Neural temporal context reinstatement of event structure during memory recall

Although life unfolds continuously, experiences are generally perceived and remembered as discrete events. Accumulating evidence suggests that event boundaries disrupt temporal representations and weaken memory associations. However, less is known about the consequences of event boundaries on temporal representations during retrieval, especially when temporal information is not tested explicitly. Using a neural measure of temporal context extracted from scalp electroencephalography, we found reduced temporal context similarity between studied items separated by an event boundary when compared to items from the same event. Further, while participants free recalled list items, neural activity reflected reinstatement of temporal context representations from study, including temporal disruption. A computational model of episodic memory, the Context Maintenance and Retrieval model (CMR; Polyn, Norman & Kahana, 2009), predicted these results, and made novel predictions regarding the influence of temporal disruption on recall order. These findings implicate the impact of event structure on memory organization via temporal representations.

Event boundary detection using autonomous agents in a sensor network

We propose a novel approach to event boundary detection, where autonomous agents are deployed in order to minimize the number of transmissions required to discover an event boundary. The goal of our algorithm is to reduce the number of non-boundary node transmissions (i.e. nodes within the event area and not within transmission distance to the boundary), since the sensory data from these nodes are not required for event boundary detection. The algorithm works by first randomly generating a fraction of agents within the event nodes, then discovering and mapping the boundary, and finally reporting the aggregated results to the user. Simulations demonstrate that the algorithm exhibits O(n) efficiency relationship with the event area, which is an improvement over existing methods that show O(n²) relationships. Furthermore, we demonstrate that the boundary of an event may be successfully mapped using the proposed algorithm.

Event boundary detection using autonomous agents in a sensor network

We propose a novel approach to event boundary detection, where autonomous agents are deployed in order to minimize the number of transmissions required to discover an event boundary. The goal of our algorithm is to reduce the number of non-boundary node transmissions (i.e. nodes within the event area and not within transmission distance to the boundary), since the sensory data from these nodes are not required for event boundary detection. The algorithm works by first randomly generating a fraction of agents within the event nodes, then discovering and mapping the boundary, and finally reporting the aggregated results to the user. Simulations demonstrate that the algorithm exhibits O(n) efficiency relationship with the event area, which is an improvement over existing methods that show O(n²) relationships. Furthermore, we demonstrate that the boundary of an event may be successfully mapped using the proposed algorithm.

Narratives as Networks: Predicting Memory from the Structure of Naturalistic Events

ABSTRACTHuman life consists of a multitude of diverse and interconnected events. However, extant research has focused on how humans segment and remember discrete events from continuous input, with far less attention given to how the structure of connections between events impacts memory. We conducted an fMRI study in which subjects watched and recalled a series of realistic audiovisual narratives. By transforming narratives into networks of events, we found that more central events—those with stronger semantic or causal connections to other events—were better remembered. During encoding, central events evoked larger hippocampal event boundary responses associated with memory consolidation. During recall, high centrality predicted stronger activation in cortical areas involved in episodic recollection, and more similar neural representations across individuals. Together, these results suggest that when humans encode and retrieve complex real-world experiences, the reliability and accessibility of memory representations is shaped by their location within a network of events.

Emergent Goal-Anticipatory Gaze in Infants via Event-Predictive Learning and Inference

From about six months of age onwards, infants start to reliably fixate the goal of an observed action, such as a grasp, before the action is complete. The available research has identified a variety of factors that influence such goal-anticipatory gaze shifts, including the experience with the shown action events and familiarity with the observed agents. However, the underlying cognitive processes are still heavily debated. We propose that our minds (i) tend to structure sensorimotor dynamics into probabilistic, generative event- and event-boundary-predictive models, and, meanwhile, (ii) choose actions with the objective to minimize predicted uncertainty. We implement this proposition by means of event-predictive learning and active inference. The implemented learning mechanism induces an inductive, event-predictive bias, thus developing schematic encodings of experienced events and event boundaries. The implemented active inference principle chooses actions by aiming at minimizing expected future uncertainty. We train our system on multiple object-manipulation events. As a result, the generation of goal-anticipatory gaze shifts emerges while learning about object manipulations: the model starts fixating the inferred goal already at the start of an observed event after having sampled some experience with possible events and when a familiar agent (i.e., a hand) is involved. Meanwhile, the model keeps reactively tracking an unfamiliar agent (i.e a mechanical claw) that is performing the same movement. We conclude that event-predictive learning combined with active inference may be critical for eliciting infant action-goal prediction.

Structuring memory through inference-based event segmentation

Although the stream of information we encounter is continuous, our experiences tend to be discretized into meaningful clusters, altering how we represent our past. Event segmentation theory proposes that clustering ongoing experience in this way is adaptive in that it promotes efficient online processing as well as later reconstruction of relevant information. A growing literature supports this theory by demonstrating its important behavioral consequences. Yet the exact mechanisms of segmentation remain elusive. Here, we provide a brief overview of how event segmentation influences ongoing processing, subsequent memory retrieval, and decision making as well as some proposed underlying mechanisms. We then explore how beliefs, or inferences, about what generates our experience may be the foundation of event cognition. In this inference‐based framework, experiences are grouped together according to what is inferred to have generated them. Segmentation then occurs when the inference changes, creating an event boundary. This offers an alternative to dominant theories of event segmentation, allowing boundaries to occur independent of perceptual change and even when transitions are predictable. We describe how this framework can reconcile seemingly contradictory empirical findings (e.g., memory can be biased toward both extreme episodes and the average of episodes). Finally, we discuss open questions regarding how time is incorporated into the inference process.