A new agenda for personality psychology in the digital age?

Personality and Individual Differences 147 (2019) 128–134

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A new agenda for personality psychology in the digital age? a,⁎

Christian Montag , Jon D. Elhai a b

T

b

Institute of Psychology and Education, Ulm University, Ulm, Germany Department of Psychology, and Department of Psychiatry, University of Toledo, Toledo, OH, USA

A R T I C LE I N FO Keywords: Personality psychology Psychoinformatics Digital phenotyping Internet of things Neuroscience Big data Personality neuroscience Psychology

A B S T R A C T

present paper hypothesizes how research in personality psychology will change with the recent emer• The gence of the Internet of Things (IoT). In a society, where humankind is digitally interconnected and all

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machines in one's own household or at work are linked to the Internet, manifold data from human-machineinteraction can be used to predict psychological traits and states. In the present theoretical paper, we first explain why it is (still) important to study personality and we also give a short (historical) overview on different theoretical and methodological approaches to study personality. Second, this work will focus on a recent addition to the toolbox of a personality psychologist, namely digital phenotyping via methods from Psychoinformatics, which falls into the realm of the aforementioned IoT. Aside from an introduction into these new concepts, we discuss strengths and limitations of this new data layer of interest for personality psychologists. Finally, it is argued that in these modern times personality psychology research is conducted in many scattered scientific areas with researchers sometimes unaware of each other. Therefore, tremendous collaborative efforts need to be invested in order to reunify the psychological/neuroscientific oriented personality science to ultimately understand why we are the creatures we are.

1. What is personality and why is it important to study? The quest to understand individual differences in personality is as old as mankind. Galen already proposed based on work by ancient Greek philosopher Hippocrates four temperaments each linked to a bodily fluid (Stelmack & Stalikas, 1991). Modern personality psychology started much later perhaps with the important early influencer Sir Francis Galton, who also proposed to use the twin design as a means to disentangle the influence of nature versus nurture in individual differences within human behavior (Montag & Hahn, 2018). For a recent overview on this topic see also the meta-analysis by Polderman et al. (2015) demonstrating per rule of thumb that about 50% of individual differences in personality can be accounted for by genetic factors. Many definitions exist to define the term personality. Probably a common denominator of most theories is that personality consists of stable (behavioral-)tendencies of a person linked to motivational, emotional and cognitive domains over time and across situations (for more on a recent definition-discussion, please see Baumert et al., 2017). The idea of stability of a person's personality across a wide range of situations has been challenged to a large extent, because persons do not

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necessarily behave consistently in response to their inner urges across many situations. Therefore, Mischel and Shoda (1995) proposed the so called if-then functions to understand how the idea of a stable personality set - despite such behavioral inconsistencies - can manifest in everyday life: They observed that a person's behavior profile becomes more consistent, when situational behavior profiles are established (such as if I am at work then I am diligent; if I am home and have to do homework then I am less diligent; see also Mischel, Shoda, & MendozaDenton, 2002). In short context matters, and this debate led to the latent trait-state theory (Steyer, Schmitt, & Eid, 1999; for very recent developments see Ziegler, Horstmann, & Ziegler, 2019). For an overlap between the concept of trait and state see also the work by Augustine and Larsen (2012). Beyond the stability of personality across different situations many longitudinal studies have been carried out demonstrating indeed that personality is rather stable over time, such that a person being extraverted today will also likely be extraverted in ten years (Edmonds, Jackson, Fayard, & Roberts, 2008; McCrae & Costa Jr, 1994). Nevertheless, subtle changes in personality over the lifetime can be observed with persons becoming more conscientious and more agreeable when

Corresponding author at: Department of Molecular Psychology, Institute of Psychology and Education, Ulm University, Helmholtzstr. 8/1, 89081 Ulm, Germany. E-mail address: [email protected] (C. Montag).

https://doi.org/10.1016/j.paid.2019.03.045 Received 9 November 2018; Received in revised form 21 March 2019; Accepted 30 March 2019 Available online 03 May 2019 0191-8869/ © 2019 Elsevier Ltd. All rights reserved.

