• Digital Humanities and Cultural Heritage
• 2017-2021close
• Publications close
• Research data close
• Other research products close

The first issue of Nature Reviews Neuroscience was published 20 years ago, in 2000. To mark this anniversary, in this Viewpoint article we asked a selection of researchers from across the field who have authored pieces published in the journal in recent years for their thoughts on notable and interesting developments in neuroscience, and particularly in their areas of the field, over the past two decades. They also provide some thoughts on current lines of research and questions that excite them.

AbstractThis chapter reports an investigation into possible brain bases for negation. It begins with a review of negation experiments that used behavioral studies (measuring Reaction Time—RT), and functional Magnetic Resonance Imaging (fMRI) experiments that sought to identify local activations that correlate with the presence of negation. The chapter dwells on a major methodological problem that permeates the experimental study of negation processing, and proposes a solution: instead of overt negation, we study expressions that contain a covert negation—expressions that are Downward Entailing (DE) as evinced by their ability to reverse inferences and license NPIs in their scope. DE operators are thus taken to contain a hidden, or covert, negation, and contrast with the Upward Entailing counterparts (few vs. many; less vs. more). The chapter reviews behavioral experiments in healthy adults that indicate that DE has a processing cost, and an fMRI study that finds a single brain location for this computation. These results serve as a basis for an experiment on individuals with Broca’s aphasia. Tests with DE and UE quantifiers with these patients resulted in a mixed picture, which is discussed and its implications are derived.

[EN] The development of a model to quantify semantic similarity and relatedness between words has been the major focus of many studies in various fields, e.g. psychology, linguistics, and natural language processing. Unlike the measures proposed by most previous research, this article is aimed at estimating automatically the strength of associative words that can be semantically related or not. We demonstrate that the performance of the model depends not only on the combination of independently constructed word embeddings (namely, corpus- and network-based embeddings) but also on the way these word vectors interact. The research concludes that the weighted average of the cosine-similarity coefficients derived from independent word embeddings in a double vector space tends to yield high correlations with human judgements. Moreover, we demonstrate that evaluating word associations through a measure that relies on not only the rank ordering of word pairs but also the strength of associations can reveal some findings that go unnoticed by traditional measures such as Spearman's and Pearson's correlation coefficients. s Financial support for this research has been provided by the Spanish Ministry of Science, Innovation and Universities [grant number RTC 2017-6389-5], the Spanish ¿Agencia Estatal de Investigación¿ [grant number PID2020-112827GB-I00 / AEI / 10.13039/501100011033], and the European Union¿s Horizon 2020 research and innovation program [grant number 101017861: project SMARTLAGOON]. Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.

Structured information is easier to remember and recall than random one. In real life, information exhibits multi-level hierarchical organization, such as clauses, sentences, episodes and narratives in language. Here we show that multi-level grouping emerges even when participants perform memory recall experiments with random sets of words. To quantitatively probe brain mechanisms involved in memory structuring, we consider an experimental protocol where participants perform ‘final free recall’ (FFR) of several random lists of words each of which was first presented and recalled individually. We observe a hierarchy of grouping organizations of FFR, most notably many participants sequentially recalled relatively long chunks of words from each list before recalling words from another list. More-over, participants who exhibited strongest organization during FFR achieved highest levels of performance. Based on these results, we develop a hierarchical model of memory recall that is broadly compatible with our findings. Our study shows how highly controlled memory experiments with random and meaningless material, when combined with simple models, can be used to quantitatively probe the way meaningful information can efficiently be organized and processed in the brain, so to be easily retrieved.Significance StatementInformation that people communicate to each other is highly structured. For example, a story contains meaningful elements of various degrees of complexity (clauses, sentences, episodes etc). Recalling a story, we are chiefly concerned with these meaningful elements and not its exact wording. Here we show that people introduce structure even when recalling random lists of words, by grouping the words into ‘chunks’ of various sizes. Doing so improves their performance. The so formed chunks closely correspond in size to story elements described above. This suggests that our memory is trained to create a structure that resembles the one it typically deals with in real life, and that using random material like word lists can be used to quantitatively probe these memory mechanisms.

AbstractLexical Semanticsis concerned with how words encode mental representations of the world, i.e.,concepts. We call this type of concepts,classification concepts. In this paper, we focus onVisual Semantics, namely, on how humans build concepts representing what they perceive visually. We call this second type of concepts,substance concepts. As shown in the paper, these two types of concepts are different and, furthermore, the mapping between them is many-to-many. In this paper we provide a theory and an algorithm for how to build substance concepts which are in a one-to-one correspondence with classifications concepts, thus paving the way to the seamless integration between natural language descriptions and visual perception. This work builds upon three main intuitions: (i) substance concepts are modeled asvisual objects, namely, sequences of similar frames, as perceived in multipleencounters; (ii) substance concepts are organized into avisual subsumption hierarchybased on the notions of and ; (iii) the human feedback is exploitednotto name objects, but, rather, to align the hierarchy of substance concepts with that of classification concepts. The learning algorithm is implemented for the base case of a hierarchy of depth two. The experiments, though preliminary, show that the algorithm manages to acquire the notions of and with reasonable accuracy, this despite seeing a small number of examples and receiving supervision on a fraction of them.

Abstract Spontaneous collapse models (CM) have been proposed as a possible solution of the quantum measurement problem. In CM the unitary evolution of the wave function is modified by stochastic corrections which lead to a dynamical reduction at macroscopic level. Unlike interpretations of quantum mechanics, CM predict departures from standard theory that can be experimentally tested. Here, we focus on one of these predictions, a universal force noise acting on any mechanical system. In particular, we discuss recent bounds on collapse models set by ultracold cantilevers experiments and gravitational wave detectors. First notable results are a partial exclusion of the CSL parameters proposed by Adler and a full exclusion of an early model based on quantum gravity ideas.

We present a novel unsupervised framework, which links continuous visual features and symbolic textual descriptions of manipulation activity videos. First, we extract the semantic representation of visually observed manipulations by applying a bottom-up approach to the continuous image streams. We then employ a rule-based reasoning to link visual and linguistic inputs. The proposed framework allows robots 1) to autonomously parse, classify, and label sequentially and/or concurrently performed atomic manipulations (e.g., “cutting” or “stirring”), 2) to simultaneously categorize and identify manipulated objects without using any standard feature-based recognition techniques, and 3) to generate textual descriptions for long activities, e.g., “breakfast preparation.” We evaluated the framework using a dataset of $\text{120}$ atomic manipulations and $\text{20}$ long activities.