CNR Institute of Computational linguistics Antonio Zampolli

Genova, Italy

CNR Institute of Computational linguistics Antonio Zampolli

Genova, Italy
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Gigliotta O.,University of Naples Federico II | Pezzulo G.,CNR Institute of Computational linguistics Antonio Zampolli | Nolfi S.,CNR Institute of Neuroscience
Theory in Biosciences | Year: 2011

We show how simulated robots evolved for the ability to display a context-dependent periodic behavior can spontaneously develop an internal model and rely on it to fulfill their task when sensory stimulation is temporarily unavailable. The analysis of some of the best evolved agents indicates that their internal model operates by anticipating sensory stimuli. More precisely, it anticipates functional properties of the next sensory state rather than the exact state that sensors will assume. The characteristics of the states that are anticipated and of the sensorimotor rules that determine how the agents react to the experienced states, however, ensure that they produce very similar behaviour during normal and blind phases in which sensory stimulation is available or is self-generated by the agent, respectively. Agents' internal models also ensure an effective transition during the phases in which agents' internal dynamics is decoupled and re-coupled with the sensorimotor flow. Our results suggest that internal models might have arisen for behavioral reasons and successively exapted for other cognitive functions. Moreover, the obtained results suggest that self-generated internal states should not necessarily match in detail the corresponding sensory states and might rather encode more abstract and motor-oriented information. © 2011 Springer-Verlag.

Del Grosso A.M.,CNR Institute of Computational linguistics Antonio Zampolli
MMEDIA 2013 - 5th International Conferences on Advances in Multimedia | Year: 2013

This paper aims at illustrating a collaborative and modular web platform in the domain of digital and computational philology. The proposed work deals with parallel multilingual and multimedia resources. Two case studies are discussed in order to show the flexibility of the designed platform. The reusability of the components in different projects is achieved by abstract modeling and through the application of effective design patterns. The platform deals with textual resources and associated multimedia content, which can be retrieved by the metadata and shown in parallel (e.g., the page image of a manuscripts and the related transcription). The library of components will distribute under GPL 3.0 license and available at

Pezzulo G.,CNR Institute of Computational linguistics Antonio Zampolli | Pezzulo G.,CNR Institute of Cognitive Sciences and Technologies | Dindo H.,University of Palermo
Behavioral and Brain Sciences | Year: 2013

Pickering & Garrod (P&G) explain dialogue dynamics in terms of forward modeling and prediction-by-simulation mechanisms. Their theory dissolves a strict segregation between production and comprehension processes, and it links dialogue to action-based theories of joint action. We propose that the theory can also incorporate intentional strategies that increase communicative success: for example, signaling strategies that help remaining predictable and forming common ground. Copyright © 2013 Cambridge University Press.

Pezzulo G.,CNR Institute of Computational linguistics Antonio Zampolli | Pezzulo G.,CNR Institute of Cognitive Sciences and Technologies | Rigoli F.,CNR Institute of Cognitive Sciences and Technologies | Rigoli F.,University of Siena
Frontiers in Neuroscience | Year: 2011

Traditional theories of decision-making assume that utilities are based on the intrinsic value of outcomes; in turn, these values depend on associations between expected outcomes and the current motivational state of the decision-maker. This view disregards the fact that humans (and possibly other animals) have prospection abilities, which permit anticipating future mental processes and motivational and emotional states. For instance, we can evaluate future outcomes in light of the motivational state we expect to have when the outcome is collected, not (only) when we make a decision. Consequently, we can plan for the future and choose to store food to be consumed when we expect to be hungry, not immediately. Furthermore, similarly to any expected outcome, we can assign a value to our anticipated mental processes and emotions. It has been reported that (in some circumstances) human subjects prefer to receive an unavoidable punishment immediately, probably because they are anticipating the dread associated with the time spent waiting for the punishment. This article offers a formal framework to guide neuroeconomic research on how prospection affects decision-making. The model has two characteristics. First, it uses model-based Bayesian inference to describe anticipation of cognitive and motivational processes. Second, the utility-maximization process considers these anticipations in two ways: to evaluate outcomes (e.g., the pleasure of eating a pie is evaluated differently at the beginning of a dinner, when one is hungry, and at the end of the dinner, when one is satiated), and as outcomes having a value themselves (e.g., the case of dread as a cost of waiting for punishment). By explicitly accounting for the relationship between prospection and value, our model provides a framework to reconcile the utility-maximization approach with psychological phenomena such as planning for the future and dread. © 2011 Pezzulo and Rigoli.

