Semantic feature production norms for a large set of objects and events

Semantic features produced by speakers of a language when given a word corresponding to a concept have provided insight into numerous behavioral phenomena concerning semantic representation in language-impaired and -unimpaired speakers. A number of theories concerning the organization of semantic memory have used features as their starting point. Here, we provide a set of feature norms collected from approximately 280 participants for a total of 456 words (169 nouns referring to objects, 71 nouns referring to events, and 216 verbs referring to events). Whereas a number of feature norms for object concepts already exist, we provide the first set of norms for event concepts. We have used these norms (for both objects and events) in research addressing questions concerning the similarities and differences between the semantic representation of objects and events and in research concerning the interface between semantics and syntax, given that events can be expressed in language as nouns or verbs. Some of this research is summarized here. These norms may be downloaded from www.psychonomic.org/archive.     

Semantic feature production norms for a large set of living and nonliving things

Semantic features have provided insight into numerous behavioral phenomena concerning concepts, categorization, and semantic memory in adults, children, and neuropsychological populations. Numerous theories and models in these areas are based on representations and computations involving semantic features. Consequently, empirically derived semantic feature production norms have played, and continue to play, a highly useful role in these domains. This article describes a set of feature norms collected from approximately 725 participants for 541 living (dog) and nonliving (chair) basic-level concepts, the largest such set of norms developed to date. This article describes the norms and numerous statistics associated with them. Our aim is to make these norms available to facilitate other research, while obviating the need to repeat the labor-intensive methods involved in collecting and analyzing such norms. The full set of norms may be downloaded from www.psychonomic.org/archive.     

Semantic memory: A feature-based analysis and new norms for Italian

Semantic norms for properties produced by native speakers are valuable tools for researchers interested in the structure of semantic memory and in category-specific semantic deficits in individuals following brain damage. The aims of this study were threefold. First, we sought to extend existing semantic norms by adopting an empirical approach to category (Exp. 1) and concept (Exp. 2) selection, in order to obtain a more representative set of semantic memory features. Second, we extensively outlined a new set of semantic production norms collected from Italian native speakers for 120 artifactual and natural basic-level concepts, using numerous measures and statistics following a feature-listing task (Exp. 3b). Finally, we aimed to create a new publicly accessible database, since only a few existing databases are publicly available online.     

French categorization norms for 70 semantic categories and comparison with Van Overschelde et al.’s (2004) English norms

The present methodological note provides categorization norms for 70 semantic categories collected from 200 participants. The categories were mainly derived from a French translation of the Van Overschelde, Rawson, and Dunlosky (2004) inductor terms including a large set of semantic categories. Our study also extends recent French norms (Léger, Boumlak, & Tijus, 2008; Marchal & Nicolas, 2003) and tests their stability. These 70 categories constitute the widest French categorization norms to date and will be of use to studies in the fields of both linguistics and psycholinguistics. A cross-linguistic comparison, with both quantitative and qualitative results, is also performed, which should prove useful for bilingual and/or cross-linguistic studies. The norms collected for all 70 categories are available for download as supplemental materials from http://brm.psychonomic-journals.org/content/supplemental.     

Concepts as Semantic Pointers: A Framework and Computational Model

The reconciliation of theories of concepts based on prototypes, exemplars, and theory‐like structures is a longstanding problem in cognitive science. In response to this problem, researchers have recently tended to adopt either hybrid theories that combine various kinds of representational structure, or eliminative theories that replace concepts with a more finely grained taxonomy of mental representations. In this paper, we describe an alternative approach involving a single class of mental representations called “semantic pointers.” Semantic pointers are symbol‐like representations that result from the compression and recursive binding of perceptual, lexical, and motor representations, effectively integrating traditional connectionist and symbolic approaches. We present a computational model using semantic pointers that replicates experimental data from categorization studies involving each prior paradigm. We argue that a framework involving semantic pointers can provide a unified account of conceptual phenomena, and we compare our framework to existing alternatives in accounting for the scope, content, recursive combination, and neural implementation of concepts.     