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found in the works by Davis and Panksepp (2018), Gibby and Zickar (2008), Gregory (2004) and Weiner and Greene (2017). Beyond descriptive taxonomies, other personality theorists with a stronger biological background aiming at a mechanistic, biological understanding of personality proposed different but related personality dimensions,1 which all can be assessed by self-report inventories, too. Among these concepts are the assessment of individual differences in primary emotional systems as defined by Affective Neuroscience Theory by Panksepp (2011) driving the Big Five of Personality in a bottom up-fashion (Montag & Panksepp, 2017a, 2017b; see also for an update Montag & Davis, 2018). Panksepp discovered by means of electrical brain stimulation and pharmacological challenge seven primary emotional systems deeply anchored in the mammalian brain strongly impacting on our human behavior (Panksepp, 2011, look for an overlap with Ekman's emotion theory in Montag & Panksepp, 2016; Davis & Montag, 2018a, 2018b). These systems could represent the phylogenetically oldest parts of human personality as also stressed in Montag and Panksepp (2017a, 2017b). Adding to these concepts of Panksepp (2011), more classic biological personality theories comprise not only the (revised) reinforcement sensitivity theory including the highly investigated behavioral activation and inhibition systems as basic personality dimensions (BIS/BAS; Corr, 2004; Gray & McNaughton, 2000; Reuter, Cooper, Smillie, Markett, & Montag, 2015), but also the biosocial theory of Cloninger, Svrakic, and Przybeck (1993, 1998) resulting in four temperament traits (in a degree comparable to the primary emotional systems according to Panksepp, 2011) and character traits being more shaped by the environment and to a much lesser degree by genetics (the latter play a strong role as a fundament of temperament). German psychologist Hans-Jürgen Eysenck grounded his famous PEN (Psychoticism, Extraversion, Neuroticism) model on the background of biology and a mix of results from psychoanalytic work by Jung and - similar to the Big Five – on factor analysis (Eysenck, 2013; Eysenck & Eysenck, 1994). Please note, that Jeffrey Gray was a student of Eysenck and also related the BIS/BAS dimension to Eysenck's Extraversion/Neuroticism dimensions (e.g. Gray, 1987a, 1987b). For intercorrelations between the constructs see also Montag, Jurkiewicz, and Reuter (2012). Aside from these different theoretical avenues to understand human personality, there also exist different biological methodological approaches to understand personality. These different levels and approaches to be studied in personality psychology are depicted in Fig. 1. Based on insights from the aforementioned twin studies, molecular genetic approaches have been applied to detect regions on the human genome linked to individual differences in personality (e.g. Montag & Reuter, 2014; Sindermann et al., 2018). According to our modern understanding of human personality, nature and nurture cannot be seen as distinct entities, but rather interacting parties (Montag & Hahn, 2018; Reiss, Leve, & Neiderhiser, 2013). This has given way to studies ultimately testing molecular genetic by environment interaction effects on personality (e.g. Montag et al., 2013a; Pluess, Belsky, Way, & Taylor, 2010; Culverhouse et al., 2018) and in recent years also associations between the epigenome and personality (e.g. Haas et al., 2016; Haas, Smith, & Nishitani, 2018). Investigations of the epigenome among others focus on the study of methylation patterns at the promoter region of a gene, either opening or closing a gene via hypo- or hypermethylation (Zhang & Meaney, 2010). By this, researchers can also get insights into the impact of environmental factors such as stress on epigenetic mechanisms (Provençal & Binder, 2015) regulating the availability of blueprints for bodily products such as enzymes to regulate neurotransmitters. Logically, biologically oriented working personality psychologists also have to take into account the further

