Showing papers by "ParisTech published in 2003"

Object recognition is not predication.

Abstract: Predicates involved in language and reasoning are claimed to radically differ from categories applied to objects. Human predicates are the cognitive result of a contrast between perceived objects. Object recognition alone cannot generate such operations as modification and explicit negation. The mechanism studied by Hurford constitutes at best an evolutionary prerequisite of human predication ability. Jim Hurford’s claim is an impressive attempt to ground human distinctive cognitive abilities like logical reasoning and language in mammalian brain anatomy. His claim is conceptually important to help us understand how a dual where-and-what processing, leading to object recognition, may be a likely prerequisite of human predication. The claim that object recognition and predication are similar by nature, differing only in degree, is however too difficult to accept, for two groups of reasons. The first objection is a general critique that can be addressed at any gradualist account of phylogenetic descent. Modern evolutionary theory emphasizes that species are most of the time in equilibrium, and qualitatively differ by clear-cut characteristics (Gould & Eldredge 1977). This view is widely confirmed by computer simulations based on genetic algorithms, which show that evolutionary processes are rapid and produce local optima (Holland 1992; Dessalles 1996). One characteristic that our species has in proper is the cognitive ability to manipulate predicates through logical reasoning and to express them through language. The object recognition behaviour shown by mammals is not expected to be either equivalent to, or even to be a draft of, this human ability. One further argument along this line is provided by successful attempts to evolve syntactic language in populations of artificial individuals, as soon as they are granted with some predicate-argument semantics (Kirby 2000; Batali 2002). The fact that other primates seem unable to master syntactic symbolic expressions casts doubt on predicates being available to them. The second objection against equating object recognition abilities with (even simple) predication comes from the fact that the underlying cognitive processes are qualitatively different. Jim Hurford restrains the cognitive role of predication to the process of categorisation, “[…] the mental events involved when a human attends to an object in the world and classifies it perceptually as satisfying the predicate in question”. When a perceived object is categorised as an apple, many perceptual features are involved in the recognition: aspects of the shape, colours, textures, presence of two characteristic extremities, etc. This ends up, according to Hurford’s account, with a predicate like APPLE(x). Let us call this process, based on mere object recognition, R-predication. Contrary to Hurford’s account, we claim that R-predication is qualitatively different from those cognitive processes involved in language that logic represents with predicates. Let us call the latter C-predication. Available models of categorisation, and thus of R-predication, are holistic. Neural networks or standard statistical devices rely on the maximum number of common features that can be found between the object to be recognised and known classes, exemplars or prototypes. The difficulty, addressed for instance in conceptual clustering techniques (Michalski & Stepp 1983), is precisely to extract short explicit descriptions for classes and objects. C-predication radically differs by showing non-holistic features: it isolates one explicit property from the context. In (Ghadakpour 2003), this process is described as resulting from a contrast operator (hence C meaning contrast in C-predication). We are able, without any training, to contrast any object with another resembling object or with its known prototype. This allows us to characterise a perceived object as a blue apple or as a big apple. Even if the remarkable ability to form prototypes and to see global resemblance between two objects is