Persuasive appeals frequently prove ineffective or produce unintended outcomes, due to the presence of motivated reasoning. Using the example of electric cars adoption, this research delves into the impact of emotional content, message valence, and the coherence of pre-existing attitudes on biased information evaluation. By conducting a factorial survey (N = 480) and incorporating a computational model of attitude formation, we aim to gain a deeper insight into the cognitive-affective mechanisms driving motivated reasoning. Our experimental findings reveal that motivated reasoning is most pronounced when persuasive appeals employ a combination of emotional and rational elements within a negatively valenced argument. Furthermore, our computational model, which estimates belief and affect adjustments underlying attitude changes, elucidates how message framing influences cognitive-affective processes through emotional coherence. The results provide support for a negative correlation between shifts in coherence in response to new information and the propensity for motivated reasoning. The research contributes to computational models of opinion dynamics and social influence, offering a psychologically realistic framework for exploring the impact of individual reasoning on population-level dynamics, particularly in policy contexts, where it can enhance communication and informed policy discussions.

Changing people's travel behaviours and mode choices is an important mitigation option to reduce greenhouse gas emission in transport. Previous studies have shown that symbolic meanings associated with new low-emission vehicles and travel services influence people's willingness to adopt these innovations. However, little is known about the symbolic meanings of many upcoming transport innovations, which often influence habitual decision-making and their stratification within the population. This study thus examines cultural affective meanings of a broad range of conventional and novel transport mode options in a nationally representative German sample. Cluster analysis of affective meanings of travel modes identified six unique traveller segments. These consumer groups differ significantly in their intention to adopt low-emission travel modes and are characterized by specific psychographic, socio-demographic and behavioural profiles. The results demonstrate that affective meanings of choice options are predictive regarding the attitudes and (intended) behaviour of traveller segments. Moreover, the invariant positive meanings of conventional cars across segments indicate the strong cultural embeddedness of this mode in society. We discuss the implications of our approach for the development of government strategies for sustainable transport.

Psychological research practices are often prone to individualistic biases, emphasizing individual-level mechanisms of attitudes, behaviour, and persuasion, while neglecting the dynamics of communication in social networks. We illustrate with our InnoMind simulation model how agent-based modeling as a research method can account for the multi-level interactions between information processing in individual brains and flows of information in societies. InnoMind is based on theories of emotional cognition from cognitive science, theories of attitudes and persuasion from social psychology, and theories of social networks from sociology. In a case study, we show how the model can be used to address practical research questions in environmental psychology: We describe computer simulations with InnoMind that can serve as ex-ante evaluations of suitable campaign strategies for the promotion of carsharing as an innovative means of sustainable urban transportation. We discuss how empirical/experimental versus computational/theoretical research strategies in environmental psychology can and should be regarded as mutually informative.

This model is based on theories of emotional cognition from cognitive science,theories of attitudes and persuasion from social psychology, and theories of social networks from sociology. Agents’ beliefs and emotions about attitude objects are modeled as a parallel constraint satisfaction networks. In two case studies we showed, how the model can be used to address theoretical and practical research questions in the context of innovation diffusion and environmental psychology.

Symbolic interactionist principles of sociology are based on the idea that human action is guided by culturally shared symbolic representations of identities, behaviours, situations and emotions. Shared linguistic, paralinguistic, or kinesic elements allow humans to coordinate action by enacting identities in social situations. Structures of identity-based interactions can lead to the enactment of social orders that solve social dilemmas e.g., by promoting cooperation. Our goal is to build an artificial agent that mimics the identity-based interactions of humans. This paper describes a study in which humans played a repeated prisoner's dilemma game against other humans or one of three artificial agents bots. One of the bots has an explicit representation of identity and demonstrates more human-like behaviour than the other bots.