they enter older adulthood (see also a new work investigating changes of the so called Big Five of Personality across the life span, Damian, Spengler, Sutu, & Roberts, in press). We also should mention a recent review by Bleidorn, Hopwood, and Lucas (2018) providing the readers with the latest updates on how certain life events impact upon personality development. The investigation of personality is of relevance beyond human curiosity trying to understand why we are the kinds of creatures we are: Personality predicts a range of important life variables including vulnerability/resilience for psychiatric disorders (Lahey, 2009), health behavior (Roberts, Walton, & Bogg, 2005), mortality (Bogg & Roberts, 2004) and job performance (Hurtz & Donovan, 2000), to name but a few (see also for an overview the work by Montag, 2016). 2. Different approaches to study personality starting 100 years ago with the first personality self-report inventory Many approaches are used to study personality and individual differences in human behavior. These approaches are highlighted both in the area of manifold methodologies and also in the area of applying different theoretical concepts. In the realm of methods, clearly at the heart of most personality studies is the self-report inventory assessing individual differences in personality. This assessment of a person's personality is in several works also complemented with reports by peers in several studies (e.g. Youyou, Kosinski, & Stillwell, 2015). One of the most relevant taxonomies to grasp personality is the so called Big Five of Personality, carved out by a lexical approach (McCrae & John, 1992). This approach also became widely known as the Five Factor Model of Personality (FFM; McCrae & Costa, 2004), which built upon background of the Big Five of Personality, so that the terms of FFM and the Big Five of Personality are sometimes used interchangeably. Beyond the Big Five of Personality, also shortly abbreviated with the acronym OCEAN (Openness to Experience, Conscientiousness, Extraversion, Agreeableness and Neuroticism), additional personality taxonomies/theories are en vogue, such as the HEXACO model adding the dimension called Honesty/Humility to the Big Five of Personality (Lee & Ashton, 2018). For further illustrations of the still dominating self-report approach in personality psychology, we shortly depict the development from the first self-report approaches to grasp personality to the detection of the now well-known Five Factor model in the early 1960s. Arguably, the first self-report inventory used to assess personality was published in 1919, exactly 100 years ago. Woodworth (1919) aimed with his scale to predict which soldiers would show resilience against “shell shock”, hence against developing posttraumatic stress symptoms due to their experiences in the war zone. The development of this questionnaire was based on clinical observations of patient groups, a prevailing approach in scale-construction until Allport and Odbert (1936) provided the scientific community with their prominent list of 17,593 words as a basis to conduct factorial analysis to carve out personality dimensions. Aside from directly asking humans to reflect on their personality, other scientists such as Rorschach (1921) or Morgan and Murray (1935) developed projective test procedures, where traits of a person are derived from the manner in which a person describes inkblots in Rorschach's case or from the stories provided when a person is confronted with a certain picture as in the Thematic Apperception Test (TAT). These different methods to assess individual differences in personality come with benefits and costs. The self-report procedures are of importance to understand how a person sees him- or herself, although these data can be biased by lacking interoceptive abilities or tendencies to answer in a socially desirable manner. Projective test procedures have the benefit of reducing these problems but come with costs in terms of important psychometric criteria and their scientific status has been questioned (Lilienfeld, Wood, & Garb, 2000). For a more detailed view on landmark publications/achievements in early personality assessment, please see Table 1. Note that the information provided in the Table 1 has been

1 Fittingly, Costa and McCrae (1993) mentioned that the FFM “somehow resembles the Christmas tree on which each of the other personality models can serve as “decorations.” “(Montag & Panksepp, 2017b, p. 5).

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Table 1 Important historical events towards a modern understanding of personality assessment from the first self-report measures in 1874/1919 to the detection of the “Big Five” personality factors in 1961 (please note that landmark findings from ability/intelligence research are not presented; moreover the present overview clearly is not complete and other researchers might name different works). Year

Achievement

1874 1919

Sir Francis Galton asked other scholars to self-report on their temperament according to Galen's temperament dimensions (Galton, 1874, p. 199–200) Robert S. Woodworth developed the personality inventory called Woodworth Personality Data Sheet (WPDS) to identify emotionally unstable soldiers during World War I (earlier known as a scale to test Psychoneurotic Tendencies). The questionnaire was constructed on the background of observations made from case studies with patients showing neurotic symptoms. Rorschach published his work called Psychodiagnostics, where the inkblot test was first presented. In contrast to the explicit nature of Woodworth's self-report approach, participants here describe what they see in the inkblots and the manner how they do this should provide insights into psychological variables such as personality. Robert Bernreuter arguably developed the first multidimensional self-report measure in personality psychology called the Bernreuter Personality Inventory. It assesses individual differences in neurotic tendencies, self-sufficiency, introversion-extraversion and dominance submission. Raymond Cattell identified four personality factors based on a correlational study with forty-six bipolar items assessing personality. Christiana D. Morgan and Henry A. Murray publish the Thematic Apperception Test (TAT). As the Rorschach Test mentioned above, the TAT is a projective test procedure to get insights into the personality of a person. Morgan & Murray focused in their test on the detection of needs such as achievement or power. Allport and Odbert identified 17,953 terms in Webster's New International Dictionary to describe personality/behavior. This is the modern start of the lexical approach to understand personality. The Minnesota Multiphasic Personality Inventory (MMPI) was published by Hathaway & McKinley to identify psychopathology. Cattell published his Sixteen Personality Factor Questionnaire based among others on the lexical approach to understand personality. Donald Fiske worked with some of Cattell's rating scales and derived five factors (but the now common conscientiousness factor was missing; instead he observed selfexpression). Of note, Fiske also relied on a factor analytical approach to describe personality. Tupes and Christal used thirty of thirty-five Cattell scales and observed for the first time the now recognized five-factorial structure of the Big Five by means of factorial analysis.

