Intention Forecasting
Intention Forecasting - Related Videos
09:47
Children's Reliance on Artist Intentions When Identifying Pictures
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2023
Source: Laboratories of Judith Danovitch and Nicholaus Noles—University of Louisville Children are not the best artists. Sometimes it’s easy to pick out the characteristic triangular head, whiskers, and tail of a cat, but children often describe elaborate scenarios that they depict as a beautifully unrecognizable mess. Thus, given children’s questionable artistic talent, how do they know what their drawings, and the drawings of others, represent? One way children identify pictures is by relying...
07:35
How Virtual Celebrity Characteristics Drive Purchase Intention: Testing the Stimulus-Organism-Response Framework with Structural Equation Modeling
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2026
In the context of the virtual celebrity experience, content quality most strongly drives satisfaction; satisfaction, in turn, increases purchase intention indirectly via loyalty. Personalization strengthens the satisfaction-loyalty link, so firms should prioritize emotion-evoking, narrative-consistent virtual celebrity content and personalized loyalty programs.
11:14
A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
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Cited by 3 •
2015
This protocol presents a novel methodology for the neural decoding of intent from freely-behaving infants during unscripted social interaction with an actor. Neural activity is acquired using non-invasive high-density active scalp electroencephalography (EEG). Kinematic data is collected with inertial measurement units and supplemented with synchronized video recording.
01:29
Reliability and Validity
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2020
Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways. Unfortunately, being consistent in measurement does not necessarily mean that you have measured something correctly. To illustrate this concept, consider a kitchen...
11:45
Piaget's Conservation Task and the Influence of Task Demands
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2023
Source: Laboratories of Judith Danovitch and Nicholaus Noles—University of Louisville Jean Piaget was a pioneer in the field of developmental psychology, and his theory of cognitive development is one of the most well-known psychological theories. At the heart of Piaget’s theory is the idea that children’s ways of thinking change over the course of childhood. Piaget provided evidence for these changes by comparing how children of different ages responded to questions and problems that he...
FAQs
Intention forecasting typically combines several evidence streams rather than relying on one cue. Motion trajectories indicate how behavior is unfolding, while gaze, language, environmental context, and interaction history add information about possible goals. A model integrates these signals to estimate latent intentions, meaning goals that are not directly observable, and maintains uncertainty because observations may support multiple interpretations.
Uncertainty estimates are operational signals, not merely statistical outputs. When a predicted intention is sufficiently clear, an engineered system can plan around the expected action. If ambiguity remains, it can request clarification or defer to human control instead of committing to a risky response. This makes forecasting useful for managing when a machine should act, wait, or seek more information.
Environmental context and interaction history help distinguish similar movements or statements that could lead to different actions. The same trajectory may carry different implications depending on surroundings and what has already occurred, so forecasting cannot treat behavior as isolated motion. Including these variables supports more context-sensitive predictions and can improve coordination by reducing delays caused by misinterpreting the next action.
An engineering workflow begins by observing available behavioral and contextual signals, combining them in a probabilistic or machine-learning model, and estimating likely intentions with associated uncertainty. The resulting forecast can then inform system planning before the person or agent completes the action. In deployment, the uncertainty estimate also guides whether the system acts, asks for clarification, or defers control.
During human-robot collaboration, forecasts help a robot anticipate a person's likely next action and coordinate its own behavior accordingly. This can support safer planning around expected human movement and reduce the chance of collisions or unnecessary waiting. Because the prediction is uncertain, the robot can avoid treating an inference as certainty and can defer to human control when appropriate.
In autonomous navigation, anticipated human behavior can inform planning so a system responds before an interaction becomes urgent. In assistive technologies, forecasts can help tailor responses to a person's expected goals, while adaptive interfaces can adjust interaction based on predicted actions. Across these settings, the practical outcome is faster coordination, fewer delays, and more context-sensitive machine behavior.