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Related Concept Videos

State Space Representation01:27

State Space Representation

The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
Storage01:23

Storage

A schema is a mental framework that helps individuals organize and interpret information. Schemata, formed from previous experiences, influence how we process new information: how we encode it, the inferences we make, and how we retrieve it. For instance, a schema for what a typical classroom looks like might include desks, a teacher's desk, a whiteboard, and students in such an environment. This expectation helps us quickly understand and navigate new classrooms without needing to analyze each...
Framing Effects03:26

Framing Effects

Information is everywhere and its presentation—such as how and when items are presented—can impact our perceptions and decisions surrounding the info. This broad concept umbrellas framing effects—influences that occur due to the way information is framed in its appearance, whether it’s purely the order or the specific wording of a message. Let’s take a look at numerous ways in which two versions of something can objectively say the same thing, yet we respond in different ways based on the...
Elaborative Rehearsals01:07

Elaborative Rehearsals

Elaborative rehearsal is a crucial cognitive strategy that strengthens information encoding in long-term memory by making meaningful connections between new data and pre-existing knowledge. This approach contrasts with maintenance rehearsal, which involves simple repetition without delving into the significance of the information. While maintenance rehearsal might temporarily keep information active in short-term memory, it is less effective for long-term retention.
The effectiveness of...
Language Development01:22

Language Development

Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...
Encoding01:19

Encoding

Information enters the brain through encoding, which is the input of information into the memory system. Once sensory information is received from the environment, the brain labels or codes it. The information is then organized with similar information and connected to existing concepts. Encoding occurs through automatic processing and effortful processing.
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Related Experiment Videos

Constructing semantic representations from a gradually-changing representation of temporal context.

Marc W Howard1, Karthik H Shankar, Udaya K K Jagadisan

  • 1Syracuse University.

Topics in Cognitive Science
|May 10, 2011
PubMed
Summary
This summary is machine-generated.

Computational models using word co-occurrences can reveal semantic memory representations. The predictive temporal context model (pTCM) and temporal context model (TCM) may form a general declarative memory model.

Related Experiment Videos

Area of Science:

  • Cognitive Science
  • Computational Linguistics
  • Neuroscience

Background:

  • Computational models of semantic memory analyze word co-occurrences in text.
  • These models define temporal context through word proximity (window, sentence, or document).
  • The temporal context model (TCM) is established for episodic memory research.

Purpose of the Study:

  • To introduce the predictive temporal context model (pTCM) for generating semantic memory representations.
  • To explore the integration of pTCM and TCM as components of a broader declarative memory model.

Main Methods:

  • Utilizing naturally-occurring text to identify word co-occurrences.
  • Defining temporal context based on word proximity within textual data.
  • Applying the temporal context model (TCM) and developing the predictive temporal context model (pTCM).

Main Results:

  • pTCM generates semantic memory representations using the TCM's temporal context definition.
  • The combined framework of pTCM and TCM offers a unified approach to declarative memory.

Conclusions:

  • pTCM provides a novel method for semantic memory representation.
  • The integration of pTCM and TCM advances the understanding of declarative memory mechanisms.