A set of semantic norms for German and Italian

The psychological community frequently investigates semantic norms of properties produced by native speakers after being presented concept words, and these norms are of great value for a wide variety of psychological experiments. This paper presents a new set of norms that includes a collection of properties from a production experiment for the German and the Italian languages. Stimuli consisted of 50 concrete objects taken from 10 different concept classes. The data comprise annotations of semantic relation types and several statistical measures, which facilitate the comparison of the two target languages.     

Parsing the rainbow

Navigating the ontology of color used to be a simple affair. There was the naive view that colors really are in objects the way they appear, and the view that they are secondary qualities to cause certain experiences in us. Today, there are myriad well-developed views but no satisfactory taxonomy of philosophical theories on color. In this article, I first examine the two newest taxonomies on offer and argue that they are inadequate. In particular, I look at Brogaard’s taxonomy and then Cohen’s. One of the reasons I am displeased with Brogaard and Cohen’s taxonomies is that I find it implausible that dispositions are relational properties. I provide an argument against this way of classifying dispositions. Having learned from the vices and virtues of Brogaard and Cohens’ taxonomies, I provide what I believe is a much-enhanced way of taxonomizing philosophical views on color. My taxonomy rules out certain views, clarifies others, and shows that there is an unnoticed view worthy of consideration.     

Semantic segmentation via highly fused convolutional network with multiple soft cost functions

Semantic image segmentation is one of the most challenging tasks in computer vision. In this paper, we propose a highly fused convolutional network, which consists of three parts: downsampling, fused upsampling and multiple predictions. We adopt a VGG-net based downsampling structure, followed by multiple steps of upsampling. Feature maps in each pair of corresponding pooling layers and unpooling layers are combined. We also bring out multiple pre-outputs, each is generated from an unpooling layer by a one-step upsampling operation. Finally, we concatenate these pre-outputs to get the final output. As a result, our proposed network makes high use of the feature information by fusing and reusing features in low layers. In addition, when training our model, we add multiple soft cost functions on pre-outputs and the final output. In this way, we can reduce the loss reduction in backpropagation. We evaluate our model on three public segmentation datasets: CamVid, PASCAL VOC, and ADE20K. We achieve considerable segmentation performance on PASCAL VOC dataset and ADE20K dataset. Especially on CamVid dataset, we achieve state-of-the-art performance.     

English semantic word-pair norms and a searchable Web portal for experimental stimulus creation

As researchers explore the complexity of memory and language hierarchies, the need to expand normed stimulus databases is growing. Therefore, we present 1,808 words, paired with their features and concept–concept information, that were collected using previously established norming methods (McRae, Cree, Seidenberg, & McNorgan Behavior Research Methods 37:547–559, 2005). This database supplements existing stimuli and complements the Semantic Priming Project (Hutchison, Balota, Cortese, Neely, Niemeyer, Bengson, & Cohen-Shikora 2010). The data set includes many types of words (including nouns, verbs, adjectives, etc.), expanding on previous collections of nouns and verbs (Vinson & Vigliocco Journal of Neurolinguistics 15:317–351, 2008). We describe the relation between our and other semantic norms, as well as giving a short review of word-pair norms. The stimuli are provided in conjunction with a searchable Web portal that allows researchers to create a set of experimental stimuli without prior programming knowledge. When researchers use this new database in tandem with previous norming efforts, precise stimuli sets can be created for future research endeavors.     

An efficient method for estimating semantic similarity based on feature overlap: Reliability and validity of semantic feature ratings

Two studies were conducted in which human participants rated pairs of words according to the perceived degree to which the words’ referents shared semantic features. The participants found the task intuitive, simple, and quick to complete. The ratings were reliable and valid. Interrater and interstudy correlations were high, and ratings were good predictors of known feature overlap values obtained from existing semantic feature norms.     

