Time changes: Timing contexts support event segmentation in associative memory

Emotional state dynamics impacts temporal memory

Emotional fluctuations are ubiquitous in everyday life, but precisely how they sculpt the temporal organisation of memories remains unclear. Here, we designed a novel task - the Emotion Boundary Task - wherein participants viewed sequences of negative and neutral images surrounded by a colour border. We manipulated perceptual context (border colour), emotional-picture valence, as well as the direction of emotional-valence shifts (i.e., shifts from neutral-to-negative and negative-to-neutral events) to create events with a shared perceptual and/or emotional context. We measured memory for temporal order and temporal distances for images processed within and across events. Negative images processed within events were remembered as closer in time compared to neutral ones. In contrast, temporal distances were remembered as longer for images spanning neutral-to-negative shifts - suggesting temporal dilation in memory with the onset of a negative event following a previously-neutral state. The extent of negative-picture induced temporal dilation in memory correlated with dispositional negativity across individuals. Lastly, temporal order memory was enhanced for recently-presented negative (versus neutral) images. These findings suggest that emotional-state dynamics matters when considering emotion-temporal memory interactions: While persistent negative events may compress subjectively remembered time, dynamic shifts from neutral-to-negative events produce temporal dilation in memory, with implications for adaptive emotional functioning.

Emotional state dynamics impacts temporal memory

Emotional fluctuations are ubiquitous in everyday life, but precisely how they sculpt the temporal organization of memories remains unclear. Here, we designed a novel task-the Emotion Boundary Task-wherein participants viewed sequences of negative and neutral images surrounded by a color border. We manipulated perceptual context (border color), emotional valence, as well as the direction of emotional-valence shifts (i.e., shifts from neutral-to-negative and negative-to-neutral events) to create encoding events comprised of image sequences with a shared perceptual and/or emotional context. We measured memory for temporal order and subjectively remembered temporal distances for images processed within and across events. Negative images processed within events were remembered as closer in time compared to neutral ones. In contrast, temporal distance was remembered as longer for images spanning neutral-to-negative shifts-suggesting temporal dilation in memory with the onset of a negative event following a previously-neutral state. The extent of this negative-picture induced temporal dilation in memory correlated with dispositional negativity across individuals. Lastly, temporal order memory was enhanced for recently presented negative (compared to neutral) images. These findings suggest that emotional-state dynamics matters when considering emotion-temporal memory interactions: While persistent negative events may compress subjectively remembered time, dynamic shifts from neutral to negative events produce temporal dilation in memory, which may be relevant for adaptive emotional functioning.

Target detection does not influence temporal memory

Target detection has been found to enhance memory for concurrently presented stimuli under dual-task conditions. This "attentional boost effect" is reminiscent of findings in the event memory literature, where conditions giving rise to event boundaries have been shown to enhance memory for boundary items. Target detection commonly requires a working memory update (e.g., adding to a covert mental target count), which is also thought to be a key contributor to creating event boundaries. However, whether target detection impacts temporal memory in similar ways as event boundaries remains unknown, because these two parallel literatures have used different types of memory tests, making direct comparisons difficult. In a preregistered experiment with sequential Bayes factor design, we examined whether target detection influences temporal binding between items by inserting target and nontarget stimuli during encoding of trial-unique object images, and then comparing subsequent temporal order and distance memory for image pairs that span a target or nontarget. We found that target detection enhanced recognition memory for target trial images but had no effect on temporal binding between items. In a follow-up experiment, we showed that when the encoding task required updating of task set rather than target count, event segmentation-related temporal memory effects were observed. These results document that target detection as such does not disrupt inter-item associations in memory, and that attention orienting in the absence of updating task sets does not create event boundaries. This suggests a key distinction between declarative and procedural working memory updates in segmenting events in memory.

Multisensory synchrony of contextual boundaries affects temporal order memory, but not encoding or recognition

We memorize our daily life experiences, which are often multisensory in nature, by segmenting them into distinct event models, in accordance with perceived contextual or situational changes. However, very little is known about how multisensory boundaries affect segmentation, as most studies have focused on unisensory (visual or audio) segmentation. In three experiments, we investigated the effect of multisensory boundaries on segmentation in memory and perception. In Experiment 1, participants encoded lists of pictures while audio and visual contexts changed synchronously or asynchronously. After each list, we tested recognition and temporal associative memory for pictures that were encoded in the same audio-visual context or that crossed a synchronous or an asynchronous multisensory change. We found no effect of multisensory synchrony for recognition memory: synchronous and asynchronous changes similarly impaired recognition for pictures encoded at those changes, compared to pictures encoded further away from those changes. Multisensory synchrony did affect temporal associative memory, which was worse for pictures encoded at synchronous than at asynchronous changes. Follow up experiments showed that this effect was not due to the higher dimensionality of multisensory over unisensory contexts (Experiment 2), nor that it was due to the temporal unpredictability of contextual changes inherent to Experiment 1 (Experiment 3). We argue that participants formed situational expectations through multisensory synchronicity, such that synchronous multisensory changes deviated more strongly from those expectations than asynchronous changes. We discuss our findings in light of supportive and conflicting findings of uni- and multi-sensory segmentation.

Bringing Bayes and Shannon to the Study of Behavioural and Neurobiological Timing and Associative Learning

Bayesian parameter estimation and Shannon’s theory of information provide tools for analysing and understanding data from behavioural and neurobiological experiments on interval timing—and from experiments on Pavlovian and operant conditioning, because timing plays a fundamental role in associative learning. In this tutorial, we explain basic concepts behind these tools and show how to apply them to estimating, on a trial-by-trial, reinforcement-by-reinforcement and response-by-response basis, important parameters of timing behaviour and of the neurobiological manifestations of timing in the brain. These tools enable quantification of relevant variables in the trade-off between acting as an ideal observer should act and acting as an ideal agent should act, which is also known as the trade-off between exploration (information gathering) and exploitation (information utilization) in reinforcement learning. They enable comparing the strength of the evidence for a measurable association to the strength of the behavioural evidence that the association has been perceived. A GitHub site and an OSF site give public access to well-documented Matlab and Python code and to raw data to which these tools have been applied.