The diffusion of electric vehicles (EVs) is considered an effective policy strategy to meet greenhouse gas reduction targets. For large-scale adoption, however, demand-side oriented policy measures are required, based on consumers' transport needs, values and social norms. We introduce an empirically grounded, spatially explicit, agent-based model, InnoMind (Innovation diffusion driven by changing Minds), to simulate the effects of policy interventions and social influence on consumers' transport mode preferences. The agents in this model represent individual consumers. They are calibrated based on empirically derived attributes and characteristics of survey respondents. We model agent decision-making with artificial neural networks that account for the role of emotions in information processing. We present simulations of 4 scenarios for the diffusion of EVs in the city of Berlin, Germany (3 policy scenarios and 1 base case). The results illustrate the varying effectiveness of measures in different market segments and the need for appropriate policies tailored to the heterogeneous needs of different travelers. Moreover, the simulations suggest that introducing an exclusive zone for EVs in the city would accelerate the early-phase diffusion of EVs more effectively than financial incentives only.

In traditional market analysis aggregated data sets of individual consumer preferences and/ or consumer behavior are frequently used to identify future potential for innovation. These methods, however, neglect the effects of social interactions and temporal dynamics relevant for innovation diffusion. Novel approaches rooted in artificial intelligence and social network analysis research enable to model these complex processes in realistic computer models and to simulate potential market developments. In a case study investigating the acceptance of electric vehicles we developed an agent-based model and simulated target group specific scenarios of diffusion. Our results indicate that there is a high potential for electric vehicles in certain segments of the market, which could achieve considerable market share if a suitable choice of products is available. Moreover, the flexible structure of our model allows to explore the impact of various interventions (e.g. marketing strategies) as well as the analysis of potential acceptance of further innovation fields.

Recent studies with magnetoencephalography (MEG) have shown that the human auditory cortex is particularly sensitive to reduction in sound and speech qual ity. In this paper , we examine whether this sensitivity is also visible in the electroencephalogram (EEG) and whether it is possible to detect subconscious processes which can then be used to improve the behavioral assessment of speech quality. In order to compare the physiological results with behavioral measurements, we degraded a speech stimulus (vowel /a/) in a scalable way and asked for a pair comparison (PC ) and a comparison category rating (CCR) of the degraded stimuli . In addition, the brain activit y of eleven healthy subjects was measured with EEG, focusing on event-related potential s (ERPs). We found that the threshold as set by the Modulated Noise Reference Unit (MNRU) for the PC and CCR are on a similar signal -to-noise level . We trained classifie rs, which were found capable of distinguish ing between events which are seemingly similar at the behavioral leve l (i.e., no button press). Converging evidence suggests that the classifier results could reflect subconscious cortical sensitivity to sound degradations.

Due to the growing amount of digital media an increasing need to automatically categorize media such as music or pictures has been emerged. One of the metadata standards that has been established to search and retrieve media is MPEG-7. But it does not yet exist a query format that enables the user to query multimedia metadata databases. Therefore the MPEG committee decided to instantiate a call for proposal (N8220) for an MPEG-7 query format (MP7QF) framework and specified a set of requirements (N8219). This paper introduces a MP7QF framework and describes its main components and associated MP7QF XML schema types. The framework makes use of the MPEG-21 digital item declaration language (DIDL) for exchanging MP7QF Items along various MP7QF frameworks and client applications. An MP7QF Item acts as container for the input query format and output query format of a user query request. This paper concentrates on components of the framework such as session management, service retrieval and its usability and excludes consciously definitions and explanations of the input and output query format.

