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Related Concept Videos

Bias01:22

Bias

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Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
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Bias in Epidemiological Studies01:29

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Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
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Confirmation Biases01:31

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The confirmation bias is the tendency to focus on information that confirms our existing beliefs and ignore information that is inconsistent with our expectations. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Have you ever fallen prey to the confirmation bias, either as the source or target of such bias?
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Halo Effect01:27

Halo Effect

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The halo effect is a cognitive bias in which an individual's overall impression influences judgments about their specific traits. This psychological phenomenon leads people to associate positive characteristics with those they perceive as generally good and negative characteristics with those they view as bad. This effect is particularly influential in social perception, professional evaluations, and decision-making processes.The Psychological Basis of the Halo EffectThe halo effect is rooted...
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Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

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The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
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Dose-Response Relationship: Selectivity and Specificity01:25

Dose-Response Relationship: Selectivity and Specificity

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Drugs exert their therapeutic effects by interacting with receptors, enzymes, or ion channels that are present throughout the human body. The strength and duration of the interaction between a drug and its target receptor are characterized by the selectivity and specificity of the drug. Selectivity refers to a drug's strong preference for its intended target over other targets. For instance, isoprenaline, a non-selective β-adrenergic agonist, interacts with both β1- and...
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Related Experiment Video

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Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues
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Distinguishing bias from sensitivity effects in multialternative detection tasks.

Devarajan Sridharan1, Nicholas A Steinmetz2, Tirin Moore3

  • 1Department of Neurobiology, Stanford University School of Medicine, Stanford, CA, USA.

Journal of Vision
|August 23, 2014
PubMed
Summary

A new signal detection model separates perceptual sensitivity from choice bias in multialternative tasks. This advances understanding of perception, attention, and decision-making by accurately quantifying behavioral contributions.

Keywords:
attentionmultidimensional modelsnonforced choiceoptimal decision theoryperceptual decision-makingsignal detection theoryunforced choice

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Area of Science:

  • Cognitive Neuroscience
  • Computational Neuroscience
  • Psychophysics

Background:

  • Multialternative detection tasks are crucial for studying cognitive processes.
  • Distinguishing perceptual sensitivity from choice bias is vital but challenging with current methods.

Purpose of the Study:

  • To introduce a novel signal detection model.
  • To decouple the effects of choice bias from perceptual sensitivity in multialternative tasks.
  • To provide a framework for analyzing complex perceptual decisions.

Main Methods:

  • Developed a signal detection model operating in a multidimensional decision space.
  • Employed analytical and numerical methods to map model parameters to choice probabilities.
  • Validated the model using published data from ternary choice experiments.

Main Results:

  • The model successfully quantifies the independent contributions of bias and sensitivity.
  • Demonstrated an optimal mapping between model parameters and choice probabilities for numerous alternatives.
  • Provided new insights into sensory noise and competitive interactions in perception.

Conclusions:

  • The new model accurately and parsimoniously explains observer behavior in multialternative tasks.
  • It offers a valuable tool for interpreting behavioral and neural perturbations.
  • Facilitates research in perception, attention, and decision-making.