1921 1931 1933 1935 1936 1943 1949 1949 1961

The following literature has not been cited in the main body of the paper, but is mentioned in the Table 1: Bernreuter, 1931, Cattell (1933; 1949), Fiske, 1949, Hathaway and McKinley (1943) and Tupes and Christal (1961).

Fig. 1. A holistic view of personality will require integration of manifold psychobiological-technological data levels (the head and computer display have been taken from the free resource platform pixabay.com).

Eisenberger, Lieberman, & Satpute, 2005; Hornboll et al., 2018; Montag, Reuter, & Axmacher, 2011; Montag, Reuter, Jurkiewicz, Markett, & Panksepp, 2013; Montag et al., 2013b; Passamonti, Riccelli, Lacquaniti, Staab, & Indovina, in press). MRI technologies include structural brain imaging (such as analyzing T1 data sets with voxelbased morphometry approaches) and task based functional MRI, where the BOLD response of a person is recorded while he/she is following a task in the scanner, and recent advances to understand the functional connectome in the context of Personality Neuroscience by applying resting state fMRI (Markett, Montag, & Reuter, 2018; Mulders, Llera, Tendolkar, van Eijndhoven, & Beckmann, 2018). Beyond these neuroscientific techniques classic experimental approaches with reaction time measurement, etc. (e.g. Gupta & Nicholson, 1985; Stelmack, Houlihan, & McGarry-Roberts, 1993) are used to obtain a deeper

molecular layers of mRNA expression (e.g. resulting from the degree of the methylation), and neurotransmitter/-peptide/hormone levels (Adler, Wedekind, Pilz, Weniger, & Huether, 1997; Chang et al., 2018; Czermak et al., 2004; Kalbitzer et al., 2009; Shoal, Giancola, & Kirillova, 2003). Signaling between neurons relies on information exchange of these brain-transmitters, in turn shaping also brain structure and functions. On this next higher investigation level, brain imaging techniques are often used to map personality on human brain architecture and function including studies using electroencephalography (e.g. Lenci et al., n.d.; Hagemann et al., 1999; Knyazev, Merkulova, Savostyanov, Bocharov, & Saprigyn, 2019; Wacker, Chavanon, & Stemmler, 2010) and positron emission tomography (Buckholtz et al., 2010), but in recent years especially magnetic resonance imaging (MRI; e.g.

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Stachl et al., 2017; see also a new work on sensation seeking and smartphone variables by Schoedel et al., 2018). Other platforms can also be used to explore personality: among these are Twitter (Quercia, Kosinski, Stillwell, & Crowcroft, 2011) and Instagram (Ferwerda, Schedl, & Tkalcic, 2015). In the near future all traces from the Internet of Things (IoT), hence a fully connected environment, where everything from household machines to cars are connected with the Internet, can be used to predict person variables including personality traits and gender (e.g. Stachl & Bühner, 2015). Already now the lights or the thermostat are linked to the Internet in some households. A noteworthy recent study of more than 18,000 students in China could predict how orderly and as a consequence how good their academic performance was from their regularity in taking a shower and eating meals (tracked via a smart card) (Cao et al., 2018). This kind of approach to conducting psychodiagnostics has been coined digital phenotyping (Onnela & Rauch, 2016; Insel, 2017), perhaps falling in the realm of a new research discipline called Psychoinformatics (Markowetz et al., 2014; Montag, Duke, & Markowetz, 2016; Yarkoni, 2012), where methods from computer science such as machine learning are used to predict, and ultimately also to understand human personality. Linking digital traces derived from human-machine interaction to personality represents only one facet of a greater research movement currently developing in the health sciences (Insel, 2017) and recent work by Montag et al. (2017) already demonstrated the feasibility of linking real-world variables from smartphones with brain imaging data (smaller gray matter volume of the nucleus accumbens were associated with longer and more frequent use of the Facebook application on the smartphones) and with a molecular genetic variable (Sariyska, Rathner, Baumeister, & Montag, 2018). Additional work groups already provided evidence that these digital data layers can also be used to predict cognitive (Dagum, 2018) and depressed states (Elhai et al., 2017; Rozgonjuk, Levine, Hall, & Elhai, 2018). A study from Stange et al. (2018) even linked bipolar disorder to keyboard typing styles.

