Papers in the Biological Sciences


Date of this Version



Published in Trends in Neurosciences, July 2015, Vol. 38, No. 7, pp 405–407. doi 10.1016/j.tins.2015.04.008


Copyright © 2015 Elsevier Ltd. Used by permission.


Definitions of learning vary widely across disciplines, driven largely by different approaches used to assess its occurrence. These definitions can be better reconciled with each other if each is recognized as coherent with a common conceptualization of learning, while appreciating the practical utility of different learning definitions in different contexts.

Learning is a major focus of research in psychology, neuro- science, behavioral ecology, evolutionary theory, and computer science, as well as in many other disciplines. Despite its conceptual prevalence, definitions of learning differ enormously both within and between these disciplines, and new definitions continue to be proposed [1]. Ongoing disputes over the definition of learning generate uncertainty regarding the boundaries of the learning concept and confuse assessments about which phenomena genuinely constitute learning. These disputes impair transdisciplinary collaboration and synthesis between conceptually related fields. Many of the definitions in use by these different disciplines, however, can be aligned with a common “umbrella concept” of learning that can be applied across disciplines by considering learning simply as the processing of information derived from experience to update system properties [2–5]. Many of the definitions also have clear practical utility in that they reflect a variety of approaches to determine whether or how learning has occurred. We argue that embracing the multiple definitions defined by individual subfields (Table S1 in the supplementary material online) – while simultaneously recognizing their shared relationship to this umbrella concept – will facilitate the integration of neurophysiological, psychological, computational, and evolutionary approaches to learning.

Includes supplementary materials.