Search results for "Causa"

Abstract. Towards a Theory of Cognitive Responsibility: Action, Perception and Normativity from Plato to Searle.

2011

The talk articulates the normative commitments allowing us to consider perceptual experience as a form of knowledge - that is, as a form of the human activity situated in the normative space of reason of which we can be held responsible. More specifically, John Searle's characterization of the logical structure of perceptual experiences as causally selfreferential intentional states can be developed into an account of the causal and normative-intentional aspects of experience, the genealogy of which can be traced back to Plato's Theaetetus and Meno. In these dialogues, in fact, a picture of experience as "knowledge" seems to be based on a specific "reasoning about the cause" as the specific…

1993

We may state that the path of development of an organism can play an important role in its immediate realization and also in its possible transformation. This leads to the problem of the existence of a causative link between individual development (ontogeny) and evolutionary history (phylogeny). This problem which has been dealt with by numerous authors, has led to contradictory answers, depending on the direction of the supposed connection: from the evolutionary history to individual development or vice versa, that is, from individual development to the evolutionary history.

Laudatio (Investidura como Doctor "Honoris Causa" por la Universitat de València a José Vidal Beneyto)

2006

Taking historical embeddedness seriously : Three historical approaches to advance strategy process and practice research

2016

International audience; Despite the proliferation of strategy process and practice research, we lack understanding of the historical embeddedness of strategic processes and practices. In this paper, we present three historical approaches with the potential to remedy this deficiency. First, realist history can contribute to a better understanding of the historical embeddedness of strategic processes; in particular, comparative historical analysis can explicate the historical conditions, mechanisms, and causality in strategic processes. Second, interpretative history can add to our knowledge of the historical embeddedness of strategic practices, and microhistory can specifically help to under…

Costly punishment prevails in intergroup conflict.

2011

Understanding how societies resolve conflicts between individual and common interests remains one of the most fundamental issues across disciplines. The observation that humans readily incur costs to sanction uncooperative individuals without tangible individual benefits has attracted considerable attention as a proximate cause as to why cooperative behaviours might evolve. However, the proliferation of individually costly punishment has been difficult to explain. Several studies over the last decade employing experimental designs with isolated groups have found clear evidence that the costs of punishment often nullify the benefits of increased cooperation, rendering the strong human tenden…

Advancing Deep Learning for Earth Sciences: From Hybrid Modeling to Interpretability

2020

Machine learning and deep learning in particular have made a huge impact in many fields of science and engineering. In the last decade, advanced deep learning methods have been developed and applied to remote sensing and geoscientific data problems extensively. Applications on classification and parameter retrieval are making a difference: methods are very accurate, can handle large amounts of data, and can deal with spatial and temporal data structures efficiently. Nevertheless, several important challenges need still to be addressed. First, current standard deep architectures cannot deal with long-range dependencies so distant driving processes (in space or time) are not captured, and the…

Estimation of brain connectivity through Artificial Neural Networks

2019

Among different methods available for estimating brain connectivity from electroencephalographic signals (EEG), those based on MVAR models have proved to be flexible and accurate. They rely on the solution of linear equations that can be pursued through artificial neural networks (ANNs) used as MVAR model. However, when few data samples are available, there is a lack of accuracy in estimating MVAR parameters due to the collinearity between regressors. Moreover, the assessment procedure is also affected by the lack of data points. The mathematical solution to these problems is represented by penalized regression methods based on l 1 norm, that can reduce collinearity by means of variable sel…

Multiscale Granger causality analysis by à trous wavelet transform

2017

Since interactions in neural systems occur across multiple temporal scales, it is likely that information flow will exhibit a multiscale structure, thus requiring a multiscale generalization of classical temporal precedence causality analysis like Granger's approach. However, the computation of multiscale measures of information dynamics is complicated by theoretical and practical issues such as filtering and undersampling: to overcome these problems, we propose a wavelet-based approach for multiscale Granger causality (GC) analysis, which is characterized by the following properties: (i) only the candidate driver variable is wavelet transformed (ii) the decomposition is performed using the…

Temporal Binding in Multisensory and Motor-Sensory Contexts: Toward a Unified Model

2021

Our senses receive a manifold of sensory signals at any given moment in our daily lives. For a coherent and unified representation of information and precise motor control, our brain needs to temporally bind the signals emanating from a common causal event and segregate others. Traditionally, different mechanisms were proposed for the temporal binding phenomenon in multisensory and motor-sensory contexts. This paper reviews the literature on the temporal binding phenomenon in both multisensory and motor-sensory contexts and suggests future research directions for advancing the field. Moreover, by critically evaluating the recent literature, this paper suggests that common computational prin…

Bayesian Metanetwork for Context-Sensitive Feature Relevance

2006

Bayesian Networks are proven to be a comprehensive model to describe causal relationships among domain attributes with probabilistic measure of appropriate conditional dependency. However, depending on task and context, many attributes of the model might not be relevant. If a network has been learned across multiple contexts then all uncovered conditional dependencies are averaged over all contexts and cannot guarantee high predictive accuracy when applied to a concrete case. We are considering a context as a set of contextual attributes, which are not directly effect probability distribution of the target attributes, but they effect on a “relevance” of the predictive attributes towards tar…