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Decision Making01:20

Decision Making

175
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.
Automatic decision-making is fast, intuitive, and relies on gut feelings...
175
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

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The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
4.1K
Reason and Intuition01:37

Reason and Intuition

6.5K
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...
6.5K
Decision Making: P-value Method01:09

Decision Making: P-value Method

5.6K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
5.6K
Factors Affecting Perception01:25

Factors Affecting Perception

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Perception is influenced by perceptual set, context, motivation, and emotion. Perceptual set, or perceptual expectancy, refers to the tendency to perceive things in a particular way, influenced by previous experiences and expectations. This phenomenon affects the interpretation of stimuli, creating a set of mental tendencies and assumptions that impact sensory perceptions of sound, taste, touch, and sight.
An illustrative example of a perceptual set is the scenario where an airline pilot told...
1.7K
Cognitive Theories: Lazarus Mediational Theory of Emotion01:17

Cognitive Theories: Lazarus Mediational Theory of Emotion

1.1K
Richard Lazarus' cognitive mediational theory highlights the pivotal role of cognitive appraisal in shaping emotional responses. According to this theory, the evaluation of a stimulus — based on personal values, goals, beliefs, and expectations — mediates the emotional response. This appraisal process is immediate and often occurs unconsciously, influencing the intensity and nature of the resulting emotion.
Cognitive Appraisal and Emotional Response
Lazarus proposed that...
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Updated: Aug 19, 2025

The Adventures of Fundi Intervention Based on the Cognitive and Emotional Processing in Attention Deficit Hyperactive Disorder Patients
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Quantum affective processes for multidimensional decision-making.

Johnny K W Ho1, Johan F Hoorn2,3,4,5

  • 1Laboratory for Artificial Intelligence in Design, Hong Kong Science Park, New Territories, Hong Kong. kawaiho@aidlab.hk.

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|November 28, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces Quantum Coppélia (Q-Coppélia), a quantum-computational framework for simulating robot affect. It addresses challenges in modeling ambiguous emotional states and parallel processing in human-robot interaction.

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Area of Science:

  • Artificial Intelligence
  • Quantum Computing
  • Cognitive Science

Background:

  • Modeling human affective systems for human-robot interaction presents challenges in handling ambiguous emotional states, probabilistic decisions, and parallel cognitive processes.
  • Conventional computation struggles with representing contextual behaviors and self-directed neuroplasticity, hindering realistic affective simulations.
  • Existing fuzzy-based systems like Silicon Coppélia offer a foundation but lack the theoretical capacity for quantum-level ambiguity and parallelism.

Purpose of the Study:

  • To develop a quantum-computational framework for simulating robot affect that can handle indefinite and ambiguous emotional states.
  • To address the limitations of conventional computation in modeling parallel cognitive processes and contextual aspects of behavior.
  • To translate fuzzy-based affective models into quantum logics for enhanced simulation capabilities in human-robot interaction.

Main Methods:

  • Developed Quantum Coppélia (Q-Coppélia) by translating the fuzzy-based Silicon Coppélia system into quantum logics.
  • Utilized quantum algorithms to represent and process ambiguous affective states and parallel cognitive functions.
  • Detailed the circuitry of the Q-Coppélia framework for simulating the progression of a robot's attitude towards a user.

Main Results:

  • Demonstrated the theoretical capability of Q-Coppélia to account for indefinite and ambiguous affective states.
  • Showcased the framework's ability to handle parallelism, a key feature of cognitive processes.
  • Provided a system for simulating affective interactions by linking quantum algorithms with psychological principles.

Conclusions:

  • Q-Coppélia offers a novel approach to simulating robot affect by leveraging quantum computation.
  • The framework theoretically supports the modeling of complex psychological phenomena like ambiguity and parallelism in affective states.
  • This work bridges quantum algorithms and psychology, paving the way for more sophisticated affective human-robot interaction.