The reminiscence bump is a robust finding demonstrated mostly with the cue-word method in Western cultures. The first aim of the study was to replicate the reminiscence bump using a life history timeline method and to extend reminiscence bump research to a Turkish sample. The second aim was to empirically examine the recently proposed life story account (Gluck & Bluck, 2007) for the reminiscence bump. The sample consisted of 40 women and 32 men aged 52 to 66 years. Participants' lives were divided into 5-year intervals and they verbally reported as many memories as possible in a standard timeframe from each interval (in random order) and provided ratings of several memory characteristics. As expected, the lifespan distribution of the resulting 6373 memories demonstrated a reminiscence bump. In support of the life story account, bump memories were found to be more novel, more important for identity development, more distinct, and more likely to involve developmental transitions than memories from other age periods. Findings are discussed in terms of the life story account, which synthesises lifespan developmental theory and life story theory.

Ageing, by definition, involves moving across lived time. Grounded in developmental psychology, particularly lifespan developmental theory, this study examines two time-related factors that may affect psychological wellbeing in adulthood. Particularly, chronological age and perceived time left to live (i.e. future time perspective) are predicted to act as opposing forces in the construction of psychological wellbeing. Young (N=285, 19-29 years) and middle-aged adults (N=135, 14-64 years) self-reported their current psychological wellbeing (across six dimensions) and their sense of future time perspective. As predicted, mediation analyses show that higher levels of chronological age (being in midlife), and having a more open-ended, positive future time perspective are both related to higher psychological wellbeing. Note, however, that being in midlife is related to a more limited and negative future time perspective. As such, confirming our conceptual argument, while both age and future perspective are measures of time in a general sense, analyses show that they act as unique, opposing forces in the construction of psychological wellbeing. The current research suggests that individuals can optimise psychological wellbeing to the extent that they maintain an open-ended and positive sense of the future. Ageing, by definition, involves moving across lived time. Grounded in developmental psychology, particularly lifespan developmental theory, this study examines two timerelated factors that may affect psychological wellbeing in adulthood. Particularly, chronological age and perceived time left to live (i.e. future time perspective) are predicted to act as opposing forces in the construction of psychological wellbeing. Young (N = , - years) and middle-aged adults (N = , - years) selfreported their current psychological wellbeing (across six dimensions) and their sense of future time perspective. As predicted, mediation analyses show that higher levels of chronological age (being in midlife), and having a more open-ended, positive future time perspective are both related to higher psychological wellbeing. Note, however, that being in midlife is related to a more limited and negative future time perspective. As such, confirming our conceptual argument, while both age and future perspective are measures of time in a general sense, analyses show that they act as unique, opposing forces in the construction of psychological wellbeing. The current research suggests that individuals can optimise psychological wellbeing to the extent that they maintain an open-ended and positive sense of the future.KEY WORDS -adult development, psychological wellbeing, future time perspective, midlife.

This is the first study to take a naturalistic observation approach to reminiscence and to build on self-report data.

Background Reminiscence is the act of thinking or talking about personal experiences that occurred in the past. It is a central task of old age that is essential for healthy aging, and it serves multiple functions, such as decision-making and introspection, transmitting life lessons, and bonding with others. The study of social reminiscence behavior in everyday life can be used to generate data and detect reminiscence from general conversations. Objective The aims of this original paper are to (1) preprocess coded transcripts of conversations in German of older adults with natural language processing (NLP), and (2) implement and evaluate learning strategies using different NLP features and machine learning algorithms to detect reminiscence in a corpus of transcripts. Methods The methods in this study comprise (1) collecting and coding of transcripts of older adults’ conversations in German, (2) preprocessing transcripts to generate NLP features (bag-of-words models, part-of-speech tags, pretrained German word embeddings), and (3) training machine learning models to detect reminiscence using random forests, support vector machines, and adaptive and extreme gradient boosting algorithms. The data set comprises 2214 transcripts, including 109 transcripts with reminiscence. Due to class imbalance in the data, we introduced three learning strategies: (1) class-weighted learning, (2) a meta-classifier consisting of a voting ensemble, and (3) data augmentation with the Synthetic Minority Oversampling Technique (SMOTE) algorithm. For each learning strategy, we performed cross-validation on a random sample of the training data set of transcripts. We computed the area under the curve (AUC), the average precision (AP), precision, recall, as well as F1 score and specificity measures on the test data, for all combinations of NLP features, algorithms, and learning strategies. Results Class-weighted support vector machines on bag-of-words features outperformed all other classifiers (AUC=0.91, AP=0.56, precision=0.5, recall=0.45, F1=0.48, specificity=0.98), followed by support vector machines on SMOTE-augmented data and word embeddings features (AUC=0.89, AP=0.54, precision=0.35, recall=0.59, F1=0.44, specificity=0.94). For the meta-classifier strategy, adaptive and extreme gradient boosting algorithms trained on word embeddings and bag-of-words outperformed all other classifiers and NLP features; however, the performance of the meta-classifier learning strategy was lower compared to other strategies, with highly imbalanced precision-recall trade-offs. Conclusions This study provides evidence of the applicability of NLP and machine learning pipelines for the automated detection of reminiscence in older adults’ everyday conversations in German. The methods and findings of this study could be relevant for designing unobtrusive computer systems for the real-time detection of social reminiscence in the everyday life of older adults and classifying their functions. With further improvements, these systems could be deployed in health interventions aimed at improving older adults’ well-being by promoting self-reflection and suggesting coping strategies to be used in the case of dysfunctional reminiscence cases, which can undermine physical and mental health.