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Previous psychophysiological research suggests that pain measurement needs to go beyond the assessment of Pain Intensity and Unpleasantness by adding the evaluation of Pain-Related Suffering. Based on this three-dimensional approach, we attempted to elucidate who is more likely to suffer by identifying reasons that may lead individuals to report Pain and Pain-Related Suffering more than others. A sample of 24 healthy participants (age range 18–33) underwent four different sessions involving the evaluation of experimentally induced phasic and tonic pain. We applied two decision tree models to identify variables (selected from psychological questionnaires regarding pain and descriptors from post-session interviews) that provided a qualitative characterization of the degrees of Pain Intensity, Unpleasantness and Suffering and assessed the respective impact of contextual influences. The overall classification accuracy of the decision trees was 75% for Intensity, 77% for Unpleasantness and 78% for Pain-Related Suffering. The reporting of suffering was predominantly associated with fear of pain and active cognitive coping strategies, pain intensity with bodily competence conveying strength and resistance and unpleasantness with the degree of fear of pain and catastrophizing. These results indicate that the appraisal of the three pain dimensions was largely determined by stable psychological constructs. They also suggest that individuals manifesting higher active coping strategies may suffer less despite enhanced pain and those who fear pain may suffer even under low pain. The second decision tree model revealed that suffering did not depend on pain alone, but that the complex rating-related decision making can be shifted by situational factors (context, emotional and cognitive). The impact of coping and fear of pain on individual Pain-Related Suffering may highlight the importance of improving cognitive coping strategies in clinical settings.

Conversational interfaces also called chatbots recently disrupted the Internet and opened up endless opportunities for assessment and learning. Formative feedback providing learners with a practical instruction for improvement is one of the challenging tasks in, for instance self-assessment settings and self-directed learning. This becomes even more challenging if user's personal information such as learning history and previous achievements cannot be exploited for data protection reasons or are simply not available. This study seeks to explore the opportunities of providing formative feedback in chatbot-based assessment. Two main challenges were faced: the limitations of the messenger as an interface that restricts visual representation of the quiz questions, and zero information about the user to generate adaptive feedback. Two types of feedback were investigated regarding their formative effect: immediate feedback, which was given after answering a question, and cumulative feedback detailing strengths and weaknesses of the user in each of the topics covered along with the directives for improvement. A chatbot called SQL Quizbot was deployed on Facebook Messenger for the purposes of this study 1. A survey conducted to disclose users' perception of the feedback reveals that more than 80% of the users find immediate feedback helpful. Overall this study shows that chatbots have a great potential as an aiding tool for e-learning systems to include an interactive component into feedback in order to increase user motivation and retention.

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In this paper, we look beyond the traditional population-level sentiment modeling and consider the individuality in a person's expressions by discovering both textual and contextual information. In particular, we construct a hierarchical neural network that leverages valuable information from a person's past expressions, and offer a better understanding of the sentiment from the expresser's perspective. Additionally, we investigate how a person's sentiment changes over time so that recent incidents or opinions may have more effect on the person's current sentiment than the old ones. Psychological studies have also shown that individual variation exists in how easily people change their sentiments. In order to model such traits, we develop a modified attention mechanism with Hawkes process applied on top of a recurrent network for a userspecific design. Implemented with automatically labeled Twitter data, the proposed model has shown positive results employing different input formulations for representing the concerned information.

The explorative mind-map is a dynamic framework, that emerges automatically from the input, it gets. It is unlike a verificative modeling system where existing (human) thoughts are placed and connected together. In this regard, explorative mind-maps change their size continuously, being adaptive with connectionist cells inside; mind-maps process data input incrementally and offer lots of possibilities to interact with the user through an appropriate communication interface. With respect to a cognitive-motivated situation like a conversation between partners, mind-maps become interesting as they are able to process stimulating signals whenever they occur. If these signals are close to an own understanding of the world, then the conversational partner becomes automatically more trustful than if the signals do not or less match the own knowledge scheme. In this (position) paper, we therefore motivate explorative mindmaps as a cognitive engine and propose these as a decision support engine to foster trust. Explorative Mind-mapsThe principles of explorative mind-maps M i have already been described in [11], where we accent that mind-maps rely on the natural principle on sensations and the corresponding propagation of stimuli to a final destination. Indeed, explorative mind-maps share this principle through an associative architecture that incrementally processes accepted stimuli to a consistent informational structure. This is similar to the natural paradigm, but on contrast to a verificative processing of a user's thoughts, the explorative mind-maps are built from the bottom up, meaning that their existence exclusively interdepend on incoming signals.Explorative mind-maps share a sub-symbolic architecture that is composed of interacting entity cells e i . As mentioned above for the natural principle, these cells foster on a processing of data streams and a stimulation/inhibitionprinciple of adjacent connections. The activation of such a connectionist architecture bases on a dynamic construction of cell structures during the processing of the input stream.In the stimulation phase, a stream data is stimulated and absorbed by receptor (input) cells r i , which decompose the Figure 1: Merge between the existing mind-map a) and a (newly) mini-network b) to an updated mind-map c). Entity cells e 2 and e 5 are higher activated; the connection in between has been learned, the activation increased as well.stream to its entities. For example, the text streams are decomposed into the word entities, transactional streams to item entities, and so on. Using filter cells f i , those receptor cells r i are inhibited that do not address a semantic interest. In the Mini-Network phase, the collection of entities, which occur at such a specific time-point, form a mini-network [6] with fully connected mini-network cells m i . The Mindmap Merger starts once the mini-network is established: in this phase, the mini-network is sent to the mind-map and is merged with the existing entity cells in the mindmap (initially, the mind-map is empty...

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People use different words when expressing their opinions. Sentiment analysis as a way to automatically detect and categorize people's opinions in text, needs to reflect this diversity and individuality. One possible approach to analyze such traits is to take a person's past opinions into consideration. In practice, such a model can suffer from the data sparsity issue, thus it is difficult to develop. In this article, we take texts from social platforms and propose a preliminary model for evaluating the effectiveness of including user information from the past, and offer a solution for the data sparsity. Furthermore, we present a finer-designed, enhanced model that focuses on frequent users and offers to capture the decay of past opinions using various gaps between the creation time of the text. An attention-based Hawkes process on top of a recurrent neural network is applied for this purpose, and the performance of the model is evaluated with Twitter data. With the proposed framework, positive results are shown which opens up new perspectives for future research.