understanding of human personality. Studying morphological markers (Sindermann et al., 2016) or eye-tracking (Rauthmann, Seubert, Sachse, & Furtner, 2012) in personality psychology should also be mentioned. Perhaps the most recent addition to the personality psychologist's toolbox are methods from Psychoinformatics (Markowetz, Błaszkiewicz, Montag, Switala, & Schlaepfer, 2014), which will be introduced in the following section. 3. Digital phenotyping by applying methods from psychoinformatics in personality psychology Recent years have seen a rise in personality studies introducing digital data from Facebook and similar platforms to predict human traits. Prominent examples are works by Kosinski, Stillwell, and Graepel (2013) predicting human personality traits from the ‘Likes’ a person gave to different topics on the Facebook platform. A further study by Youyou et al. (2015) demonstrated that the algorithm predicting personality from Facebook-Likes ultimately might be more accurate than evaluations by one's friends or partner in predicting what kind of personality one has (when based on how the participant answered the inventory). A recent meta-analysis by Azucar, Marengo, and Settanni (2018) demonstrated that personality predictions from Facebook-Likes and related digital sources indeed are practical, but only at a group level rather than the individual level. Correlations between the humandigital-machine-interaction data such as ‘Likes’ and the Big Five ranged between 0.29 (Agreeableness) and 0.40 (Extraversion). Here it becomes apparent that some personality traits can be better predicted from a certain digital trace than others: This means, that it is logical that increased communication platform use should be positively linked to extraverted behavior, even to such extraverted behavior of a person in the offline world. Aside from this, also the opposite, hence introversion, could be linked to other kinds of online usage; perhaps introverts are characterized with less socializing, but more non-social browsing. The review work by Lambiotte and Kosinski (2014) point to a bit different direction: They observed that “Introverts tend to belong to fewer but larger and denser communities, while extroverts tend to act as bridges between more frequent, smaller, and overlapping communities.” (p. 1936). This all shows that it is of importance to take a detailed look into what people are exactly doing online. From the data it also becomes visible, that insights into other personality traits might be better derived from other digital traces, not necessarily stemming from social media platforms. It would be imaginable that a digital trace left via opening a door via a smartcard at the office could give insights into punctuality and as a consequence into conscientiousness. The future will show which data are particularly useful in predicting different kinds of personality traits. Not only the aforementioned ‘Like-structure’ on Facebook, but also textmining from posts on Facebook or online diaries reveal interesting insights into personality (Kern et al., 2016; Park et al., 2015; Schwartz et al., 2013) and even into “ovulatory changes in sexual desire and behavior” (Arslan, Schilling, Gerlach, & Penke, in press). Textmining includes the analysis of language giving insights into the trait or state of person. As a neurotic person likely more often experiences negative emotions than an emotional healthy person, the neurotic person should also use more negative words in online-posts, etc. This phenomenon has been indeed shown in the cited works above (please see also literature distinguishing between the use of the closed vs. open vocabulary approach in the context of textmining; for examples see Pennebaker and King (1999) or the aforementioned literature). Adding to data from Facebook, directly tracked smartphone variables (Andone et al., 2016) have been used to predict personality, such that increased time spent on the WhatsApp smartphone application is associated with higher extraversion and lower conscientiousness (Montag et al., 2015b). Moreover, recent studies linked call behavior on smartphones to extraversion (Chittaranjan, Blom, & Gatica-Perez, 2013; Montag et al., 2014, 2019; Mønsted, Mollgaard, & Mathiesen, 2018;

