Ai-based Image Judgment
Ai-based Image Judgment - Related Videos
09:20
Using fMRI to Dissect Moral Judgment
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2023
Source: William Brady & Jay Van Bavel—New York University In examining the roles of reason and emotion in moral judgments, psychologists and philosophers alike point to the trolley dilemma and the footbridge dilemma. With the trolley dilemma, most people say that it is appropriate to pull a switch to stop a train from hitting five people by diverting it to kill one person. However, with the footbridge dilemma, most people say it is inappropriate to push a large man off of a bridge in order...
08:04
Evaluating the Accuracy of Snap Judgments
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2023
Source: Diego Reinero & Jay Van Bavel—New York University Social psychologists have long been interested in the way people form impressions of others. Much of this work has focused on the errors people make in judging others, such as the exaggerated influence of central traits (such as "warm" and "cold"), the insufficient weight given to the context in which others' behavior takes place, and the tendency for people to make judgments that conform to their initial expectations about another.
13:00
Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
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Cited by 20 •
2017
Temporal-order judgments can be used to estimate processing speed parameters and attentional weights and thereby to infer the mechanisms of attentional processing. This methodology can be applied to a wide range of visual stimuli and works with many attention manipulations.
08:12
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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Cited by 3 •
2022
The protocol presents an experimental psychophysics paradigm to obtain large quantities of similarity judgments, and an accompanying analysis workflow. The paradigm probes context effects and enables modeling of similarity data in terms of Euclidean spaces of at least five dimensions.
12:10
Assessment of Mouse Judgment Bias through an Olfactory Digging Task
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2022
This article provides a detailed description of a novel mouse judgment bias protocol. Evidence of this olfactory digging task's sensitivity to affective state is also demonstrated and its utility across diverse research fields is discussed.
FAQs
Annotated training images provide examples that allow a machine-learning model to learn visual patterns associated with developmental features. The model can then apply those learned patterns to new samples, supporting consistent interpretation across a larger image set. This process is especially useful when researchers need to recognize recurring differences in cell shape, position, movement, or tissue organization.
Cell shape, spatial position, movement, and tissue organization are key visual features that can inform analysis. Their significance depends on the developmental question being studied, such as whether cells are changing location or tissues are becoming organized. Examining these features systematically helps connect image-level patterns with developmental stages, morphogenesis, or cellular abnormalities.
Automated analysis can examine large image datasets using consistent criteria rather than relying entirely on repeated visual scoring by individuals. This reduces the burden of manual interpretation and can make subtle changes easier to quantify across experiments. As a result, researchers may identify developmental patterns that are difficult to compare reliably when images are evaluated one at a time.
A typical workflow begins with annotated images that represent the visual patterns of interest. A machine-learning model learns from those examples, after which the trained model is applied to new developmental samples. The resulting interpretations can support classification, tracking, or quantification of image features, providing structured information for comparisons across samples and experiments.
Researchers can use this approach when they need to track how cells and tissues change shape, position, movement, or organization during morphogenesis. By analyzing images across developmental samples, the method supports examination of how form emerges over time. It can also help compare changes across experiments rather than relying solely on qualitative descriptions of tissue appearance.
In developmental biology, image-based AI analysis can assist with classifying developmental stages, tracking morphogenesis, identifying cellular abnormalities, and quantifying changes across experiments. These outcomes provide more structured evidence about how cells and tissues form over time. The approach is therefore relevant both to describing normal developmental organization and to recognizing visual patterns associated with abnormal cellular behavior.