Joint perceptual decision-making: a case study in explanatory pluralism.

Traditionally different approaches to the study of cognition have been viewed as competing explanatory frameworks. An alternative view, explanatory pluralism, regards different approaches to the study of cognition as complementary ways of studying the same phenomenon, at specific temporal and spatial scales, using appropriate methodological tools. Explanatory pluralism has been often described abstractly, but has rarely been applied to concrete cases. We present a case study of explanatory pluralism. We discuss three separate ways of studying the same phenomenon: a perceptual decision-making task (Bahrami et al., 2010), where pairs of subjects share information to jointly individuate an oddball stimulus among a set of distractors. Each approach analyzed the same corpus but targeted different units of analysis at different levels of description: decision-making at the behavioral level, confidence sharing at the linguistic level, and acoustic energy at the physical level. We discuss the utility of explanatory pluralism for describing this complex, multiscale phenomenon, show ways in which this case study sheds new light on the concept of pluralism, and highlight good practices to critically assess and complement approaches

Are Chinese and German Children Taxonomic, Thematic, or Shape Biased? Influence of Classifiers and Cultural Contexts

This paper explores the effect of classifiers on young children's conceptual structures. For this purpose we studied Mandarin Chinese- and German-speaking 3- and 5-year-olds on non-lexical classification, novel-noun label extension, and inductive inference of novel properties. Some effect of the classifier system was found in Chinese children, but this effect was observed only in a non-lexical categorization task. In the label extension and property generalization tasks, children of the two language groups show strikingly similar behavior. The implications of the results for theories of the relation between language and thought as well as cultural influence on thought are discussed

Multimodal Grounding for Language Processing

This survey discusses how recent developments in multimodal processing facilitate conceptual grounding of language. We categorize the information flow in multimodal processing with respect to cognitive models of human information processing and analyze different methods for combining multimodal representations. Based on this methodological inventory, we discuss the benefit of multimodal grounding for a variety of language processing tasks and the challenges that arise. We particularly focus on multimodal grounding of verbs which play a crucial role for the compositional power of language.Comment: The paper has been published in the Proceedings of the 27 Conference of Computational Linguistics. Please refer to this version for citations: https://www.aclweb.org/anthology/papers/C/C18/C18-1197

Feature biases in early word learning : network distinctiveness predicts age of acquisition

Do properties of a word’s features influence the order of its acquisition in early word learning? Combining the principles of mutual exclusivity and shape bias, the present work takes a network analysis approach to understanding how feature distinctiveness predicts the order of early word learning. Distance networks were built from nouns with edge lengths computed using various distance measures. Feature distinctiveness was computed as a distance measure, showing how far an object in a network is from other objects based on shared and non-shared features. Feature distinctiveness predicted order of acquisition across all measures; words that were further away from other words in the network space were learned earlier. The best distance measures were based only on non-shared features (object dissimilarity) and did not include shared features (object similarity). This indicates that shared features may play less of a role in early word learning than non-shared features. In addition, the strongest effects were found for visual form and surface features. Cluster analysis further revealed that this effect is a localized effect in the object feature space, where objects’ distances from their cluster centroid were inversely correlated with their age of acquisition. Together, these results suggest a role for feature distinctiveness in early word learning

Discourse comprehension and simulation of positive emotions

Recent research has suggested that emotional sentences are understood by constructing an emotion simulation of the events being described. The present study aims to investigate whether emotion simulation is also involved in online and offline comprehension of larger language segments such as discourse. Participants read a target text describing positive events while their facial postures were manipulated to be either congruent (matching condition) or incongruent (mismatching condition) with emotional valence of the text. In addition, a control condition was included in which participants read the text naturally (without a manipulation of facial posture). The influence of emotion simulation on discourse understanding was assessed by online (self-paced reading times) and offline (verbatim and inference questions) measures of comprehension. The major result was that participants read faster the target text describing positive emotional events while their bodily systems were prepared for processing of positive emotions (matching condition) rather than unprepared (control condition) or prevented from positive emotional processing (mismatching condition). Simulation of positive emotions did not have a significant impact on offline explicit and implicit discourse comprehension. This pattern of results suggests that emotion simulation has an impact on online comprehension, but may not have any effect on offline discourse processing

Discovering information flow using a high dimensional conceptual space

This paper presents an informational inference mechanism realized via the use of a high dimensional conceptual space. More specifically, we claim to have operationalized important aspects of G?rdenforss recent three-level cognitive model. The connectionist level is primed with the Hyperspace Analogue to Language (HAL) algorithm which produces vector representations for use at the conceptual level. We show how inference at the symbolic level can be implemented by employing Barwise and Seligmans theory of information flow. This article also features heuristics for enhancing HAL-based representations via the use of quality properties, determining concept inclusion and computing concept composition. The worth of these heuristics in underpinning informational inference are demonstrated via a series of experiments. These experiments, though small in scale, show that informational inference proposed in this article has a very different character to the semantic associations produced by the Minkowski distance metric and concept similarity computed via the cosine coefficient. In short, informational inference generally uncovers concepts that are carried, or, in some cases, implied by another concept, (or combination of concepts)