54,437 research outputs found
Towards Deep Learning Models for Psychological State Prediction using Smartphone Data: Challenges and Opportunities
There is an increasing interest in exploiting mobile sensing technologies and
machine learning techniques for mental health monitoring and intervention.
Researchers have effectively used contextual information, such as mobility,
communication and mobile phone usage patterns for quantifying individuals' mood
and wellbeing. In this paper, we investigate the effectiveness of neural
network models for predicting users' level of stress by using the location
information collected by smartphones. We characterize the mobility patterns of
individuals using the GPS metrics presented in the literature and employ these
metrics as input to the network. We evaluate our approach on the open-source
StudentLife dataset. Moreover, we discuss the challenges and trade-offs
involved in building machine learning models for digital mental health and
highlight potential future work in this direction.Comment: 6 pages, 2 figures, In Proceedings of the NIPS Workshop on Machine
Learning for Healthcare 2017 (ML4H 2017). Colocated with NIPS 201
Connecting Levels of Analysis in Educational Neuroscience: A Review of Multi-level Structure of Educational Neuroscience with Concrete Examples
In its origins educational neuroscience has started as an endeavor to discuss implications of neuroscience studies for education. However, it is now on its way to become a transdisciplinary field, incorporating findings, theoretical frameworks and methodologies from education, and cognitive and brain sciences. Given the differences and diversity in the originating disciplines, it has been a challenge for educational neuroscience to integrate both theoretical and methodological perspective in education and neuroscience in a coherent way. We present a multi-level framework for educational neuroscience, which argues for integration of multiple levels of analysis, some originating in brain and cognitive sciences, others in education, as a roadmap for the future of educational neuroscience with concrete examples in moral education
Attention-Based LSTM for Psychological Stress Detection from Spoken Language Using Distant Supervision
We propose a Long Short-Term Memory (LSTM) with attention mechanism to
classify psychological stress from self-conducted interview transcriptions. We
apply distant supervision by automatically labeling tweets based on their
hashtag content, which complements and expands the size of our corpus. This
additional data is used to initialize the model parameters, and which it is
fine-tuned using the interview data. This improves the model's robustness,
especially by expanding the vocabulary size. The bidirectional LSTM model with
attention is found to be the best model in terms of accuracy (74.1%) and
f-score (74.3%). Furthermore, we show that distant supervision fine-tuning
enhances the model's performance by 1.6% accuracy and 2.1% f-score. The
attention mechanism helps the model to select informative words.Comment: Accepted in ICASSP 201
Learning Hierarchical Visual Representations in Deep Neural Networks Using Hierarchical Linguistic Labels
Modern convolutional neural networks (CNNs) are able to achieve human-level
object classification accuracy on specific tasks, and currently outperform
competing models in explaining complex human visual representations. However,
the categorization problem is posed differently for these networks than for
humans: the accuracy of these networks is evaluated by their ability to
identify single labels assigned to each image. These labels often cut
arbitrarily across natural psychological taxonomies (e.g., dogs are separated
into breeds, but never jointly categorized as "dogs"), and bias the resulting
representations. By contrast, it is common for children to hear both "dog" and
"Dalmatian" to describe the same stimulus, helping to group perceptually
disparate objects (e.g., breeds) into a common mental class. In this work, we
train CNN classifiers with multiple labels for each image that correspond to
different levels of abstraction, and use this framework to reproduce classic
patterns that appear in human generalization behavior.Comment: 6 pages, 4 figures, 1 table. Accepted as a paper to the 40th Annual
Meeting of the Cognitive Science Society (CogSci 2018
Character and theory of mind: an integrative approach
Traditionally, theories of mindreading have focused on the representation of beliefs and desires. However, decades of social psychology and social neuroscience have shown that, in addition to reasoning about beliefs and desires, human beings also use representations of character traits to predict and interpret behavior. While a few recent accounts have attempted to accommodate these findings, they have not succeeded in explaining the relation between trait attribution and belief-desire reasoning. On my account, character-trait attribution is part of a hierarchical system for action prediction, and serves to inform hypotheses about agents’ beliefs and desires, which are in turn used to predict and interpret behavior
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