predictions. An example of active inference is mammalian infants deriving pleasure from suckling on an object, making it possible to “fool” them with a pacifier.In a recent paradigm shift, brain is no longer viewed as a passive receiver and processor of information. Instead, it is thought of as a predictive coding machine operating by rules of Bayesian inferential statistics . In shorMore recently, PPF has been integrated with the free-energy principle (FEP) . The FEPprecision estimate or weighting  they may be refractory to PE and fail to update according to the changing environment, rendering the generative model insensitive. On the other hand, too malleable priors (low precision) may lead to an unstable model, hypersensitive to environmental contingencies, and thus lacking in predictive power. Precision weighting is thought to be mediated by neuromodulatory control of the synaptic gain of PE units . Then, the meaning of an adaptive stress response  is an accurate prediction of internal states that optimally accommodate the future challenge.DSM-5). On the other hand, the posttraumatic model of the world is believed to rely on hyperprecise predictions of threat that do not require sensory confirmation for triggering a metabolic “fight or flight” response  including symptoms of hypervigilance and unrealistic negative beliefs about the world. Hyperprecise “threat” priors will result in relatively underweighted exteroceptive PEs (ePE). I suggest that, in PPF, precision weighting functions as a mechanism of allostasis, where the ratio between a prior and the PE precisions determines the current homeostatic set point for the prior, that is, its mean and variance. Under allostatic load, these parameters change in a way that minimizes the model’s free energy.I hypothesize that in TSR the generative model of the world undergoes a drastic recalibration of its predictions (exteroceptive priors). As a result, the model generates a set of hyperprecise predictions of the world’s dangerousness to accommodate the traumatic experience in a way that suppresses the PE, thus minimizing the model’s free energy . Such reTrauma-induced exteroceptive “blindness” is expected to be trauma-specific; however, it may generalize to unrelated or loosely related sensory information. For example, PTSD patients show hyperactivation of the amygdala in response to both trauma-specific and neutral images. Moreover, this hyperreactivity is observed even without recognition of presented stimuli, indicating that it is likely due to the top-down, that is, prediction-driven, information processing .DSM-5) can be seen as both an active inference (hypervigilant behaviors) “fulfilling” the hyperprecise exteroceptive “threat” priors and interoceptive active inference (visceromotor manifestations) “fulfilling” the hyperprecise interoceptive predictions of physiological emergency response , it is difficult to predict an appropriate visceromotor response. This challenge was aptly expressed in application to depression; “major depression occurs when the brain is certain that it will encounter an uncertain environment; that is, the world is inherently volatile, capricious, unpredictable, and uncontrollable” , p. 2278ontinuum . Becauseontinuum  can be tontinuum .The above model of stress response may also be applicable to its developmental dynamics. This aspect, although important and far-reaching, is beyond the scope of this article and will be addressed only cursory. Stress response is both constrained and biased by innate and genetic factors, which may determine the organism’s allostatic range. Genetic differences in the stress responsivity have been observed since long ago in strains of rats . Stress In order to elaborate the described model in greater clinical detail, below, I compare TSR to PSR, using examples of their prototypical clinical conditions, PTSD and depression, respectively. A detailed description of the PSR model of depression can be found elsewhere .DSM-5), where patients avoid reminders, thoughts, and feelings associated with the traumatic event. Depressed people, on the contrary, tend to engage in rumination, that is, prolonged reminiscing and brooding about their past and present misfortune , which in PPF can be regarded as active interoceptive inference meant to minimize the uncertainty of a generative model that overpredicts threat. Selective underweighting of iPE helps sustain the hyperprecise “allostasis” priors. Thus, PTSD generative model is defined by a combination of “runaway” exteroceptive “threat” and interoceptive “allostasis” priors (DSM-5). Noteworthy, such recreating often happens in night dreams, when there is little to no sensory input and, consequently, no ePE.On the interoceptive side, PTSD is characterized by chronic hyperarousal .Where depressive and PTSD generative models differ is exteroceptive inference . WithoutIn accord with the highlighted differences between TSR and PRS generative models , I suggeTo be considered traumatic, a stress response to an event must meet the necessary condition that the event be outside of the person’s normative life experience, causing an abrupt recalibration of exteroceptive priors, and the sufficient condition that the response include a breakdown of self-regulatory functions manifested in a malfunction of predictive processing with selectively underweighted precision of exteroceptive and iPEs.Whereas the described model of TSR , 2 is sp3.Depression, too, has been associated with low interoceptive sensory sensitivity, that is, underweighted iPE. Phenomenologically, it is manifested in the emotional flatness of depressed people . DepressThe central claim of my model is that, in contrast to underweighted ePEs in trauma, depression is associated with their selective overweighting. According to In aggregate, the existing evidence points to the qualitative difference between neural dynamics in PTSD and depression. The suggested model  interprePerhaps the most significant aspect of the suggested model of stress continuum  is its pThe proposed model implies different therapeutic strategies for TSR and PSR. All efficacious therapies for PTSD have exposure at their core. This appears a straightforward approach because it allows for reshaping the patient’s perceptual and active inference (including interoceptive) response by changing the external and internal context of traumatic imagery, as well as behavioral reaction to it. On the other hand, using exposure for the treatment of depression may not provide significant benefits because, unlike PTSD patients, depressed people do not avoid the negative material but self-expose to it through rumination . AccordiThe author confirms being the sole contributor of this work and has approved it for publication.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


