Socio-Cognitive and Affective Computing

Social cognition focuses on how people process, store, and apply information about other people and social situations. It focuses on the role that cognitive processes play in social interactions. On the other hand, the term cognitive computing is generally used to refer to new hardware and/or software that mimics the functioning of the human brain and helps to improve human decision-making. In this sense, it is a type of computing with the goal of discovering more accurate models of how the human brain/mind senses, reasons, and responds to stimuli. Socio-Cognitive Computing should be understood as a set of theoretical interdisciplinary frameworks, methodologies, methods and hardware/software tools to model how the human brain mediates social interactions. In addition, Affective Computing is the study and development of systems and devices that can recognize, interpret, process, and simulate human affects, a fundamental aspect of socio-cognitive neuroscience. It is an interdisciplinary field spanning computer science, electrical engineering, psychology, and cognitive science. Physiological Computing is a category of technology in which electrophysiological data recorded directly from human activity are used to interface with a computing device. This technology becomes even more relevant when computing can be integrated pervasively in everyday life environments. Thus, Socio-Cognitive and Affective Computing systems should be able to adapt their behavior according to the Physiological Computing paradigm. This book integrates proposals from researchers who use signals from the brain and/or body to infer people's intentions and psychological state in smart computing systems. The design of this kind of systems combines knowledge and methods of ubiquitous and pervasive computing, as well as physiological data measurement and processing, with those of socio-cognitive and affective computing

Comparison of Neurological Activation Patterns of Children with and without Autism Spectrum Disorders When Verbally Responding to a Pragmatic Task

This study examined the neurological activation of children with autism spectrum disorders (ASD) while performing a pragmatic judgment task. In this study, children between the ages of 9 and 15 years responded to questions regarding a social situation, taken from the Comprehensive Assessment of Spoken Language, while concurrently having their brain activity measured. We targeted four brain regions for analysis: dorsolateral prefrontal cortex (DLPFC), orbitofrontal cortex (OFC), superior temporal gyrus (STG), and the inferior parietal lobule (IPL). Ten children with ASD and 20 typically developing (TD) children participated. Matching occurred in a bracketing manner with each child in the ASD group being matched to two control children to account for natural variability. Neuroimgaging was conducted utilizing functional Near‐Infrared Spectroscopy (fNIRS). Oxygenated and deoxygenated blood concentration levels were measured through Near‐Infrared light cap with 44 channels. The cap was placed over frontal lobe and the left lateral cortex. The placement was spatially registered using the Polhemus. Analysis indicated that children in the ASD group performed significantly poorer than their controls on the pragmatic judgment task. Mixed repeated measures analysis of variance of neurological data indicated that the children with ASD had lower concentration levels of oxygenated and total hemoglobin across the four regions. There were significantly higher concentration levels for oxygenated and total hemoglobin in the STG. Analysis of correct and incorrect responses revealed significantly more activation in the OFC when responses were correct. Additionally, there was a significant interaction of Accuracy and Group in left DLPFC. Children with ASD presented higher oxygenated hemoglobin concentration values when responding correctly, while children in the control group presented higher oxygenated hemoglobin concentration values for the incorrect items. Statistical Parametric Mapping was performed for each triad to assess the diffusion of neural activation across the frontal cortex and the left lateral cortex. Individual comparisons revealed that 7 out of 10 children with ASD demonstrated patterns consistent with more diffuse brain activation than their TD controls. Findings from this study suggest that an fNIRS study can provide important information about the level and diffusion of neural processing of verbal children and adolescents with ASD

Check Me If You Can: Detecting ChatGPT-Generated Academic Writing using CheckGPT

With ChatGPT under the spotlight, utilizing large language models (LLMs) for academic writing has drawn a significant amount of discussions and concerns in the community. While substantial research efforts have been stimulated for detecting LLM-Generated Content (LLM-content), most of the attempts are still in the early stage of exploration. In this paper, we present a holistic investigation of detecting LLM-generate academic writing, by providing a dataset, evidence, and algorithms, in order to inspire more community effort to address the concern of LLM academic misuse. We first present GPABenchmark, a benchmarking dataset of 600,000 samples of human-written, GPT-written, GPT-completed, and GPT-polished abstracts of research papers in CS, physics, and humanities and social sciences (HSS). We show that existing open-source and commercial GPT detectors provide unsatisfactory performance on GPABenchmark, especially for GPT-polished text. Moreover, through a user study of 150+ participants, we show that it is highly challenging for human users, including experienced faculty members and researchers, to identify GPT-generated abstracts. We then present CheckGPT, a novel LLM-content detector consisting of a general representation module and an attentive-BiLSTM classification module, which is accurate, transferable, and interpretable. Experimental results show that CheckGPT achieves an average classification accuracy of 98% to 99% for the task-specific discipline-specific detectors and the unified detectors. CheckGPT is also highly transferable that, without tuning, it achieves ~90% accuracy in new domains, such as news articles, while a model tuned with approximately 2,000 samples in the target domain achieves ~98% accuracy. Finally, we demonstrate the explainability insights obtained from CheckGPT to reveal the key behaviors of how LLM generates texts