Recent research in visual object recognition has shown that context can facilitate object recognition. This study assessed the effect of self-relevant familiarity of context in object recognition. Participants performed a task in which they had to recognize degraded objects shown under varying levels of contextual information. The level of degradation at which they could successfully recognize the target object was used as a measure of performance. There were five contextual conditions: (1) no context, (2) context, (3) context and size, (4) context and location, (5) context, size and location. Within each contextual condition, we compared the performance of "Expert" participants who viewed objects in the context of their own house and "Novice" participants who viewed those particular settings for the first time. Ratings were performed to assess each object's consistency, frequency, position consistency, typicality and shape distinctiveness. Object's size was the only contextual info rmation that did not affect performance. Contextual information significantly reduced the amount of bottom-up visual information needed for object identification for both experts and novices. An interaction (Contextual Information x Level of Familiarity) was observed. Expert participants' performance improved significantly more than novice participants' performance by the presence of contextual information. Location information affected the performance of expert participants, only when objects that occupied stable positions were considered. Both expert and novice participants performed better with objects that rated high in typicality and shape distinctiveness. Object's consistency, frequency and position consistency did not seem to affect expert participants' performance but did affect novice participants' performance. / A regression analysis model that included Level of Familiarity, Contextual Information Level, Shape and Typical performance. Our results are in accordance with the priming model of visual object recognition. We concluded that a self-relevant context has its own consistency rules and that it affects visual object recognition by narrowing down the number of expectations and the search space significantly more than a non-self-relevant context does. Keywords: visual object recognition, self-relevant familiarity, location, size, probability. / by Evangelie Daskagianni. / Thesis (Ph.D.)--Florida Atlantic University, 2011. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2011. Mode of access: World Wide Web.

Natural categories seem to be comprised of clustered stimuli that contain a myriad of correlated features; birds, for example, tend to fly, have wings, lay eggs, and make nests. Nonetheless, the evidence that people use these correlations during intentional category learning is overwhelmingly negative (Murphy, 2002). People do, however, show evidence of correlational sensitivity during other types of category learning tasks (e.g., feature prediction). The usual explanation is that intentional category learning tasks promote rule use, which discards the correlated feature information; whereas, other types of category learning tasks promote exemplar storage, which preserves correlated feature information. However, all of the intentional category learning tasks employed to examine correlational sensitivity to date have only used deterministic mappings of stimuli to categories (i.e., each stimulus belongs to only one category). The current thesis is concerned primarily with the effects introducing the probabilistic assignment of stimuli to categories on the acquisition of different types of correlational knowledge. If correlational knowledge depends on whether or not people selectively attend to the correlation then probabilistic reinforcement, which is predicted to increase attention shifting (Kruschke & Johansen, 1999), should lead to increased correlational sensitivity. The first paper of this thesis confirms that selective attention provides a way to explain the presence or absence of correlational knowledge in different tasks. However, selective attention models have been unable to account for tasks in which people use the correlation between a non-relevant cue and regions of the category space to switch between the application of multiple rules. This phenomenon, known as knowledge partitioning, is explored in the second paper of this thesis. This thesis also extends the empirical implications of the first two papers to existing research (see included paper 3) and also provides recommendations of how utilize this conceptualization of knowledge for practitioners in the applied setting (see included paper 4). Finally, in addition to increasing attention shifting, probabilistic feedback is also assumed to result in an attenuation of learning over time (Kruschke & Johansen, 1999); the fifth paper in this thesis provides empirical confirmation that people attenuate learning in response to unavoidable error.

Thesis (Ph. D.)--Ohio State University, 2006. / Title from first page of PDF file. Includes bibliographical references (p. 125-130).

A fundamental requirement of any autonomous robot system is the ability to predict the affordances of its environment, which define how the robot can interact with various objects. In this dissertation, we demonstrate that the conventional direct perception approach can indeed be applied to the task of training robots to predict affordances, but it does not consider that objects can be grouped into categories such that objects of the same category have similar affordances. Although the connection between object categorization and the ability to make predictions of attributes has been extensively studied in cognitive science research, it has not been systematically applied to robotics in learning to predict a number of affordances from recognizing object categories. We develop a computational framework of learning and predicting affordances where a robot explicitly learns the categories of objects present in its environment in a partially supervised manner, and then conducts experiments to interact with the objects to both refine its model of categories and the category-affordance relationships. In comparison to the direct perception approach, we demonstrate that categories make the affordance learning problem scalable, in that they make more effective use of scarce training data and support efficient incremental learning of new affordance concepts. Another key aspect of our approach is to leverage the ability of a robot to perform experiments on its environment and thus gather information independent of a human trainer. We develop the theoretical underpinnings of category-based affordance learning and validate our theory on experiments with physically-situated robots. Finally, we refocus the object categorization problem of computer vision back to the theme of autonomous agents interacting with a physical world consisting of categories of objects. This enables us to reinterpret and extend the Gluck-Corter category utility function for the task of learning categorizations for affordance prediction.

Thesis (M.S.)--Auburn University, 2008. / Abstract. Vita. Includes bibliographical references (p. 75-79).

Thesis (Ph. D.)--Drexel University, 1994. / eContent provider-neutral record in process. Description based on print version record.

Thesis (Ph. D.)--University of Wisconsin--Madison, 1970. / Typescript. Vita. Description based on print version record. Includes bibliographical references.

Thesis (Ph.D.)--Ohio State University, 2005. / Title from first page of PDF file. Document formatted into pages; contains xii, 110 p.; also includes graphics. Includes bibliographical references (p. 103-110). Available online via OhioLINK's ETD Center

Sidman's (2000) theory of stimulus equivalence states that all positive elements in a reinforcement contingency enter an equivalence class. The theory also states that if an element from an equivalence class conflicts with a programmed reinforcement contingency, the conflicting element will drop out of the equivalence class. Minster et al. (2006) found evidence suggesting that a conflicting element does not drop out of an equivalence class. In an effort to explain maintained accuracy on programmed reinforcement contingencies, the authors seem to suggest that participants will behave in accordance with a particular partitioning of the equivalence class which continues to include the conflicting element. This hypothesis seems to explain their data well, but their particular procedures are not a good test of the notion of "dropping out" due to the pre-establishment of equivalence classes before the conflicting member entered the class. The current experiment first developed unpartitioned equivalence classes and only later exposed participants to reinforcement contingencies that conflicted with pre-established equivalence classes. The results are consistent with the notion that a partition developed such that the conflicting element had dropped out of certain subclasses of the original equivalence class. The notion of a partitioning of an equivalence class seems to provide a fuller description of the phenomenon Sidman (1994, 2000) described as "dropping out" of an equivalence class.

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