As reproductive genetic technologies advance, families have more options to choose what sort of child they want to have. Using preimplantation genetic diagnosis (PGD), for example, allows parents to evaluate several existing embryos before selecting which to implant via in vitro fertilization (IVF). One of the traits PGD can identify is genetic deafness, and hearing embryos are now preferentially selected around the globe using this method. Importantly, some Deaf families desire a deaf child, and PGD–IVF is also an option for them. Selection for genetic deafness, however, encounters widespread disapproval in the hearing community, including mainstream philosophy and bioethics. In this paper I apply Elizabeth Barnes’ value-neutral model of disability as mere-difference to the case of selecting for deafness. I draw on evidence from Deaf Studies and Disability Studies to build an understanding of deafness, the Deaf community, and the circumstances relevant to reproductive choices that may obtain for some Deaf families. Selection for deafness, with deafness understood as mere-difference and valued for its cultural identity, need not necessitate impermissible moral harms. I thus advocate that it is sometimes morally permissible to select for deafness in one’s child. Reproductive genetic technologies continue to advance, providing families with increasing options to choose what sort of child they want to have. Preimplantation genetic diagnosis (PGD), for example, allows parents to evaluate several existing embryos and select which to implant via in vitro fertilization (IVF) . Setting aside the fact that the argument from direct harm also begs the question, it has at least one simple dismissal: “[a]s many authors have acknowledged,” summarizes Melissa Seymour Fahmy , p. 129,10Julian Savulescu and Guy Kahane , p. 276 Another way to respond to the harming-the-child objection is to show that it might be possible that it is actually a good thing to be deaf. Parents who select for deafness are not doing serious harm to their child because being deaf actually accrues benefits and goods, not harm. Rachel Cooper  exploresFirst, signed languages are now recognized as full natural languages Sacks , p. 90. In terms of sense perception, deaf people can have enhanced visual and vibrational qualia experiences. A deaf child can be referred to  as a “visual child”  of Deaf culture would be impossible. Certainly, many Deaf people report valuing Deaf culture very highly , because both worlds and languages are open to their children: both the hearing and the not hearing worlds, both the verbal and the nonverbal languages. , p. 90But what about Deaf parents raising a hearing child in the same way? This objection is considered by Camporesi:still prefer a deaf child is telling. Perhaps there is an asymmetry introduced when a hearing child is included in Deaf culture; namely, the parents cannot fully engage their child with respect to the child’s hearing culture. For example, when the child makes hearing friends who cannot Sign, the Deaf parents face communication barriers with those friends and probably their parents, too. The hearing child will have many experiences which the Deaf parents might have difficulty sharing or helping them with.The point Camporesi raises here, that Deaf parents could include a hearing child in Deaf culture  is frequently asserted. There is good counterevidence, however, from children of Deaf adults (CODAs) that this is indeed challenging and “imperfect” . There will be suffering and/or limited opportunity in the deaf child’s future world which could have been avoided in the selection of a different child. This formulation avoids the Parfit non-identity problem, which we could use to dismiss the first two kinds of objections. However, the harmful-state-of-affairs objection faces its own issues.Firstly, David Wasserman and Adrienne Asch , p. 30 hSecondly, one might argue that allowing families to select for deafness places an undue burden on society, since society will be responsible for providing many of the accommodations, such as specialized education and interpreting services, that the child will grow to need. I think this concern is largely unwarranted. It is not at all clear that any prospective parents could make the balancing calculations required to evaluate the future impacts of their children. Characterizing this worry in another way Fahmy , p. 132 20against disabilities at all. For example, Barnes  culture” . He then goes on to give a good account of why a deaf child parented by Deaf adults might benefit to a much greater extent than a hearing child would. In conclusion, Sparrow holds:If we acknowledge the reasonableness of a cultural understanding of Deafness and an identification with Deaf culture, and if we allow the use of genetic technologies to parents wishing to have children of a certain sort, then we have no legitimate grounds to deny Deaf parents the right to use these technologies in order to have deaf babies, if they wish to do so. , p. 16Sparrow , p. 14 pThose who believe that we should always produce “the best children we can” are likely to argue, against my conclusions, that deafness is a disability, and that we should never deliberately bring disabled individuals into existence. My question to them is, why not? All human beings live the best life they