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Related Concept Videos

Deductive Reasoning01:16

Deductive Reasoning

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Deductive reasoning, or deduction, is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the opposite direction as compared to inductive reasoning, which means that it uses a general principle or law to predict specific results. From those general principles, a scientist can deduce and predict the specific results that would be valid as long as the general principles are valid.
For example, a researcher can deduce specific predictions...
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Inductive Reasoning00:59

Inductive Reasoning

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Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. It is uncertain and operates in degrees to which the conclusions are credible. As such, inductive arguments can be weak or strong, rather than valid or invalid, and conclusions can be used to formulate testable, falsifiable hypotheses.
Inductive reasoning is common in descriptive science. A life scientist makes observations and records them. This data can be qualitative or...
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Reasoning01:30

Reasoning

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Reasoning is the action of thinking about something in a logical, sensible way. It is integral to problem-solving, decision-making, and critical thinking. Reasoning can be inductive or deductive. Reasoning involves transforming information into conclusions, which is essential for problem-solving, decision-making, and critical thinking.
Inductive reasoning involves deriving generalizations from specific observations. This type of reasoning helps form beliefs about the world. For example,...
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Visual Agnosia01:12

Visual Agnosia

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Visual agnosia is a condition characterized by the inability to recognize visually presented objects despite having normal vision. For instance, a person with visual agnosia can describe the shape and color of an object but cannot identify or name it. This impairment does not affect their visual field, acuity, color vision, brightness discrimination, language, or memory. An example of this condition in a social setting is someone at a dinner party asking for "that silver thing with a round...
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Decision Making: P-value Method01:09

Decision Making: P-value Method

6.8K
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...
6.8K
Reason and Intuition01:37

Reason and Intuition

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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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Updated: Jan 17, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Data-And Knowledge-Driven Visual Abductive Reasoning.

Chen Liang, Wenguan Wang, Ling Chen

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |September 23, 2025
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    Summary
    This summary is machine-generated.

    We introduce Visual Abductive Reasoning (VAR), a new AI task for explaining visual events. Our model, Reasonerv2, shows promise but still lags behind human abductive reasoning capabilities.

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    Area of Science:

    • Artificial Intelligence
    • Computer Vision
    • Cognitive Science

    Background:

    • Abductive reasoning, the process of finding the likeliest explanation for observations, is common in human cognition but under-explored in AI.
    • Existing computer vision models often lack the ability to infer explanations for incomplete visual information.

    Purpose of the Study:

    • To introduce Visual Abductive Reasoning (VAR), a novel task enabling AI to infer explanatory hypotheses for observed visual events.
    • To develop and evaluate a model capable of performing knowledge-driven, causal, and cascaded reasoning for VAR.

    Main Methods:

    • Creation of the first large-scale VAR dataset with 9,000 examples.
    • Development of Reasonerv2, a transformer-based model utilizing contextualized directional position embeddings for temporal structure and causal inference.
    • Integration of external knowledge bases and a cascaded decoder architecture for progressive sentence refinement and cross-sentence information flow.

    Main Results:

    • Reasonerv2 demonstrated superior performance compared to established video-language models on the VAR task.
    • The model successfully captured causal temporal structures and leveraged external knowledge for reasoning.
    • Despite advancements, Reasonerv2's performance remains significantly below human-level abductive reasoning.

    Conclusions:

    • Visual Abductive Reasoning (VAR) presents a challenging yet crucial direction for advancing machine intelligence.
    • The Reasonerv2 model offers a strong foundation for knowledge-driven visual reasoning, highlighting the potential of transformer architectures.
    • Further research is needed to bridge the gap between AI and human capabilities in complex abductive reasoning tasks.