Learning Membership Functions in a Function-Based Object Recognition System

Functionality-based recognition systems recognize objects at the category level by reasoning about how well the objects support the expected function. Such systems naturally associate a ``measure of goodness'' or ``membership value'' with a recognized object. This measure of goodness is the result of combining individual measures, or membership values, from potentially many primitive evaluations of different properties of the object's shape. A membership function is used to compute the membership value when evaluating a primitive of a particular physical property of an object. In previous versions of a recognition system known as Gruff, the membership function for each of the primitive evaluations was hand-crafted by the system designer. In this paper, we provide a learning component for the Gruff system, called Omlet, that automatically learns membership functions given a set of example objects labeled with their desired category measure. The learning algorithm is generally applicable to any problem in which low-level membership values are combined through an and-or tree structure to give a final overall membership value.Comment: See http://www.jair.org/ for any accompanying file

Unusual use of objects after unilateral brain damage. The technical reasoning model.

International audienceIt has been suggested that gesture engrams, conceptual knowledge and/or the ability to infer function from structure can support object use. The present paper proposes an alternative view which is based upon the idea that object use requires solely the ability to reason about technical means provided by objects. Technical means are abstract principles which are not linked with any object representation (e.g., cutting involves the opposition between dense and permeable material). The technical reasoning model predicts that the inability to perform technical reasoning should impair performance in any situation requiring the use of objects (in a conventional way or not). Twenty left brain-damaged (LBD) patients, 11 right brain-damaged (RBD) patients and 41 healthy controls were examined on experimental tests assessing the conventional use of objects (e.g., screwing a screw with a screwdriver), conceptual knowledge about object function, pantomime of object use and recognition of object utilization gestures. We also designed the Unusual Use of Objects test, which demands unusual applications of objects to achieve a purpose for which the usually applied object is not provided (e.g., screwing a screw with a knife). The key findings are that only LBD patients have more difficulties on the Unusual Use of Objects Test than controls or RBD patients, and that the severity of their impairment is correlated with that on conventional use of objects. Correlations with tests assessing conceptual knowledge as well as with tests of pantomime of object use and recognition of object utilization gestures were weaker. These results support the technical reasoning model and question the role of conceptual knowledge and gesture engrams in object use. Since the technical reasoning model also predicts two distinct technical disorders, the discussion focuses on the existence of these disorders in regard to individual performance profiles obtained in the Unusual Use of Objects test

Ranking relations using analogies in biological and information networks

Analogical reasoning depends fundamentally on the ability to learn and generalize about relations between objects. We develop an approach to relational learning which, given a set of pairs of objects $\mathbf{S}=\{A^{(1)}:B^{(1)},A^{(2)}:B^{(2)},\ldots,A^{(N)}:B ^{(N)}\}$, measures how well other pairs A:B fit in with the set $\mathbf{S}$. Our work addresses the following question: is the relation between objects A and B analogous to those relations found in $\mathbf{S}$? Such questions are particularly relevant in information retrieval, where an investigator might want to search for analogous pairs of objects that match the query set of interest. There are many ways in which objects can be related, making the task of measuring analogies very challenging. Our approach combines a similarity measure on function spaces with Bayesian analysis to produce a ranking. It requires data containing features of the objects of interest and a link matrix specifying which relationships exist; no further attributes of such relationships are necessary. We illustrate the potential of our method on text analysis and information networks. An application on discovering functional interactions between pairs of proteins is discussed in detail, where we show that our approach can work in practice even if a small set of protein pairs is provided.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS321 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Neural Correlates of Fluid Reasoning in Children and Adults

Fluid reasoning, or the capacity to think logically and solve novel problems, is central to the development of human cognition, but little is known about the underlying neural changes. During the acquisition of event-related fMRI data, children aged 6–13 (N = 16) and young adults (N = 17) performed a task in which they were asked to identify semantic relationships between drawings of common objects. On semantic problems, participants indicated which of five objects was most closely semantically related to a cued object. On analogy problems, participants solved a visual propositional analogy (e.g., shoe is to foot as glove is to…?) by indicating which of four objects would complete the problem; these problems required integration of two semantic relations, or relational integration. Our prior research on analogical reasoning in adults implicated left anterior ventrolateral prefrontal cortex (VLPFC) in the controlled retrieval of individual semantic relationships, and rostrolateral prefrontal cortex (RLPFC) in relational integration. In this study, age-related changes in the recruitment of VLPFC, temporal cortex, and other cortical regions were observed during the retrieval of individual semantic relations. In contrast, age-related changes in RLPFC function were observed during relational integration. Children aged 6–13 engage RLPFC too late in the analogy trials to influence their behavioral responses, suggesting that important changes in RLPFC function take place during adolescence