1,721,196 research outputs found
Linguistic Representations of Motion Do Not Depend on the Visual Motion System
No full textEmbodied semantics proposes that constructing the meaning of motion verb phrases relies on representations of motion in sensory cortex. However, the data reported by earlier studies as evidence for this claim are also explained by a symbolic-semantics view proposing interactions between dissociable systems. In the experiments reported here, participants were visually adapted to real and implied leftward or rightward motion, which produced a motion aftereffect opposite to the direction of the adapting stimulus. Participants then decided whether a directionally ambiguous or a leftward- or rightward-directional verb phrase implied leftward or rightward motion. Because the visual system is engaged in the motion aftereffect, embodied semantics predicts that responses in the motion-aftereffect direction (opposite to the direction of the adapting stimulus) are facilitated, whereas symbolic semantics predicts response facilitation in the direction of the adapting stimulus (opposite to the direction of the motion aftereffect). We found response facilitation in the direction of real- and implied-motion adapting stimuli in ambiguous and directional verb phrases. These results suggest that visual and linguistic representations of motion can be dissociated. © The Author(s) 2012
"Pragmatism and Theories of Emergence" in "European Journal of Pragmatism and American Philosophy" XI-2 | 2019
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Gesture, meaning, and intentionality: from radical to pragmatist enactive theory of language
No full textThe article argues in favour of a pragmatist enactive interpretation of the emergence of the symbolic and contentful mind from a basic form of social communicative interaction in which basic cognitive capacities are involved. Through a critical overview of Radical Enactivists (RECers)’ view about language, the article focuses on Mead’s pragmatist behavioural theory of meaning that refers to the gestural conversation as the origin of the evolution of linguistic conversation. The article develops as follows. After exposing the main elements of REC’s theory of cognition and language that involve the construction of a theory of natural signs (teleosemiotics) and basic directionality (Ur-intentionality), some critical points of Hutto and Myin’s proposal will be highlighted. To foster a continuist perspective of language, the behavioural theory of meaning and language that Mead develops from the notion of gesture will be analysed. His theory is akin to REC and could augment the bare bones of REC’s sketched perspective, helping to include Ur-intentionality in a broader non-dualistic phylogenetic and ontogenetic theory of symbolic language from gestural communication, thus helping to overcome the distinction between a content-less intentionality and a content-involving intentionality, i.e., a semantic propositional intentionality. Furthermore, a recent revival of Mead’s theory testifies to its up-to-date relevance to explain the innate social dimension of human and non-human animals, and the human communicative capacity through the conditioning of bio-social canons and structures
Evolution and Emergence. Comparing C. Lloyd Morgan’s Emergentism and G.H. Mead’s Processual Ontology
No full textReview of "Razinsky Hili, Ambivalence. A Philosophical Exploration, London-New York, Rowman & Littlefield, 2017, p. ix, 285"
No full textFurther Results on the Convergence of the Pavon–Ferrante Algorithm for Spectral Estimation
No full textIn this paper, we provide a detailed analysis of the global convergence properties of an extensively studied and extremely effective fixed-point algorithm for the Kullback-Leibler approximation of spectral densities, proposed by Pavon and Ferrante in their paper 'On the Georgiou-Lindquist approach to constrained Kullback-Leibler approximation of spectral densities.' Our main result states that the algorithm globally converges to one of its fixed points
On the Existence of a Solution to a Spectral Estimation Problem à la Byrnes-Georgiou-Lindquist
No full textA parametric spectral estimation problem in the style of Byrnes, Georgiou, and Lindquist was posed in [1], but the existence of a solution was only proved in a special case. Based on their results, we show that a solution indeed exists given an arbitrary matrix-valued prior density. The main tool in our proof is the topological degree theory
Parametrization of minimal spectral factors of discrete-time rational spectral densities
No full textIn this paper, the problem of providing a complete parametrization of the minimal spectral factors of a discrete-time rational spectral density is considered. The desired parametrization, given in terms of the all-pass divisors of a certain all-pass function, is established in the most general setting: after several partial results, mostly in the continuous-time case, this is indeed the first complete parametrization obtained without resorting to any facilitating assumption. This result provides a positive answer to a conjecture raised in 2016
Non-Normality Improves Information Transmission Performance of Network Systems
No full textIn this paper, we propose a new measure of communication performance of linear network systems, the information gain, and we show that this measure is strongly affected by the degree of non-normality of the networks adjacency matrix. Specifically, we prove that the numerical abscissa of the networks adjacency matrix, a well-known indicator of matrix non-normality, regulates the behavior of the information gain. Further, we establish a lower bound on the information gain of positive networks, i.e., weighted networks with positive weights. This bound reveals that the information gain may exhibit an exponential dependence on the graphical distance between the transmitter and the receiver nodes. Finally, we present a design methodology which provably enhances the information gain while keeping the networks weights bounded in magnitude. We illustrate and validate our theoretical findings by means of examples with structured and random networks
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