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Related Concept Videos

Decision Making01:20

Decision Making

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Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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Decision Making: P-value Method01:09

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The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...
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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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Multi-alternative decision-making with non-stationary inputs.

Luana F Nunes1, Kevin Gurney1

  • 1Department of Psychology , University of Sheffield , Sheffield S10 2TP, UK.

Royal Society Open Science
|November 18, 2016
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Summary

We enhanced the multihypothesis sequential probability ratio test (MSPRT) for dynamic environments. Our new methods allow MSPRT to adapt to changing information, improving decision-making in real-world scenarios.

Keywords:
MSPRTbasal gangliadecision-makingmulti-alternativenon-stationary

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Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Decision Theory

Background:

  • The multihypothesis sequential probability ratio test (MSPRT) is a widely used model for multi-alternative decision-making.
  • MSPRT is optimal and applicable to biological decision-making but limited to discrete, static environments.
  • Real-world decision-making is continuous and involves non-stationary stimuli, requiring evidence discarding.

Purpose of the Study:

  • To adapt the MSPRT for continuous, non-stationary environments.
  • To develop a decision mechanism that can dynamically select and deselect options based on changing evidence.
  • To improve the performance and resource efficiency of MSPRT in dynamic settings.

Main Methods:

  • Augmented MSPRT with a rectangular integration window and a transparent decision boundary.
  • Introduced adaptive window sizing based on problem difficulty.
  • Developed an alternative exponential evidence decay method for reduced memory usage.

Main Results:

  • The augmented MSPRT successfully handled dynamic evidence selection and deselection.
  • Adaptive window sizing enhanced performance in non-stationary environments.
  • Exponential decay method offered comparable performance with significant memory savings.

Conclusions:

  • The novel windowing techniques enable MSPRT to function effectively in non-stationary environments.
  • These adaptations address limitations of traditional MSPRT in real-world continuous decision-making.
  • The methods offer a more robust and resource-efficient approach to sequential decision-making under changing conditions.