On the reciprocal interaction between believing and feeling: an adaptive agent modelling perspective

Confronting, Representing, and Believing Counterintuitive Concepts: Navigating the Natural and the Supernatural

Recent research shows that even preschoolers are skeptical; they frequently reject claims from other people when the claims conflict with their own perceptions and concepts. Yet, despite their skepticism, both children and adults come to believe in a variety of phenomena that defy their first-hand perceptions and intuitive conceptions of the world. In this review, we explore how children and adults acquire such concepts. We describe how a similar developmental process underlies mental representation of both the natural and the supernatural world, and we detail this process for two prominent supernatural counterintuitive ideas-God and the afterlife. In doing so, we highlight the fact that conceptual development does not always move in the direction of greater empirical truth, as described within naturalistic domains. We consider factors that likely help overcome skepticism and, in doing so, promote belief in counterintuitive phenomena. These factors include qualities of the learners, aspects of the context, qualities of the informants, and qualities of the information.

Multi-stream LSTM-HMM decoding and histogram equalization for noise robust keyword spotting

Highly spontaneous, conversational, and potentially emotional and noisy speech is known to be a challenge for today's automatic speech recognition (ASR) systems, which highlights the need for advanced algorithms that improve speech features and models. Histogram Equalization is an efficient method to reduce the mismatch between clean and noisy conditions by normalizing all moments of the probability distribution of the feature vector components. In this article, we propose to combine histogram equalization and multi-condition training for robust keyword detection in noisy speech. To better cope with conversational speaking styles, we show how contextual information can be effectively exploited in a multi-stream ASR framework that dynamically models context-sensitive phoneme estimates generated by a long short-term memory neural network. The proposed techniques are evaluated on the SEMAINE database-a corpus containing emotionally colored conversations with a cognitive system for "Sensitive Artificial Listening".

Mathematical model of the dynamics of psychotherapy

The success of psychotherapy depends on the nature of the therapeutic relationship between a therapist and a client. We use dynamical systems theory to model the dynamics of the emotional interaction between a therapist and client. We determine how the therapeutic endpoint and the dynamics of getting there depend on the parameters of the model. Previously Gottman et al. used a very similar approach (physical-sciences paradigm) for modeling and making predictions about husband–wife relationships. Given that this novel approach shed light on the dyadic interaction between couples, we have applied it to the study of the relationship between therapist and client. The results of our computations provide a new perspective on the therapeutic relationship and a number of useful insights. Our goal is to create a model that is capable of making solid predictions about the dynamics of psychotherapy with the ultimate intention of using it to better train therapists.