Subordinate-level object classification reexamined

Abstract The classification of a table as round rather than square, a car as a Mazda rather than a Ford, a drill bit as 3/8-inch rather than 1/4-inch, and a face as Tom have all been regarded as a single process termed “subordinate classification.” Despite the common label, the considerable heterogeneity of the perceptual processing required to achieve such classifications requires, minimally, a more detailed taxonomy. Perceptual information relevant to subordinate-level shape classifications can be presumed to vary on continua of (a) the type of distinctive information that is present, nonaccidental or metric, (b) the size of the relevant contours or surfaces, and (c) the similarity of the to-be-discriminated features, such as whether a straight contour has to be distinguished from a contour of low curvature versus high curvature. We consider three, relatively pure cases. Case 1 subordinates may be distinguished by a representation, a geon structural description (GSD), specifying a nonaccidental characterization of an object's large parts and the relations among these parts, such as a round table versus a square table. Case 2 subordinates are also distinguished by GSDs, except that the distinctive GSDs are present at a small scale in a complex object so the location and mapping of the GSDs are contingent on an initial basic-level classification, such as when we use a logo to distinguish various makes of cars. Expertise for Cases 1 and 2 can be easily achieved through specification, often verbal, of the GSDs. Case 3 subordinates, which have furnished much of the grist for theorizing with “view-based” template models, require fine metric discriminations. Cases 1 and 2 account for the overwhelming majority of shape-based basic- and subordinate-level object classifications that people can and do make in their everyday lives. These classifications are typically made quickly, accurately, and with only modest costs of viewpoint changes. Whereas the activation of an array of multiscale, multiorientation filters, presumed to be at the initial stage of all shape processing, may suffice for determining the similarity of the representations mediating recognition among Case 3 subordinate stimuli (and faces), Cases 1 and 2 require that the output of these filters be mapped to classifiers that make explicit the nonaccidental properties, parts, and relations specified by the GSDs.

[1]  G. A. Miller THE PSYCHOLOGICAL REVIEW THE MAGICAL NUMBER SEVEN, PLUS OR MINUS TWO: SOME LIMITS ON OUR CAPACITY FOR PROCESSING INFORMATION 1 , 1956 .

[2]  Landsborough Thomson,et al.  Birds of North America , 1962, Nature.

[3]  R. Shepard,et al.  Perceptual-cognitive explorations of a toroidal set of free-form stimuli , 1973 .

[4]  Dave Bartram,et al.  The role of visual and semantic codes in object naming , 1974 .

[5]  Wayne D. Gray,et al.  Basic objects in natural categories , 1976, Cognitive Psychology.

[6]  M. Potter Short-term conceptual memory for pictures. , 1976, Journal of experimental psychology. Human learning and memory.

[7]  Brian L Kottas,et al.  Comparison of Potential Critical Feature Sets for Simulator-Based Target Identification Training , 1980 .

[8]  M. C. Smith,et al.  Tracing the time course of picture--word processing. , 1980, Journal of experimental psychology. General.

[9]  I. Biederman,et al.  Scene perception: Detecting and judging objects undergoing relational violations , 1982, Cognitive Psychology.

[10]  Edward E. Smith,et al.  Basic-level superiority in picture categorization , 1982 .

[11]  B. Tversky,et al.  Journal of Experimental Psychology : General VOL . 113 , No . 2 JUNE 1984 Objects , Parts , and Categories , 2005 .

[12]  Stephen M. Kosslyn,et al.  Pictures and names: Making the connection , 1984, Cognitive Psychology.

[13]  H. Brownell,et al.  Category differentiation in object recognition: typicality constraints on the basic category advantage. , 1985, Journal of experimental psychology. Learning, memory, and cognition.

[14]  I. Biederman,et al.  Sexing day-old chicks: A case study and expert systems analysis of a difficult perceptual-learning task. , 1987 .

[15]  S. Grossberg The Adaptive Self-Organization of Serial Order in Behavior: Speech, Language, And Motor Control , 1987 .

[16]  E. Rolls,et al.  Functional subdivisions of the temporal lobe neocortex , 1987, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[17]  I. Biederman Recognition-by-components: a theory of human image understanding. , 1987, Psychological review.

[18]  T. Poggio,et al.  A network that learns to recognize three-dimensional objects , 1990, Nature.

[19]  I. Biederman,et al.  Evidence for Complete Translational and Reflectional Invariance in Visual Object Priming , 1991, Perception.

[20]  B Tversky,et al.  Parts and the basic level in natural categories and artificial stimuli: Comments on Murphy (1991) , 1991, Memory & cognition.

[21]  I. Biederman,et al.  Priming contour-deleted images: Evidence for intermediate representations in visual object recognition , 1991, Cognitive Psychology.

[22]  J. Tanaka,et al.  Object categories and expertise: Is the basic level in the eye of the beholder? , 1991, Cognitive Psychology.

