Object Recognition in Man, Monkey, and Machine edited by Michael J. Tarr and Heinrich H. Bülthoff

MIT Press, 1999. £17.50/$25.00 (217 pages)ISBN 0 262 70070 0Back in graduate school, I remember reading a collection of papers from a special issue of Cognition that was published as a book under the title, Visual Cognition1xVisual cognition. Pinker, S. : 1–63See all References1. Edited by Steven Pinker, Visual Cognition gave voice to an emerging field of research that connected the processes of object perception with the processes of object recognition. For a graduate student with a burgeoning interest in these issues, the book served as sort of an introductory field guide to the ‘big’ problems in this relatively new area of research. As Pinker explained in the book’s introduction, a big, if not the biggest, problem in the field involved the question of shape recognition. Specifically, how is it that people are able to recognize an object from its retinal image given that a single object can cast an infinite number of retinal projections?An elegant and influential solution, proposed by Marr and Nishihara, was that recognition was not based on the identification of an object’s retinal image, but on the identification of its reconstructed, 3-D representation2xRepresentation and recognition of the spatial organization of three-dimensional shapes. Marr, D. and Nishihara, H.K. Proc. R. Soc. London Ser. B. 1978; 200: 269–294Crossref | PubMedSee all References2. The appeal of this structural approach was that the same 3-D representation can be computed regardless of the viewpoint of the observer; hence, this class of recognition models was referred to as ‘viewpoint invariant’. For the last decade, viewpoint-invariant models, such as Biederman’s recognition-by-components (RBC) model3xRecognition-by-components: a theory of human image understanding. Biederman, I. Psychol. Rev. 1987; 94: 115–147Crossref | PubMedSee all References3, have formed the dominant and most widely accepted approach to object recognition. However, for all its computational elegance and appeal, empirical results that support the viewpoint-invariant position have been sparse and controversial.Now, in a recent collection of essays from Cognition entitled Object Recognition in Man, Monkey, and Machine, Mike Tarr and Heinrich Bulthoff argue persuasively and emphatically that object recognition is after all affected by the viewpoint of the observer. According to the ‘viewpoint-dependent‘ approach, an object can be recognized on the basis of an image obtained from a particular stored view, or from a restricted set of images obtained from multiple stored views. To bolster the cause for viewpoint dependency, Tarr and Bulthoff have assembled an impressive lineup of vision scientists who show that image-based recognition is computationally feasible (Ullman), neurologically realizable (Perrett, Oram and Ashbridge) and behaviorally robust (Tarr and Gauthier). Moreover, other evidence indicates that recovering 3-D structural information from 2-D input is not all that easy, suggesting that viewpoint-invariant recognition might not be as straightforward as one might think (Moore and Cavanagh).Read as a collective package, these essays constitute a tour de force for the viewpoint-dependent approach and spell trouble for the viewpoint-invariant models. However, as compelling as the viewpoint-dependent arguments made in these essays are, long-standing theories in psychology do not go so gently into that good night (nor should they). What seems to be missing from the Man, Monkey, and Machine volume is the response from the opposing ‘viewpoint’. For example, it would be informative to hear how proponents of the viewpoint-invariant position might respond to criticisms raised by the protagonists of the viewpoint-dependent position, or how they might reconcile the two models. Regrettably, one shortcoming of this otherwise eminently readable and valuable book is that we hear arguments from only one side of the debate. Despite this oversight, the book provides the reader with clear, satisfying answers to one of the most enduring puzzles in visual cognition.While the volume helps to settle the score on one long-standing question in the literature, its concluding essays open the door to new, equally intriguing questions. In his chapter, Schyns broaches the potentially messy topic of individual differences in object recognition. Rather than assuming a ‘one-size-fits-all’ approach to recognition, Schyns effectively demonstrates how object representations are adapted to the individual goals and experiences of the perceiver. In their essay, Goodale and Humphrey cite electrophysiological evidence from monkey studies and clinical evidence from brain-damaged patients to show that distinct neural representations are generated when objects are treated as objects of perception as opposed to when they are regarded as objects of action. So, for example, our representation of a corkscrew depends on whether we want to use it to open a bottle of wine or locate it amongst other objects in a kitchen drawer. Whereas traditional models of object recognition see the perceiver as playing a relatively passive role in recognition, Schyns, Goodale and Humphrey adopt a utilitarian approach in which object representations are created and modified according to the intentions of the user (perceiver). These studies signal an important new direction in research aimed towards understanding the contextual factors involved in object recognition. Moreover, like viewpoint-dependent models and unlike viewpoint-invariant models, the utilitarian approach acknowledges that prior experience influences recognition.Object Recognition in Man, Monkey, and Machine demonstrates how research in the cognitive sciences should work. First, identify a significant problem in the field; second, develop testable theories that can be tackled using a range of methodologies; and, finally, draw reasonable conclusions based on converging results from these methodologies. Taken as a whole, these essays present an impressive body of research that pack a powerful take-home message. This is what good science is all about.