Dataset Information

Attention in Psychology, Neuroscience, and Machine Learning.

ABSTRACT: Attention is the important ability to flexibly control limited computational resources. It has been studied in conjunction with many other topics in neuroscience and psychology including awareness, vigilance, saliency, executive control, and learning. It has also recently been applied in several domains in machine learning. The relationship between the study of biological attention and its use as a tool to enhance artificial neural networks is not always clear. This review starts by providing an overview of how attention is conceptualized in the neuroscience and psychology literature. It then covers several use cases of attention in machine learning, indicating their biological counterparts where they exist. Finally, the ways in which artificial attention can be further inspired by biology for the production of complex and integrative systems is explored.

SUBMITTER: Lindsay GW

PROVIDER: S-EPMC7177153 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Attention in Psychology, Neuroscience, and Machine Learning.

Lindsay Grace W GW

Frontiers in computational neuroscience 20200416

Attention is the important ability to flexibly control limited computational resources. It has been studied in conjunction with many other topics in neuroscience and psychology including awareness, vigilance, saliency, executive control, and learning. It has also recently been applied in several domains in machine learning. The relationship between the study of biological attention and its use as a tool to enhance artificial neural networks is not always clear. This review starts by providing an ...[more]

PMID: 32372937

Similar Datasets

Project description:This article examines the relation of contemplative exercises with enhancement of students' experience during neuroscience studies. Short contemplative exercises inspired by the Buddhist tradition of self-inquiry were introduced in an undergraduate neuroscience course for psychology students. At the start of the class, all students were asked to participate in short "personal brain investigations" relevant to the topic presented. These investigations were aimed at bringing stable awareness to a specific perceptual, emotional, attentional, or cognitive process and observing it in a non-judgmental, non-personal way. In addition, students could choose to participate, for bonus credit, in a longer exercise designed to expand upon the weekly class activity. In the exercise, students continued their "personal brain investigations" for 10 min a day, 4 days a week. They wrote "lab reports" on their daily observations, obtained feedback from the teacher, and at the end of the year reviewed their reports and reflected upon their experiences during the semester. Out of 265 students, 102 students completed the bonus track and their final reflections were analyzed using qualitative methodology. In addition, 91 of the students answered a survey at the end of the course, 43 students participated in a quiz 1 year after course graduation, and the final grades of all students were collected and analyzed. Overall, students reported satisfaction from the exercises and felt they contributed to their learning experience. In the 1-year follow-up, the bonus-track students were significantly more likely than their peers to remember class material. The qualitative analysis of bonus-track students' reports revealed that the bonus-track process elicited positive feelings, helped students connect with class material and provided them with personal insights. In addition, students acquired contemplative skills, such as increased awareness and attention, non-judgmental attitudes, and better stress-management abilities. We provide examples of "personal brain investigations" and discuss limitations of introducing a contemplative approach.

Dataset Information

Attention in Psychology, Neuroscience, and Machine Learning.

Publications

Attention in Psychology, Neuroscience, and Machine Learning.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets