The standard cognitive explanation for the emergence of human communication is that it rests largely on the expression and attribution of communicative intentions which are, in turn, enabled by ...complex metarepresentations of mental states. This complexity is at odds with the limited metapsychological abilities of infants. But mentalistic metarepresentations are neither necessary nor sufficient in explaining communication. Coded ostensive signals (e.g., eye contact) and established channels (e.g., speech) allow that communicative episodes be identified through decoding rather than metarepresentational inferences. Thus, some metarepresentations may be unnecessary. However, metapsychology is also insufficient for explaining communication: the logic of instrumental actions permits interpreting their effect as following from intentions, yet the effect of communicative actions is often unavailable for inferring meaning. Moreover, current evidence for the developmental trajectory of communication and mental state attribution does not support the emergence of the former from the latter. My proposal is that our primitive concept of communication targets, instead, representational action. When we communicate, we typically convey a propositional content that is detached from our acts—a property absent in ordinary goal-directed actions. This view additionally raises the possibility that metarepresentational capacities evolved for representing external, communicative representations and were only later exapted for other purposes.
To support a victim of violence and establish the correct penalty for the perpetrator, it is crucial to correctly evaluate and communicate the severity of the violence. Recent data have shown these ...communications to be biased. However, computational language models provide opportunities for automated evaluation of the severity to mitigate the biases.
We investigated whether these biases can be removed with computational algorithms trained to measure the severity of violence described.
In phase 1 (P1), participants (N=71) were instructed to write some text and type 5 keywords describing an event where they experienced physical violence and 1 keyword describing an event where they experienced psychological violence in an intimate partner relationship. They were also asked to rate the severity. In phase 2 (P2), another set of participants (N=40) read the texts and rated them for severity of violence on the same scale as in P1. We also quantified the text data to word embeddings. Machine learning was used to train a model to predict the severity ratings.
For physical violence, there was a greater accuracy bias for humans (r
=0.22) compared to the computational model (r
=0.31; t
=-2.37, P=.023). For psychological violence, the accuracy bias was greater for humans (r
=0.058) than for the computational model (r
=0.35; t
=-14.58, P<.001). Participants in P1 experienced psychological violence as more severe (mean 6.46, SD 1.69) than participants rating the same events in P2 (mean 5.84, SD 2.80; t
=-2.22, P=.029<.05), whereas no calibration bias was found for the computational model (t
=1.30, P=.195). However, no calibration bias was found for physical violence for humans between P1 (mean 6.59, SD 1.81) and P2 (mean 7.54, SD 2.62; t
=1.32, P=.19) or for the computational model (t
=0.62, P=.534). There was no difference in the severity ratings between psychological and physical violence in P1. However, the bias (ie, the ratings in P2 minus the ratings in P1) was highly negatively correlated with the severity ratings in P1 (r
=0.29) and in P2 (r
=0.37), whereas the ratings in P1 and P2 were somewhat less correlated (r
=0.11) using the psychological and physical data combined.
The results show that the computational model mitigates accuracy bias and removes calibration biases. These results suggest that computational models can be used for debiasing the severity evaluations of violence. These findings may have application in a legal context, prioritizing resources in society and how violent events are presented in the media.
As problems of injustice observed in the decarbonization process arose, energy scholars have recently sought remedies to address social justice concerns under the banner of just transition. What ...remains elusive in the existing literature is the role of communication between proponents of policy ideas and the public in fostering social consensus around just transition, particularly within non-Western contexts. The research presented here aims to fill the aforementioned knowledge gap by investigating reasons behind the vanished momentum of a just transition policy in South Korea, despite a public atmosphere accepting of the need for low-carbon energy transition. Employing natural language processing on 2022 news articles and 32,211 online comments, our research reveals that the public perception of just transition has been influenced heavily by ideologically-driven interpretations of the meaning of justice. This is due primarily to the failure of the speakers of just transition to effectively communicate its intended scope and content. The findings underscore the importance of communication in building a shared understanding of just transition aligned with deep core beliefs of a society to ensure its public acceptance and long-term viability.
Human-machine communication has emerged as a new relational context of education and should become a priority for instructional scholarship in the coming years. With artificial intelligence and ...robots offering personalized instruction, teachers' roles may shift toward overseers who design and select machine-led instruction, monitor student progress, and provide support. In this essay, we argue that bringing the sensibilities of instructional researchers to bear on these issues involving machine agents, within and outside the traditional classroom walls, is vitally important.
PurposeArtificial intelligence (AI) might change the communication profession immensely, but the academic discourse is lacking an investigation of the perspective of practitioners on this. This ...article addresses this research gap. It offers a literature overview and reports about an empirical study on AI in communications, presenting first insights on how professionals in the field assess the technology.Design/methodology/approachA quantitative cross-national study among 2,689 European communication practitioners investigated four research questions: RQ1 – How much do professionals know about AI and to what extent are they already using AI technologies in their everyday lives? RQ2 – How do professionals rate the impact of AI on communication management? RQ3 – Which challenges do professionals identify for implementing AI in communication management? RQ4 – Which risks do they perceive?FindingsCommunication professionals revealed a limited understanding of AI and expected the technology to impact the profession as a whole more than the way their organisations or themselves work. Lack of individual competencies and organisations struggling with different levels of competency and unclear responsibilities were identified as key challenges and risks.Research limitations/implicationsThe results highlight the need for communication managers to educate themselves and their teams about the technology and to identify the implementation of AI as a leadership issue.Originality/valueThe article offers the first cross-national quantitative study on AI in communication management. It presents valuable empirical insights on a trending topic in the discipline, highly relevant for both academics and practitioners.
With the rapid development of modern communication systems, the amount of data has exploded, the system structure has become increasingly complex, and existing communication theories and technologies ...are facing huge challenges. The successful application of deep learning technology in the fields of images, speech, natural language processing, and games provides a possible solution for the theory and technology of communication systems that goes beyond traditional ideas and performance. This article mainly summarizes the application cases of deep learning methods in channel estimation, signal detection, and modulation recognition, and shows their outstanding performance compared to traditional communication theory and technology. Finally, we analyze the opportunities and challenges faced by deep learning-based communication technologies.
Despite most studies on the neurobiology of language demonstrating the central part of the perisylvian network involved in language and speech function, this review attempts to complement this view ...by focusing on the role of the orbitofrontal cortex (OFC). This region is primarily involved in goal-directed adaptive behavior. Recently, there has been increasing evidence that the OFC is involved in language and speech tasks. This review demonstrates that not only the linguistic tasks that involve the processing of socially, pragmatically and emotionally relevant information engage OFC and its neurobiological mechanisms, but also specific receptive and expressive language performances rely on specific neurophysiological properties of this region (e.g., the gray matter volume and the functional activation of OFC and the uncinate fasciculus that connects OFC), which in many cases, demand executive functions. These findings highlight: (1) The OFC plays a relevant role in the adaptive neurobiological function of language; (2) the neurobiological mechanisms beyond linguistic and speech processes complement and interplay with the language-unique processes to achieve successful comprehension and production in the changing communicative contexts.
This study aims to provide an overview to the Communication and Information Department Mojokerto regarding the maturity level of physical and environmental security management at the agency, as well ...as to provide future recommendations. The results of research related to physical and environmental safety using the ISO 27002 standard, indicate that the level of physical and environmental security at the Communication and Information Department Mojokerto is still relatively low. Things that are still lacking include the lack of protection from external threats such as natural disasters, as well as the lack of care and maintenance of infrastructure. The maturity level of physical and environmental security control is 0.85 which is still at level 1 or Initial Ad Hoc from a maximum value of 5, which is at the Optimized level. It can be concluded that Communication and Information Department Mojokerto only knows that there are things that need attention but there is no standardization of the process. With this research, it is hoped that the Communication and Information Department Mojokerto can make improvements to improve physical and environmental security. In addition, it is also a consideration to obtain ISMS Certification with the ISO 27002 standard in the future.
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