Internal Autobiographical Map Model for Understanding and Transforming Repetitive Human Behavioral Patterns
DOI:
https://doi.org/10.29070/b8pf4r68Keywords:
Internal Autobiographical Map (IAM), Behavioral Patterns, Autobiographical Memory, Psychological Identity, Behavioral Change, Cognitive Structures, Childhood ExperiencesAbstract
The research is based on the mechanisms of sustainable human behavioral change which is studied on the basis of structural psychological model referred to as Internal Autobiographical Map (IAM). This model postulates that human behavior is never determined by the conscious choices or immediate situation alone but a powerful contribution of internal cognitive structures which are established through autobiographical experiences. The study is centered on how childhood experiences have a role to play in the development of an inner meaning of self, other individuals and the environment around them. Subsequently, these interpretations become stable cognitive patterns in which emotional reactions, anticipations, and patterns of behavior are directed in the course of life. These internal structures are triggered automatically as persons face scenarios that are similar to those previously encountered, and they tend to initiate repetitive emotional responses and behavioral reactions in other aspects of life like relationship, career development and personal development. To describe these processes, the research combines the knowledge of neuroscience, autobiographical memory organization theories, and psychological identity development theories. Another concept that is presented in the research is the Direct Change Solution (DCS) which is an organized intervention process that is aimed at assisting people in recognizing the fundamental experiences in autobiography and the inferences that people made on the basis of those experiences and the rules and roles of behavior that can shape their behavior. By doing this, people can be in a better position of recognizing the psychological frameworks behind their choices and responses. The results show that these internal autobiographical frameworks and not external situations per se are a major determinant of repetitive behavioral patterns. Through the raising of awareness, re-perceiving the past, and making intentional decisions about new behavioral practices people can slowly change the woven patterns of the psyche. The research findings are that sustainable behavioral change is not only a motivational or a willpower process but a process of restructuring the internal autobiographical system that regulates human perception, emotions and decision making processes.
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References
1. E. Welch, V. Schelkun, C. Gasser, K. Lockwood, and L. Davachi, “Rich autobiographical memory benefits from both novelty and similarity to other daily experiences,” Sci. Rep., vol. 15, no. 1, 2025, doi: 10.1038/s41598-025-29036-9.
2. B. Guerin, “The Forces that Shape Human Behavior: Combining the Behavior Analysis Approach with Social Science Knowledge for a More Exhaustive Framework to Analyze and Change Human Behavior,” Psychol. Rec., vol. 75, no. 3, pp. 355–371, 2025, doi: 10.1007/s40732-025-00654-z.
3. E. Donarelli, C. Civilotti, G. Di Fini, G. Gandino, and A. Celeghin, “Autobiographical Memory: A Scoping Meta-Review of Neuroimaging Data Enlightens the Inconsistencies Between Theory and Experimentation,” Brain Sci., vol. 15, no. 5, pp. 1–20, 2025, doi: 10.3390/brainsci15050515.
4. L. Petiot, H. Sauzeon, and P. Dragicevic, “The Effect of Augmented Reality on Involuntary Autobiographical Memory,” Conf. Hum. Factors Comput. Syst. - Proc. , no. March, 2025, doi: 10.1145/3706598.3713922.
5. C. M. Fang et al., “How AI and Human Behaviors Shape Psychosocial Effects of Extended Chatbot Use: A Longitudinal Randomized Controlled Study,” 2025, [Online]. Available: http://arxiv.org/abs/2503.17473
6. F. Hutmacher, M. Appel, and S. Schwan, “Understanding Autobiographical Memory in the Digital Age: The AMEDIA-Model,” Psychol. Inq., vol. 35, no. 2, pp. 83–105, 2024, doi: 10.1080/1047840X.2024.2384125.
7. R. D. I. van Genugten and D. L. Schacter, “Automated scoring of the autobiographical interview with natural language processing,” Behav. Res. Methods, vol. 56, no. 3, pp. 2243–2259, 2024, doi: 10.3758/s13428-023-02145-x.
8. T. J. Prescott and P. F. Dominey, “Synthesizing the temporal self: Robotic models of episodic and autobiographical memory,” Philos. Trans. R. Soc. B Biol. Sci., vol. 379, no. 1913, 2024, doi: 10.1098/rstb.2023.0415.
9. M. Monzel et al., “Hippocampal-occipital connectivity reflects autobiographical memory deficits in aphantasia,” Elife, vol. 13, pp. 1–20, 2024, doi: 10.7554/elife.94916.3.
10. I. Peters, N. F. Kemper, F. Schmiedek, and T. Habermas, “Individual differences in revising the life story—Personality and event characteristics influence change in the autobiographical meaning of life events,” J. Pers., vol. 91, no. 5, pp. 1207–1222, 2023, doi: 10.1111/jopy.12793.
11. J. S. Park, J. O’Brien, C. J. Cai, M. R. Morris, P. Liang, and M. S. Bernstein, “Generative Agents: Interactive Simulacra of Human Behavior,” UIST 2023 - Proc. 36th Annu. ACM Symp. User Interface Softw. Technol., no. March, 2023, doi: 10.1145/3586183.3606763.
12. I. A. Clark, S. Mohammadi, M. F. Callaghan, and E. A. Maguire, “Conduction velocity along a key white matter tract is associated with autobiographical memory recall ability,” Elife, vol. 11, pp. 1–44, 2022, doi: 10.7554/eLife.79303.
