Everyday fluctuations in mood can influence our ability to remember to carry out intentions (prospective memory). Theories of mood-cognition interaction make differing predictions about the effects of positive and negative mood states on cognition that may change in aging. To test these predictions, we looked at the effects of age and induced mood on different types of prospective memory tasks. Results showed that on a task which required constant attentional monitoring (event-based prospective memory) young adults' performance was impaired by negative mood, while older adults' performance was not influenced by mood. Further analyses indicated that the deleterious effects of negative mood states in young participants were related to decreased monitoring. In another task which required more intermittent monitoring (time-based prospective memory), older adults' performance actually improved under positive mood, whereas young adults showed no effect of mood. Contrary to predictions, these age differences were not related to improved emotion regulation in old age. We conclude that young adults are more likely than older to show prospective memory failures caused by negative mood. Future research priorities are outlined to better understand the motivational and task characteristics which influence this phenomenon.

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The present study aimed to investigate the affect-cognition interplay in young and older adults by studying prospective memory (PM), the realisation of delayed intentions. While most previous studies on the topic were conducted in the laboratory, we examined the influence of naturally occurring affect on PM tasks carried out in participants' everyday lives. For seven consecutive days, participants were asked to rate their affective state nine times per day and send text messages either at specific times (time-based PM) or when a particular event occurred (event-based PM). Resultsshowed that within-participants changes in valence from more positive to more negative affect were associated with decreased PM performance. This was similarly true for young and older adults. The design used allowed linkage of within-participants fluctuations of affect and cognitive functions, constituting a methodological advancement. Results suggest that positive affect has the potential to improve cognitive functioning in everyday life.

The characterization of the relationship between predictions and one-shot episodic encoding poses an important challenge for memory research. On the one hand, events that are compatible with our previous knowledge are thought to be remembered better than incompatible ones. On the other hand, unexpected situations, by virtue of their surprise, are known to cause enhanced learning. Several theoretical accounts try to solve this apparent paradox by conceptualizing prediction error (PE) as a continuum ranging from low PE (for expectation matching events) to high PE (for expectation mismatching ones). Under such framework, the relationship between PE and memory encoding would be described by a U-shape function with higher memory performance for extreme levels of PE and lower memory for middle levels of PE. In this study we used a gradual manipulation of the strength of association between scenes and objects to render different levels of PE and then tested for episodic memory of the (mis)matching events. In two experiments, and in contrast to what was anticipated, recognition memory as a function of PE followed an inverted U-shape, with higher performance for intermediate levels of PE. Furthermore, in two additional experiments we showed the relevance of explicit predictions at encoding to reveal such inverted U pattern, thus providing the boundary conditions of the effect. We discuss our current findings in the light of the uncertainty in the environment and the importance of the operations underlying encoding tasks.

Expectations can lead to prediction errors of varying degrees depending on the extent to which the information encountered in the environment conforms with prior knowledge. While there is strong evidence on the computationally specific effects of such prediction errors on learning, relatively less evidence is available regarding their effects on episodic memory. Here, we had participants work on a task in which they learned context/object-category associations of different strengths based on the outcomes of their predictions. We then used a reinforcement learning model to derive subject-specific trial-to-trial estimates of prediction error at encoding and link it to subsequent recognition memory. Results showed that model-derived prediction errors at encoding influenced subsequent memory as a function of the outcome of participants’ predictions (correct vs. incorrect). When participants correctly predicted the object category, stronger prediction errors (as a consequence of weak expectations) led to enhanced memory. In contrast, when participants incorrectly predicted the object category, stronger prediction errors (as a consequence of strong expectations) led to impaired memory. These results highlight the important moderating role of choice outcome that may be related to interactions between the hippocampal and striatal dopaminergic systems.

Predictive processing accounts propose that our brain constantly tries to match top-down internal representations with bottom-up incoming information from the environment. Predictions can lead to prediction errors of varying degrees depending on the extent to which the information encountered in the environment conforms with prior expectations. Theoretical and computational models assume that prediction errors have beneficial effects on learning and memory. However, while there is strong evidence on the effects of prediction error on learning, relatively less evidence is available regarding its effects on memory. Moreover, most of the studies available so far manipulated prediction error by using monetary rewards, whereas in everyday life learning does not always occur in the presence of explicit rewards. We used a task in which participants leaned context/object-category associations of different strength based on the outcomes of their predictions. After learning these associations, participants were presented with trial-unique objects that could match or violate their predictions. Finally, participants were asked to complete a surprise recognition memory test. We used a reinforcement learning model to derive subject-specific trial-to-trial estimates of prediction error at encoding and link it to subsequent recognition memory. Results showed that model-derived prediction errors at encoding influenced subsequent memory as a function of the outcome of participants’ predictions (correct vs incorrect). When participants correctly predicted the object category, stronger prediction errors (as a consequence of weak expectations) led to enhanced memory. In contrast, when participants incorrectly predicted the object category, stronger prediction errors (as a consequence of strong expectations) led to impaired memory. These results reveal a computationally specific influence of prediction error on memory formation, highlighting the important moderating role of choice outcome that may be related to interactions between the hippocampal and striatal dopaminergic systems.

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