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In this article, we present two novel aging clocks created within the deep learning paradigm – PsychoAge and SubjAge

In this article, we present two novel aging clocks created within the deep learning paradigm – PsychoAge and SubjAge

DISCUSSION

Both these clocks use the same set of 50 psychosocial features to estimate human chronological age and subjective age, respectively. These clocks showed superior performance during CV in MIDUS 1 (MAEPsychoAge= 6.70 years; MAESubjAge= 7.32 years) and were verified in two other large data sets – MIDUS 2 and MIDUS Refresher ( Table 1 ). In terms of epsilon accuracy, PsychoAge reached a score of 0.78 in MIDUS 1, and SubjAge – 0.74.

Having trained and verified the final models, we aimed to understand how PsychoAge and SubjAge see human aging and what features they pay the most attention to. With a tandem PFI-DFS approach we ranked all features according to their relative importance. Top-5 important features in both clocks were associated with health conditions (e.g. headache frequency) and relationship status (marital status, expectations from sex life in 10 years). Less significant features greatly differ in their relative importance for SubjAge and PsychoAge predictions. For example, top-20 PsychoAge features contain only one personality trait – neuroticism. Meanwhile, the only personality traits encountered among top-20 SubjAge features are – extraversion and openness.

These three personality traits, along with conscientiousness and agreeableness form “the big five traits”, which are commonly used in practice and scientific research to describe the human mental state landscape. High neuroticism is characteristic of emotional instability and common mental disorders, such as mood disorders, Colombo cute girls anxiety, and substance use disorders. Openness and extraversion, on the other hand, are considered more balanced traits, although their abnormally low scores are also related to social phobia and agoraphobia .

We hypothesized that the human mind evolves throughout the lifespan, which results in some traits, beliefs, or priorities shifting – not always in unison or at the same speed. At certain life stages, career-related priorities may rise, while at others they may fade and be replaced by different priorities. These lifelong progressions of the psyche eventually get recognized by the neural networks we constructed to let them build an image of psychological aging.

Positive orientation, seeking warmth, social interaction, and emotional stability may play an important role in psychological aging

This idea of human mind progression is described in much more detail in the review of SST by Laura Carstensen. SST suggests that younger people are more goal-oriented, interested to obtain new knowledge and skills, while older people tend to value emotionally meaningful goals more.

To identify the psychosocial features that change while a person advances from one age group to another we trained ples from three age groups (25-39, 40-64, 65-75 years). First, we defined the psychological aging core – variables that remain highly important (top-25) across all age groups ( Table 2 ). The core contained not only strictly psychological features, however. To illustrate, marital status, hypertension medication, headaches, and body mass index were among the seven core features required for accurate chronological age prediction. Interestingly, neuroticism score also belonged to the same psychological aging core, as well as seeing the community as a source of comfort. Psychological traits within the subjective aging core contained aspirations scale, extraversion, openness, positive reappraisal prevalence, and two career-related variables – effort put into work now and work expectations in 10 years. In contrast to the first psychological core, which contained few psychological traits, the subjective core consisted almost exclusively of psychological features.

This highlights an important distinction between aging per se (as judged by PsychoAge) and our perception of it (as judged by SubjAge): subjective aging is mostly dependent on internal causes.

We also explored the uniquely important features for each age group – features that emerged only in one top-25 set. Since these features were recognized as important only in these groups, it may be assumed that they shift the most markedly during the corresponding life periods. To illustrate, young adults were not the only age group who responded affirmatively to the statement “Forceful describes you well”, but rather many of these people went through a transformation that affected their forcefulness. Detecting such a change was essential for a predictor to accurately predict whether a person was at the beginning or the end of this phase of life.