Reaching the Sustainable Development Goals requires a fundamental socio-economic transformation accompanied by substantial investment in low-carbon infrastructure. Such a sustainability transition represents a non-marginal change, driven by behavioral factors and systemic interactions. However, typical economic models used to assess a sustainability transition focus on marginal changes around a local optimum, whichby constructionlead to negative effects. Thus, these models do not allow evaluating a sustainability transition that might have substantial positive effects. This paper examines which mechanisms need to be included in a standard computable general equilibrium model to overcome these limitations and to give a more comprehensive view of the effects of climate change mitigation. Simulation results show that, given an ambitious greenhouse gas emission constraint and a price of carbon, positive economic effects are possible if (1) technical progress results (partly) endogenously from the model and (2) a policy intervention triggering an increase of investment is introduced. Additionally, if (3) the investment behavior of firms is influenced by their sales expectations, the effects are amplified. The results provide suggestions for policy-makers, because the outcome indicates that investment-oriented climate policies can lead to more desirable outcomes in economic, social and environmental terms.

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.

Purpose: The aims of this systematic review are to provide a critical overview of short-term memory (STM) and working memory (WM) treatments in stroke aphasia and to systematically evaluate the internal and external validity of STM/WM treatments. Method: A systematic search was conducted in February 2014 and then updated in December 2016 using 13 electronic databases. We provided descriptive characteristics of the included studies and assessed their methodological quality using the Risk of Bias in N-of-1 Trials quantitative scale (Tate et al., 2015), which was completed by 2 independent raters. Results: The systematic search and inclusion/exclusion procedure yielded 17 single-case or case-series studies with 37 participants for inclusion. Nine studies targeted auditory STM consisting of repetition and/or recognition tasks, whereas 8 targeted attention and WM, such as attention process training including n-back tasks with shapes and clock faces as well as mental math tasks. In terms of their methodological quality, quality scores on the Risk of Bias in N-of-1 Trials scale ranged from 4 to 17 (M = 9.5) on a 0-30 scale, indicating a high risk of bias in the reviewed studies. Effects of treatment were most frequently assessed on STM, WM, and spoken language comprehension. Transfer effects on communication and memory in activities of daily living were tested in only 5 studies. Conclusions: Methodological limitations of the reviewed studies make it difficult, at present, to draw firm conclusions about the effects of STM/WM treatments in poststroke aphasia. Further studies with more rigorous methodology and stronger experimental control are needed to determine the beneficial effects of this type of intervention. To understand the underlying mechanisms of STM/WM treatment effects and how they relate to language functioning, a careful choice of outcome measures and specific hypotheses about potential improvements on these measures are required. Future studies need to include outcome measures of memory functioning in everyday life and psychosocial functioning more generally to demonstrate the ecological validity of STM and WM treatments.