can, and if life is a good thing, then why deprive some potential individuals of that opportunity because of their personal qualities? , p. 511While I generally agree with Sparrow here, one can argue that this view neglects to consider appropriately the biological nature of disability, or the impacts of hearing impairment that we may wish to account for. Matti Häyry takes a stance similar to Sparrow’s:Both Sparrow  and Häyrassume that disability is necessarily an overall “bad” and then justify our rejection of selecting for deafness simply because we label it a disability. Instead we need to examine, and perhaps change, our concept of disability. We can then discern the relevance of disability, in this case deafness, to a future child’s well-being.Many of the arguments in “Erik Parens  examinesagainst choosing disability, because the premises assume that making the choice between particular features can be made independently of all other considerations. In Parens’ view, the choice takes place within a framework that renders a condition typically considered to be a disability into something else. “When people speak of choosing deafness or short stature,” writes Parens, “they are not choosing those traits because they take them to be disabilities. They are choosing those traits because they take them to be enhancements, where by ‘enhancement’ I mean an intervention that will facilitate a child’s or a family’s flourishing, as they understand it” . So, according to Parens, a couple that selects for deafness in their child is, in a sense, selecting not for disability, but instead for flourishing. Here again we see the assumption that disability, as a kind of bad-difference, must be opposed to well-being; this is because (for Parens) selecting for disability and selecting for flourishing are mutually exclusive. Parens’ evaluation, though, does seem consistent with some Deaf people’s desires during genetic counseling. I. King Jordan . This “all else being equal” often plays a role in arguments g Jordan , scholarUnfortunately, the way Parens’ account erases disability in the case of selection is not ideal. His conception of selecting disability does not actually challenge the bad-difference view; it seems instead to assume that some disabilities (such as deafness or dwarfism) can be flourishing-promoting “enhancements”. A different theory of disability may provide more traction on the question of selecting for genetic deafness. The situation identified by Parens, however, that parents who choose an attribute such as deafness are in a sense choosing something positive (flourishing) for their child, is something we should keep in mind. Perhaps the contribution here is simply a reminder to acknowledge that, when it comes to their potential future children, disabled parents can be thoughtful, dedicated, and invested choosers. Our task is now to describe an account of disability which neither assumes the bad-difference view nor fails to adequately capture what a disability like deafness is .The Minority Body: A Theory of Disability  make you worse off.” Barnes’ proposal counters the bad-difference view. Disability is the kind of feature, so the bad-difference view usually goes, that makes an overall negative difference in one’s life; that is, to have a disability is to have a suboptimal body, to have a harmful overall deficit in one’s biology. Further, “[a]ccording to bad-difference views of disability, not only is having a disability bad for you, having a disability would still be bad for you even if society was fully accommodating of disabled people” .” Barnes , p. 6.cause a person (particularly one’s own child) serious harm—even if that harm stands a good chance of being outweighed by other benefits.”The biggest (immediate) challenges for the value-neutral model concern causing and removing disability.local harms to the child do not seem serious enough  to warrant an injunction against selecting for it. In terms of global harms, the testimony and scholarship about Deafness suggests that deafness itself is not such a harm. As Parens suggests, reasonable parents can desire a deaf child over a hearing one from a positive mindset about their child’s potential to flourish in their family and community.Deafness, however, is a condition that does not cause serious overall harm and suffering; therefore, I argue, in an open, accepting, and accommodating society, parents might not be obligated to choose a hearing embryo over a deaf one.against disability but concedes that her account cannot maintain this. The most her account can do is hold it morally blameworthy to select against disability. Motivations to select against disability seem grounded in the bad-difference view, a view Barnes , might seem especially harmful, does not mean that deafness, when considered individually, is defined by that level of harm.One of the strongest objections to a mere-difference view comes from Jeff McMahan . SettingAnother response to McMahan’s objection is that the harm accruing to multiple disabilities is an emergent phenomenon, i.e., a new effect accruing to a set of causes and not merely a sum of the individual effects of those causes. Singly, a disability can be neutral with respect to overall well-being, but perhaps, summed together, multiple disabilities can interact to produce substantial, emergent harm. One of the mechanisms by which this could occur is the compounding reduction of capabilities that a person might use to address local harms. Emergent properties are more than the sum of their parts, so in this way we can account for the seriousness of multiple disabilities without designating individual disabilities as bad-differences. I do not think that McMahan’s objection from multiple disabilities should require us to reject Barnes’ analysis of particular disabilities, especially in the narrow case being considered in this paper.Barnes , p. 352 Savulescu and Kahane, scholars who argue strongly in favor of technologies like PGD, write: “[w]hat determines whether there are moral reasons for or against selecting a child with a congenital