Abstract. Recent works in Computer Science, Neurosciences, Education, and Psychology have shown that emotions play an important role in learning. Learner's cognitive ability depends on his emotions. We will point out the role of emotions in learning, distinguishing the different types and models of emotions which have been considered until now. We will address an important issue concerning the different means to detect emotions and introduce recent approaches to measure brain activity using Electroencephalograms (EEG). Knowing the influence of emotional events on learning it becomes important to induce specific emotions so that the learner can be in a more adequate state for better learning or memorization. To this end, we will introduce the main components of an emotionally intelligent tutoring system able to recognize, interpret and influence learner's emotions. We will talk about specific virtual agents that can influence learner's emotions to motivate and encourage him and involve a more cooperative work, particularly in narrative learning environments. Pushing further this paradigm, we will present the advantages and perspectives of subliminal learning which intervenes without conscious perception. Finally, we conclude with new directions to emotional learning. Emotions and LearningLearning involves mainly two processes: reasoning and memorizing. Reasoning is developed during cognitive tasks in which a learner tries to solve a problem using deductions or inductions. If a new knowledge (fact, rule) is obtained then the knowledge will be memorized in long term memory. Memorizing is also triggered when a learner tries to remember a previously acquired element using several means such as similarity, imagery, case-based analysis, etc. The two processes work alternatively and can be considered as a "Cartesian" approach of brain functions. Recent researches in neurosciences, education, and psychology have shown that emotions play an important role in learning. People often separate emotions and reason, believing that emotions are an obstacle in rational decision making or

This paper presents results from an empirical study conducted with a subliminal teaching technique aimed at enhancing learner's performance in Intelligent Systems through the use of physiological sensors. This technique uses carefully designed subliminal cues (positive) and miscues (negative) and projects them under the learner's perceptual visual threshold. A positive cue, called answer cue, is a hint aiming to enhance the learner's inductive reasoning abilities and projected in a way to help them figure out the solution faster but more importantly better. A negative cue, called miscue, is also used and aims at obviously at the opposite (distract the learner or lead them to the wrong conclusion). The latest obtained results showed that only subliminal cues, not miscues, could significantly increase learner performance and intuition in a logic-based problem-solving task. Nonintrusive physiological sensors (EEG for recording brainwaves, blood volume pressure to compute heart rate and skin response to record skin conductivity) were used to record affective and cerebral responses throughout the experiment. The descriptive analysis, combined with the physiological data, provides compelling evidence for the positive impact of answer cues on reasoning and intuitive decision making in a logic-based problem-solving paradigm.

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In this chapter we describe the challenges and learnings in establishing processes for developing actionable biometric procedures for production teams. The chapter, divided in four main sections, describes the ongoing efforts of recent years to facilitate the incorporation of the science of biometrics into the culture of video game production, as illustrated through several case studies. The end goal is making biometric data an accessible option in the tool chest of user researchers and an ally in the team’s decision-making processes. Throughout the chapter, references to related work in games user research and academia are presented.