Modality-Specific Effects of Perceptual Load in Multimedia Processing

Digital media are sensory-rich, multimodal, and often highly interactive. An extensive collection of theories and models within the field of media psychology assume the multimodal nature of media stimuli, yet there is current ambiguity as to the independent contributions of visual and auditory content to message complexity and to resource availability in the human processing system. In this article, we argue that explicating the concepts of perceptual and cognitive load can create progress toward a deeper understanding of modality-specific effects in media processing. In addition, we report findings from an experiment showing that perceptual load leads to modality-specific reductions in resource availability, whereas cognitive load leads to a modality-general reduction in resource availability. We conclude with a brief discussion regarding the critical importance of separating modality-specific forms of load in an increasingly multisensory media environment

Modality-Specific Effects of Perceptual Load in Multimedia Processing

Digital media are sensory-rich, multimodal, and often highly interactive. An extensive collection of theories and models within the field of media psychology assume the multimodal nature of media stimuli, yet there is current ambiguity as to the independent contributions of visual and auditory content to message complexity and to resource availability in the human processing system. In this article, we argue that explicating the concepts of perceptual and cognitive load can create progress toward a deeper understanding of modality-specific effects in media processing. In addition, we report findings from an experiment showing that perceptual load leads to modality-specific reductions in resource availability, whereas cognitive load leads to a modality-general reduction in resource availability. We conclude with a brief discussion regarding the critical importance of separating modality-specific forms of load in an increasingly multisensory media environment

A Graph-Learning Approach for Detecting Moral Conflict in Movie Scripts

Moral conflict is central to appealing narratives, but no methodology exists for computationally extracting moral conflict from narratives at scale. In this article, we present an approach combining tools from social network analysis and natural language processing with recent theoretical advancements in the Model of Intuitive Morality and Exemplars. This approach considers narratives in terms of a network of dynamically evolving relationships between characters. We apply this method in order to analyze 894 movie scripts encompassing 82,195 scenes, showing that scenes containing moral conflict between central characters can be identified using changes in connectivity patterns between network modules. Furthermore, we derive computational models for standardizing moral conflict measurements. Our results suggest that this method can accurately extract moral conflict from a diverse collection of movie scripts. We provide a theoretical integration of our method into the larger milieu of storytelling and entertainment research, illuminating future research trajectories at the intersection of computational communication research and media psychology

The extended Moral Foundations Dictionary (eMFD): Development and applications of a crowd-sourced approach to extracting moral intuitions from text.

Moral intuitions are a central motivator in human behavior. Recent work highlights the importance of moral intuitions for understanding a wide range of issues ranging from online radicalization to vaccine hesitancy. Extracting and analyzing moral content in messages, narratives, and other forms of public discourse is a critical step toward understanding how the psychological influence of moral judgments unfolds at a global scale. Extant approaches for extracting moral content are limited in their ability to capture the intuitive nature of moral sensibilities, constraining their usefulness for understanding and predicting human moral behavior. Here we introduce the extended Moral Foundations Dictionary (eMFD), a dictionary-based tool for extracting moral content from textual corpora. The eMFD, unlike previous methods, is constructed from text annotations generated by a large sample of human coders. We demonstrate that the eMFD outperforms existing approaches in a variety of domains. We anticipate that the eMFD will contribute to advance the study of moral intuitions and their influence on social, psychological, and communicative processes