Strength of visual percept generated by famous faces perceived without awareness: effects of affective valence, response latency, and visual field

Participants who were unable to detect familiarity from masked 17 ms faces ([Stone and Valentine, 2004] and [Stone and Valentine, in press-b]) did report a vague, partial visual percept. Two experiments investigated the relative strength of the visual percept generated by famous and unfamiliar faces, using masked 17 ms exposure. Each trial presented simultaneously a famous and an unfamiliar face, one face in LVF and the other in RVF. In one task, participants responded according to which of the faces generated the stronger visual percept, and in the other task, they attempted an explicit familiarity decision. The relative strength of the visual percept of the famous face compared to the unfamiliar face was moderated by response latency and participants’ attitude towards the famous person. There was also an interaction of visual field with response latency, suggesting that the right hemisphere can generate a visual percept differentiating famous from unfamiliar faces more rapidly than the left hemisphere. Participants were at chance in the explicit familiarity decision, confirming the absence of awareness of facial familiarity.

Narrative Personae and Visual Signs: Reading Leonard’s intimate photo-memoir. a/b: Auto/Biography Studies.

: In this paper, I look at Joanne Leonard’s Being in Pictures and engage in a critical dialogue with an assemblage of visual and textual narratives that comprise her intimate photo memoir. In doing this I draw on Hannah Arendt’s take on narratives as tangible traces of uniqueness and plurality, political traits par excellence in the cultural histories of the human condition. Being aware of my role as a reader/viewer/interpreter of a woman artist’s auto/biographical narratives, I move beyond dilemmas of representation or questions of unveiling “the real Leonard”. The artist is instead configured as a narrative persona, whose narratives respond to three interrelated themes of inquiry, namely the visualization of spatial technologies, vulnerability and the gendering of memory. Key words: gendered memories, narrative persona, spatial technologies, photo memoir, vulnerability

Probabilistic Graphical Modelling for Software Product Lines: A Frameweork for Modeling and Reasoning under Uncertainty

This work provides a holistic investigation into the realm of feature modeling within software product lines. The work presented identifies limitations and challenges within the current feature modeling approaches. Those limitations include, but not limited to, the dearth of satisfactory cognitive presentation, inconveniency in scalable systems, inflexibility in adapting changes, nonexistence of predictability of models behavior, as well as the lack of probabilistic quantification of model’s implications and decision support for reasoning under uncertainty. The work in this thesis addresses these challenges by proposing a series of solutions. The first solution is the construction of a Bayesian Belief Feature Model, which is a novel modeling approach capable of quantifying the uncertainty measures in model parameters by a means of incorporating probabilistic modeling with a conventional modeling approach. The Bayesian Belief feature model presents a new enhanced feature modeling approach in terms of truth quantification and visual expressiveness. The second solution takes into consideration the unclear support for the reasoning under the uncertainty process, and the challenging constraint satisfaction problem in software product lines. This has been done through the development of a mathematical reasoner, which was designed to satisfy the model constraints by considering probability weight for all involved parameters and quantify the actual implications of the problem constraints. The developed Uncertain Constraint Satisfaction Problem approach has been tested and validated through a set of designated experiments. Profoundly stating, the main contributions of this thesis include the following: • Develop a framework for probabilistic graphical modeling to build the purported Bayesian belief feature model. • Extend the model to enhance visual expressiveness throughout the integration of colour degree variation; in which the colour varies with respect to the predefined probabilistic weights. • Enhance the constraints satisfaction problem by the uncertainty measuring of the parameters truth assumption. • Validate the developed approach against different experimental settings to determine its functionality and performance.