191 research outputs found

    How similar are objects and events?

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    Semanticists often assume an ontology for natural language that includes not only ordinary objects, but also events, and other sorts of entities. We link this ontology to how speakers represent static and dynamic entities. Specifically, we test how speakers determine whether an entity counts as “atomic” by using count vs. mass (e.g., some gleebs, some gleeb) and distributive vs. non-distributive descriptions (e.g., gleeb every second or so, gleeb around a little). We then seek evidence for atomic representation in a non-linguistic task. Ultimately we suggest that natural language ontology reveals properties of language-independent conceptualization

    Agribusiness Firm Reactions to Regulations: The Case of Investments in Traceability Systems

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    The regulatory framework of food production has changed a lot in recent years. As a result, traceability of food products has become mandatory in the European Union, nonetheless leaving room for more advanced solutions. This study answers the question what exactly determines firms' investments in traceability systems by first developing a theoretical framework - the so-called Tracking and Tracing System Investment Model - and then analyzing empirical data from the German food industry that provide in-depth insights into companies' investment behaviour.Altogether, 234 companies representing more than fifteen different sub‐sectors of the food‐processing industry participated in an online survey. The results show that German food firms can be divided into four clusters based on their dominant motives for investing (or not investing) in traceability systems. Moreover, the results of a partial‐least squares (PLS) analysis provide a good understanding of the major factors influencing the investment behaviour of companies concerning tracking and tracing systems

    Metacognition: pre-verbal infants adapt their behaviour to their knowledge states

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    Metacognitive abilities, such as knowing we know something or that we made the wrong decision, can be powerful tools for adapting behaviour and accelerating learning. Apes, dolphins, and even rats demonstrate some such abilities; a new study provides evidence that human infants can too

    Four-Day-Old Human Neonates Look Longer at Non-Biological Motions of a Single Point-of-Light

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    BACKGROUND: Biological motions, that is, the movements of humans and other vertebrates, are characterized by dynamic regularities that reflect the structure and the control schemes of the musculo-skeletal system. Early studies on the development of the visual perception of biological motion showed that infants after three months of age distinguished between biological and non-biological locomotion. METHODOLOGY/PRINCIPAL FINDINGS: Using single point-light motions that varied with respect to the “two-third-power law” of motion generation and perception, we observed that four-day-old human neonates looked longer at non-biological motions than at biological motions when these were simultaneously presented in a standard preferential looking paradigm. CONCLUSION/SIGNIFICANCE: This result can be interpreted within the “violation of expectation” framework and can indicate that neonates' motion perception — like adults'—is attuned to biological kinematics

    Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense

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    Recent progress in deep learning is essentially based on a "big data for small tasks" paradigm, under which massive amounts of data are used to train a classifier for a single narrow task. In this paper, we call for a shift that flips this paradigm upside down. Specifically, we propose a "small data for big tasks" paradigm, wherein a single artificial intelligence (AI) system is challenged to develop "common sense", enabling it to solve a wide range of tasks with little training data. We illustrate the potential power of this new paradigm by reviewing models of common sense that synthesize recent breakthroughs in both machine and human vision. We identify functionality, physics, intent, causality, and utility (FPICU) as the five core domains of cognitive AI with humanlike common sense. When taken as a unified concept, FPICU is concerned with the questions of "why" and "how", beyond the dominant "what" and "where" framework for understanding vision. They are invisible in terms of pixels but nevertheless drive the creation, maintenance, and development of visual scenes. We therefore coin them the "dark matter" of vision. Just as our universe cannot be understood by merely studying observable matter, we argue that vision cannot be understood without studying FPICU. We demonstrate the power of this perspective to develop cognitive AI systems with humanlike common sense by showing how to observe and apply FPICU with little training data to solve a wide range of challenging tasks, including tool use, planning, utility inference, and social learning. In summary, we argue that the next generation of AI must embrace "dark" humanlike common sense for solving novel tasks.Comment: For high quality figures, please refer to http://wellyzhang.github.io/attach/dark.pd

    Early contributions to infants’ mental rotation abilities

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    Some cognitive abilities exhibit reliable gender differences, with females outperforming males in specific aspects of verbal ability, and males showing an advantage on certain spatial tasks. Among these cognitive gender differences, differences in mental rotation are the most robust, and appear to be present even in infants. A large body of animal research suggests that gonadal hormones, particularly testosterone, during early development could contribute to this gender difference in mental rotation. Also, substantial evidence supports an influence of socialization on mental rotation performance. The present study investigated the relationship of two types of factors, early postnatal testosterone exposure and parental attitudes about gender, to mental rotation performance in 61 healthy infants (29 males, 32 females). We measured salivary testosterone at two time points: 1-2.5 months of age and 5-6 months of age. Infants' mental rotation performance and parents' attitudes about gender were assessed at 5-6 months of age. As predicted, testosterone concentrations were significantly higher in boys than girls in early infancy (d = 0.54), and boys performed significantly better than girls on mental rotation (d = 0.64). A significant positive correlation between testosterone at age 1-2.5 months and mental rotation was found only in boys (r = 0.50, p = .01). A significant negative correlation between parents' gender-stereotypical attitudes and mental rotation performance was found only in girls (r = -.57, p = .002). These findings suggest that the early postnatal testosterone surge (also known as "mini-puberty") may have organizational influences on mental rotation performance in boys, and that parents may influence their daughters' mental rotation abilities beginning very early in life
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