condition such as deafness is factual information about the expected well-being of such a child, when compared to other possible children” , p. 290.First, a value-neutral model of deafness better aligns with many testimonies of deaf people, as I briefly demonstrate in what follows.Ouch! that:[…] any claim that “I’m not disabled, I’m Deaf” should be made with care, not least because in saying so you appear to take the stance that disability is such an abhorrent state that you wish to disassociate yourself from it. To me, though, the term “disability” should not be synonymous with low status, failure and undesirability, but instead signify pride and solidarity. Disability should be part of the spectrum of human life, upon which we all, deaf included, stand. Atkinson Because many Deaf advocates define deafness without using a deficit or dysfunction model, and because their evaluation arises from the lived experiences of Deaf people, the next step might be to deny entirely, as evident in the quotation above, the “disability” label.Atkinson’s testimony fits with Barnes’ model.on balance”  and similarly authorizes parents:In “Can It Be a Good Thing to Be Deaf?” Rachel Cooper  explainsIn this paper I applied Elizabeth Barnes’s value-neutral model of disability as a mere-difference to the case of selection for genetic deafness. Weighing advantages and disadvantages of being deaf using this model—and doing so in consultation with lines of thought from Deaf Studies and Disability Studies—helps address the epistemic injustice of doubting disability testimony. Barnes , p. 143 My aim here was to suggest that, in the case of genetic deafness and PGD, it may sometimes be morally permissible for parents to select deafness for their future child. I recognize this places significant responsibility on parents to appropriately consider the kind of life their future child will have. Deaf parents are duly capable. It is my hope that the reader now better understands how and why some parents might select for deafness. And I further advocate that this selection, contrary to widespread hearing intuition, be considered morally permissible.The author of this paper is hearing.


The established level of measurement invariance allows for comparisons of latent means across cultures. We conclude that the brief measure of moral vitalism is invariant across 28 cultures and can be used to estimate levels of moral vitalism with the same precision across very different cultural settings.Moral vitalism refers to a tendency to view good and evil as actual forces that can influence people and events. The Moral Vitalism Scale had been designed to assess moral vitalism in a brief survey form. Previous studies established the reliability and validity of the scale in US-American and Australian samples. In this study, the cross-cultural comparability of the scale was tested across 28 different cultural groups worldwide through measurement invariance tests. A series of exact invariance tests marginally supported partial  Moral vitalism is a concept that captures how lay people think about and explain morally relevant actions and events in the world –2. It isThe construct of moral vitalism provides an important avenue through which to examine the role of spiritual beliefs in moral reasoning and judgement. It goes beyond prior work focused on how a belief in God may shape thinking and behavior  or workBy focusing on how people explain their moral worlds, moral vitalism provides an account of moral cognition which diverges from the view that morality arises from the need to protect persons, groups, or norms . While In short, moral vitalism provides a filler explanation, or a placeholder concept, for why morally relevant events occur. As an approach to understanding moral cognition, it suggests that morality may be as much characterized by a set of beliefs about the nature of the world as it is by the basis on which people make moral judgements.As a formal theory, moral vitalism reflects a basic form of cognition (belief) that is likely universal and probably arose as an explanation for life threatening events, such as disease in contexts where other  explanations were not available (see ). From tThe theory and measurement of moral vitalism provides exciting and novel avenues for cross-cultural research. For example,  showed iThe current version of the MVS features five items assessing the belief in real, agentic forces of good and evil  on a 6-point Likert scale ranging from 1 = strongly disagree to 6 = strongly agree . However, applying this model in the multiple group settings was challenging, as in many groups it resulted in a non-positive definite matrix, which signaled potential problems with the model and the data . Moreover, running the two-factor model separately in each group resulted in various model problems across groups.The MVS initially included eight items: three reverse-coded items and five non-reversed items. Non-reversed items are listed in We concluded that configural invariance across groups could not be detected with the eight items. Therefore, we dropped the reverse-coded items and examined measurement invariance for a one-factor model with the five straight-coded items only.p-values of χ2 tests of model fit were above .01 in every group. Therefore, this model was further used in the invariance testing as a configural model. The alternative model with two factors, one including “existence”, “natural”, and “responsible”, and the other including “motivates” and “awareness” showed acceptable fit on the pooled sample but did not converge in multiple-group analysis .A simple multiple-group factor model with a single factor at the pooled sample showed unacceptable fit as indicated by a high value of RMSEA . Modification indices suggested adding a covariance between items “awareness” and “natural” which improved the model and yielded a marginally acceptable value of .078 for RMSEA. At the next step, the modification indices suggested to add a covariance between residuals of the items “natural” and “existence” which makes sense, because