Pezzulo G.,CNR Institute of Computational linguistics Antonio Zampolli | Pezzulo G.,CNR Institute of Cognitive Sciences and Technologies
IEEE Transactions on Autonomous Mental Development | Year: 2012

Recent research in cognitive psychology, neuroscience, and robotics has widely explored the tight relations between language and action systems in primates. However, the link between the pragmatics of linguistic and nonlinguistic interactions has received less attention up to now. In this paper, we argue that cognitive agents exploit the same cognitive processes and neural substratea general pragmatic competenceacross linguistic and nonlinguistic interactive contexts. Elaborating on Levinson's idea of an interaction engine that permits to convey and recognize communicative intentions in both linguistic and nonlinguistic interactions, we offer a computationally guided analysis of pragmatic competence, suggesting that the core abilities required for successful linguistic interactions could derive from more primitive architectures for action control, nonlinguistic interactions, and joint actions. Furthermore, we make the case for a novel, embodied approach to human-robot interaction and communication, in which the ability to carry on face-to-face communication develops in coordination with the pragmatic competence required for joint action. © 2012 IEEE.

Fantoni G.,University of Pisa | Apreda R.,University of Pisa | Apreda R.,Erre Quadro S.r.l. | Dell'Orletta F.,CNR Institute of Computational linguistics Antonio Zampolli | Monge M.,Erre Quadro S.r.l.
Advanced Engineering Informatics | Year: 2013

Patents contain a large quantity of technical information not available elsewhere and therefore very interesting for both academia and industry. The purpose of the research is to try to detect and extract information about the functions, the physical behaviours and the states of the system directly from the text of a patent in an automatic way. The above three categories constitute a well-known set of relevant entities in the theory of engineering design, and their study allows powerful analysis of individual artefacts as well as that of groups of products or technologies. The focus is in providing a handy tool that could speed up and facilitate human analysis and allow tackling also large corpora of documents. A second goal is to develop a protocol based on free software and database resources, so that it could be replicable with limited effort by everyone without having to rely on commercial databases. Extracting technical and design information from a document whose aim is more legal than technical, and that is written using a specific jargon, is not a trivial task. The approach chosen to overcome the various issues is to support state-of-the-art Computational Linguistic tools with a large Knowledge Base. The latter has been constructed both manually and automatically and comprises not only keywords but also concepts, relationships and regular expressions. A case study about a very recent patent describing a mechanical device has been included to show the functioning and output of the entire system. © 2013 Elsevier Ltd. All rights reserved.

Chersi F.,CNR Institute of Neuroscience | Pezzulo G.,CNR Institute of Neuroscience | Pezzulo G.,CNR Institute of Computational linguistics Antonio Zampolli
Cognitive Processing | Year: 2012

The hippocampus plays a central role in spatial representation, declarative and episodic memory. In this area, so-called place cells possess high spatial selectivity, firing preferentially when the individual is within a small area of the environment. Interestingly, it has been found in rats that these cells can be active also when the animal is outside the location or context of their corresponding place field producing so-called "forward sweeps". These typically occur at decision points during task execution and seem to be utilized, among other things, for the evaluation of potential alternative paths. Anticipatory firing is also found in the ventral striatum, a brain area that is strongly interconnected with the hippocampus and is known to encode value and reward. In this paper, we describe a biologically based computational model of the hippocampal-ventral striatum circuit that implements a goal-directed mechanism of choice, with the hippocampus primarily involved in the mental simulation of possible navigation paths and the ventral striatum involved in the evaluation of the associated reward expectancies. The model is validated in a navigation task in which a rat is placed in a complex maze with multiple rewarding sites. We show that the rat mentally activates place cells to simulate paths, estimate their value, and make decisions, implementing two essential processes of model-based reinforcement learning algorithms of choice: look-ahead prediction and the evaluation of predicted states. © 2012 Marta Olivetti Belardinelli and Springer-Verlag.

