scholarly journals Structured Event Memory: A neuro-symbolic model of event cognition.

2020 ◽  
Vol 127 (3) ◽  
pp. 327-361 ◽  
Author(s):  
Nicholas T. Franklin ◽  
Kenneth A. Norman ◽  
Charan Ranganath ◽  
Jeffrey M. Zacks ◽  
Samuel J. Gershman
Keyword(s):  
Event Memory ◽  
Event Cognition ◽  
Symbolic Model

scholarly journals Structured event memory: a neuro-symbolic model of event cognition

2019 ◽  
Author(s):  
Nicholas T. Franklin ◽  
Kenneth A. Norman ◽  
Charan Ranganath ◽  
Jeffrey M. Zacks ◽  
Samuel J. Gershman
Keyword(s):  
Probabilistic Reasoning ◽  
Scale Up ◽  
Generative Model ◽  
Video Data ◽  
Event Memory ◽  
Event Cognition ◽  
Event Segmentation ◽  
Symbolic Model ◽  
Continuous Space ◽  
High Level

AbstractHumans spontaneously organize a continuous experience into discrete events and use the learned structure of these events to generalize and organize memory. We introduce theStructured Event Memory(SEM) model of event cognition, which accounts for human abilities in event segmentation, memory, and generalization. SEM is derived from a probabilistic generative model of event dynamics defined over structured symbolic scenes. By embedding symbolic scene representations in a vector space and parametrizing the scene dynamics in this continuous space, SEM combines the advantages of structured and neural network approaches to high-level cognition. Using probabilistic reasoning over this generative model, SEM can infer event boundaries, learn event schemata, and use event knowledge to reconstruct past experience. We show that SEM can scale up to high-dimensional input spaces, producing human-like event segmentation for naturalistic video data, and accounts for a wide array of memory phenomena.

Knowing Your World: Event Cognition

1988 ◽  
Vol 33 (8) ◽  
pp. 690-691
Author(s):  
Betty Tuller
Keyword(s):  
Event Cognition

The Co-Evolution of Event Memory and Knowledge

2008 ◽  
Author(s):  
Angela B. Nelson ◽  
Richard M. Shiffrin
Keyword(s):  
Event Memory

scholarly journals Dynamics of Systems with a Discontinuous Hysteresis Operator and Interval Translation Maps

Axioms ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 80
Author(s):  
Sergey Kryzhevich ◽  
Viktor Avrutin ◽  
Nikita Begun ◽  
Dmitrii Rachinskii ◽  
Khosro Tajbakhsh
Keyword(s):  
Risk Management ◽  
Invariant Measure ◽  
Invariant Measures ◽  
Interval Exchange ◽  
Management Model ◽  
Closing Lemma ◽  
Symbolic Model ◽  
Metric Properties ◽  
Ergodic Measures ◽  
Maximal Invariant

We studied topological and metric properties of the so-called interval translation maps (ITMs). For these maps, we introduced the maximal invariant measure and demonstrated that an ITM, endowed with such a measure, is metrically conjugated to an interval exchange map (IEM). This allowed us to extend some properties of IEMs (e.g., an estimate of the number of ergodic measures and the minimality of the symbolic model) to ITMs. Further, we proved a version of the closing lemma and studied how the invariant measures depend on the parameters of the system. These results were illustrated by a simple example or a risk management model where interval translation maps appear naturally.

Representing a probabilistic linguistic term set with an interval type-2 fuzzy set and the application in green supplier selection

2021 ◽  
pp. 1-18
Author(s):  
Le Jiang ◽  
Hongbin Liu
Keyword(s):  
Decision Making ◽  
Membership Function ◽  
Fuzzy Set ◽  
Supplier Selection ◽  
Symbolic Model ◽  
Green Supplier Selection ◽  
Linguistic Term ◽  
Interval Type ◽  
Decision Making Model

The use of probabilistic linguistic term sets (PLTSs) means the process of computing with words. The existing methods computing with PLTSs mainly use symbolic model. To provide a semantic model for computing with PLTSs, we propose to represent a PLTS by using an interval type-2 fuzzy set (IT2FS). The key step is to compute the footprint of uncertainty of the IT2FS. To this aim, the upper membership function is computed by aggregating the membership functions of the linguistic terms contained in the PLTS, and the lower membership function is obtained by moving the upper membership function downward with the step being total entropy of the PLTS. The comparison rules, some operations, and an aggregation operator for PLTSs are introduced. Based on the proposed method of computing with PLTSs, a multi-criteria group decision making model is introduced. The proposed decision making model is then applied in green supplier selection problem to show its feasibility.

A novel study: long-lasting event memory

Memory ◽  
2021 ◽  
pp. 1-20
Author(s):  
Abigail C. Doolen ◽  
Gabriel A. Radvansky
Keyword(s):  
Event Memory
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