4. Strengths and limitations of digital phenotyping via methods from psychoinformatics Applying methods from Psychoinformatics to personality psychology bears great potential in bringing this research field to a more advanced level. In the past it was very difficult to examine real-world variables and therefore many researchers had no other chance but to ‘only’ ask people about their lives, about their personalities and test them in researchers' (artificial) laboratories (see Montag et al., 2016). Due to ubiquitously available smartphone technology it has now became relatively easy to track human behavior on a longitudinal level to obtain insights into a wide range of personality related variables (see also the smartphone manifesto by Miller, 2012). In the future and in the realm of personality psychology, Mischel & Shoda's if-then functions can be well investigated by using GPS coordinates or other variables such as time/hour of the day to see in what context persons behave in a consistent or inconsistent manner. To illustrate this: One smartphone derived GPS coordinate could represent the participant's work place, and another GPS coordinate could represent a person's home. Now, the researcher could search for typical behavior happening only at one of these or both of these places on the smartphone (see early work using GPS in personality psychology by Müller et al., 2017). In the future all variables will be of interest, when they are linked to the Internet of Things. The aforementioned time/hour variable could be used to investigate the concepts of morningness and eveningness types of personality, to give an example (Horne & Östberg, 1976) - when is a person most active, according to his/her digital traced data, in the morning or in the evening? Although current developments are fascinating they come with problems and definitely will not be the solution to all questions posed in personality psychology. First of all, although current studies demonstrate the feasibility to predict personality from Facebook and related 131

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develop towards a Homo Digitalis (see also Montag, 2018). Please note also the discussion on unintended side effects of digitization (Scholz et al., 2018; Twenge & Campbell, 2018) and how technology itself can be used to reduce such effects (Montag, Reuter, & Markowetz, 2015, 2017).

data, they mainly do so on the group level, and hence it is still not possible to predict a person's personality with high certainty on an individual level from the available digital data. Second, and of importance, traces from a person's interaction with the IoT will ‘only’ be an additional layer to the already applied methods. Clearly, it is an important layer, but it will not lead to the exclusion of self-report inventories or other data layers in personality psychology. We assume the same to be true for other fields of psychology. We arrive at this conclusion because a) self-report or peer-rated inventories are necessary to link psychoinformatic data sets to well-validated personality constructs and b) often the discrepancy between how a person sees him- or herself and how he/she really behaves gives genuine insights, e.g. in the context of more clinical oriented personality psychologists (see as an example, the discrepancies between self-assessed and directly tracked smartphone consumption in Montag et al., 2015a). Third (or c)) data from the IoT come in great volume, high velocity and great variety (the three V's). Therefore, many issues arise regarding how to correctly handle these data and correct observed findings from a study for multiple testing, accordingly. E.g. without straight forward hypothesis, a very large number of variables derived from a person's interaction with the smartphone can be correlated with self-report data or other sources probably resulting in false positive findings. It also needs to be mentioned that tracking smartphone variables such as call behavior will be valuable in terms of comparability across studies because the same dependent variables are produced (e.g., the variable of minutes of smartphone phone use is a standardized variable – minutes by one investigator can be translated to minutes by another investigator). But before these variables really become comparable, researchers have to find a standard concerning how long and how often a certain variable is recorded/tracked on a person's smartphone or other technological device in the future (in older work we could demonstrate that phone usage behavior might only need to be observed for a few days/weeks to obtain robust insights into this behavior, whereas seldom used features such as SMS sending need longer observation periods; Montag et al., 2014). A major issue in applying methods from Psychoinformatics to conduct digital phenotyping lies on ethical grounds (Martinez-Martin, Insel, Dagum, Greely, & Cho, 2018). As it is possible to obtain insights into clinically relevant states of a person from recorded smartphones variables (e.g. Elhai et al., 2017; Rozgonjuk et al., 2018), strong ethical guidelines need to be established, so that this kind of data is not misused in settings such as by the insurance industry or illustrated by the recent Facebook scandal (Cadwalladr & Graham-Harrison, 2018; Kosinski, Matz, Gosling, Popov, & Stillwell, 2015; Markowetz et al., 2014). Finally, we will see how these new Big Data analysis approach in personality psychology, being mainly atheoretical, will reunify with a long history of largely theoretical driven science. Theory must be also the guiding light in a digital age (although Big Data will clearly also provide surprising new findings, which then will be in need to be independently replicated and explained by theory). But as mentioned above in the context of the multiple testing issue: By applying Big Data millions of data points may be merged together and researchers might falsely conclude that two personality variables are related to each other. And so this is where theory comes in – to focus the search on a smaller set of variables so that we don't artificially inflate error rates by trying to correlate thousands of variables with each other. Another potential problem in personality prediction from the sources of the IoT might arise, when studies reveal that digital usage itself impacts on personality development. For instance, work by Kushlev, Proulx, and Dunn (2016) provided evidence that high frequent use of the smartphone might result into an increase of inattention. Hadar et al. (2017) provided evidence for rising social concerns due to smartphone usage. Also on neuroscientific levels it has been demonstrated that playing video games (Zhou et al., 2019) or using the smartphone (Gindrat, Chytiris, Balerna, Rouiller, & Ghosh, 2015) influence brain structure and function. In this context Montag and Diefenbach (2018) ask the question if Homo Sapiens