When is best-worst best? A comparison of best-worst scaling,numeric estimation,and rating scales for collection of semantic norms

Large-scale semantic norms have become both prevalent and influential in recent psycholinguistic research. However, little attention has been directed towards understanding the methodological best practices of such norm collection efforts. We compared the quality of semantic norms obtained through rating scales, numeric estimation, and a less commonly used judgment format called best-worst scaling. We found that best-worst scaling usually produces norms with higher predictive validities than other response formats, and does so requiring less data to be collected overall. We also found evidence that the various response formats may be producing qualitatively, rather than just quantitatively, different data. This raises the issue of potential response format bias, which has not been addressed by previous efforts to collect semantic norms, likely because of previous reliance on a single type of response format for a single type of semantic judgment. We have made available software for creating best-worst stimuli and scoring best-worst data. We also made available new norms for age of acquisition, valence, arousal, and concreteness collected using best-worst scaling. These norms include entries for 1,040 words, of which 1,034 are also contained in the ANEW norms (Bradley & Lang, Affective norms for English words (ANEW): Instruction manual and affective ratings (pp. 1-45). Technical report C-1, the center for research in psychophysiology, University of Florida, 1999).     

Dual career pathways of transnational athletes

ObjectivesTransnationalism, as part of the globalization processes, has transformed the lifestyle and the course of athletes' careers. This presents previously unexplored challenges encountered by student-athletes in combining athletic and academic pursuits. In this article, we propose a conceptual framework for the taxonomy of transnational dual careers (DC).Design and methodNarrative inquiry from the life story perspective was used to elicit and analyze career narratives of six transnational athletes (3 male and 3 female), generating about five interview hours per athlete. The developmental transition from secondary to higher education was chosen as a key transition to classify the DC pathways. Additional insights into DC mobilization across international borders were gleaned by employing the typologies of sport migrants developed in the sport labor migration research.ResultsThree patterns of transnational DC were discerned from the narratives based on the direction of geographic mobility and the core migration motive underpinning the storyline. Within the present dataset, the taxonomies are: (1) Within EU mobility: the sport exile DC pathway; (2) Mobility to the U.S.A.: the sport mercenary DC pathway; and (3) Mobility to the U.S.A.: the nomadic cosmopolitan DC pathway.ConclusionsThe identified transnational DC paths are not exhaustive, and highlight possibilities of individual development, unfolding through the matrices of social structures in a given location. Further research with a diverse set of transnational athletes is needed to test and expand the proposed taxonomy.     

Semantic memory disorders

Semantic memory encompasses knowledge of objects, facts and words. A number of brain regions are probably involved, but the left infero-lateral temporal lobe appears to play a key role. The separability of semantic memory from episodic (or autobiographical) memory is a focus of current debate. Impaired semantic memory is a common feature of Alzheimer's disease but is invariably overshadowed by a profound deficit in episodic memory. In semantic dementia, a rarer disorder associated with focal temporal-lobe atrophy, there is selective loss of semantic memory, characterized by preservation of superordinate knowledge of words, and objects, but loss of finer-grained information. This pattern can be interpreted as a degradation of features from a distributed network of semantic representations. Following Herpes simplex encephalitis, patients sometimes show disproportionate loss of knowledge for natural kinds (e.g. animals) with relative preservation of knowledge about artefacts, this may reflect differential damage to neural systems critical for perceptual as opposed to functional features, perceptual properties being more salient in knowledge about natural kinds.     

Semantic Holism is seriously false

Semantic Holism is the claim that any semantic path from inferential semantics (the indeterminate semantics forced by the classical inference rules of PC) reaches all the way to classical semantics if it is even one step long. In our joint paper Semantic Holism, Belnap and I showed that some such semantic paths are two steps long, but we left open a number of questions about the lengths of semantic paths. Here I answer the most important of these questions by showing that there are infinitely long semantic paths that begin at inferential semantics but that do not even reach classical semantics. I do this by showing how to construct such an infinite semantic path from the members of the family of (n–1)-out-of-n-disjunction connectives.     

