AI-driven influencer marketing: Comparing the effects of virtual and human influencers on consumer perceptions

Computer generated virtual influencers are currently one of the most important brand communication trends driven by artificial intelligence. While numerous studies on human social media influencers already exist, the field of virtual influencers is still largely unexplored, which is especially true regarding their impact on consumer perceptions. Against this background, the aim of this study is to empirically investigate consumer perceptions of virtual influencers in comparison to traditional social media influencers. We conduct an exploratory experiment to test the effect of virtual and human influencers on credibility, competence, likability, and purchase intentions. The results show no significant differences between virtual and human influencers, except for the variable likeability. Implications for management and future research are discussed

The resting human brain and motor learning.

Functionally related brain networks are engaged even in the absence of an overt behavior. The role of this resting state activity, evident as low-frequency fluctuations of BOLD (see [1] for review, [2-4]) or electrical [5, 6] signals, is unclear. Two major proposals are that resting state activity supports introspective thought or supports responses to future events [7]. An alternative perspective is that the resting brain actively and selectively processes previous experiences [8]. Here we show that motor learning can modulate subsequent activity within resting networks. BOLD signal was recorded during rest periods before and after an 11 min visuomotor training session. Motor learning but not motor performance modulated a fronto-parietal resting state network (RSN). Along with the fronto-parietal network, a cerebellar network not previously reported as an RSN was also specifically altered by learning. Both of these networks are engaged during learning of similar visuomotor tasks [9-22]. Thus, we provide the first description of the modulation of specific RSNs by prior learning--but not by prior performance--revealing a novel connection between the neuroplastic mechanisms of learning and resting state activity. Our approach may provide a powerful tool for exploration of the systems involved in memory consolidation

Couponing Eine Chance fuer deutsche Printmedien?

''Couponing' - dieses Marketingstichwort wurde im letzten Jahr oft zitiert und sehr kontrovers diskutiert. In den USA ist Couponing seit langem Volkssport, in Deutschland seit dem Wegfall von Rabattgesetz und Zugabeverordnung ueberhaupt erst moeglich. Dieser Beitrag soll die Chancen dieses alten/ neuen Marketinginstrumentes fuer das Instrument an sich und damit fuer deutsche Verlage beleuchten und erste Erfahrungen mit einer nationalen Couponbeilage vermitteln.' (Autorenreferat)Available from FHB Gelsenkirchen(1010)-AMH506 / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekSIGLEDEGerman

Time-dependent hebbian rules for the learning of templates for visual motion recognition

Experimental evidence suggests that the visual recognition of biological movements is based on learned spatio-temporal templates. Work in computational vision shows that movement recognition can be accomplished by recognizing temporal sequences of form or optic flow patterns. Recurrent neural networks with asymmetric lateral connections are one physiologically plausible way for the encoding of spatio-temporal templates. We demonstrate that time-dependent hebbian plasticity is suitable for establishing the required lateral connectivity patterns. We tested different hebbian plasticity rules and compared their efficiency and stability properties in simulations and by mathematical analysis. We found the most robust behavior for a learning rule that assumes a normalization of the total afferent synaptic connectivity that can be supported by each neuron. Consistent with psychophysical data our model learns the appropriate lateral connections after less than 30 stimulus repetitions. The resulting recurrent neural network shows strong sequence selectivity.status: publishe

Time-dependent hebbian rules for the learning of templates

for visual motion recognitio

Visual learning shapes the processing of complex movement stimuli in the human brain

Recognition of actions and complex movements is fundamental for social interactions and action understanding. While the relationship between motor expertise and visual recognition of body movements has received a vast amount of interest, the role of visual learning remains largely unexplored. Combining psychophysics and fMRI experiments, we investigated neural correlates of visual learning of complex movements. Subjects were trained to visually discriminate between very similar complex movement stimuli generated by motion morphing that were either compatible (Experiments 1 and 2) or incompatible (Experiment 3) with human movement execution. Employing an fMRI adaptation paradigm as index of discriminability, we scanned human subjects before and after discrimination training. The results of Experiment 1 revealed three different effects as a consequence of training; 1) Emerging fMRI-selective adaptation in general motion related areas (hMT/V5+, KO/V3b) for the differences between human-like movements. 2) Enhanced of fMRI-selective adaptation already present before training in biological motion related areas (pSTS, FBA). 3) Changes covarying with task difficulty in frontal areas. Moreover, the observed activity changes were specific to the trained movement patterns (Experiment 2). The results of Experiment 3, testing artificial movement stimuli, were strikingly similar to the results obtained for human movements. General and biological motion related areas showed movement-specific changes in fMRI-selective adaptation for the differences between the stimuli after training. These results support the existence of a powerful visual machinery for the learning of complex motion patterns that is independent of motor execution. We propose thus a key role of visual learning in action recognition.status: publishe

Learning to discriminate complex movements: biological versus artificial trajectories

The recognition of complex body movements and actions is a fundamental visual capacity very important for social communication. It seems possible that movement recognition is based on a general capability of the visual system to learn complex visual motion patterns. Alternatively, this visual function might exploit specialized mechanisms for the analysis of biologically relevant movements, for example, of humans or animals. To investigate this question, we trained human observers to discriminate novel motion patterns that were generated, exploiting a new technique for stimulus generation by motion morphing. We tested the learning of different classes of novel movement stimuli. One group of stimuli was fully consistent with human movements. A second class of stimuli was based on artificial skeleton models that were inconsistent with human and animal bodies. A third group of stimuli specified the same local motion information as human movements but was inconsistent with an underlying articulated shape. Participants learned both classes of articulated movements very fast in an orientation-dependent manner. Learning speed and accuracy were strikingly similar and independent of the similarity of the stimuli with biologically relevant body shapes. For the class of stimuli without underlying articulated shape, however, we did not observe significant improvements of the discrimination performance after training. Our results indicate the existence of a fast visual learning process for complex articulated movement patterns, which likely is relevant for biological motion perception. This process seems to operate independently of the consistency of the patterns with biologically relevant body shapes but seems to require the compatibility of the learned movements with a global underlying shape.status: publishe

Common neural correlates of emotion perception in humans

Whether neuroimaging findings support discriminable neural correlates of emotion categories is a longstanding controversy. Two recent meta-analyses arrived at opposite conclusions, with one supporting (Vytal and Hamann []: J Cogn Neurosci 22:2864-2885) and the other opposing this proposition (Lindquist et al. []: Behav Brain Sci 35:121-143). To obtain direct evidence regarding this issue, we compared activations for four emotions within a single fMRI design. Angry, happy, fearful, sad and neutral stimuli were presented as dynamic body expressions. In addition, observers categorized motion morphs between neutral and emotional stimuli in a behavioral experiment to determine their relative sensitivities. Brain-behavior correlations revealed a large brain network that was identical for all four tested emotions. This network consisted predominantly of regions located within the default mode network and the salience network. Despite showing brain-behavior correlations for all emotions, muli-voxel pattern analyses indicated that several nodes of this emotion general network contained information capable of discriminating between individual emotions. However, significant discrimination was not limited to the emotional network, but was also observed in several regions within the action observation network. Taken together, our results favor the position that one common emotional brain network supports the visual processing and discrimination of emotional stimuli. Hum Brain Mapp, 2015. © 2015 Wiley Periodicals, Inc.status: publishe