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Related Concept Videos

Attribution Theory00:56

Attribution Theory

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Behavior is a product of both the situation (e.g., cultural influences, social roles, and the presence of bystanders) and of the person (e.g., personality characteristics). Subfields of psychology tend to focus on one influence or behavior over others. Situationism is the view that our behavior and actions are determined by our immediate environment and surroundings. In contrast, dispositionism holds that our behavior is determined by internal factors (Heider, 1958).
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Fundamental Attribution Error01:14

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According to some social psychologists, people tend to overemphasize internal factors as explanations—or attributions—for the behavior of other people. They tend to assume that the behavior of another person is a trait of that person, and to underestimate the power of the situation on the behavior of others. They tend to fail to recognize when the behavior of another is due to situational variables, and thus to the person’s state. This erroneous assumption is...
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Attribution01:26

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In social interactions, individuals frequently seek to understand the motivations and causes behind others' behaviors. This fundamental aspect of social perception, known as attribution, plays a crucial role in shaping interpersonal relationships and guiding future actions. Attribution refers to the cognitive process through which people infer the reasons behind others' behaviors, allowing them to assess character traits, intentions, and situational influences.Attribution Theory and Its...
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What is a Hypothesis?01:14

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A hypothesis can be a simple sentence or statement about a property or any phenomenon observed or predicted for a population. It is usually a claim about a  property of the population. It can be stated for any field observations or experiments. A hypothesis statement cannot be said to be right or wrong as it is merely a statement. It needs to be tested through an elaborate data collection process and an appropriate statistical test. A hypothesis should be a general but not a vague...
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Understanding the Self01:28

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The self is a central aspect of human identity, encompassing an individual’s beliefs, emotions, perceptions, and experiences. It is a cognitive and psychological construct that enables individuals to interpret their traits and behaviors, influencing how they perceive themselves and interact with the world. While personality consists of stable and enduring characteristics, the self is shaped by self-perception and social experiences. This distinction highlights the dynamic nature of the...
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Types of Hypothesis Testing01:11

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There are three types of hypothesis tests: right-tailed, left-tailed, and two-tailed.
When the null and alternative hypotheses are stated, it is observed that the null hypothesis is a neutral statement against which the alternative hypothesis is tested. The alternative hypothesis is a claim that instead has a certain direction. If the null hypothesis claims that p = 0.5, the alternative hypothesis would be an opposing statement to this and can be put either p > 0.5, p < 0.5, or p...
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Understanding Cerebellar Pattern Formation
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JANUS: A hypothesis-driven Bayesian approach for understanding edge formation in attributed multigraphs.

Lisette Espín-Noboa1,2, Florian Lemmerich1,2, Markus Strohmaier1,2

  • 11GESIS - Leibniz Institute for the Social Sciences, Unter Sachsenhausen 6-8, Cologne, 50667 Germany.

Applied Network Science
|November 17, 2018
PubMed
Summary
This summary is machine-generated.

We introduce JANUS, a new Bayesian method for network analysis. This approach helps compare theories on why connections form in complex networks.

Keywords:
Attributed multigraphsBayesian inferenceEdge formationHypTrailsMultiplex

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Area of Science:

  • Network analysis
  • Graph theory
  • Statistical modeling

Background:

  • Edge formation is a fundamental problem in network analysis.
  • Existing methods include network growth models and statistical regression.
  • A gap exists in intuitively comparing hypotheses about edge formation in multigraphs.

Purpose of the Study:

  • To introduce JANUS, a novel hypothesis-driven Bayesian approach.
  • To enable intuitive comparison of hypotheses regarding edge formation in multigraphs.
  • To model edge multiplicity using a categorical model and express hypotheses as priors.

Main Methods:

  • Developed JANUS, a hypothesis-driven Bayesian framework.
  • Utilized a categorical model for edge multiplicity.
  • Employed Bayesian model comparison to evaluate hypothesis plausibility.
  • Tested on synthetic and empirical network data.

Main Results:

  • JANUS effectively compares hypotheses about edge formation.
  • The approach integrates prior beliefs about parameters.
  • Demonstrated utility on both simulated and real-world network data.
  • Successfully applied Bayesian model comparison to network hypotheses.

Conclusions:

  • JANUS provides a powerful new tool for network analysis.
  • It facilitates the study of mechanisms driving edge formation.
  • Relevant for researchers studying network structure and dynamics.
  • Offers both empirical and methodological contributions to the field.