A bstract. Neural network models of categorical perception (compression of withincategory similarity and dilation of between-category differences) are applied to the symbol-grounding problem (of how to connect symbols with meanings) by connecting analogue sensorimotor projections to arbitrary symbolic representations via learned category-invariance detectors in a hybrid symbolic/non-symbolic system. O ur nets are trained to categorize and name 50 ´50 pixel images (e.g. circles, ellipses, squares and rectangles) projected on to the receptive eld of a 7 ´7 retina. They rst learn to do prototype matching and then entry-level naming for the four kinds of stimuli, grounding their names directly in the input patterns via hidden-unit representations ('sensorimotor toil'). We show that a higher-level categorization (e.g. 'symmetric' versus 'asymmetric') can be learned in two very different ways: either (1) directly from the input, just as with the entry-level categories (i.e. by toil); or (2) indirectly, fro m Boolean combinations of the grounded category names in the form of propositions describing the higher-order category ('symbolic theft'). We analyse the architectures and input conditions that allow grounding (in the form of compression/ separation in internal similarity space ) to be 'transferred' in this second way from directly grounded entry-level category names to higher-order category names. Such hybrid models have implications for the evolution and learning of language.
To cite this article: Language and Cognit ive Processes (2013): The cognit ive load of presupposit ion t riggers: mandat ory and opt ional repairs in presupposit ion failure, Language and Cognit ive Processes, DOI: 10.1080DOI: 10. / 01690965.2013 To link to this article: ht t p:/ / dx.doi.org/ 10.1080/ 01690965.2013.830185 PLEASE SCROLL DOWN FOR ARTI CLETaylor & Francis m akes every effort t o ensure t he accuracy of all t he inform at ion ( t he " Cont ent " ) cont ained in t he publicat ions on our plat form . However, Taylor & Francis, our agent s, and our licensors m ake no represent at ions or warrant ies what soever as t o t he accuracy, com plet eness, or suit abilit y for any purpose of t he Cont ent . Any opinions and views expressed in t his publicat ion are t he opinions and views of t he aut hors, and are not t he views of or endorsed by Taylor & Francis. The accuracy of t he Cont ent should not be relied upon and should be independent ly verified wit h prim ary sources of inform at ion. Taylor and Francis shall not be liable for any losses, act ions, claim s, proceedings, dem ands, cost s, expenses, dam ages, and ot her liabilit ies what soever or howsoever caused arising direct ly or indirect ly in connect ion wit h, in relat ion t o or arising out of t he use of t he Cont ent . This art icle m ay be used for research, t eaching, and privat e st udy purposes. Any subst ant ial or syst em at ic reproduct ion, redist ribut ion, reselling, loan, sub-licensing, syst em at ic supply, or dist ribut ion in any form t o anyone is expressly forbidden. If a speaker utters a sentence p containing a presupposition trigger that activates a presupposition q, and q does not belong to the common ground of presuppositions, it is a case of presupposition failure. If this occurs, speakers are required to repair the failure to make sense of the utterance. According to Glanzberg, two subcategories of being infelicitous may emerge in the case of presupposition failure: one is that strong presuppositions lead to obligatory repair, and the other is that weak presuppositions lead only to an optional repair. Following Glanzberg's suggestion, in this paper we present the results of an experiment supporting the idea that, depending on the kind of trigger, processing the information conveyed by a presupposition can be either optional or mandatory in case of presupposition failure. The conclusion of this paper is that the cognitive demands of different presupposition triggers do not primarily depend on whether they optionally or obligatorily lead to process the presuppositions activated. Rather, their cognitive demands seem to be related with the complexity of the mental representation of the presupposition required.
Scholars studying the origins and evolution of language are also interested in the general issue of the evolution of cognition. Language is not an isolated capability of the individual, but has intrinsic relationships with many other behavioral, cognitive, and social abilities. By understanding the mechanisms underlying the evolution of linguistic abilities, it is possible to understand the evolution of cognitive abilities. Cognitivism, one of the current approaches in psychology and cognitive science, proposes that symbol systems capture mental phenomena, and attributes cognitive validity to them. Therefore, in the same way that language is considered the prototype of cognitive abilities, a symbol system has become the prototype for studying language and cognitive systems. Symbol systems are advantageous as they are easily studied through computer simulation (a computer program is a symbol system itself), and this is why language is often studied using computational models.A symbol system is made up by a set of arbitrary "physical tokens" (i.e., symbols) that can be manipulated on the basis of explicit rules (i.e., syntax). Some of the main properties of such a symbol system are: (a) compositeness, that is symbols and rules can be recursively composed; and (b) semantic interpretability, specifying that the entire system and its parts can be systematically assigned a meaning (Pylyshyn, 1984;Harnad, 1990). Some significant issues arise when studying such symbol systems as a direct metaphor and model of language. These will also have direct implications for the study of the origins and evolution of language. The first issue is to establish exactly what a symbol is, by giving a clear
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