Designing better graphs by including distributional information and integrating words, numbers, and images.

2015 ◽  
pp. 333-353
Author(s):  
David M. Lane ◽  
Anikó Sándor
Keyword(s):  
Distributional Information

Synopsis of Cnidoscolus (Euphorbiaceae) in midwestern Brazil, including taxonomic updates, a new species, and a reestablishment of C. neglectus

Phytotaxa ◽  
2021 ◽  
Vol 500 (3) ◽  
pp. 179-200
Author(s):  
CELLINI CASTRO DE OLIVEIRA ◽  
ANDRÉ LAURÊNIO DE MELO ◽  
MARCOS JOSÉ DA SILVA
Keyword(s):  
New Species ◽  
Geographic Distribution ◽  
Conservation Status ◽  
Systematic Position ◽  
The Other ◽  
Dichotomous Key ◽  
Distributional Information ◽  
A New Species

A synopsis of the genus Cnidoscolus is presented for the midwestern region of Brazil, which resulted from the analysis of about 1,200 specimens from 62 national and foreign herbaria, including type collections. Observations of populations in field were also made. Nine species are recognized, one of which, C. mcvaughii, is new to science. It is described and illustrated, and comments about its geographic distribution, morphological relationships, systematic position, phenology, and conservation status are provided, as well as images and a map. The other species are contrasted by a dichotomous key. Also, distributional information, maps, conservation evaluations, images and morphologically diagnoses are included. Eight synonymizations, one lectotypification, a neotypifcation, and the re-establishment of C. neglectus are proposed.

scholarly journals Predicting the distribution of Omalonyx (Mollusca: Pulmonata: Succineidae) species from literature review, museum databases and new sampling efforts in Brazil

2018 ◽  
Vol 18 (1) ◽  
Author(s):  
Daniel Coscarelli ◽  
Lângia C. Montresor ◽  
Philip Russo ◽  
Alan Lane de Melo ◽  
Teofânia H.D.A. Vidigal
Keyword(s):  
Literature Review ◽  
South America ◽  
Species Distribution ◽  
First Record ◽  
Neotropical Region ◽  
Bioclimatic Variables ◽  
Distributional Information ◽  
Consistent Performance ◽  
Using Data ◽  
Hydrographic Regions

Abstract Accurate distributional information is crucial for studies on systematics, biodiversity and conservation. To improve the knowledge regarding the geographical distribution of Omalonyx in South America, we present updated information based on data from a literature review, institutional collections and malacological surveys. All this information composed the dataset used to predict species distribution employing the Maximum Entropy Algorithm (MaxEnt). The model was run using data on species distribution, altitude and bioclimatic variables (WorldClim database). The model had consistent performance, and areas presenting similar conditions to areas where the species were recorded were considered areas of occurrence. The predicted occurrence areas included those that were already surveyed and those that are considered potential occurrence areas. The results demonstrate that the genus has widespread distribution in the Neotropical region and occurs in the tropical, temperate and arid regions of South America and Lesser Antilles. Omalonyx spp. were recorded in all South American countries and hydrographic regions. However, in some countries, there were only isolated records (ex: Colombia and Ecuador). Here, we also present the first record of Omalonyx spp. in four Brazilian States (Acre, Rondônia, Piaui, and Amapá). The genus was found in all hydrographic regions within Brazil and among 27 federative unities; it was absent from only two unities (Roraima State and Distrito Federal). This work contributes to the knowledge on Omalonyx spp. distribution and provides an important basis for the work of ecologists and taxonomists.

scholarly journals Avoiding Conflict: When Speaker Coordination Does Not Require Conceptual Agreement

2021 ◽  
Vol 3 ◽  
Author(s):  
Alexandre Kabbach ◽  
Aurélie Herbelot
Keyword(s):  
Ad Hoc ◽  
Semantic Space ◽  
Social Contexts ◽  
Linguistic Community ◽  
Dense Matrix ◽  
Lexical Representations ◽  
Successful Communication ◽  
Distributional Information ◽  
Distributional Semantic Models ◽  
Value Decomposition

In this paper we discuss the socialization hypothesis—the idea that speakers of the same (linguistic) community should share similar concepts given that they are exposed to similar environments and operate in highly-coordinated social contexts—and challenge the fact that it is assumed to constitute a prerequisite to successful communication. We do so using distributional semantic models of meaning (DSMs) which create lexical representations via latent aggregation of co-occurrence information between words and contexts. We argue that DSMs constitute particularly adequate tools for exploring the socialization hypothesis given that 1) they provide full control over the notion of background environment, formally characterized as the training corpus from which distributional information is aggregated; and 2) their geometric structure allows for exploiting alignment-based similarity metrics to measure inter-subject alignment over an entire semantic space, rather than a set of limited entries. We propose to model coordination between two different DSMs trained on two distinct corpora as dimensionality selection over a dense matrix obtained via Singular Value Decomposition This approximates an ad-hoc coordination scenario between two speakers as the attempt to align their similarity ratings on a set of word pairs. Our results underline the specific way in which linguistic information is spread across singular vectors, and highlight the need to distinguish agreement from mere compatibility in alignment-based notions of conceptual similarity. Indeed, we show that compatibility emerges from idiosyncrasy so that the unique and distinctive aspects of speakers’ background experiences can actually facilitate—rather than impede—coordination and communication between them. We conclude that the socialization hypothesis may constitute an unnecessary prerequisite to successful communication and that, all things considered, communication is probably best formalized as the cooperative act of avoiding conflict, rather than maximizing agreement.

