Rapid allocation of temporal attention in the attentional blink paradigm. - Hilkenmeier, Frederic; Scharlau, Ingrid (European Journal of Cognitive Psychology)
How fast can information of a first target (T1) in a rapid serial visual presentation be used for top-down allocation of attention in time? A valid cue about the temporal position of a second target (T2) was integrated into T1. The data show that 100 ms after T1 onset, T2 was identified better than without cue, raising the conditional T2 performance. T1 apparently triggers a facilitative effect of attention, known from other paradigms such as peripheral cueing. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
Keywords: temporal attention; attentional blink paradigm; first target information; top-down allocation; rapid serial visual presentation; Stimulus Presentation Methods; Visual Stimulation; Visual Attention

Early Top-Down Influences in Control of Attention: Evidence from the Attentional Blink - Hilkenmeier, Frederic; Tünnermann, Jan; Scharlau, Ingrid (KI 2009: Advances in Artificial Intelligence. Proceedings of the 32nd Annual Conference on Artificial Intelligence.)
The relevance of top-down information in the deployment of attention has more and more been emphasized in cognitive psychology. We present recent findings about the dynamic of these processes and also demonstrate that task relevance can be adjusted rapidly by incoming bottom-up information. This adjustment substantially increases performance in a subsequent task. Implications for artificial visual models are discussed.
Keywords: visuo-spatial attention; top-down control; task relevance; artificial visual attention; attentional blink

People modify their tutoring behavior in robot-directed interaction for action learning - Vollmer, Anna-Lisa; Lohan, Katrin Solveig; Fischer, Kerstin; Nagai, Yukie; Pitsch, Karola; Fritsch, Jannik; Rohlfing, Katharina; Wrede, Britta (Development and Learning, 2009. ICDL 2009. IEEE 8th International Conference on Development and Learning)
In developmental research, tutoring behavior has been identified as scaffolding infants' learning processes. It has been defined in terms of child-directed speech (Motherese), child-directed motion (Motionese), and contingency. In the field of developmental robotics, research often assumes that in human-robot interaction (HRI), robots are treated similar to infants, because their immature cognitive capabilities benefit from this behavior. However, according to our knowledge, it has barely been studied whether this is true and how exactly humans alter their behavior towards a robotic interaction partner. In this paper, we present results concerning the acceptance of a robotic agent in a social learning scenario obtained via comparison to adults and 8-11 months old infants in equal conditions. These results constitute an important empirical basis for making use of tutoring behavior in social robotics. In our study, we performed a detailed multimodal analysis of HRI in a tutoring situation using the example of a robot simulation equipped with a bottom-up saliency-based attention model. Our results reveal significant differences in hand movement velocity, motion pauses, range of motion, and eye gaze suggesting that for example adults decrease their hand movement velocity in an Adult-Child Interaction (ACI), opposed to an Adult-Adult Interaction (AAI) and this decrease is even higher in the Adult-Robot Interaction (ARI). We also found important differences between ACI and ARI in how the behavior is modified over time as the interaction unfolds. These findings indicate the necessity of integrating top-down feedback structures into a bottom-up system for robots to be fully accepted as interaction partners.
Keywords: robot simulation; hand movement velocity; robotic interaction partner; robotic agent; robot-directed interaction; multimodal analysis; Motionese; Motherese; intelligent tutoring systems; immature cognitive capability; human computer interaction; eye gaze; child-directed speech; child-directed motion; bottom-up system; bottom-up saliency-based attention model; adult-robot interaction; adult-child interaction; adult-adult interaction; human-robot interaction; action learning; social learning scenario; social robotics; software agents; top-down feedback structures; tutoring behavior