generaliseringsevner

Generaliseringsevner, or generalization abilities, refer to the capacity to apply what has been learned in one context to new, unseen situations. In psychology and education, it describes the ability to transfer knowledge, rules, or skills beyond the original training task. In machine learning, it denotes performance on data or tasks not encountered during training.

Human generalization relies on abstraction, schemas, and prior knowledge; analogical reasoning and pattern recognition enable applying a rule across domains. Effective generalization improves with varied practice, explicit rule extraction, feedback, and metacognitive strategies. Misgeneralization occurs when a rule is applied too broadly or inappropriately.

In education, researchers study transfer of learning, noting that learners may excel on trained tasks but struggle with novel applications. Assessments may include transfer tasks across contexts.

In AI, generalization is central to model usefulness. A model that overfits fails to generalize; strategies to improve generalization include regularization, diverse and augmented training data, robust representations, and meta-learning. Challenges include covariate shift and distribution shift.

Measuring generalization involves test data drawn from the same distribution or from related domains, cross-domain evaluation, and few-shot or zero-shot benchmarks. Understanding generalization informs education, cognitive science, and AI system design, aiming for flexible, robust performance in real-world settings.