13. Y. Pang, Q. Ke, H. Rahmani, J. Bailey, and J. Liu, “IGFormer: Interaction Graph Transformer for Skeleton-Based Human Interaction Recognition,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 13685 LNCS, pp. 605–622, 2022, doi: 10.1007/978-3-031-19806-9_35.
14. V. Mazzia, S. Angarano, F. Salvetti, F. Angelini, and M. Chiaberge, “Action Transformer: A self-attention model for short-time pose-based human action recognition,” Pattern Recognit., vol. 124, 2022, doi: 10.1016/j.patcog.2021.108487.
15. V. Wardell, C. L. Esposito, C. R. Madan, and D. J. Palombo, “Semi-automated transcription and scoring of autobiographical memory narratives,” Behav. Res. Methods, vol. 53, no. 2, pp. 507–517, 2021, doi: 10.3758/s13428-020-01437-w.
16. A. A. Wank, J. R. Andrews-Hanna, and M. D. Grilli, “Searching for the past: Exploring the dynamics of direct and generative autobiographical memory reconstruction among young and cognitively normal older adults,” Mem. Cogn., vol. 49, no. 3, pp. 422–437, 2021, doi: 10.3758/s13421-020-01098-2.
17. T. J. Barry, D. J. Hallford, and K. Takano, “Autobiographical Memory Impairments as a Transdiagnostic Feature of Mental Illness: A Meta-Analytic Review of Investigations Into Autobiographical Memory Specificity and Overgenerality Among People With Psychiatric Diagnoses,” Psychol. Bull., vol. 147, no. 10, pp. 1054–1074, 2021, doi: 10.1037/bul0000345.
18. A. W. Gilmore et al., “Evidence supporting a time-limited hippocampal role in retrieving autobiographical memories,” Proc. Natl. Acad. Sci. U. S. A., vol. 118, no. 12, 2021, doi: 10.1073/pnas.2023069118.
19. M. Descamps, O. Boucher, D. K. Nguyen, and I. Rouleau, “Emotional autobiographical memory associated with insular resection in epileptic patients: A comparison with temporal lobe resection,” Brain Sci., vol. 11, no. 10, 2021, doi: 10.3390/brainsci11101316.
20. A. W. Gilmore et al., “The human hippocampus plays a time-limited role in retrieving autobiographical memories,” bioRxiv, p. 2020.11.19.390526, 2020.
21. L. H. Chen et al., “MotionLLM: Understanding Human Behaviors from Human Motions and Videos,” IEEE Trans. Pattern Anal. Mach. Intell., pp. 1–35, 2025, doi: 10.1109/TPAMI.2025.3627546.
22. B. Hong, M. A. Tran, H. Cheng, B. Arenas Rodriguez, K. E. Li, and M. D. Barense, “The influence of event similarity on the detailed recall of autobiographical memories,” Memory, vol. 33, no. 1, pp. 100–112, 2025, doi: 10.1080/09658211.2024.2406307.
23. A. M. Agron, A. Martin, and A. W. Gilmore, “Scene construction and autobiographical memory retrieval in autism spectrum disorder,” Autism Res., vol. 17, no. 2, pp. 204–214, 2024, doi: 10.1002/aur.3066.
24. L. A. E. M. van Houtum et al., “Aberrant neural network activation during reliving of autobiographical memories in adolescent depression,” Cortex, vol. 168, pp. 14–26, 2023, doi: 10.1016/j.cortex.2023.06.021.
25. F. Z. Zhang, D. Campbell, and S. Gould, “Efficient Two-Stage Detection of Human-Object Interactions with a Novel Unary-Pairwise Transformer,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2022-June, pp. 20072–20080, 2022, doi: 10.1109/CVPR52688.2022.01947.
26. R. Setton, L. Mwilambwe-Tshilobo, S. Sheldon, G. R. Turner, and R. N. Spreng, “Hippocampus and temporal pole functional connectivity is associated with age and individual differences in autobiographical memory,” Proc. Natl. Acad. Sci. U. S. A., vol. 119, no. 41, pp. 1–12, 2022, doi: 10.1073/pnas.2203039119.
27. G. Chhabra, E. M. Onyema, S. Kumar, M. Goutham, S. Mandapati, and C. Iwendi, “Human Emotions Recognition, Analysis and Transformation by the Bioenergy Field in Smart Grid Using Image Processing,” Electron., vol. 11, no. 23, 2022, doi: 10.3390/electronics11234059.
28. J. Erez, M. E. Gagnon, and A. M. Owen, “Differentiating real-world autobiographical experiences without recourse to behaviour,” Brain Sci., vol. 11, no. 4, 2021, doi: 10.3390/brainsci11040521.
29. A. W. Gilmore, A. Quach, S. E. Kalinowski, S. J. Gotts, D. L. Schacter, and A. Martin, “Dynamic content reactivation supports naturalistic autobiographical recall in humans,” J. Neurosci., vol. 41, no. 1, pp. 153–166, 2021, doi: 10.1523/JNEUROSCI.1490-20.2020.
30. K. M. Lempert, K. A. MacNear, D. A. Wolk, and J. W. Kable, “Links between autobiographical memory richness and temporal discounting in older adults,” Sci. Rep., vol. 10, no. 1, pp. 1–13, 2020, doi: 10.1038/s41598-020-63373-1.
