The starting point of this paper is the observation that methods based on the direct match of keywords are inadequate because they do not consider the cognitive ability of concept formation and abstraction. We argue that keyword evaluation needs to be based on a semantic model of language capturing the semantic relatedness of words to satisfy the claim of the human-like ability of concept formation and abstraction and achieve better evaluation results. Evaluation of keywords is difficult since semantic informedness is required for this purpose. This model must be capable of identifying semantic relationships such as synonymy, hypernymy, hyponymy, and location-based abstraction. For example, when gathering texts from online sources, one usually finds a few keywords with each text. Still, these keyword sets are neither complete for the text nor are they in themselves closed, i.e., in most cases, the keywords are a random subset of all possible keywords and not that informative w.r.t. the complete keyword set. Therefore all algorithms based on this cannot achieve good evaluation results and provide good/better keywords or even a complete keyword set for a text. As a solution, we propose a word graph that captures all these semantic relationships for a given language. The problem with the hyponym/hyperonym relationship is that, unlike synonyms, it is not bidirectional. Thus the space of keyword sets requires a metric that is non-symmetric, in other words, a quasi-metric. We sketch such a metric that works on our graph. Since it is nearly impossible to obtain such a complete word graph for a language, we propose for the keyword task a simpler graph based on the base text upon which the keyword sets should be evaluated. This reduction is usually sufficient for evaluating keyword sets.

Being able to generate predictions about what is going to happen next while observing other people’s actions plays a crucial role in our daily lives. Different theoretical explanations for the underlying processes of humans’ action prediction abilities have been suggested. Whereas an embodied account posits that predictive gaze relies on embodied simulations in the observer’s motor system, other accounts do not assume a causal role of the motor system for action prediction. The general aim of this thesis was to augment current knowledge about the functional mechanisms behind humans’ action prediction abilities. In particular, the present thesis outlines and tests an embodied account of action prediction. The second aim of this thesis was to extend prior action prediction studies by exploring infants’ online gaze during observation of social interactions. The thesis reports 3 eye-tracking studies that were designed to measure adults’ and infants’ predictive eye movements during observation of different manual and social actions. The first two studies used point-light displays of manual reaching actions as stimuli to isolate human motion information. Additionally, Study II used transcranial magnetic stimulation (TMS) to directly modify motor cortex activity. Study I showed that kinematic information from biological motion can be used to anticipate the goal of other people’s point-light actions and that the presence of biological motion is sufficient for anticipation to occur. Study II demonstrated that TMS-induced temporary lesions in the primary motor cortex selectively affected observers’ gaze latencies. Study III examined 12-month-olds’ online gaze during observation of a give-and-take interaction between two individuals. The third study showed that already at one year of age infants shift their gaze from a passing hand to a receiving hand faster when the receiving hand forms a give-me gesture compared to an inverted hand shape. The reported results from this thesis make two major contributions. First, Studies I and II provide evidence for an embodied account of action prediction by demonstrating a direct connection between anticipatory eye movements and motor cortex activity. These findings support the interpretation that predictive eye movements are driven by a recruitment of the observer’s own motor system. Second, Study III implicates that properties of social action goals influence infants’ online gaze during action observation. It further suggests that at one year of age infants begin to show sensitivity to social goals within the context of give-and-take interactions while observing from a third-party perspective.

Visual simultaneous localization and mapping (SLAM) as field has been researched for ten years, but with recent advances in mobile performance visual SLAM is entering the consumer market in a completely new way. A visual SLAM system will however be sensitive to non cautious use that may result in severe motion, occlusion or poor surroundings in terms of visual features that will cause the system to temporarily fail. The procedure of recovering from such a fail is called relocalization. Together with two similar problems localization, to find your position in an existing SLAM session, and loop closing, the online reparation and perfection of the map in an active SLAM session, these can be grouped as visual location recognition (VLR). This thesis presents novel results by combining the scalability of FabMap and the precision of 13th Lab's tracking yielding high-precision VLR, +/- 10 cm, while maintaining above 99 % precision and 60 % recall for sessions containing thousands of images. Everything functional purely on a normal mobile phone. The applications of VLR are many. Indoors, where GPS is not functioning, VLR can still provide positional information and navigate you through big complexes like airports and museums. Outdoors, VLR can improve the precision of GPS tenfold yielding a new level of navigational experience. Virtual and augmented reality applications are other areas that benefit from improved positioning and localization.