Skip to main content

Advertisement

SpringerLink
Log in

Novel Computational Algorithms to Index Lexical Markers of Psychological Distancing and Their Relationship to Emotion Regulation Efficacy Over Time

  • RESEARCH ARTICLE
  • Published:
Affective Science Aims and scope Submit manuscript

Abstract

The degree to which one employs an objective or spatially/temporally distant perspective via language, i.e., linguistic distancing, has previously been shown to be positively associated with well-being. We sought to further elucidate relationships among language and emotion over time as a function of the implementation of sub-tactics of psychological distancing. In Study 1, we developed novel deep machine learning algorithms to identify the degree to which linguistic patterns reflect two types of psychological distancing, namely objective (OBJ) and spatial/temporal (FAR) distancing. In Study 2, in an expressive writing-based longitudinal emotion regulation training task, participants transcribed their thoughts while viewing negative or neutral stimuli over 5 sessions in one of three ways: by implementing objective language (objective group), by implementing spatially/temporally distant language (far group), or by responding naturally. We found that the OBJ and FAR algorithms significantly predicted changes in task-based self-reported negative affect in the objective group and found no significant associations in the far group. The relationship between the algorithm scores and self-reported negative affect was stronger in the objective group compared to the far group. These findings describe sensitive linguistic distancing algorithms that are capable of tracking changes in self-reported negative affect. These results may be useful in developing novel, unobtrusive emotion regulation assessments and interventions that utilize natural language processing.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Cohn, M. A., Mehl, M. R., & Pennebaker, J. W. (2004). Linguistic markers of psychological change surrounding September 11, 2001. Psychological Science, 15(10), 687–693. https://doi.org/10.1111/j.0956-7976.2004.00741.x

    Article  PubMed  Google Scholar 

  • Denny, B. T., & Ochsner, K. N. (2014). Behavioral effects of longitudinal training in cognitive reappraisal. Emotion, 14(2), 425–433. https://doi.org/10.1037/a0035276

  • Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. ArXiv:1810.04805 [Cs]. http://arxiv.org/abs/1810.04805. Accessed 1 Dec 2020

  • Doré, B., Ort, L., Braverman, O., & Ochsner, K. N. (2015). Sadness shifts to anxiety over time and distance from the national tragedy in Newtown Connecticut. Psychological Science, 26(4), 363–373. https://doi.org/10.1177/0956797614562218

    Article  PubMed  Google Scholar 

  • Doré, B. P., Silvers, J. A., & Ochsner, K. N. (2016). Toward a personalized science of emotion regulation. Social and Personality Psychology Compass, 10(4), 171–187. https://doi.org/10.1111/spc3.12240

    Article  PubMed  PubMed Central  Google Scholar 

  • Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41(4), 1149–1160. https://doi.org/10.3758/BRM.41.4.1149

  • Gross, J. (1998). Antecedent- and response-focused emotion regulation: Divergent consequences for experience, expression, and physiology. Journal of Personality and Social Psychology, 74(1), 224–237.

    Article  Google Scholar 

  • Hallgren, K. A. (2012). Computing inter-rater reliability for observational data: An overview and tutorial. Tutorials in Quantitative Methods for Psychology, 8(1), 23–34.

    Article  Google Scholar 

  • Hayes, A., & Krippendorff, K. (2007). Answering the call for a standard reliability measure for coding data. Communication Methods and Measures, 1, 77–89. https://doi.org/10.1080/19312450709336664

    Article  Google Scholar 

  • Huo, H., & Iwaihara, M. (2020). Utilizing BERT pretrained models with various fine-tune methods for subjectivity detection. In X. Wang, R. Zhang, Y.-K. Lee, L. Sun, & Y.-S. Moon (Eds.), Web and Big Data (pp. 270–284). Springer International Publishing. https://doi.org/10.1007/978-3-030-60290-1_21

  • IBM, Corp. (n.d.). IBM SPSS Statistics, Version 26.0. IBM Corp.

  • Krippendorff, K. (2004). Content Analysis: An Introduction to Its Methodology. SAGE.

  • Krippendorff, K. (2011). Computing Krippendorff’s alpha-reliability. Departmental Papers (ASC). https://repository.upenn.edu/asc_papers/43. Accessed 12 Feb 2020

  • Kross, E., & Ayduk, O. (2017). Chapter two - self-distancing: theory, research, and current directions. In J. M. Olson (Ed.), Advances in Experimental Social Psychology (Vol. 55, pp. 81–136). Academic Press. https://doi.org/10.1016/bs.aesp.2016.10.002

  • Kross, E., & Ayduk, O. (2008). Facilitating adaptive emotional analysis: Distinguishing distanced-analysis of depressive experiences from immersed-analysis and distraction. Personality & Social Psychology Bulletin, 34(7), 924–938. https://doi.org/10.1177/0146167208315938

    Article  Google Scholar 

  • Kross, Ethan, Ayduk, O., & Mischel, W. (2005). When asking “why” does not hurt. Distinguishing rumination from reflective processing of negative emotions. Psychological Science, 16(9), 709–715. https://doi.org/10.1111/j.1467-9280.2005.01600.x

  • Lang, P. J., Greenwald, M. K., Bradley, M. M., & Hamm, A. O. (1993). Looking at pictures: Affective, facial, visceral, and behavioral reactions. Psychophysiology, 30(3), 261–273

  • McRae, K., Ciesielski, B., & Gross, J. J. (2012). Unpacking cognitive reappraisal: Goals, tactics, and outcomes. Emotion, 12(2), 250–255. https://doi.org/10.1037/a0026351