both items claim the existence of good and evil forces in a natural world. The resulting model  had apprNext, we tested partially invariant models. We examined the factor loadings in each group from the configural model in which they were estimated independently in every group. This information as well as modification indices indicated that factor loadings of items “existence” and “awareness” had the largest differences across groups, so we relaxed these equality constraints. Comparing the model fit of the partial metric invariance model (model 4) to the fit of the configural model revealed a decrease in CFI of .011 and increase in RMSEA of .013, which almost fell in the recommended range. So, we were able to conditionally accept this partial metric invariance model. However, when we tested the partial scalar invariance model, the decrease in CFI and increase in RMSEA were very large (model 5), thus, it was rejected. We also tried a minimal partial metric invariance model, releasing equality constraints on loadings of “existence”, “nature”, and “awareness” items, which expectedly showed an acceptable decrease in the model fit (model 6) compared to configural invariance model. This model was then used to test for the partial scalar invariance (model 7), a decrease in CFI was .014, close to the suggested threshold, but increase in RMSEA was .022, substantially larger than the recommended threshold of .015, and the absolute value of RMSEA of .096 pointed to the model’s overall poor fit to the data.The resulting partial metric invariance model is prone to criticism, because, first, the RMSEA statistic, although technically being under the recommended threshold of .08, had an upper confidence bound which was beyond this threshold. Moreover, these models had a very few degrees of freedom, which may imply that the models could have been overfitted, that is, be applicable only to the current sample. Given the nature of the convenience sample, the problem of overfitting is especially important issue, so the minimally fitting model might not be the population model. Due to these concerns, we switched to the approximate measurement invariance approach –22, whic2 including zero. The metric model showed a similarly good fit with PPP = .060. The scalar invariance model showed a deterioration in fit with a PPP dropping below the recommended threshold of .05 to essentially zero, and BIC increasing compared to the metric and configural models. Increasing prior variance of the intercepts of the items “natural”, “awareness”, and “responsible” resulted in a partial approximate scalar invariance model that fitted the data sufficiently well: PPP of .061 was in the recommended range, χ2 confidence interval included zero and the increase in BIC was relatively small. We consider this model final and conclude that the measurement model of moral vitalism with five items and two residual covariances  is approximately and partially invariant at the scalar level, which implies that the means and regression/correlation coefficients can be compared across all 28 cultural groups.The latent means estimated by the partial approximate scalar invariance model are listed in It is noteworthy that the latent means estimated by different models were highly similar. For example, the latent means from the exact scalar invariance model Click here for additional data file.


The framework of depicting put forward by  We communicate meaning to each other in different ways: by creating a physical analog, by relating ourselves to the physical world, or by assigning a sign to the meaning . The comTo contextualize, as well as better understand, phenomena that fall within this overlooked domain, we turned to the framework of language use proposed by Specifically, The Tonight Show Starring Jimmy Fallon, the following example illustrates what a typical depiction in real-life language use looks like. In the excerpt, Kaley Cuoco, the talk show guest, recounts her experience of doing a ‘‘canyon swing.’’1Found in an episode of (1)swings right arm back and forth, parallel to frontal plane)athen you swing, for ten minutes.’’2Kaley Cuoco explains what canyon swing is: “… and you’re supposed to just walk off, and it’s a six-second free fall, and  form-meaning relation associated with the verbal phrase. With the aid of the depiction, the audience is afforded additional semiotic resources with which to imagine and therefore understand the swinging — including the manner, direction, and speed — in a more direct, albeit highly schematic, manner, as the link between the form of the depiction and its meaning is iconically motivated.By creating and displaying the depiction, Cuoco provides her audience — in this case Fallon, the audience in the recording studio, and the audience of the show as broadcast — with rich semiotic resources with which to imagine and comprehend the canyon swing scene, in a way that is concrete and perceptually tangible: Normally, with the descriptive verbal phrase meow and whack are descriptive as well as depictive . A closer examination reveals potential insufficiencies of the typology, leading to problems in categorization. In light of this, we tap into a corpus of American TV talk shows that we constructed specifically for this purpose (section “Methods”). Through systematic data annotation and operationalization of relevant theoretical notions in Clark’s framework, we establish a methodology for researching depictions that is both empirically grounded and theoretically valid. Confronting Clark’s proposed typology with real-life usage events taken from the corpus (section “Depiction Type Attribution”), we zero in on the issues encountered in depiction type attribution, pinpointing underspecification and form-function conflation as the major underlying causes. This critical examination further leads to an alternative, gradient conceptualization of Clark’s original typology. Crucially, this alternative conceptualization brings the aforementioned overlooked domain to the fore, namely cases where meaning is communicated iconically, nonverbally, and