Structuring time: The hippocampus constructs sequence memories that generalize temporal relations across experiences

AbstractThe hippocampal-entorhinal region supports memory for episodic details, such as temporal relations of sequential events, and mnemonic constructions combining experiences for inferential reasoning. However, it is unclear whether hippocampal event representations reflect temporal relations derived from mnemonic constructions, event order, or elapsing time, and whether they generalize temporal relations across similar sequences. Here, participants mnemonically constructed times of events from multiple sequences using infrequent cues and their experience of passing time. After learning, event representations in the anterior hippocampus reflected sequence relations based on constructed times. These event representations generalized across sequences, revealing distinct representational formats for events from the same or different sequences. Structural knowledge about time patterns, abstracted from different sequences, biased the construction of specific event times. These findings demonstrate that the hippocampus reconciles representations of specific relations with the generalization across different episodes, consistent with memory-based constructions combining episodic details and general knowledge to simulate scenarios.

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From improvisation to learning: how naturalness and systematicity shape language evolution

10.31234/osf.io/7ed89 ◽

2021 ◽

Author(s):

Yasamin Motamedi ◽

Lucie Wolters ◽

Danielle Naegeli ◽

Simon Kirby ◽

Marieke Schouwstra

Keyword(s):

Word Order ◽

Cultural Transmission ◽

Cognitive Biases ◽

Language Evolution ◽

Direct Object ◽

Natural Languages ◽

Gesture Studies ◽

Event Order ◽

Linguistic Backgrounds ◽

Learning Event

Silent gesture studies, in which hearing participants from different linguistic backgrounds produce gestures to communicate events, have been used to test hypotheses about the cognitive biases that govern cross-linguistic word order preferences. In particular, the differential use of SOV and SVO order to communicate, respectively, extensional events (where the direct object exists independently of the event; e.g., girl throws ball) and intensional events (where the meaning of the direct object is potentially dependent on the verb; e.g., girl thinks of ball), has been suggested to represent a natural preference, demonstrated in improvisation contexts. However, natural languages tend to prefer systematic word orders, where a single order is used regardless of the event being communicated. We present a series of studies that investigate ordering preferences for SOV and SVO orders using an online forced-choice experiment, where participants select orders for different events i) in the absence of conventions and ii) after learning event-order mappings in different frequencies in a regularisation experiment. Our results show that natural ordering preferences arise in the absence of conventions, replicating previous findings from production experiments. In addition, we show that participants regularise the input they learn in the manual modality in two ways, such that, while the preference for systematic order patterns increases through learning, it exists in competition with the natural ordering preference, that conditions order on the semantics of the event. Using our experimental data in a computational model of cultural transmission, we show that this pattern is expected to persist over generations, suggesting that we should expect to see evidence of semantically-conditioned word order variability in at least some languages.

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Event2Vec: Learning Event Representations Using Spatial-Temporal Information for Recommendation

Advances in Knowledge Discovery and Data Mining - Lecture Notes in Computer Science ◽

10.1007/978-3-030-16142-2_25 ◽

2019 ◽

pp. 314-326 ◽

Cited By ~ 2

Author(s):

Yan Wang ◽

Jie Tang

Keyword(s):

Temporal Information ◽

Event Representations ◽

Learning Event

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Learning Event Representations for Temporal Segmentation of Image Sequences by Dynamic Graph Embedding

IEEE Transactions on Image Processing ◽

10.1109/tip.2020.3044448 ◽

2021 ◽

Vol 30 ◽

pp. 1476-1486

Author(s):

Mariella Dimiccoli ◽

Herwig Wendt

Keyword(s):

Graph Embedding ◽

Image Sequences ◽

Dynamic Graph ◽

Temporal Segmentation ◽

Event Representations ◽

Learning Event

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Learning Event Representations by Encoding the Temporal Context

Lecture Notes in Computer Science - Computer Vision – ECCV 2018 Workshops ◽

10.1007/978-3-030-11015-4_44 ◽

2019 ◽

pp. 587-596 ◽

Cited By ~ 1

Author(s):

Catarina Dias ◽

Mariella Dimiccoli

Keyword(s):

Temporal Context ◽

Event Representations ◽

Learning Event

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The hippocampal formation and action at a distance

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.2119670118 ◽

2021 ◽

Vol 118 (51) ◽

pp. e2119670118

Author(s):

Lynn Nadel

Keyword(s):

Immanuel Kant ◽

Hippocampal Formation ◽

Critical Role ◽

Cognitive Maps ◽

Space And Time ◽

Action At A Distance ◽

The Mind ◽

Event Representations ◽

With Memory ◽

Close Temporal Proximity

The question of why our conceptions of space and time are intertwined with memory in the hippocampal formation is at the forefront of much current theorizing about this brain system. In this article I argue that animals bridge spatial and temporal gaps through the creation of internal models that allow them to act on the basis of things that exist in a distant place and/or existed at a different time. The hippocampal formation plays a critical role in these processes by stitching together spatiotemporally disparate entities and events. It does this by 1) constructing cognitive maps that represent extended spatial contexts, incorporating and linking aspects of an environment that may never have been experienced together; 2) creating neural trajectories that link the parts of an event, whether they occur in close temporal proximity or not, enabling the construction of event representations even when elements of that event were experienced at quite different times; and 3) using these maps and trajectories to simulate possible futures. As a function of these hippocampally driven processes, our subjective sense of both space and time are interwoven constructions of the mind, much as the philosopher Immanuel Kant postulated.

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Actin Automata with Memory

International Journal of Bifurcation and Chaos ◽

10.1142/s021812741650019x ◽

2016 ◽

Vol 26 (01) ◽

pp. 1650019 ◽

Cited By ~ 4

Author(s):

Ramón Alonso-Sanz ◽

Andy Adamatzky

Keyword(s):

Actin Filaments ◽

State Transitions ◽

State Machines ◽

Double Chain ◽

Neighboring Node ◽

Current State ◽

Time Patterns ◽

Finite State ◽

With Memory ◽

Momentary State

Actin is a globular protein which forms long polar filaments in eukaryotic. The actin filaments play the roles of cytoskeleton, motility units, information processing and learning. We model actin filament as a double chain of finite state machines, nodes, which take states “0” and “1”. The states are abstractions of absence and presence of a subthreshold charge on actin units corresponding to the nodes. All nodes update their state in parallel to discrete time. A node updates its current state depending on states of two closest neighbors in the node chain and two closest neighbors in the complementary chain. Previous models of actin automata consider momentary state transitions of nodes. We enrich the actin automata model by assuming that states of nodes depend not only on the current states of neighboring node but also on their past states. Thus, we assess the effect of memory of past states on the dynamics of acting automata. We demonstrate in computational experiments that memory slows down propagation of perturbations, decrease entropy of space-time patterns generated, transforms traveling localizations to stationary oscillators, and stationary oscillations to still patterns.

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