[23]  Heinrich H. Bülthoff,et al.  Psychophysical support for a 2D view interpolation theory of object recognition , 1991 .

[24]  I Biederman,et al.  Metric invariance in object recognition: a review and further evidence. , 1992, Canadian journal of psychology.

[25]  I. Biederman,et al.  Size invariance in visual object priming , 1992 .

[26]  Azriel Rosenfeld,et al.  3-D Shape Recovery Using Distributed Aspect Matching , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[27]  I. Biederman,et al.  Dynamic binding in a neural network for shape recognition. , 1992, Psychological review.

[28]  T. Sanocki Time course of object identification: evidence for a global-to-local contingency. , 1993, Journal of experimental psychology. Human perception and performance.

[29]  Joachim M. Buhmann,et al.  Distortion Invariant Object Recognition in the Dynamic Link Architecture , 1993, IEEE Trans. Computers.

[30]  K Tanaka,et al.  Neuronal mechanisms of object recognition. , 1993, Science.

[31]  M. Farah,et al.  Parts and Wholes in Face Recognition , 1993, The Quarterly journal of experimental psychology. A, Human experimental psychology.

[32]  T. Poggio,et al.  Symmetric 3D objects are an easy case for 2D object recognition. , 1994, Spatial vision.

[33]  Keiji Tanaka,et al.  Neuronal selectivities to complex object features in the ventral visual pathway of the macaque cerebral cortex. , 1994, Journal of neurophysiology.

[34]  N. Logothetis,et al.  View-dependent object recognition by monkeys , 1994, Current Biology.

[35]  M J Tarr,et al.  Is human object recognition better described by geon structural descriptions or by multiple views? Comment on Biederman and Gerhardstein (1993). , 1995, Journal of experimental psychology. Human perception and performance.

[36]  S. Edelman Representation of Similarity in 3D Object Discrimination , 1995 .

[37]  N. Logothetis,et al.  Shape representation in the inferior temporal cortex of monkeys , 1995, Current Biology.

[38]  M. Tarr Rotating objects to recognize them: A case study on the role of viewpoint dependency in the recognition of three-dimensional objects , 1995, Psychonomic bulletin & review.

[39]  M. Goodale,et al.  The visual brain in action , 1995 .

[40]  E. E. Cooper,et al.  Recognizing objects with an irregular part , 1995 .

[41]  I. Biederman,et al.  Viewpoint-dependent mechanisms in visual object recognition: Reply to Tarr and Bülthoff (1995). , 1995 .

[42]  I Biederman,et al.  To what extent can matching algorithms based on direct outputs of spatial filters account for human object recognition? , 1996, Spatial vision.

[43]  I Biederman,et al.  Neurocomputational bases of object and face recognition. , 1997, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[44]  Peter Kalocsai Biologically inspired recognition model with extension fields , 1997, Proceedings of International Conference on Image Processing.

[45]  G. Winocur,et al.  What Is Special about Face Recognition? Nineteen Experiments on a Person with Visual Object Agnosia and Dyslexia but Normal Face Recognition , 1997, Journal of Cognitive Neuroscience.

[46]  Ronald A. Rensink,et al.  TO SEE OR NOT TO SEE: The Need for Attention to Perceive Changes in Scenes , 1997 .

[47]  M. Tarr,et al.  Becoming a “Greeble” Expert: Exploring Mechanisms for Face Recognition , 1997, Vision Research.

[48]  P. Goldman-Rakic,et al.  Areal segregation of face-processing neurons in prefrontal cortex. , 1997, Science.

[49]  M. Tarr,et al.  Testing conditions for viewpoint invariance in object recognition. , 1997, Journal of experimental psychology. Human perception and performance.

[50]  John E. Hummel,et al.  Distributed representations of structure: A theory of analogical access and mapping. , 1997 .

[51]  Gregory L. Murphy,et al.  Hierarchical structure in concepts and the basic level of categorization. , 1997 .

[52]  Koen Lamberts,et al.  The time course of categorization. , 1998 .

[53]  Isabel Gauthier,et al.  Three-dimensional object recognition is viewpoint dependent , 1998, Nature Neuroscience.

[54]  J. Hummel,et al.  The role of attention in priming for left-right reflections of object images: evidence for a dual representation of object shape. , 1998, Journal of experimental psychology. Human perception and performance.

[55]  Glyn W. Humphreys,et al.  From objects to names: A cognitive neuroscience approach , 1999, Psychological research.

[56]  Irving Biederman,et al.  One-shot viewpoint invariance in matching novel objects , 1999, Vision Research.

[57]  Richard Freeman,et al.  Building object representations from parts: Tests of a stochastic sampling model. , 1999 .

[58]  Thomas J. Palmeri The time course of perceptual categorization , 2001, Similarity and Categorization.

[59]  R. K. Simpson Nature Neuroscience , 2022 .