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Subjective judgements - no more, no less? A response to Malterud, Bjelland and Elvbakken.

ABSTRACT: In 2016, three researchers published a report where they concluded that systematic reviews from my institution, the Norwegian Knowledge Centre for the Health Services, are of little use for health policy decision-making. Based on their reading of the conclusion section in 14 reports, they argue that our systematic reviews are not useful due to their lack of clear and conclusive findings. I have reviewed the same documents and I beg to differ. Unfortunately, the description of their methodological approach is both sparse and difficult to grasp, making it very hard to understand how they arrived at their conclusions.

SUBMITTER: Fretheim A

PROVIDER: S-EPMC6245815 | biostudies-other | 2018 Nov

REPOSITORIES: biostudies-other

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Subjective judgements - no more, no less? A response to Malterud, Bjelland and Elvbakken.

Fretheim Atle A

Health research policy and systems 20181120 1

In 2016, three researchers published a report where they concluded that systematic reviews from my institution, the Norwegian Knowledge Centre for the Health Services, are of little use for health policy decision-making. Based on their reading of the conclusion section in 14 reports, they argue that our systematic reviews are not useful due to their lack of clear and conclusive findings. I have reviewed the same documents and I beg to differ. Unfortunately, the description of their methodologica ...[more]

PMID: 30458794

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Project description:Fairness violations elicit powerful behavioral and affective responses. Indeed, people are willing to incur costs to sanction unfair behavior. Here we study the possible impact of long-term mental training in socio-affective capacities such as compassion on altruistic punishment and compensatory behavior in economic games. To this end we recruited a group of long-term meditation practitioners (LTPs) who had engaged in an average of 40 K h of mental training exercises including compassion-related meditation, along with a group of meditation-naïve controls. Participants played several adaptations of the dictator game in which they had the opportunity to punish the dictator both when they were the recipients of the dictator's offer and when they were third-party witnesses to the dictator's treatment of an anonymous second player. Compared to controls, LTPs were less likely to punish when they were the victims of fairness violations. However, both groups punished equivalently when they witnessed others receiving unfair treatment. In post-task questionnaires, controls reported significantly more anger in response to unfair offers than LTPs, although fairness judgments did not differ between groups. These data suggest that because the LTPs were less angered by unfair treatment of themselves, they punished that behavior less. However, when they witnessed the unfair treatment of others, they engaged in norm-reinforcing punishment. Finally, when participants played an additional game which included the opportunity to recompense victims, LTPs were more likely to do so. Together these data point to differential approaches to justice whereby LTPs engaged less in vengeful, retributive justice and focused more on norm reinforcement and the restoration of equity. These differences suggest that social preferences are plastic and that altruistic responses to unfairness may be shaped by the prolonged cultivation of prosocial motivation, altruism, and compassion.

Project description:Background: The increasing involvement of social robots in human lives raises the question as to how humans perceive social robots. Little is known about human perception of synthesized voices. Aim: To investigate which synthesized voice parameters predict the speaker's eeriness and voice likability; to determine if individual listener characteristics (e.g., personality, attitude toward robots, age) influence synthesized voice evaluations; and to explore which paralinguistic features subjectively distinguish humans from robots/artificial agents. Methods: 95 adults (62 females) listened to randomly presented audio-clips of three categories: synthesized (Watson, IBM), humanoid (robot Sophia, Hanson Robotics), and human voices (five clips/category). Voices were rated on intelligibility, prosody, trustworthiness, confidence, enthusiasm, pleasantness, human-likeness, likability, and naturalness. Speakers were rated on appeal, credibility, human-likeness, and eeriness. Participants' personality traits, attitudes to robots, and demographics were obtained. Results: The human voice and human speaker characteristics received reliably higher scores on all dimensions except for eeriness. Synthesized voice ratings were positively related to participants' agreeableness and neuroticism. Females rated synthesized voices more positively on most dimensions. Surprisingly, interest in social robots and attitudes toward robots played almost no role in voice evaluation. Contrary to the expectations of an uncanny valley, when the ratings of human-likeness for both the voice and the speaker characteristics were higher, they seemed less eerie to the participants. Moreover, when the speaker's voice was more humanlike, it was more liked by the participants. This latter point was only applicable to one of the synthesized voices. Finally, pleasantness and trustworthiness of the synthesized voice predicted the likability of the speaker's voice. Qualitative content analysis identified intonation, sound, emotion, and imageability/embodiment as diagnostic features. Discussion: Humans clearly prefer human voices, but manipulating diagnostic speech features might increase acceptance of synthesized voices and thereby support human-robot interaction. There is limited evidence that human-likeness of a voice is negatively linked to the perceived eeriness of the speaker.

Dataset Information

Subjective judgements - no more, no less? A response to Malterud, Bjelland and Elvbakken.

Publications

Subjective judgements - no more, no less? A response to Malterud, Bjelland and Elvbakken.

Similar Datasets

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