Terranova G.,Risk Management Unit | Ferro M.,CNR Institute of Computational linguistics Antonio Zampolli | Carpeggiani C.,CNR Institute of Clinical Physiology | Recchia V.,CNR Institute of Clinical Physiology | And 3 more authors.
JACC: Cardiovascular Imaging | Year: 2012

Guidelines on informed consent for clinical practice exhort physicians to use standard plain language to enhance patient comprehension and facilitate shared decision making. The aim of this study was to assess and improve quality and readability of current informed consent forms used in cardiology. We evaluated the currently used informed consent forms, previously written in Italian and English, of 7 common imaging examinations, according to the recommendations of scientific societies. For each text, we also developed a revised informed consent form according to reference standards, including Federal Plain Language guidelines. Regarding readability scores, we analyzed each text (standard and revised) with Flesch-Kincaid (F-K) grade level (higher numbers indicating harder-to-read text) and the Italian language-tailored Gulpease level (from 0 [difficult] to 100 [easy]). Overall quality and readability was poor for both the original English and Italian versions, and readability was improved with the revised form, with higher readability evidenced by changes in both F-K grade level (standard 10.2 ± 2.37% vs. revised 6.5 ± 0.41%; p < 0.001) for English and Gulpease (standard 45.7 ± 2% vs. revised 84.09 ± 2.98%; p < 0.0001) for Italian. In conclusion, current informed consent forms are complex, incomplete, and unreadable for the average patient. Substantial quality improvement and higher readability scores can be achieved with revised forms that explicitly discuss risks and are prepared following standard recommendations of plain writing. © 2012 American College of Cardiology Foundation.

Barca L.,CNR Institute of Cognitive Sciences and Technologies | Pezzulo G.,CNR Institute of Cognitive Sciences and Technologies | Pezzulo G.,CNR Institute of Computational linguistics Antonio Zampolli
PLoS ONE | Year: 2012

Visual lexical decision is a classical paradigm in psycholinguistics, and numerous studies have assessed the so-called "lexicality effect" (i.e., better performance with lexical than non-lexical stimuli). Far less is known about the dynamics of choice, because many studies measured overall reaction times, which are not informative about underlying processes. To unfold visual lexical decision in (over) time, we measured participants' hand movements toward one of two item alternatives by recording the streaming x,y coordinates of the computer mouse. Participants categorized four kinds of stimuli as "lexical" or "non-lexical:" high and low frequency words, pseudowords, and letter strings. Spatial attraction toward the opposite category was present for low frequency words and pseudowords. Increasing the ambiguity of the stimuli led to greater movement complexity and trajectory attraction to competitors, whereas no such effect was present for high frequency words and letter strings. Results fit well with dynamic models of perceptual decision-making, which describe the process as a competition between alternatives guided by the continuous accumulation of evidence. More broadly, our results point to a key role of statistical decision theory in studying linguistic processing in terms of dynamic and non-modular mechanisms. © 2012 Barca, Pezzulo.

Pezzulo G.,CNR Institute of Computational linguistics Antonio Zampolli | Pezzulo G.,CNR Institute of Cognitive Sciences and Technologies | Ognibene D.,Intelligent Group
Motor Control | Year: 2012

In this paper, we aim to elucidate the processes that occur during action preparation from both a conceptual and a computational point of view. We first introduce the traditional, serial model of goal-directed action and discuss from a computational viewpoint its subprocesses occurring during the two phases of covert action preparation and overt motor control. Then, we discuss recent evidence indicating that these subprocesses are highly intertwined at representational and neural levels, which undermines the validity of the serial model and points instead to a parallel model of action specification and selection. Within the parallel view, we analyze the case of delayed choice, arguing that action preparation can be proactive, and preparatory processes can take place even before decisions are made. Specifically, we discuss how prior knowledge and prospective abilities can be used to maximize utility even before deciding what to do. To support our view, we present a computational implementation of (an approximated version of) proactive action preparation, showing its advantages in a simulated tennis-like scenario. © 2012 Human Kinetics, Inc.

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