Manifold embedding for zero-shot recognition

Zero-Shot Recognition (ZSR) has gained its popularity recently owing to its promising characteristic that extends the classifiers to the unseen classes. It is typically addressed by resorting to a class semantic space to transfer the knowledge from the seen classes to unseen ones. Therefore, constructing the effective interactions between the visual space and the class semantic space is the key for ZSR. In this paper, under the assumption that the distribution of the semantic categories in the semantic space has an intrinsic manifold structure, we propose two manifold embedding-based ZSR approaches to capture the intrinsic structures of both the visual space and the class semantic space, i.e., ME-ZSR and MCCA-ZSR. Specifically, ME-ZSR builds embedding from visual space to semantic space, while MCCA-ZSR explores to embed both visual and semantic features into a common space. The linear, closed-form solutions make both methods efficient to optimize. Extensive experiments on three popular datasets AwA, CUB and NAB validate the effectiveness of both methods.     

Entitlement and conscientiousness in the prediction of organizational deviance

Responding to Wu and LeBreton’s (2011) call for further study, this paper examines dispositional predictors of organizational deviance. In a sample of 428 participants, self-report data were collected anonymously. Using hierarchical regression, the dispositional variables of entitlement and conscientiousness were similarly strong and statistically significant predictors of organizational deviance. The total variance explained in deviance by these variables and some demographic variables was .31. Additionally, the specificity matching principle suggests that narrow band traits like entitlement are better at predicting narrowly measured behaviors like deviance than are broad band traits like conscientiousness. Using dominance analysis, entitlement was a stronger predictor of organizational deviance than is conscientiousness.     

Modelling the effects of semantic ambiguity in word recognition

Most words in English are ambiguous between different interpretations; words can mean different things in different contexts. We investigate the implications of different types of semantic ambiguity for connectionist models of word recognition. We present a model in which there is competition to activate distributed semantic representations. The model performs well on the task of retrieving the different meanings of ambiguous words, and is able to simulate data reported by Rodd, Gaskell, and Marslen-Wilson [J. Mem. Lang. 46 (2002) 245] on how semantic ambiguity affects lexical decision performance. In particular, the network shows a disadvantage for words with multiple unrelated meanings (e.g., bark) that coexists with a benefit for words with multiple related word senses (e.g., twist). The ambiguity disadvantage arises because of interference between the different meanings, while the sense benefit arises because of differences in the structure of the attractor basins formed during learning. Words with few senses develop deep, narrow attractor basins, while words with many senses develop shallow, broad basins. We conclude that the mental representations of word meanings can be modelled as stable states within a high-dimensional semantic space, and that variations in the meanings of words shape the landscape of this space.     

Evolution of word meanings through metaphorical mapping: Systematicity over the past millennium

One way that languages are able to communicate a potentially infinite set of ideas through a finite lexicon is by compressing emerging meanings into words, such that over time, individual words come to express multiple, related senses of meaning. We propose that overarching communicative and cognitive pressures have created systematic directionality in how new metaphorical senses have developed from existing word senses over the history of English. Given a large set of pairs of semantic domains, we used computational models to test which domains have been more commonly the starting points (source domains) and which the ending points (target domains) of metaphorical mappings over the past millennium. We found that a compact set of variables, including externality, embodiment, and valence, explain directionality in the majority of about 5000 metaphorical mappings recorded over the past 1100 years. These results provide the first large-scale historical evidence that metaphorical mapping is systematic, and driven by measurable communicative and cognitive principles.     

Language knowledge and event knowledge in language use

This paper examines how semantic knowledge is used in language comprehension and in making judgments about events in the world. We contrast knowledge gleaned from prior language experience (“language knowledge”) and knowledge coming from prior experience with the world (“world knowledge”). In two corpus analyses, we show that previous research linking verb aspect and event representations have confounded language and world knowledge. Then, using carefully chosen stimuli that remove this confound, we performed four experiments that manipulated the degree to which language knowledge or world knowledge should be salient and relevant to performing a task, finding in each case that participants use the type of knowledge most appropriate to the task. These results provide evidence for a highly context-sensitive and interactionist perspective on how semantic knowledge is represented and used during language processing.     

The discovery and comparison of symbolic magnitudes

Humans and other primates are able to make relative magnitude comparisons, both with perceptual stimuli and with symbolic inputs that convey magnitude information. Although numerous models of magnitude comparison have been proposed, the basic question of how symbolic magnitudes (e.g., size or intelligence of animals) are derived and represented in memory has received little attention. We argue that symbolic magnitudes often will not correspond directly to elementary features of individual concepts. Rather, magnitudes may be formed in working memory based on computations over more basic features stored in long-term memory. We present a model of how magnitudes can be acquired and compared based on BARTlet, a representationally simpler version of Bayesian Analogy with Relational Transformations (BART; Lu, Chen, & Holyoak, 2012). BARTlet operates on distributions of magnitude variables created by applying dimension-specific weights (learned with the aid of empirical priors derived from pre-categorical comparisons) to more primitive features of objects. The resulting magnitude distributions, formed and maintained in working memory, are sensitive to contextual influences such as the range of stimuli and polarity of the question. By incorporating psychological reference points that control the precision of magnitudes in working memory and applying the tools of signal detection theory, BARTlet is able to account for a wide range of empirical phenomena involving magnitude comparisons, including the symbolic distance effect and the semantic congruity effect. We discuss the role of reference points in cognitive and social decision-making, and implications for the evolution of relational representations.     

A review of literature on the link between action observation and action language: advancing a shared semantic theory

Understanding each other is a core concept of social cohesion and, consequently, has immense value in human society. Importantly, shared information leading to cohesion can come from two main sources: observed action and/or language (word) processing. In this paper, we propose a theoretical framework for the link between action observation and action verb processing. Based on the activation of common semantic representations of actions through semantic resonance, this model can account for the neurophysiological, behavioral and neuropsychological domains in the link between action observation and language. Semantic resonance is hypothesized to play a role beyond that of the mere observation of others and can benefit future studies trying to connect action production and language.