Book contents
- Frontmatter
- Contents
- List of Contributors
- Preface
- 1 Introductory Information Theory and the Brain
- Part One Biological Networks
- Part Two Information Theory and Artificial Networks
- 5 Experiments with Low-Entropy Neural Networks
- 6 The Emergence of Dominance Stripes and Orientation Maps in a Network of Firing Neurons
- 7 Dynamic Changes in Receptive Fields Induced by Cortical Reorganization
- 8 Time to Learn About Objects
- 9 Principles of Cortical Processing Applied to and Motivated by Artificial Object Recognition
- 10 Performance Measurement Based on Usable Information
- Part Three Information Theory and Psychology
- Part Four Formal Analysis
- Bibliography
- Index
8 - Time to Learn About Objects
from Part Two - Information Theory and Artificial Networks
Published online by Cambridge University Press: 04 May 2010
- Frontmatter
- Contents
- List of Contributors
- Preface
- 1 Introductory Information Theory and the Brain
- Part One Biological Networks
- Part Two Information Theory and Artificial Networks
- 5 Experiments with Low-Entropy Neural Networks
- 6 The Emergence of Dominance Stripes and Orientation Maps in a Network of Firing Neurons
- 7 Dynamic Changes in Receptive Fields Induced by Cortical Reorganization
- 8 Time to Learn About Objects
- 9 Principles of Cortical Processing Applied to and Motivated by Artificial Object Recognition
- 10 Performance Measurement Based on Usable Information
- Part Three Information Theory and Psychology
- Part Four Formal Analysis
- Bibliography
- Index
Summary
Introduction
To successfully interact with the everyday objects that surround us we must be able to recognise these objects under widely differing conditions, such as novel viewpoints or changes in retinal size and location. Only if we can do this correctly can we determine the behavioural significance of these objects and decide whether the sphere in front of us should, for example, be kicked or eaten. Similar, although often finer discriminations are required in face recognition. One might be presented with the task of deciding which side of the aisle is reserved for the groom's family at your cousin's wedding – a problem of familiar versus unfamiliar categorisation. On the other hand, the faces may be familiar and the task becomes one of distinguishing family members, such as your aunt from your sister. Such decisions have clear social significance and are crucial in deciding how to interact with other people.
Quite how we succeed in recognising people's faces or indeed any other objects remains the subject of much debate. Theories for how we represent objects and ultimately solve object recognition abound. One suggestion is that we construct mental 3D models which we can manipulate in size and orientation until a match to the observed object is found in our repertoire of objects (Marr and Nishihara, 1978; Marr, 1982). Other theories also work on the assumption that we store libraries of objects, but at the level of innumerable, deformable outline sketches or “templates” that can be matched to edges and other features detected in the viewed object (Ullman, 1989; Yuille, 1991; Hinton et al., 1992).
- Type
- Chapter
- Information
- Information Theory and the Brain , pp. 139 - 163Publisher: Cambridge University PressPrint publication year: 2000