Mimicking aphasic semantic errors in normal speech production: evidence from a novel experimental paradigm

Semantic errors are commonly found in semantic dementia (SD) and some forms of stroke aphasia and provide insights into semantic processing and speech production. Low error rates are found in standard picture naming tasks in normal controls. In order to increase error rates and thus provide an experimental model of aphasic performance, this study utilised a novel method- tempo picture naming. Experiment 1 showed that, compared to standard deadline naming tasks, participants made more errors on the tempo picture naming tasks. Further, RTs were longer and more errors were produced to living items than non-living items a pattern seen in both semantic dementia and semantically-impaired stroke aphasic patients. Experiment 2 showed that providing the initial phoneme as a cue enhanced performance whereas providing an incorrect phonemic cue further reduced performance. These results support the contention that the tempo picture naming paradigm reduces the time allowed for controlled semantic processing causing increased error rates. This experimental procedure would, therefore, appear to mimic the performance of aphasic patients with multi-modal semantic impairment that results from poor semantic control rather than the degradation of semantic representations observed in semantic dementia [Jefferies, E. A., & Lambon Ralph, M. A. (2006). Semantic impairment in stoke aphasia vs. semantic dementia: A case-series comparison. Brain, 129, 2132-2147]. Further implications for theories of semantic cognition and models of speech processing are discussed.     

The MCMI as a measure of DSM-III

The Millon Clinical Multiaxial Inventory (MCMI) has been interpreted as a measure of DSM-III disorders. However, the MCMI was constructed and validated primarily as a measure of Millon's (1969, 1981) taxonomy, not DSM-III. Comparison of the two taxonomies and examination of the MCMI's content validity for two of the MCMI scales indicate only a partial congruence between the Millon and DSM-III taxonomies. There has been no published empirical research concerning the relationship between the MCMI and DSM-III, and the derivation and cross-validation research for the MCMI scales employed Millon's taxonomy and not DSM-III. It is suggested that until such data have been presented one should be cautious in one's interpretation of the MCMI as a measure of DSM-III disorders.     

Combining feature norms and text data with topic models

Many psychological theories of semantic cognition assume that concepts are represented by features. The empirical procedures used to elicit features from humans rely on explicit human judgments which limit the scope of such representations. An alternative computational framework for semantic cognition that does not rely on explicit human judgment is based on the statistical analysis of large text collections. In the topic modeling approach, documents are represented as a mixture of learned topics where each topic is represented as a probability distribution over words. We propose feature-topic models, where each document is represented by a mixture of learned topics as well as predefined topics that are derived from feature norms. Results indicate that this model leads to systematic improvements in generalization tasks. We show that the learned topics in the model play in an important role in the generalization performance by including words that are not part of current feature norms.     

External details revisited – A new taxonomy for coding ‘non-episodic’ content during autobiographical memory retrieval

Autobiographical memory (ABM) is typically held to comprise episodic and semantic elements, with the vast majority of studies to date focusing on profiles of episodic details in health and disease. In this context, ‘non-episodic’ elements are often considered to reflect semantic processing or are discounted from analyses entirely. Mounting evidence suggests that rather than reflecting one unitary entity, semantic autobiographical information may contain discrete subcomponents, which vary in their relative degree of semantic or episodic content. This study aimed to (1) review the existing literature to formally characterize the variability in analysis of ‘non-episodic’ content (i.e., external details) on the Autobiographical Interview and (2) use these findings to create a theoretically grounded framework for coding external details. Our review exposed discrepancies in the reporting and interpretation of external details across studies, reinforcing the need for a new, consistent approach. We validated our new external details scoring protocol (the ‘NExt’ taxonomy) in patients with Alzheimer's disease (n = 18) and semantic dementia (n = 13), and 20 healthy older Control participants and compared profiles of the NExt subcategories across groups and time periods. Our results revealed increased sensitivity of the NExt taxonomy in discriminating between ABM profiles of patient groups, when compared to traditionally used internal and external detail metrics. Further, remote and recent autobiographical memories displayed distinct compositions of the NExt detail types. This study is the first to provide a fine-grained and comprehensive taxonomy to parse external details into intuitive subcategories and to validate this protocol in neurodegenerative disorders.