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      Machine learning accurately classifies age of toddlers based on eye tracking

      , , ,
      Scientific Reports
      Springer Nature

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          Abstract

          How people extract visual information from complex scenes provides important information about cognitive processes. Eye tracking studies that have used naturalistic, rather than highly controlled experimental stimuli, reveal that variability in looking behavior is determined by bottom-up image properties such as intensity, color, and orientation, top-down factors such as task instructions and semantic information, and individual differences in genetics, cognitive function and social functioning. These differences are often revealed using areas of interest that are chosen by the experimenter or other human observers. In contrast, we adopted a data-driven approach by using machine learning (Support Vector Machine (SVM) and Deep Learning (DL)) to elucidate factors that contribute to age-related variability in gaze patterns. These models classified the infants by age with a high degree of accuracy, and identified meaningful features distinguishing the age groups. Our results demonstrate that machine learning is an effective tool for understanding how looking patterns vary according to age, providing insight into how toddlers allocate attention and how that changes with development. This sensitivity for detecting differences in exploratory gaze behavior in toddlers highlights the utility of machine learning for characterizing a variety of developmental capacities.

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          Most cited references43

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          ImageNet: A large-scale hierarchical image database

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            Choosing Prediction Over Explanation in Psychology: Lessons From Machine Learning.

            Psychology has historically been concerned, first and foremost, with explaining the causal mechanisms that give rise to behavior. Randomized, tightly controlled experiments are enshrined as the gold standard of psychological research, and there are endless investigations of the various mediating and moderating variables that govern various behaviors. We argue that psychology's near-total focus on explaining the causes of behavior has led much of the field to be populated by research programs that provide intricate theories of psychological mechanism but that have little (or unknown) ability to predict future behaviors with any appreciable accuracy. We propose that principles and techniques from the field of machine learning can help psychology become a more predictive science. We review some of the fundamental concepts and tools of machine learning and point out examples where these concepts have been used to conduct interesting and important psychological research that focuses on predictive research questions. We suggest that an increased focus on prediction, rather than explanation, can ultimately lead us to greater understanding of behavior.
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              Emotion, cognition, and behavior.

              R J Dolan (2002)
              Emotion is central to the quality and range of everyday human experience. The neurobiological substrates of human emotion are now attracting increasing interest within the neurosciences motivated, to a considerable extent, by advances in functional neuroimaging techniques. An emerging theme is the question of how emotion interacts with and influences other domains of cognition, in particular attention, memory, and reasoning. The psychological consequences and mechanisms underlying the emotional modulation of cognition provide the focus of this article.
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                Author and article information

                Journal
                Scientific Reports
                Sci Rep
                Springer Nature
                2045-2322
                December 2019
                April 18 2019
                December 2019
                : 9
                : 1
                Article
                10.1038/s41598-019-42764-z
                38c1ba1e-1395-4474-9908-7b3bbdd42155
                © 2019

                https://creativecommons.org/licenses/by/4.0

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