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Pazienti con lesioni focali all’emisfero destro (RHD), pur mostrando abilità linguistiche complessivamente preservate, riportano frequentemente una compromissione delle abilità comunicative (Cummings, 2009), che interessa in modo particolare la sfera pragmatica: deficit nelle abilità conversazionali (Lehman, 2006), nella comprensione di espressioni non letterali (Papagno et al., 2006), nel riconoscimento della prosodia (Pell, 2007) e delle espressioni facciali (Kucharska-Pietura et al., 2003). La maggior parte degli studi (e.g Cheang & Pell, 2006; McDonald, 2000) ha valutato le competenze pragmatiche dei pazienti RHD attraverso il canale linguistico, mentre è stata meno studiata la componente gestuale (Brownell, 1999). La Teoria della Pragmatica Cognitiva (Bara, 2010), propone un modello unitario della comunicazione, in cui la competenza comunicativa è indipendente dal mezzo, linguistico o extralinguistico, utilizzato per veicolare significati; conferme a tale modello si ritrovano in studi su popolazioni cliniche (Gabbatore et al., 2014, Angeleri et al., 2008). Cutica et al. (2006), in particolare, hanno studiato le abilità pragmatiche in comprensione con un campione di pazienti con lesioni all’emisfero destro e sinistro (LHD), evidenziando come i pazienti RHD, rispetto a quelli LHD, mostrassero deficit più gravi nella componente extralinguistica, a conferma di una specializzazione dell’emisfero destro nelle componenti extralinguistiche e paralinguistiche. Data la grande variabilità di profili associati ai deficit comunicativi nei pazienti RHD (Myers, 2005), è fondamentale l’utilizzo di strumenti di assessment che permettano una valutazione globale ed approfondita (Angeleri et al., 2012). Un assessment puntuale è inoltre necessario per impostare trattamenti riabilitativi mirati (e.g. Bosco et al., 2013).

We outline some components of a mentalist theory of human communicative competence. Communication in our species is an intentional and overt type of social interaction, based on each agent's capability of entertaining shared mental states and of acting so as to make certain mental states shared with the other. Communicative meaning is a matter of ascription: it is not an intrinsic property of a communicative act, but is instead created here and now as the shared construction of the interlocutors. We then discuss how communicative actions are superficially realized by our species, focusing in particular on the difference between linguistic and extralinguistic (that is, gestural) means of expression. Linguistic communication is the communicative use of a symbol system, whereas extralinguistic communication is the communicative use of a set of symbols. The difference turns out to be a matter of processing rather than of intrinsic structure.

During interaction with computer-based 3-D simulations like virtual reality, users may experience a sense of involvement called presence. Presence is commonly defined as the subjective feeling of "being there". We discuss the state of the art in this inno vative research area and introduce a situated cognition perspective on presence. We argue that presence depends on the proper integration of aspects relevant to an agent's movement and perception, to her actions, and to her conception of the overall situ a tion in which she finds herself, as well as on how these aspects mesh with the possibilities for action afforded in the interaction with the artifact. We also aim at showing that studies of presence offer a test-bed for different theories of situated co gnition.›

The debate over the evolution of an innate language capacity seems to divide into two principle schools of thought. Jackendoff (1999a, 1999b) has argued that language processing is based on three autonomous generative components, phonological, syntactic, and semantic/conceptual and he is committed to the view that the language faculty evolved incrementally through natural selection. Pinker (1994, 333) also sees ``no reason to doubt that the principle explanation is the same as for any other complex instinct or organ, Darwin's theory of natural selection", when theorizing about language evolution. An alternative approach has been taken recently by Hauser, Chomsky and Fitch (2002). They argue that the property that makes human language unique (recursion), may be a recent emergence in hominid evolution. It follows from this line of thought that most of the anatomical characteristics that support language (e.g. vocal tract and controlled breathing) may be merely variations of previously evolved biological structures, and not of a different kind. Leaving aside the argument of whether these structures evolved gradually, they did not evolve nor were they ``tuned" to serve the faculty of language. Jackendoff (1999a) accuses those who do not accept that language arose gradually through natural selection as having been ``forced to devalue evolutionary argumentation". Jackendoff's concern seems to stem from the view that there is only one way that evolution can proceed, through gradual change honed by natural selection. My concern is for the neglect of the vast amount of evidence supporting the theory that modern humans did not emerged in a gradual, step-wise fashion. Here I argue that hominins evolved through major evolutionary leaps, which may have numbered only two or three significant mutation ``events". Neoteny (the retention of infant or juvenile growth rates) has been the major force in the evolution of our primate ancestors and this process can explain the sudden emergence of many of the traits that define what it means to be human, including the sudden emergence of human cognition and language.

Many activities in Cognitive Science involve complex computer models and simulations of both theoretical and real entities. Artificial Intelligence and the study of artificial neural nets in particular, are seen as major contributors in the quest for understanding the human mind. Computational models serve as objects of experimentation, and results from these virtual experiments are tacitly included in the framework of empirical science. Cognitive functions, like learning to speak, or discovering syntactical structures in language, have been modeled and these models are the basis for many claims about human cognitive capacities. Artificial neural nets (ANNs) have had some successes in the field of Artificial Intelligence, but the results from experiments with simple ANNs may have little value in explaining cognitive functions. The problem seems to be in relating cognitive concepts that belong in the `top-down' approach to models grounded in the `bottom-up' connectionist methodology. Merging the two fundamentally different paradigms within a single model can obfuscate what is really modeled. When the tools (simple artificial neural networks) to solve the problems (explaining aspects of higher cognitive functions) are mismatched, models with little value in terms of explaining functions of the human mind are produced. The ability to learn functions from data-points makes ANNs very attractive analytical tools. These tools can be developed into valuable models, if the data is adequate and a meaningful interpretation of the data is possible. The problem is, that with appropriate data and labels that fit the desired level of description, almost any function can be modeled. It is my argument that small networks offer a universal framework for modeling any conceivable cognitive theory, so that neurological possibility can be demonstrated easily with relatively simple models. However, a model demonstrating the possibility of implementation of a cognitive function using a distributed methodology, does not necessarily add support to any claims or assumptions that the cognitive function in question, is neurologically plausible.