85,030 research outputs found

    Dimensional Relevance Shifts in Category Learning

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    Associative learning in baboons and humans: Species differences in learned attention to visual features

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    We examined attention shifting in baboons and humans during the learning of visual categories. Within a conditional matching-to-sample task, participants of the two species sequentially learned two two-feature categories which shared a common feature. Results showed that humans encoded both features of the initially learned category, but predominantly only the distinctive feature of the subsequently learned category. Although baboons initially encoded both features of the first category, they ultimately retained only the distinctive features of each category. Empirical data from the two species were analyzed with the 1996 ADIT connectionist model of Kruschke. ADIT fits the baboon data when the attentional shift rate is zero, and the human data when the attentional shift rate is not zero. These empirical and modeling results suggest species differences in learned attention to visual features

    Temporal Cross-Media Retrieval with Soft-Smoothing

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    Multimedia information have strong temporal correlations that shape the way modalities co-occur over time. In this paper we study the dynamic nature of multimedia and social-media information, where the temporal dimension emerges as a strong source of evidence for learning the temporal correlations across visual and textual modalities. So far, cross-media retrieval models, explored the correlations between different modalities (e.g. text and image) to learn a common subspace, in which semantically similar instances lie in the same neighbourhood. Building on such knowledge, we propose a novel temporal cross-media neural architecture, that departs from standard cross-media methods, by explicitly accounting for the temporal dimension through temporal subspace learning. The model is softly-constrained with temporal and inter-modality constraints that guide the new subspace learning task by favouring temporal correlations between semantically similar and temporally close instances. Experiments on three distinct datasets show that accounting for time turns out to be important for cross-media retrieval. Namely, the proposed method outperforms a set of baselines on the task of temporal cross-media retrieval, demonstrating its effectiveness for performing temporal subspace learning.Comment: To appear in ACM MM 201

    Аналіз відмінностей тем, якості письма та стилістичного контексту в есеях студентів коледжу на основі комп’ютерної програми Linguistic Inquiry and Word Count (LIWC).

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    Machine methods for automatically analyzing text have been investigated for decades. Yet the availability and usability of these methods for classifying and scoring specialized essays in small samples–as is typical for ordinary coursework–remains unclear. In this paper we analyzed 156 essays submitted by students in a first-year college rhetoric course. Using cognitive and affective measures within Linguistic Inquiry and Word Count (LIWC), we tested whether machine analyses could i) distinguish among essay topics, ii) distinguish between high and low writing quality, and iii) identify differences due to changes in rhetorical context across writing assignments. The results showed positive results for all three tests. We consider ways that LIWC may benefit college instructors in assessing student compositions and in monitoring the effectiveness of the course curriculum. We also consider extensions of machine assessments for instructional applications.Машинні методи автоматичного аналізу тексту та їхні можливості вивчалися впродовж десятиліть. Однак питання доступності та зручності використання цих методів для класифікації та оцінки спеціалізованих есеїв у невеликих зразках, як, наприклад, курсових роботах, залишається досі малодослідженим питанням. У статті проаналізовано 139 есеїв із курсу стилістики, написаних студентами першого курсу. На основі використання когнітивних та афективних категорій програми Linguistic Inquiry and Word Count (LIWC) було перевірено здатність машинного аналізу: а) розмежовувати теми есеїв, б) розрізняти високу та низьку якість письма та в) виявляти відмінності через зміни стилістичного контексту написаних завдань. Дослідження засвідчило позитивні результати для всіх трьох тестових перевірок. Увагу авторів зосереджено на тому, як LIWC може полегшити роботу університетських викладачів під час оцінки ними студентських творів та моніторингу ефективності навчальної програми курсу. Крім того, у статті розглянуто питання перспектив машинного оцінювання викладацьких застосунків

    View-Invariant Object Category Learning, Recognition, and Search: How Spatial and Object Attention Are Coordinated Using Surface-Based Attentional Shrouds

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    Air Force Office of Scientific Research (F49620-01-1-0397); National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624

    Systematic reviews of health effects of social interventions: 2. Best available evidence: how low should you go?

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    Study objective: There is little guidance on how to select the best available evidence of health effects of social interventions. The aim of this paper was to assess the implications of setting particular inclusion criteria for evidence synthesis. Design: Analysis of all relevant studies for one systematic review, followed by sensitivity analysis of the effects of selecting studies based on a two dimensional hierarchy of study design and study population. Setting: Case study of a systematic review of the effectiveness of interventions in promoting a population shift from using cars towards walking and cycling. Main results: The distribution of available evidence was skewed. Population level interventions were less likely than individual level interventions to have been studied using the most rigorous study designs; nearly all of the population level evidence would have been missed if only randomised controlled trials had been included. Examining the studies that were excluded did not change the overall conclusions about effectiveness, but did identify additional categories of intervention such as health walks and parking charges that merit further research, and provided evidence to challenge assumptions about the actual effects of progressive urban transport policies. Conclusions: Unthinking adherence to a hierarchy of study design as a means of selecting studies may reduce the value of evidence synthesis and reinforce an "inverse evidence law" whereby the least is known about the effects of interventions most likely to influence whole populations. Producing generalisable estimates of effect sizes is only one possible objective of evidence synthesis. Mapping the available evidence and uncertainty about effects may also be important
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