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Long-term, multi-event surprise correlates with enhanced autobiographical memory

Nature Human Behaviour volume 7, pages 2152–2168 (2023)Cite this article

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Abstract

Neurobiological and psychological models of learning emphasize the importance of prediction errors (surprises) for memory formation. This relationship has been shown for individual momentary surprising events; however, it is less clear whether surprise that unfolds across multiple events and timescales is also linked with better memory of those events. We asked basketball fans about their most positive and negative autobiographical memories of individual plays, games and seasons, allowing surprise measurements spanning seconds, hours and months. We used advanced analytics on National Basketball Association play-by-play data and betting odds spanning 17 seasons, more than 22,000 games and more than 5.6 million plays to compute and align the estimated surprise value of each memory. We found that surprising events were associated with better recall of positive memories on the scale of seconds and months and negative memories across all three timescales. Game and season memories could not be explained by surprise at shorter timescales, suggesting that long-term, multi-event surprise correlates with memory. These results expand notions of surprise in models of learning and reinforce its relevance in real-world domains.

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Fig. 1: Memory task, basic response characteristics and analytical approach.
Fig. 2: Surprise predicts memory for positive and negative plays.
Fig. 3: Full- and within-game surprises predict memory for positive and negative games.
Fig. 4: Surprise within and across full seasons predicts positive and negative season memories.

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Data availability

All relevant data necessary to reproduce these results are available at https://github.com/JamesWardAntony/bball_am. Other databases include the following: NBA API for play-by-play data (https://github.com/swar/nba_api); expert website with play-by-play win probabilities (https://www.inpredictable.com); year-by-year data on points scored versus points scored against for each team (for calculating team strength) (https://www.basketball-reference.com). Note that data we obtained on game-by-game betting odds are no longer available from the source that we obtained them from, but many of them can be found at https://www.kaggle.com/datasets/ehallmar/nba-historical-stats-and-betting-data and all the ones used in our dataset are in our data repository.

Code availability

All relevant code necessary to reproduce these results as Google Colab notebooks are available at https://github.com/JamesWardAntony/bball_am.

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Acknowledgements

The following individuals assisted with data collection and scoring: J. V. Figueroa, T. Guerra, J. Henige, N. Saito and M. Smith. We thank E. Robinson and B. Holladay for help with statistics. We received no specific funding for this work.

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Authors and Affiliations

  1. Department of Psychology and Child Development, California Polytechnic State University, San Luis Obispo, CA, USA

    James W. Antony, Jacob Van Dam, Jarett R. Massey & Kelly A. Bennion

  2. Department of Psychology, University of Toronto, Toronto, Ontario, Canada

    Alexander J. Barnett

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  1. James W. Antony
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Contributions

J.W.A. conceived, programmed and analysed data from the experiment, and drafted the manuscript. J.V.D. and J.R.M. contributed to study design and scored a large portion of the recall data. A.J.B. contributed to study design. K.A.B. contributed to study design and coordinated all data collection.

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Correspondence to James W. Antony.

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Extended data

Extended Data Fig. 1 Extra participant response attributes.

(a) Distribution of responses for each team combined for plays, games, and seasons. (b, c) Memory ages. Data were collected over the course of 9 months, so as an alternative to Fig. 1c,d, we plotted the time in days between the game date and study date for plays and games (b) or the time in years between the season start and study date (c).

Extended Data Fig. 2 Full win probability model.

(a, b) We plotted win probability by expected home minus away score difference and game time remaining when the home (a) and away (b) teams had possession of the ball. To illustrate the importance of team possession, we also plotted the contrast of home possession minus away possession (c). All graphs show data only from the 4th quarter of games.

Extended Data Fig. 3 Prediction algorithm validation against an expert.

Play surprise values from our algorithm correlated strongly with those from an expert.

Extended Data Fig. 4 Unsigned surprise over time across a game.

(a, b) We plotted the mean (a) and standard deviation of unsigned surprise (b) against game time using our metric. Mean surprise had momentary troughs and peaks at the beginning and end of each game, likely due to the momentary decrease and increase in shots taken within these short time frames. Interestingly, mean surprise tended to decrease throughout the game, owing to the probability that the outcome of many basketball games would be nearly decided at that point. However, standard deviation increased exponentially until the end of games, due to the combination of games with a near-certain outcome, which have almost no surprise, and close games, which involve increasingly rapid swings in win probability as one nears the finish.

Extended Data Fig. 5 Prediction algorithm validations related to game memories.

Signed full-game (a) and within-game surprise (b) values correlated strongly with those from an expert site. Similarly, full-game surprise values from the algorithm correlated strongly with those based on pre-game Las Vegas betting odds data (c).

Extended Data Fig. 6 Control analyses ruling out maximum play surprise as an alternative explanation for game surprise findings.

(a) Maximum unsigned play surprise within a game was greater for the positive and negative games chosen by participants than a null distribution of all games (left) [positive vs. null Mann-Whitney U = 6.9*10^5, p = 0.002, r = 0.20, 95% confidence interval: (−0.03,−0.007); negative vs. null Mann-Whitney U = 5.8*10^5, p < 0.001, r = 0.30, 95% confidence interval: (−0.05,−0.02)]. We therefore selected subsets of the positive and negative games that did not differ from the null distribution (right) (p > 0.10, enforced). (b) Using these modified distributions, negative games still had greater unsigned full-game (left) and within-game surprise (right) values than the null distribution (99% and 100% of the samples, respectively). Shown are averages across resamples. (c, d) We plotted the t-statistics of subsets of the game data that did not differ significantly in maximum play surprise. (c) t-statistics for positive (top) and negative (bottom) full-game surprise. (d) t-statistics for positive (top) and negative (bottom) within-game surprise. Both negative distributions remained significant, whereas both positive ones were not significant.

Extended Data Fig. 7 Season surprise after adjusting for differences in maximum full- and within-game surprise.

(a–d) We plotted the t-statistics of subsets of the season data that did not differ from the null distribution in maximum game surprise. (a-b) t-statistics for positive (a) and negative (b) seasons, adjusting for maximum full-game surprise. (c-d) t-statistics for positive (c) and negative (d) seasons, adjusting for within-game maximum surprise. All distributions remained significant after adjusting for game surprise deviations from the null.

Extended Data Fig. 8 Characteristics of participant memories.

We plotted metrics for each type of memory on (a) how long fans were affected by the outcome (1=minutes, 2=hours, 3=days, 4=months, 5=still affected), (b) emotionality with respect to everyday life (−5 to 5; worst to best thing that happened on a given day, week, month, year, ever), (c) unsigned emotionality, (d) their level of fandom (1=almost indifferent, 2=preferred team in that sport, 3=favorite team in that sport, 4=favourite team in any sport), and (e) approximate number of times they re-watched the play, parts of the game, or parts of the season.

Extended Data Table 1 Sample memories in which participants included the preceding or following play, game, or season context in their description
Full size table

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Antony, J.W., Van Dam, J., Massey, J.R. et al. Long-term, multi-event surprise correlates with enhanced autobiographical memory. Nat Hum Behav 7, 2152–2168 (2023). https://doi.org/10.1038/s41562-023-01631-8

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