scholarly journals Word meaning access: The one-to-many mapping from form to meaning

2019 ◽  
Author(s):  
Jennifer M Rodd
Keyword(s):  
A Priori ◽  
Word Meaning ◽  
Semantic Features ◽  
Contextual Cues ◽  
Word Meanings ◽  
Wide Range ◽  
Core Feature ◽  
Distributional Information ◽  
Current Context ◽  
The One

This chapter focuses on the process by which stored knowledge about a word’s form (orthographic or phonological) maps onto stored knowledge about its meaning. This mapping is made challenging by the ambiguity that is ubiquitous in natural language: most familiar words can refer to multiple different concepts. This one-to-many mapping from form to meaning within the lexicon is a core feature of word-meaning access. Fluent, accurate word-meaning access requires that comprehenders integrate multiple cues in order to determine which of a word’s possible semantic features are relevant in the current context. Specifically, word-meaning access is guided by (i) distributional information about the a priori relative likelihoods of different word meanings and (ii) a wide range of contextual cues that indicate which meanings are most likely in the current context.

scholarly journals An explicit investigation of the roles that feature distributions play in rapid visual categorization

2020 ◽  
Author(s):  
Hee Yeon Im ◽  
Natalia Tiurina ◽  
Igor Utochkin
Keyword(s):  
Visual System ◽  
Size Distribution ◽  
Size Distributions ◽  
Visual Array ◽  
Visual Categorization ◽  
Range Distribution ◽  
Distribution Shape ◽  
Ensemble Statistics ◽  
Distributional Information ◽  
Ensemble Representations

Ensemble representations are often described as efficient tools when summarizing features of multiple similar objects as a group. However, it can sometimes be more useful not to compute a single summary description for all of the objects if they are substantially different, for example, when they belong to entirely different categories. It was proposed that the visual system can efficiently use the distributional information of ensembles to decide whether simultaneously displayed items belong to single or several different categories. Here we directly tested how the feature distribution of items in a visual array affects an ability to discriminate individual items (Experiment 1) and sets (Experiments 2-3) when participants were instructed explicitly to categorize individual objects based on the median of size distribution. We varied the width (narrow or fat) as well as the shape (smooth or two-peaked) of distributions in order to manipulate the ease of ensemble extraction from the items. We found that observers unintentionally relied on the grand mean as a natural categorical boundary and that their categorization accuracy increased as a function of the size differences among individual items and a function of their separation from the grand mean. For ensembles drawn from two-peaked size distributions, participants showed better categorization performance. They were more accurate at judging within-category ensemble properties in other dimensions (centroid and orientation) and less biased by superset statistics. This finding corroborates the idea that the two-peaked feature distributions support the “segmentability” of spatially intermixed sets of objects. Our results emphasize important roles of ensemble statistics (mean, range, distribution shape) in explicit visual categorization.

scholarly journals Decoding the Information Structure Underlying the Neural Representation of Concepts

2021 ◽  
Author(s):  
Leonardo Fernandino ◽  
Lisa L. Conant ◽  
Colin J. Humphries ◽  
Jeffrey R. Binder
Keyword(s):  
Information Structure ◽  
Conceptual Knowledge ◽  
Neural Representation ◽  
Neural Activation ◽  
Activation Patterns ◽  
Concept Representation ◽  
Native Space ◽  
Distributional Information ◽  
Similarity Structure ◽  
Types Of Information

The nature of the neural code underlying conceptual knowledge remains a major unsolved problem in cognitive neuroscience. Three main types of information have been proposed as candidates for the neural representations of lexical concepts: taxonomic (i.e., information about category membership and inter-category relations), distributional (i.e., information about patterns of word co-occurrence in natural language use), and experiential (i.e., information about sensory-motor, affective, and other features of phenomenal experience engaged during concept acquisition). In two experiments, we investigated the extent to which these three types of information are encoded in the neural activation patterns associated with hundreds of English nouns from a wide variety of conceptual categories. Participants made familiarity judgments on the meaning of written nouns while undergoing functional MRI. A high-resolution, whole-brain activation map was generated for each noun in each participant′s native space. These word-specific activation maps were used to evaluate different representational spaces corresponding to the three types of information described above. In both studies, we found a striking advantage for experience-based models in most brain areas previously associated with concept representation. Partial correlation analyses revealed that only experiential information successfully predicted concept similarity structure when inter-model correlations were taken into account. This pattern of results was found independently for object concepts and event concepts. Our findings indicate that the neural representation of conceptual knowledge primarily encodes information about features of experience, and that - to the extent that it is represented in the brain - taxonomic and distributional information may rely on such an experience-based code.

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