without simultaneously co-occurring speech. A review of existing studies then reveals a curious imbalance in the literature, between gesture employed with and without co-occurring speech: Ubiquitous in language use, cases of gesture without temporally overlapping speech have been widely acknowledged by researchers, but unlike those with cotemporal speech, they have not received proportionate scholarly attention.In view of this, we zoom in on a subset of phenomena within this marginalized domain: “speech-embedded nonverbal depictions” — which we define in more technical terms as “depictions that are embedded in speech, but that are not depictions of non-depictive speech” . This definition excludes depictions of descriptive and indicative speech , but takes into account cases of depictive speech , allowing us to focus on depictions that have until recently been largely overlooked. Taking embedding in a strictly formal sense — in terms of temporal overlap — this approach also steers clears of the problems of Clark’s original typology. Following a delimitation of speech-embedded nonverbal depictions, a brief sketch is offered of how such depictions function in naturally occurring discourse. With examples from the TV talk show corpus, we demonstrate the theoretical significance of speech-embedded nonverbal depictions in relation to current topics in the literature of relevant fields of inquiry, calling for further research on this marginalized topic along various dimensions.Essentially, the aim of the present paper is first and foremost to establish a case for speech-embedded nonverbal depictions, by demonstrating their theoretical significance, but also by laying out the methodological groundwork for systematic empirical investigations. The findings — methodological, theoretical, and empirical — of the present study are therefore reported throughout sections “Methods,” “Depiction Type Attribution,” and “Speech-Embedded Nonverbal Depictions,” although the more technical discussions of empirical tokens are concentrated in section “Speech-Embedded Nonverbal Depictions.”As the term “speech-embedded nonverbal depiction” suggests, the main argument of the present study builds in part on distinctions such as “verbal vs. nonverbal.” The use of such dichotomous terms calls for clarification. On the technical level, modality and signaling method need to be teased apart. Like other modalities, speech can be depictive, indicative, descriptive, or any combination thereof. Since the focus here is on depicting, unless otherwise specified, we use “speech,” as well as related terms and modifiers such as “verbal” and “utterance,” as a shorthand term for non-depictive speech, that is descriptive or indicative speech, where speech is understood in a modality-agnostic  sense cothen you swing, thereby adding to it iconic imagistic details of the event, such as the physical configuration of the swing, and the manner and directionality of the movement, though only schematically. The depictions in (3)–(5), which are manipulated variations based on (2), illustrate the other three types of depictions in Clark’s typology.Based on the functional relations between depictions and their adjacent or accompanying (non-depictive) speech, (2)swings right arm back and forth, parallel to frontal plane) then you swing, for ten minutes.”Adjunct depiction: “… and you’re supposed to just walk off, and it’s a six-second free fall, and ((3)swings right arm back and forth, parallel to frontal plane) this, for ten minutes.”Indexed depiction: “… and you’re supposed to just walk off, and it’s a six-second free fall, and then you swing like ((4)swings right arm back and forth, parallel to frontal plane], for ten minutes.”Embedded depiction: “… and you’re supposed to just walk off, and it’s a six-second free fall, and then you ”Independent depiction: Jimmy Fallon: “How does a canyon swing work?” Kaley Cuoco: “a — b.”Lauren Ambrose on backstage costume change on Broadway: “I mean sometimes it’s like twenty seconds, for like, full-on, a, and then b, bingo wings, c (.) d.’’7Tracy Morgan explains what bingo wing is: ‘‘When an old woman hits bingo, she goes, a.’’10Tracy Morgan on the quality of his facial muscles: ‘‘Yeah, I’m your rubber-band man, . In theFor cases closer to the right half of the continuum, relatively more information is communicated through depictive signals, and relatively less information comes from non-depictive speech. Embedded and independent depictions are located on this side of the continuum: Embedded depictions  convey meaning without the accompaniment of temporally co-occurring non-depictive speech, but are formally and functionally framed by the non-depictive speech surrounding the syntactic slot that they fill. Independent depictions also convey meaning without simultaneous non-depictive speech, and do so, according to Thus conceptualized, the four depiction types as defined by Clark, of which the prototypical cases can be located as four points along the continuum, really capture the different levels of “division of labor” between non-depictive speech and depictive signals — or more generally speaking, between speech and depictions. In some cases, speech takes up more of the “load” of meaning communication; in others, the depiction “takes over,” showing meaning in iconically motivated ways. Importantly, the reconceptualization is not meant as a solution to the issues of the original typology. As the choice of term “continuum” suggests, it presupposes gradience rather than categoriality. Given the challenge of quantifying the amount of information communicated through speech as compared to depictions, the continuum is not one with strictly defined criteria either. Rather, it serves as a heuristic for identifying the varying levels of “trade-off” in terms of meaning contribution between non-depictive speech and depictive signals — not as dichotomous oppositions, but as two of the many sets of communicative resources available to the speaker in language use. It shows how, in staging different types of depictions, the speaker “packages” information in different ways, “distributing” it over speech and depictions, be they co-expressive, with or without “redundancy.”In addition to providing an alternative vantage point from which to consider the speech-depiction relations in the four depiction types identified by Largely coinciding with the left half of the continuum, where speech plays a relatively dominant role, and where adjunct and indexed depictions are located, the topic of iconic co-speech gesture has been core to modern gesture studies, with an extended body of dedicated research. Some scholars, for instance, have explored how gestures complement or supplement the semantics of their co-occurring speech , where the enactments can function in place of fully lexicalized manual signs, filling syntactic gaps as well as inferring or expressing semantic relations . Based oFinally, recent years have seen attempts at incorporating gesture into the theoretical framework of linguistic analysis, coming from various theoretical orientations and with different approaches a, starts to b, starts to jiggle.”Bob Newhart on getting feedback from the audience when performing in the rain: “This one umbrella starts to a. She needs to go on the plane.”Zooey Deschanel on being refused priority boarding when traveling with her baby daughter: “and I was like, but a, like big boats.”Jennifer Garner on accidentally kayaking into a busy harbor: “There were like a, and I hit him.”Chris Evans on bullying his brother , in childhood: “And I just had the book, and just, ”Conan O’Brien: “How do you do that, do that again?” Kristin Chenoweth: “, and I hit him.”Chris Evans on bullying his brother , in childhood: “And I just had the book, and just, , starts to , starts to jiggle.”Bob Newhart on getting feedback from the audience when performing in the rain: “This one umbrella starts to .”Tracy Morgan on the quality of his facial muscles: “Yeah, I’m your rubber-band man, a.”Conan O’Brien on his lack of quick wit: “I don’t have that, quick, a.”Emily Blunt on her impassive backchannels: “I just go like this , but that that, quick, [snaps fingers], as a whole, indexes the kind of quick wit the host is referring to. In contrast, the indexical this in (19) — where Blunt simply depicts her own nodding — is a pronoun with indexical functions. As such, this by itself directly indexes the speaker’s peculiar head nod. In other words, where a depiction is employed in connection to an indexical modifier, what is indexed is not the depiction; where an indexical pronoun is used in conjunction with a depiction, it is the depiction that is indexed. Consequently, if indexed depictions are those that are indexed by indexical speech, they should only include cases of indexical pronouns, as in (19), and not cases of indexical modifiers, such as (18) — since in the latter, it is not the depiction, but the combination of the verbal index and the depiction, that is indexical.Although verbal indices are present in both excerpts, they function in distinct manners. In (18) — where Conan depicts quick wit with finger snapping, which is associated with moments of epiphany, and therefore, metonymically, with people adept at witty comebacks — the verbal demonstrative Importantly, while the distinction between indexical modifiers and pronouns is crucial, the relations between verbal indices and depictions — specifically whether a depiction is indexed by a verbal index — are a functional concern. In other words, indexation is an issue on a dimension independent of our form-based definition of temporal embedding: A depiction can be indexed, embedded, neither, or both. Since both of the depictions in (18) and (19) occupy a temporal gap in speech without their stroke overlapping with speech, they are categorized as embedded depictions in our corpus, regardless of the fact that one of them is also verbally indexed and the other not. Though crucial to the understanding of depictions in general, a full-fledged exploration of the relations between depictions and verbal indices is left for further research.In addition to the issues concerning embedding alone, the tokens of speech-embedded nonverbal depictions in our corpus also exhibit complexities in other regards, of which we now offer a brief sketch. While the primary aim of the present paper is to draw attention to the phenomenon of speech-embedded nonverbal depictions, rather than to present an in-depth analysis, the following offers a glimpse into their theoretical and empirical potential, which further underscores the need for research on this overlooked topic.like.Consider the depictions in (20), where Mulaney recalls stumbling wearing high heels, and where the depiction is preceded by (20)stretches out both arms sideways, tilts torso in different directions, as if trying to find balance]a, when a, when a cow’s born.”John Mulaney on walking on heels: “It’s like, it was like, a, I would so panic all the time.”Tina Fey on doing serious choreography: ‘‘If I had to be on Dancing with the Stars, I would be so shark-eyes,The Ellen DeGeneres Show— like. Unlike (20), the depiction in (21) does not precede, but rather follows, shark-eyes, the verbal element whose meaning is similar to that of the depiction. In other words, the speaker first communicates the meaning verbally, saying shark-eyes, before staging a nonverbal depiction with highly similar semantics. The fact that the speaker first communicates her idea verbally, and then still proceeds to stage a depiction that is semantically “repetitive” — with the identical speech frame of I would be no less — shows that such nonverbal depictions cannot be conveniently dismissed as word-search fillers.Here Fey depicts how awkward and uncoordinated her movements would be if she were ever to go on a dance show. Like (20), the depiction in (21) is also preceded by Indeed, cases of “multimodal iteration” , such toAlso strengthening the case for speech-embedded nonverbal depictions is the fact that they are observed embedded across different syntactic levels, from the level of the word , phrase , clause , all the way to the level of the discourse . The following depictions further exemplify this versatility.