Affective computing adopts a computational approach to study affect. We highlight the AC approach towards automated affect measures that jointly model machine-readable physiological/behavioral signals with affect estimates as reported by humans or experimentally elicited. We describe the conceptual and computational foundations of the approach followed by two case studies: one on discrimination between genuine and faked expressions of pain in the lab, and the second on measuring nonbasic affect in the wild. We discuss applications of the measures, analyze measurement accuracy (...) and generalizability, and highlight advances afforded by computational tipping points, such as big data, wearable sensing, crowdsourcing, and deep learning. We conclude by advocating for increasing synergies between AC and affective science and offer suggestions toward that direction. (shrink)

The currently leading cognitive theory of consciousness, Global Workspace Theory,1,2 postulates that the primary functions of consciousness include a global broadcast serving to recruit internal resources with which to deal with the current situation and to modulate several types of learning. In addition, conscious experiences present current conditions and problems to a "self" system, an executive interpreter that is identifiable with brain structures like the frontal lobes and precuneus.1Be it human, animal or artificial, an autonomous agent3 is said to be (...) functionally conscious if its control structure (mind) implements Global Workspace Theory and the LIDA Cognitive Cycle, which includes unconscious memory and control functions needed to integrate the conscious component of the system. We would therefore consider humans, many animals4 and even some virtual or robotic agents5,6 to be functionally conscious. Such entities may approach phenomenal consciousness, as found in human and other biological brains, as additional brain-like features are added.Here we argue that adding mechanisms to produce a stable, coherent perceptual field7 in a LIDA controlled mobile robot might provide a small but significant step toward phenomenal consciousness in machines.>8 We also propose that implementing several of the various notions of self in such a LIDA controlled robot may well prove another step toward phenomenal consciousness in machines. (shrink)

Reading is one of the most common everyday activities, yet research elucidating how affective influence reading processes and outcomes is sparse with inconsistent results. To investigate this question, we randomly assigned participants (N = 136) to happiness (positive affect), sadness (negative affect), and neutral video-induction conditions prior to engaging in self-paced reading of a long, complex science text. Participants completed assessments targeting multiple levels of comprehension (e.g. recognising factual information, integrating different textual components, and open-ended responses of concepts from memory) (...) after reading and after a week-long delay. Results indicated that the Sadness (vs. Happiness) condition had higher comprehension scores, with the largest effects emerging for assessments targeting deeper levels comprehension immediately after reading. Eye-tracking analyses revealed that such benefits may be partly driven by sustained attentional focus over the 20-minute reading session. We discuss results with respect to theories on affect, cognition, and text comprehension. (shrink)