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The distribution and the functions of autobiographical memories: Why do older adults remember autobiographical memories from their youth?

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Abstract

In the present study, the distribution of autobiographical memories was examined from a functional perspective: we examined whether the extent to which long-term autobiographical memories were rated as having a self-, a directive, or a social function affects the location (mean age) and scale (standard deviation) of the memory distribution. Analyses were based on a total of 5598 autobiographical memories generated by 149 adults aged between 50 and 81 years in response to 51 cue-words. Participants provided their age at the time when the recalled events had happened and rated how frequently they recall these events for self-, directive, and social purposes. While more frequently using autobiographical memories for self-functions was associated with an earlier mean age, memories frequently shared with others showed a narrower distribution around a later mean age. The directive function, by contrast, did not affect the memory distribution. The results strengthen the assumption that experiences from an individual’s late adolescence serve to maintain a sense of self-continuity throughout the lifespan. Experiences that are frequently shared with others, in contrast, stem from a narrow age range located in young adulthood.

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Notes

  1. Naturally, distributions (of AM) might differ in more respects than location and scale. However, many probability distributions—notably those distributions from the exponential dispersion family (Jørgensen 1987)—can be described by two parameters that correspond to location and scale of the distribution.

  2. Another 54 participants completed the first measurement occasion only and were, therefore, not included into the present study.

  3. The decision to exclude AMs older than 10 years represents a rather conservative criterion. However, we aimed to eliminate the recency effect for two reasons. First, more recent memories may be qualitatively different from long-term AMs because their distribution closely resembles a forgetting function (where accessibility decreases with the passage of time), and it remains an open question whether recent memories ever (and if so, which of them) turn into AMs that can be accessed many years later. Second, an increase of recent memories would be difficult to handle methodologically, because this would require the underlying probability distribution to increase in its right tail—which is impossible with common probability distributions.

  4. To obtain these results, one calculates \(\frac{{\exp (\varvec{\beta}_{0} )}}{{1 + \exp (\varvec{\beta}_{0} )}} = \frac{\exp ( - 1.2577)}{1 + \exp ( - 1.2577)} = 0.22\) for the location of and \(\frac{{\exp (\varvec{\upsilon}_{0} )}}{{1 + \exp (\varvec{\upsilon}_{0} )}} = \frac{\exp ( - 0.1296)}{1 + \exp ( - 0.1296)} = 0.468\) for the scale of the memory distribution.

  5. For an individual of average age (i.e. 62 years), mean-centred age is 0, and thus the location estimate in original age units can be calculated as \(\frac{{\exp (\varvec{\beta}_{0} +\varvec{\beta}_{1} \times age)}}{{1 + \exp (\varvec{\beta}_{0} +\varvec{\beta}_{1} \times age)}} = \frac{\exp ( - 1.2577 + 0.0253 \times 0)}{1 + \exp ( - 1.2577 + 0.0253 \times 0)} = 0.22\). For an individual 15 years older than the average age (i.e. 77 years), the location can be calculated as \(\frac{\exp ( - 1.2577 + 0.0253 \times 15)}{1 + \exp ( - 1.2577 + 0.0253 \times 15)} = 0.29\) Likewise, the scale estimate in original age units can be calculated as \(\frac{{\exp (\varvec{\upsilon}_{0} +\varvec{\upsilon}_{1} \times age)}}{{1 + \exp (\varvec{\upsilon}_{0} +\varvec{\upsilon}_{1} \times age)}} = \frac{\exp ( - 0.1296 + 0.0103 \times 0)}{1 + \exp ( - 0.1296 + 0.0103 \times 0)} = 0.468\) for an individual of average age and \(\frac{\exp ( - 0.1296 + 0.0103 \times 15)}{1 + \exp ( - 0.1296 + 0.0103 \times 15)} = 0.506\) for an individual 15 years older.

  6. To obtain these results, one calculates \(\frac{{\exp (\varvec{\beta}_{0} +\varvec{\beta}_{3} \times valence)}}{{1 + \exp (\varvec{\beta}_{0} +\varvec{\beta}_{3} \times valence)}} = \frac{\exp ( - 1.2577 - 0.1270 \times 1)}{1 + \exp ( - 1.2577 - 0.1270 \times 1)} = 0.20\) for the location and \(\frac{{\exp (\varvec{\upsilon}_{0} +\varvec{\upsilon}_{3} \times valence)}}{{1 + \exp (\varvec{\upsilon}_{0} +\varvec{\upsilon}_{3} \times valence)}} = \frac{\exp ( - 0.1296 + 0.0533 \times 1)}{1 + \exp ( - 0.1296 + 0.0533 \times 1)} = 0.481\) for the scale of a valence-score one standard deviation above the mean. For valence-scores one standard deviation below the mean, one calculates \(\frac{\exp ( - 1.2577 + 0.1270 \times 1)}{1 + \exp ( - 1.2577 + 0.1270 \times 1)} = 0.24\) for location and \(\frac{\exp ( - 0.1296 - 0.0533 \times 1)}{1 + \exp ( - 0.1296 - 0.0533 \times 1)} = 0.454\) for scale.

  7. More specifically, self-functions accounted for 4 %, directive for 1 %, and social for 8 % of variance in location. In the scale parameter, the self-, directive, and social functions explained 2.5, 0, and 7.5 % of variance, respectively.

  8. Zimprich and Wolf (2015, 2016) used a very similar approach to model individual differences in the distribution of autobiographical memories. See their study for a detailed discussion of possible advantages and drawbacks.

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Authors and Affiliations

  1. Department of Developmental Psychology, Institute of Psychology and Education, Ulm University, Albert-Einstein-Allee 47, 89081, Ulm, Germany

    Tabea Wolf & Daniel Zimprich

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  1. Tabea Wolf
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  2. Daniel Zimprich
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Correspondence to Tabea Wolf.

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Responsible editor: Hans-Werner Wahl.

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Wolf, T., Zimprich, D. The distribution and the functions of autobiographical memories: Why do older adults remember autobiographical memories from their youth?. Eur J Ageing 13, 241–250 (2016). https://doi.org/10.1007/s10433-016-0372-5

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  • DOI: https://doi.org/10.1007/s10433-016-0372-5

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