Apr 19, 2018 · a graph marking the similarity or difference between two stimuli versus the similarity or difference in their elicited responses.

Learn what generalization gradient means in psychology and behavioral sciences. It is the gradual change in behavior or performance as stimulus similarity increases or decreases.

Learn the definition and examples of generalization gradient in psychology, a graphic description of the strength of responding to similar stimuli. Find out how generalization gradient applies to …

5 days ago · In stimulus generalization, a change in the strength or frequency of a response to a variable stimulus as a function of the degree to which it resembles a particular stimulus to …

In this study, we examined the persistence of shifts in generalization gradients after training with sounds. University students were trained to differentiate two sounds that varied along a …

A stimulus generalization gradient illustrates the extent to which stimuli similar to the original or trained stimulus elicit a response. It shows the gradual decline in responding as stimuli …

Aug 1, 2011 · The present study shows that generalization gradients are sensitive to discrimination processes in human predictive learning. This is in line with most results from …

The shape of the generalization gradient reflects the degree of stimulus generalization that an organism exhibits. What is the typical shape of a stimulus generalization gradient? steep …

Because explaining individual differences in generalization is a central topic in the research literature about generalization, we immediately introduce a random intercept/random slope …

The term generalization gradient is used to describe a graphic representation of the strength of two different stimuli, an original conditioned stimulus and a new stimulus with similar …

Why do children initially call all four-legged creatures by the same name (“doggie!”) but later narrow the application of the verbal label to the class of dogs? The most important factors that …

Feb 25, 2020 · We propose an approach to answering this question based on a hypothesis about the dynamics of gradient descent that we call Coherent Gradients: Gradients from similar …

Jan 16, 2024 · The degree to which organisms respond is determined by their physical resemblance to the original conditioned stimulus (CS+), with a stronger response elicited by …

Stimulus generalization is often quantified using generalization gradients which graph the strength of responses to stimuli that increasingly differ from the original CS. Experimenters can quantify …

Apr 20, 2021 · Here, we propose an inter-domain gradient matching objective that targets domain generalization by maximizing the inner product between gradients from different domains.

Mar 25, 2019 · Generalization gradients are sensitive to the manner in which participants are trained and tested. Discrimination training causes the generalization gradient to shift away …

The problem of generalization and the shape of the generalization gradient is examined in light of recent theoretical and empirical evidence.

Highly ordered generalization gradients (number of responses vs. wavelength) were obtained. The postdiscrimination gradients showed displacement away from the negative stimulus in the …

May 28, 2015 · Because explaining individual differences in generalization is a central topic in the research literature about generalization, we immediately introduce a random intercept/random …

1 day ago · Domain Generalization (DG) refers to a machine learning approach that enables models to perform well across different domains, even when trained only on data from specific …

3 days ago · However, the existing methods predominantly gather the sensor data from multiple source domains together for model training, which poses a threat to data privacy in the IIOT. …

3 days ago · Domain generalization seeks to acquire knowledge from limited source data and apply it to an unknown target domain. Current approaches primarily tackle this challenge by …

Moreover, our theoretical results provide numerically favorable upper bounds of testing errors (e.g., $0.037$ on MNIST). Using similar technique, we can also obtain new generalization …