  • Mehl, M. R., Robbins, M. L., & Holleran, S. E. (2013). How taking a word for a word can be problematic: Context-dependent linguistic markers of extraversion and neuroticism. Journal of Methods and Measurement in the Social Sciences, 3(2), 30–50. https://doi.org/10.2458/v3i2.16477

  • Nook, E. C., Schleider, J. L., & Somerville, L. H. (2017). A linguistic signature of psychological distancing in emotion regulation. Journal of Experimental Psychology. General, 146(3), 337–346. https://doi.org/10.1037/xge0000263

    Article  PubMed  Google Scholar 

  • Orvell, A., Ayduk, Ö., Moser, J. S., Gelman, S. A., & Kross, E. (2019). Linguistic shifts: A Relatively effortless route to emotion regulation? Current Directions in Psychological Science, 0963721419861411. https://doi.org/10.1177/0963721419861411

  • Parkinson, C., Liu, S., & Wheatley, T. (2014). A common cortical metric for spatial, temporal, and social distance. Journal of Neuroscience, 34(5), 1979–1987. https://doi.org/10.1523/JNEUROSCI.2159-13.2014

    Article  PubMed  Google Scholar 

  • Pennebaker, J. W., Booth, R. J., Boyd, R. L., & Francis, M. E. (2015). Linguistic inquiry and word count: LIWC2015. Austin, TX: Pennebaker Conglomerates (https://www.LIWC.net)

  • Pennebaker, J. W., & Chung, C. K. (2007). Expressive writing, emotional upheavals, and health. In Foundations of Health Psychology (pp. 263–284). Oxford University Press.

  • Pennebaker, J. W., & King, L. A. (1999). Linguistic styles: Language use as an individual difference. Journal of Personality and Social Psychology, 77(6), 1296–1312.

    Article  Google Scholar 

  • Powers, J., & LaBar, K. (2018). Regulating Emotion Through Distancing: A Taxonomy, Neurocognitive Model, and Supporting Meta-Analysis. Neuroscience&Biobehavioral Reviews.

  • R Core Team. (2013). A language and environment for statistical computing [R Foundation for Statistical Computing]. http://www.R-project.org/

  • Sanh, V., Debut, L., Chaumond, J., & Wolf, T. (2020). DistilBERT, a distilled version of BERT: Smaller, faster, cheaper and lighter. ArXiv:1910.01108 [Cs]. http://arxiv.org/abs/1910.01108. Accessed 15 Dec 2020

  • Shahane, A. D., & Denny, B. T. (2019). Predicting emotional health indicators from linguistic evidence of psychological distancing. 35, 200–210. https://doi.org/10.1002/smi.2855

  • Shahane, A. D., & Denny, B. T. (2021). Emotion regulation and writing. In G. Schiewer, J. Altarriba, & N. B. Chin (Eds.), The handbook on language and emotion. De Gruyter Mouton (in press)

  • Socher, R., Perelygin, A., Wu, J. Y., Chuang, J., Manning, C. D., Ng, A. Y., & Potts, C. (2013). Recursive deep models for semantic compositionality over a sentiment treebank. EMNLP, 1631, 1631–1642.

    Google Scholar 

  • Trope, Y., & Liberman, N. (2010). Construal-level theory of psychological distance. Psychological Review, 117(2), 440–463. https://doi.org/10.1037/a0018963

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

Many thanks to Jenna Jones, Rohini Kumar, and Jennifer Truitt.

Author information

Authors and Affiliations

  1. Department of Psychological Sciences, Rice University, BioScience Research Collaborative, 6500 Main Street, Houston, TX, 77030, USA

    Anoushka D. Shahane, Daniel C. Pham & Bryan T. Denny

  2. Bard College, Annandale-on-Hudson, NY, USA

    Richard B. Lopez

Authors
  1. Anoushka D. Shahane
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Daniel C. Pham
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Richard B. Lopez
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bryan T. Denny
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anoushka D. Shahane.

Ethics declarations

Funding

This work was supported by the National Heart Lung and Blood Institute F31 HL147394 to Anoushka D. Shahane.

Data Availability

Data and corresponding scripts for data analyses are available on the Open-Science Framework (OSF) at the following link: https://osf.io/3n9px/?view_only=88e11735dd9b4f569b6af114e36061a1.

Code Availability

Scripts for data analyses are available on the Open-Science Framework (OSF) at the link above. The novel algorithm codes are available on GitHub at the following link: https://github.com/danielcpham/reappraisal-model.

Ethics Approval

All procedures were approved by the Rice University Institutional Review Board.

Conflicts of Interests

On behalf of all authors, the corresponding author declares no competing interests.

Informed Consent

Informed consent was obtained from all research participants.

Authors’ Contributions

A. D. Shahane conducted the data collection and analysis, and wrote the manuscript under the supervision and guidance of B. T. Denny. R. B. Lopez assisted with the data analysis. D. C. Pham assisted with the algorithm development. All authors contributed to, reviewed, and approved of the final manuscript.

Additional information

Handling Editor: Jonathan Gratch

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 1509 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shahane, A.D., Pham, D.C., Lopez, R.B. et al. Novel Computational Algorithms to Index Lexical Markers of Psychological Distancing and Their Relationship to Emotion Regulation Efficacy Over Time. Affec Sci 2, 262–272 (2021). https://doi.org/10.1007/s42761-021-00053-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42761-021-00053-x

Keywords

Search

Navigation