Cognitive State Evolution
Cognitive State Evolution - Related Videos
12:02
Molecular Evolution of the Tre Recombinase
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Cited by 4 •
2008
Here we report the generation of Tre recombinase through directed, molecular evolution. Tre recombinase recognizes a pre-defined target sequence within the LTR sequences of the HIV-1 provirus, resulting in the excision and eradication of the provirus from infected human cells. While still in its infancy, directed molecular evolution will allow the creation of custom enzymes that will serve as tools of molecular surgery and molecular medicine.
08:42
Assessment of Social Cognition in Non-human Primates Using a Network of Computerized Automated Learning Device (ALDM) Test Systems
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Cited by 17 •
2015
Fagot & Paleressompoulle1 have published an automated learning device (ALDM) aimed at testing individual cognitive abilities in semi-free ranging monkeys. The main goal of our protocol is to use a network of ALDM test units to study social cognition in non-human primates.
15:00
Daily Transfers, Archiving Populations, and Measuring Fitness in the Long-Term Evolution Experiment with Escherichia coli
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Cited by 12 •
2023
This protocol describes how to maintain the Escherichia coli Long-Term Evolution Experiment (LTEE) by performing its daily transfers and periodic freeze-downs and how to conduct competition assays to measure fitness improvements in evolved bacteria. These procedures can serve as a template for researchers starting their own microbial evolution experiments.
09:08
An Introduction to Cognition
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2023
Cognition encompasses mental processes such as memory, perception, decision-making reasoning and language. Cognitive scientists are using a combination of behavioral and neuropsychological techniques to investigate the underlying neural substrates of cognition. They are interested in understanding how information is perceived, processed and how does it affect the final execution of behaviors. With this knowledge, researchers hope to develop new treatments for individuals with cognitive...
05:59
Numerical Cognition: More or Less
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2023
Source: Laboratories of Nicholaus Noles and Judith Danovitch—University of Louisville One of the goals of the modern education system is to teach children mathematical literacy. They are taught to add, subtract, multiply, and divide, and this base knowledge is used to support learning about geometry, algebra, calculus, physics, and statistics. School-aged children usually acquire these skills in formal educational settings, but the foundation of mathematical understanding is developed much...
FAQs
Incoming signals update an internal state estimate, allowing the model to represent changing conditions rather than treating the user or system as fixed. Perception supplies new information, while memory and current goals influence how that information affects the next state. The resulting transitions help engineers analyze how conditions develop and how subsequent behavior may change.
These factors shape both state updates and later actions. Perception provides information about the environment, memory contributes prior internal information, and goals influence which changes matter for decisions. Environmental feedback then indicates whether behavior is producing suitable results. Considering these influences together helps explain adaptation in complex human-machine and autonomous systems.
Stability indicates whether a modeled process remains controlled as conditions change, whereas adaptation reflects its ability to respond to new information. Engineering analysis considers both because excessive change can undermine performance, while insufficient change can preserve inaccurate assumptions. Examining these properties supports evaluation of behavior in complex socio-technical systems where users, machines, and environments continually interact.
A fixed-assumption system applies the same expectations despite changes in workload, attention, learning, or other conditions. Cognitive state evolution instead represents transitions and updates an internal estimate as signals and feedback arrive. This distinction matters when system behavior must remain responsive to changing user conditions, particularly in adaptive interfaces and human-robot collaboration.
A practical model begins by identifying the relevant states, incoming signals, and observable behaviors. Engineers then specify how perception, memory, goals, and environmental feedback influence state transitions and how attention, workload, learning, or decision rules affect subsequent behavior. The model can then support analysis of adaptation, stability, and performance under changing conditions.
It is useful when an interface must respond to changing user conditions instead of relying on a constant user profile. By representing shifts in attention, workload, learning, or decision behavior, the framework provides a basis for adapting system responses. This can help engineers study whether an interface remains effective as the user and surrounding environment change.
For autonomous systems, the framework supports responses that account for changing internal estimates and environmental feedback. In human-robot collaboration, it provides a way to consider evolving user conditions alongside machine behavior. These applications use state transitions to examine coordination, adaptation, and performance within socio-technical systems, where successful operation depends on interactions between people, machines, and environments.