(22)gazes at the front, into the distance; places both fists above lap, at lower center, flipping both thumbs up and down quickly, as if typing on a phone]a.”Lil Rel Howery on texting without looking at the screen: “People are just that good where they can just a, go to the principal’s office.”Cardi B on being mischievous as a kid: “I was like, ok I know, , starts to , starts to jiggle.”Bob Newhart on getting feedback from the audience when performing in the rain: “This one umbrella starts to a — b — c.”Conan O’Brien on being unable to refrain from savoring the food if required to eat on scene: “I’d be, even in a drama, they’d be like, Conan’s the murderer,  — .”Lauren Ambrose on backstage costume change on Broadway: “I mean sometimes it’s like twenty seconds, for like, full-on, ∗, and I hit him,and just, B:∗[D2]∗A:∗soon as I∗ hit him, [D4]and as B:∗[D3] ∗D1:whack; moves left hand in a curve, from lower right periphery to upper left extreme periphery, close to where B’s head is.Vocalizes D2:Tilts head away from A.D3:Traces scar on left forehead with left index.D4:brrr; touches forehead with fingertips of left hand, fingers touching, moves left hand toward upper left extreme periphery, spreading fingers along movement.Vocalizes Late Night with Seth Meyers— whack depiction, Scott is seen staging the second depiction, which is an enactment of his response to being hit by Chris. When Chris is at soon as I, Scott again contributes to the story, depicting the scar by locating and finger-tracing its shape on his forehead, before Chris stages the fourth depiction, which demonstrates, in an exaggerated manner, the spurting of the blood that came out of Scott’s forehead. The series of depictions, from both parties, goes on beyond the excerpt. As in (25) and (26), the depictions are co-dependent in meaning. Unlike in (25) and (26), the depictions in (27) are not all staged by one single speaker, but are staged jointly by two speakers, with causation between the depictions, bringing in the complexities of an additional, interactional dimension to the analysis.Almost as soon as Chris stages the The above is a very brief sketch of some of the complexities of speech-embedded nonverbal depictions, based only on tokens taken from our American TV talk show corpus, where the annotated data still await in-depth analysis. The rich and challenging cases this alone has already provided us with, nonetheless hint at the fact that speech-embedded depictions are not merely theoretically significant, but abundant in curiosities of language use and interaction as well.Drawn to nonverbal iconic language use, and informed by Clark’s recent account of depicting in everyday interaction, we turned to video recordings of American TV talk shows, a context rich in depictions. The annotation of the data proved less straightforward than expected, an issue that underlines our limited understanding of this domain of research. In addition to operationalizing relevant theoretical notions, a critical reconsideration of depiction-speech relations, on the basis of Clark’s typology of depictions, was carried out, resulting in a gradient reconceptualization of depictions in terms of meaning contribution from non-depictive speech and depictive signals. This led to the identification of a largely overlooked domain — cases where meaning is communicated through iconic nonverbal signals, without temporally co-occurring speech — which we zoomed in on as “speech-embedded nonverbal depictions.” Taking into consideration existing literature as well as the variety of tokens in our corpus, we arrived at a carefully delimited definition of such depictions, in turn bringing to the fore numerous observations, many of which pertain to current discussions in cognitive linguistics, gesture studies, and multimodal communication.As an initial step into the largely uncharted territory, it goes without saying the present study is limited in several ways. Among them is the type of data examined. The majority of the speakers in our corpus are professional actors or comedians, a fact that likely has an effect on the frequency, elaborateness, and spontaneity of the depictions they stage. Nonetheless, while true spontaneity is hardly attainable, it is undeniable that American TV talk shows, which are themselves a specialized context, contain unscripted elements. In addition, as the staging theory  suggestsThe scope of the data notwithstanding, the tokens in our corpus already shed light on some of the natural next steps for depiction researchers to embark on. Among them are depiction-speech relations, multimodal iteration, complex composite depictions, viewpoint in multimodal interaction, as well as jointly staged depictions and the causation therein. These are the tracks along which we are currently carrying out analysis of the tokens. Though not explicitly touched upon in the present paper, the tokens also point to a number of other directions in which future studies can proceed, such as issues pertaining to intersubjectivity, the performative aspect of depictions, depicting and language acquisition, and motivations for employing speech-embedded nonverbal depictions.Speech-embedded nonverbal depictions are situated, not only at the crossroads of numerous research traditions, but also among intertwined modalities and signaling methods, which prove tricky to untangle. Nonetheless, as showcased by the versatile and complex ways in which speech-embedded nonverbal depictions are employed in real-life interaction, a full picture of language use will not be complete without a systematic account of such depictions.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.H-CH conceptualized the study, compiled and annotated the data, carried out the analysis, and wrote the manuscript. GB and KF provided guidance in the conceptualization of the study, assisted in the analysis, and contributed to the revision of earlier versions of the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.