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Modeling the reminiscence bump in autobiographical memory with the Memory Chain Model

Modeling the reminiscence bump in autobiographical memory with the Memory Chain Model. Steve Janssen, Antonio Chessa & Jaap Murre University of Amsterdam. Reminiscence bump . People remember more events from certain period than from prior or latter periods.

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Modeling the reminiscence bump in autobiographical memory with the Memory Chain Model

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  1. Modeling the reminiscence bump in autobiographical memory with the Memory Chain Model Steve Janssen, Antonio Chessa & Jaap Murre University of Amsterdam

  2. Reminiscence bump • People remember more events from certain period than from prior or latter periods. • The exact shape of the reminiscence bump. • Whether the reminiscence bump stays the same for all ages.

  3. Modeling the reminiscence bump • Galton-Crovitz cueing method • Reminiscence bump • Distribution consists of two independent functions • Experiment • Results

  4. Sir Francis Galton (1879) • Inspection of cue word • Until an event comes to mind • Dating of event

  5. Probability, Not Amount • The Galton-Crovitz cueing method aims to obtain a random sample of all memories, stratified through the use of keywords. • The method measures a probabilitydensity function of memory age.

  6. Rubin, Wetzler & Nebes (1986) • Combined the results of four different experiments (N total = 285) • Found a reminiscence bump between 10 and 30 years, when older than 40 years.

  7. Reminiscence bump (RW&N, 1986)

  8. Reminiscence bump • Forgetting influences the memory age distribution. • Investigate the shape of the bump and whether the bump stays the same for all ages. • Therefore, we correct the memory age distribution for decline.

  9. Observed memory distribution consists of two independent functions • Decline function • (Non-constant) Encoding-sampling function

  10. 1.2 1 0.8 0.6 0.4 0.2 0 0 10 20 30 40 50 1.2 1 0.8 0.6 0.4 0.2 0 0 10 20 30 40 50 50 year Constant Encoding Non-constant Encoding

  11. 1.2 1.2 1 1 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0 0 0 10 20 30 40 50 0 10 20 30 40 50 1.2 1.2 1 1 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0 0 0 10 20 30 40 50 0 10 20 30 40 50 30 year 50 year

  12. Telescoping effect • Systematic displacement of events in time. • Subjects also have to date public news events. • Effect is smaller when subjects have to answer in ‘calendar’ format than in ‘ago’ format.

  13. Subjects • 827 men and 760 women • The Netherlands • M age = 39.89 years, SD age = 13.51 • Six age groups between 10 and 70 year. • No financial reward.

  14. 0.04 31 - 40 years old 0.06 31 - 40 years old ( N = 3983) 0.03 0.04 0.02 0.02 0.01 0 0 0 10 20 30 40 50 60 70 0 10 20 30 40 50 60 70 0.04 41 - 50 years old 0.06 41 - 50 years old ( N = 3791) 0.03 0.04 0.02 0.02 0.01 0 0 0 10 20 30 40 50 60 70 0 10 20 30 40 50 60 70 Memory age pdf Encoding Subject age Subject age

  15. 0.04 51 - 60 years old 0.06 51 - 60 years old ( N = 3190) 0.03 0.04 0.02 0.02 0.01 0 0 0 10 20 30 40 50 60 70 0 10 20 30 40 50 60 70 0.04 61 - 70 years old 0.06 61 - 70 years old ( N = 1169) 0.03 0.04 0.02 0.02 0.01 0 0 0 10 20 30 40 50 60 70 0 10 20 30 40 50 60 70 Memory age pdf Encoding Subject age Subject age

  16. Encoding combined over subject age classes N = 16955

  17. Conclusions • Replication of reminiscence bump within one experiment and more subjects. • Memory age distribution can be divided into two independent functions (namely decline and encoding-sampling). • Peak bump narrowed down between 13 and 18 years and stays the same for all age groups.

  18. Future research • The cause of the reminiscence bump: Encoding or Re-sampling. • Comparing encoding-sampling functions of males and females, and of Americans and Dutchmen.

  19. Steve Janssen, Antonio Chessa &Jaap Murre • Email: s.m.j.janssen@uva.nl • Website: http://memory.uva.nl

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