5. Reunifying approaches in personality psychology to derive a holistic understanding of individual differences in human nature The study of human (and mammalian) personality is conducted in many areas these days, and needless to say that not only psychologists, but also other disciplines such as economics or medicine are working in the field of personality science. The many different disciplines interested in personality led to the publication of personality papers in journals probably only read by a few personality psychologists. To give an example: Personality researchers from computer science often publish in IEEE journals, traditionally not highly frequented by personality psychologists. Given also the many different theoretical and methodological approaches to understand individual differences in human nature, it is a great challenge to follow up with all the interesting work published in the many areas of personality science and also to find an overarching theoretical framework helping to bring findings together. No matter what favorite questionnaires related to a certain theoretical framework a researcher is using, the future needs to find a standard personality questionnaire and standard psychological constructs, which will be applied in every personality related research piece (even if results with this questionnaire might only be presented in the supplement of a research paper). This approach would help to better link studies together. Such a standard will probably be the Big Five of Personality, although also numerous inventories exist in this area. Finding such an appropriate (Big Five) questionnaire will both warrant compromises with respect to length of the inventory, psychometric criteria and the ability to also measure the Big Five dimensions on a facet level. Aside from this focus, it would be valuable to see if the more biological oriented and non-biological working psychologists aim to learn from each other and build bridges between the often-parallel existing worlds. Fittingly, Jeffrey Gray already stated that all observations with respect to human behavior need to be in accordance with human biology (Gray, 1987b). Or as he puts it in his own words: “In the long run, any account of behavior which does not agree with the knowledge of the nervous and endocrine systems which has been gained through the direct study of physiology must be wrong.” (p. 241). Therefore, it is now more important than ever to bring the different branches from personality psychology and related sciences together and profit from the new amazing possibilities due to the technological revolution. Acknowledgement The position of CM is funded by a Heisenberg grant awarded to him by the German Research Foundation (MO2363/3-2). References Adler, L., Wedekind, D., Pilz, J., Weniger, G., & Huether, G. (1997). Endocrine correlates of personality traits: A comparison between emotionally stable and emotionally labile healthy young men. Neuropsychobiology, 35(4), 205–210. Allport, G. W., & Odbert, H. S. (1936). Trait-names: A psycho-lexical study. Psychological Monographs, 47(1), i. Andone, I., Błaszkiewicz, K., Eibes, M., Trendafilov, B., Montag, C., & Markowetz, A. (2016, September). Menthal: A framework for mobile data collection and analysis. Proceedings of the 2016 ACM international joint conference on pervasive and ubiquitous computing: Adjunct (pp. 624–629). ACM. Arslan, R. C., Schilling, K. M., Gerlach, T. M., & Penke, L. in press. Using 26,000 diary entries to show ovulatory changes in sexual desire and behavior. Journal of Personality and Social Psychology. Augustine, A. A., & Larsen, R. J. (2012). Is a trait really the mean of states? Journal of Individual Differences, 33, 131–137. Azucar, D., Marengo, D., & Settanni, M. (2018). Predicting the big 5 personality traits from digital footprints on social media: A meta-analysis. Personality and Individual

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