Computational approaches to emotional decision making

Findings on the role that emotion plays in human behavior have transformed Artificial Intelligence computations. Modern research explores how to simulate more intelligent and flexible systems. Several studies focus on the role that emotion has in order to establish values for alternative decision and decision outcomes. For instance, Busemeyer et al. (2007) argued that emotional state affects the subjectivity value of alternative choice. However, emotional concepts in these theories are generally not defined formally and it is difficult to describe in systematic detail how processes work. In this sense, structures and processes cannot be explicitly implemented. Some attempts have been incorporated into larger computational systems that try to model how emotion affects human mental processes and behavior (Becker-Asano & Wachsmuth, 2008; Marinier, Laird & Lewis, 2009; Marsella & Gratch, 2009; Parkinson, 2009; Sander, Grandjean & Scherer, 2005). As we will see, some tutoring systems have explored this potential to inform user models. Likewise, dialogue systems, mixed-initiative planning systems, or systems that learn from observation could also benefit from such an approach (Dickinson, Brew & Meurers, 2013; Jurafsky & Martin, 2009). That is, considering emotion as interaction can be relevant in order to explain the dynamic role it plays in action and cognition (see Boehner et al., 2007).Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Emotionally interactive agents

Models in language processing have researched how words are interpreted by humans. Many models presume the ability to correctly interpret the beliefs, motives and intentions underlying words. The interest relies also on how emotion motivates certain words or actions, inferences, and communicates information about mental state. As we will see below, some tutoring systems have explored this potential to inform user models. Likewise, dialogue systems, mixed-initiative planning systems, or systems that learn from observation could also benefit from such an approach. As these experimental data show, activating accessible constructs or attitudes through one set of stimuli can facilitate cognitive processing of other stimuli under certain circumstances, and can interfere with it under other circumstances. Some of the results support and converge on those centered on the constructs of current concern and emotional arousal. Future research has to take seriously into account this question: how to develop models where emotion interacts with cognitive processing. One example could be the work of Pitterman et al. (2010) where it is combined speech-based emotion recognition with adaptive human-computer modeling. With the robust recognition of emotions from speech signals as their goal, the authors analyze the effectiveness of using a plain emotion recognizer, a speech-emotion recognizer combining speech and emotion recognition, and multiple speech-emotion recognizers at the same time. The semi-stochastic dialogue model employed relates user emotion management to the corresponding dialogue interaction history and allows the device to adapt itself to the context, including altering the stylistic realization of its speech.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Emotional inferences by pragmatics

It has for long been taken for granted that, along the course of reading a text, world knowledge is often required in order to establish coherent links between sentences (McKoon & Ratcliff 1992, Iza & Ezquerro 2000). The content grasped from a text turns out to be strongly dependent upon the reader’s additional knowledge that allows a coherent interpretation of the text as a whole. The world knowledge directing the inference may be of distinctive nature. Gygax et al. (2007) showed that mental models related to human action may be of a perceptual nature and may include behavioral as well as emotional elements. Gygax (2010), however, showed the unspecific nature of emotional inferences and the prevalence of behavioral elements in readers' mental models of emotions. Inferences are made in both directions; emotional inferences based on behavior and vice versa. Harris & de Rosnay (2002) and Pons et al. (2003) proved that different linguistic skills –in particular lexicon, syntax and semantics are closely related to emotion understanding. Iza & Konstenius (2010) showed that additional knowledge about social norms affects the participants’ prediction about would be inferred as the behavioral or emotional outcome of a given social situation. Syntactic and lexical abilities are the best predictors of emotion understanding, but making inferences is the only significant predictor of the most complex components (reflective dimension) of emotion comprehension in normal children. Recently, Farina et al. (2011) showed in a study that the relation between pragmatics and emotional inferences may not be so straight forward. Children with High Functioning Autism (HFA) and Asperger Syndrome (AS) present similar diagnostic profiles, characterized by satisfactory cognitive development, good phonological, syntactic and semantic competences, but poor pragmatic skills and socio-emotional competencies. After training in pragmatics a descriptive analyses showed the whole group to display a deficit in emotion comprehension, but high levels of pragmatic competences. This indicates a further need to study the relationship between emotion and inference in normal subjects too. We also suggest that while behavioral elements may indeed be of perceptual nature and the inference between emotion and behavior less culturally dependent especially when concerned with basic emotions -the inference concerned with social norms may be more complex and require elaborative inference. We suggest that in further studies a distinction between basic emotions and non basic emotions, social settings and non-social settings should be made. The cognitive models concerned with social action may be of more complex nature, but with recognizable features on lexical and syntactic levels.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Individual differences in arithmetic fluency

Este estudio contrasta la hipótesis de que las dificultades que tienen algunos sujetos en el dominio de las tablas de multiplicar se deban a su incapacidad para afrontar la interferencia. Los resultados no muestran diferencias en medidas de interferencia en un tarea de recuerdo serial entre sujetos con alta y baja habilidad en la resolución de multiplicaciones. Sin embargo, se encuentran diferencias asociadas a su dominio de las representaciones numéricas.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Ministerio de Economía y Competitividad. Proyecto PSI-2012-38423

Procesos inferenciales durante la comprensión de textos

Kintsch (1992) propuso la Teoría Construcción-Integración (CI) para dar cuenta de la comprensión de textos. CI es un modelo híbrido, es decir, combina una parte simbólica y otra conexionista. Desde nuestro punto de vista, sin embargo, la propuesta de Kintsch está afectada por algunas limitaciones relacionadas con la realización de inferencias. El objetivo de este trabajo es analizar el modelo mencionado y hacer ver cómo algunas de estas limitaciones pueden ser subsanadas añadiendo a dicho sistema un razonador conexionista híbrido, como el propuesto por Sun (1994), y un foco atencional. Este último podría operar, como restricción teórica, con tal sistema razonador. En otras palabras, nuestra sugerencia es que el sistema propuesto por Sun (1994) puede ser ampliado mediante la incorporación de un foco con el fin de poder aplicarlo a algunos problemas que aparecen en el procesamiento de textos. En relación con esta propuesta, analizaremos sus consecuencias para el diseño de arquitecturas conexionistas para la comprensión