Neuroimaging studies have consistently identified a network of brain regions subserving inferences of other humans' mental states. This network consists of the superior temporal sulcus, temporoparietal junction, medial prefrontal cortex, temporal poles, and precuneus. Little is known, however, about the neural substrate underlying Theory of Mind processes in close to real-life conditions. To investigate those processes in more naturalistic settings, we used an fMRI adaptation of the video-based Movie for the Assessment of Social Cognition (MASC; Dziobek et al., 2006), which considers separate analysis of implicit mental state reasoning during rapidly changing perceptual cues as demanded in naturalistic settings and explicit mental state reasoning. We analyzed fMRI data by means of both a standard general linear model (GLM) approach and a tensor probabilistic independent component analysis (T-PICA), which is a novel model-free approach that allows decomposition of activation into independent spatio-temporally coherent functional networks. The model-based GLM approach revealed the typical explicit mental state reasoning network. Complementary to the GLM approach, the model-free T-PICA approach showed that those regions are also recruited during implicit mental state reasoning and that they are represented in three independent, functionally connected networks. The first component, mediating face processing and recognition, comprises the occipito-parietotemporal cortices, while the second component, involved in language comprehension, comprises the temporal lobes, lateral prefrontal cortex, and precuneus. The dorsomedial prefrontal cortex and the precuneus comprise the third component, which is likely responsible for self-referential mental activity. These results show that the mental state reasoning network can be decomposed into circumscribed functional networks mediating differential aspects of Theory of Mind.

MPEG-21 is a multimedia framework well suited for asset management of broadcast content. Especially for television broadcasters, MPEG-21 has been applied as asset management standard within the Digital Broadcast Item Model (DBIM). Within the scope of this paper, the DBIM is adopted to the needs of the IST IP ENTHRONE 2. ENTHRONE 2 devotes to the Quality of Service (QoS) management from content creation to content consumption over heterogeneous networks. This paper integrates the QoS metadata management issues from ENTHRONE 2 into the DBIM. This paper describes an MPEG-21 based metadata management solution for maintaining service quality throughout a heterogeneous network environment. It provides a metadata model based on the DBIM especially focusing on the needs of ENTHRONE 2.

We reported recently the induction of selective iodide uptake in prostate cancer cells (LNCaP) by prostate-specific antigen (PSA) promoter-directed sodium iodide symporter (NIS) expression that allowed a significant therapeutic effect of 131I. In the current study, we studied the potential of the high-energy alpha-emitter 211At, also transported by NIS, as an alternative radionuclide after NIS gene transfer in tumors with limited therapeutic efficacy of 131I due to rapid iodide efflux.

Due to limited treatment options the prognosis of patients with advanced hepatocellular cancer (HCC) has remained poor. To investigate an alternative therapeutic approach, we examined the feasibility of radioiodine therapy of HCC following human sodium iodide symporter (NIS) gene transfer using a mouse α-fetoprotein (AFP) promoter construct to target NIS expression to HCC cells. For this purpose, the murine Hepa 1–6 and the human HepG2 hepatoma cell lines were stably transfected with NIS cDNA under the control of the tumor-specific AFP promoter. The stably transfected Hepa 1–6 cell line showed a 10-fold increase in iodide accumulation, while HepG2 cells accumulated 125I approximately 60-fold. Tumor-specific NIS expression was confirmed on mRNA level by northern blot analysis, and on protein level by immunostaining, that revealed primarily membrane-associated NIS-specific immunoreactivity. In an in vitro clonogenic assay up to 78% of NIS-transfected Hepa 1–6 and 93% of HepG2 cells were killed by 131I exposure, while up to 96% of control cells survived. In vivo NIS-transfected HepG2 xenografts accumulated 15% of the total 123I administered per gram tumor with a biological half-life of 8.38 h, resulting in a tumor absorbed dose of 171 mGy MBq−1 131I. After administration of a therapeutic 131I dose (55.5 MBq) tumor growth of NIS expressing HepG2 xenografts was significantly inhibited. In conclusion, tumor-specific iodide accumulation was induced in HCC cells by AFP promoter-directed NIS expression in vitro and in vivo, which was sufficiently high to allow a therapeutic effect of 131I. This study demonstrates the potential of tumor-specific NIS gene therapy as an innovative treatment strategy for HCC.