Naive realism and the scientific narration of perception

Naive realism is a widely debated topic in the philosophy of the mind. In this article I will review the theses of naive realism through the works of one of the most influential philosophers who supported and developed them, Michael Martin. Once the reasons why naive realism should be supported are discussed, I will propose an empirical argument to show that naive realism and the most basic scientific knowledge of perceptive processes are contradictory

We Are Not Alone: Perception and The Others

In this paper, I have outlined an original Metaphysics of Perception which takes into consideration some of the most common views about perception in the contemporary debate. Then I will look at the consequences of this metaphysics about our perception of others and what we know about them. In the third section, I suggest how to make sense of certain neuroscientific discoveries about social perception and social cognition. In the conclusion, I recap what has been done to say that others are what we can know after all

Metafisica e Percezione. Una teoria contemporanea

Il libro prende in esame la percezione come il modo in cui gli oggetti del mondo esterno entrano a far parte della nostra esperienza cosciente. Il corpo, da un lato, gli oggetti del mondo che ci circonda, dall'altro, verranno messi nel loro posto di argomento rispetto alla metafisica della nostra esperienza percettiva

Ciò che non è fisico. Il carattere metafisico dell'esperienza umana

Con questo articolo vorrei dare qualche argomento e qualche esempio per mostrare la natura non fisica dell’esperienza umana. Per prima cosa argomenterò l’esistenza di qualcosa di non fisico e successivamente come e perché fenomeni come colori, emozioni, stati mentali e tutti gli altri fenomeni legati alla soggettività sono ciò che compone l’esperienza umana nel suo versante non fisico. Nel secondo capitolo mostrerò qual è la relazione tra l’esperienza umana nel suo carattere non fisico e metafisico e il funzionamento dei meccanismi cerebrali e la priorità del carattere metafisico dell’esperienza umana rispetto ai meccanismi cerebrali

Ciò che non é fisico. Il carattere metafisico sell'esperienza umama

Con questo articolo vorrei dare qualche argomento e qualche esempio per mostrare la natura non fisica dell’esperienza umana. Per prima cosa argomenterò l’esistenza di qualcosa di non fisico e successivamente come e perché fenomeni come colori, emozioni, stati mentali e tutti gli altri fenomeni legati alla soggettività sono ciò che compone l’esperienza umana nel suo versante non fisico. Nel secondo capitolo mostrerò qual è la relazione tra l’esperienza umana nel suo carattere non fisico e metafisico e il funzionamento dei meccanismi cerebrali e la priorità del carattere metafisico dell’esperienza umana rispetto ai meccanismi cerebrali

starvars: An R Package for Analysing Nonlinearities in Multivariate Time Series

Although linear autoregressive models are useful to practitioners in different fields, often a nonlinear specification would be more appropriate in time series analysis. In general, there are many alternative approaches to nonlinearity modelling, one consists in assuming multiple regimes. Among the possible specifications that account for regime changes in the multivariate framework, smooth transition models are the most general, since they nest both linear and threshold autoregressive models. This paper introduces the starvars package which estimates and predicts the Vector Logistic Smooth Transition model in a very general setting which also includes predetermined variables. In comparison to the existing R packages, starvars offers the estimation of the Vector Smooth Transition model both by maximum likelihood and nonlinear least squares. The package allows also to test for nonlinearity in a multivariate setting and detect the presence of common breaks. Furthermore, the package computes multi-step-ahead forecasts. Finally, an illustration with financial time series is provided to show its usage

Multi-Modal RGB-D Scene Recognition Across Domains

Scene recognition is one of the basic problems in computer vision research with extensive applications in robotics. When available, depth images provide helpful geometric cues that complement the RGB texture information and help to identify discriminative scene image features. Depth sensing technology developed fast in the last years and a great variety of 3D cameras have been introduced, each with different acquisition properties. However, those properties are often neglected when targeting big data collections, so multi-modal images are gathered disregarding their original nature. In this work, we put under the spotlight the existence of a possibly severe domain shift issue within multi-modality scene recognition datasets. As a consequence, a scene classification model trained on one camera may not generalize on data from a different camera, only providing a low recognition performance. Starting from the well-known SUN RGB-D dataset, we designed an experimental testbed to study this problem and we use it to benchmark the performance of existing methods. Finally, we introduce a novel adaptive scene recognition approach that leverages self-supervised translation between modalities. Indeed, learning to go from RGB to depth and vice-versa is an unsupervised procedure that can be trained jointly on data of multiple cameras and may help to bridge the gap among the extracted feature distributions. Our experimental results confirm the effectiveness of the proposed approach

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Forecasting realized volatility: a review

Modeling financial volatility is an important part of empirical finance. This paper provides a literature review of the most relevant volatility models, with a particular focus on forecasting models. We firstly discuss the empirical foundations of different kinds of volatility. The paper, then, analyses the non-parametric measure of volatility, named realized variance, and its empirical applications. A wide range of realized volatility models, both univariate and multivariate, is presented, such as time series models, MIDAS and GARCH-MIDAS models, Realized GARCH, and HEAVY models. We further discuss forecasting evaluation methods specifically suited for volatility models

Where and What: Two Experiments for Dualism

In 2007, two experiments that have now become very famous have appeared in the neuroscientific literature. With over of one thousand of citation, that moved neuroscientist to speculate about the self- representation and other conscious phenomena and to create new experiments, Henrik Ehrsson and Bigna Lengenhagger produce in two studies out of the body experiences in healthy subjects. The literature reports this kind of experience as consequence of neurological disease or drug use. In this article, I will prove that the where, and the what, of the out of the body experience and the normal experience are something different from the bodily one and I will argue in favor of some kind of dualism and, in particular, a dualism called property dualism. ER