Semantic memory redux: an experimental test of hierarchical category representation

Four experiments investigated the classic issue in semantic memory of whether people organize categorical information in hierarchies and use inference to retrieve information from them, as proposed by Collins & Quillian (1969). Past evidence has focused on RT to confirm sentences such as “All birds are animals” or “Canaries breathe.” However, confounding variables such as familiarity and associations between the terms have led to contradictory results. Our experiments avoided such problems by teaching subjects novel materials. Experiment 1 tested an implicit hierarchical structure in the features of a set of studied objects (e.g., all brown objects were large). Experiment 2 taught subjects nested categories of artificial bugs. In Experiment 3, subjects learned a tree structure of novel category hierarchies. In all three, the results differed from the predictions of the hierarchical inference model. In Experiment 4, subjects learned a hierarchy by means of paired associates of novel category names. Here we finally found the RT signature of hierarchical inference. We conclude that it is possible to store information in a hierarchy and retrieve it via inference, but it is difficult and avoided whenever possible. The results are more consistent with feature comparison models than hierarchical models of semantic memory

Abstraction and context in concept representation

This paper develops the notion of abstraction in the context of the psychology of concepts, and discusses its relation to context dependence in knowledge representation. Three general approaches to modelling conceptual knowledge from the domain of cognitive psychology are discussed, which serve to illustrate a theoretical dimension of increasing levels of abstraction

Mind in Action: Action Representation and the Perception of Biological Motion

The ability to understand and communicate about the actions of others is a fundamental aspect of our daily activity. How can we talk about what others are doing? What qualities do different actions have such that they cause us to see them as being different or similar? What is the connection between what we see and the development of concepts and words or expressions for the things that we see? To what extent can two different people see and talk about the same things? Is there a common basis for our perception, and is there then a common basis for the concepts we form and the way in which the concepts become lexicalized in language? The broad purpose of this thesis is to relate aspects of perception, categorization and language to action recognition and conceptualization. This is achieved by empirically demonstrating a prototype structure for action categories and by revealing the effect this structure has on language via the semantic organization of verbs for natural actions. The results also show that implicit access to categorical information can affect the perceptual processing of basic actions. These findings indicate that our understanding of human actions is guided by the activation of high level information in the form of dynamic action templates or prototypes. More specifically, the first two empirical studies investigate the relation between perception and the hierarchical structure of action categories, i.e., subordinate, basic, and superordinate level action categories. Subjects generated lists of verbs based on perceptual criteria. Analyses based on multidimensional scaling showed a significant correlation for the semantic organization of a subset of the verbs for English and Swedish speaking subjects. Two additional experiments were performed in order to further determine the extent to which action categories exhibit graded structure, which would indicate the existence of prototypes for action categories. The results from typicality ratings and category verification showed that typicality judgments reliably predict category verification times for instances of different actions. Finally, the results from a repetition (short-term) priming paradigm suggest that high level information about the categorical differences between actions can be implicitly activated and facilitates the later visual processing of displays of biological motion. This facilitation occurs for upright displays, but appears to be lacking for displays that are shown upside down. These results show that the implicit activation of information about action categories can play a critical role in the perception of human actions

Toward a Taxonomy and Computational Models of Abnormalities in Images

The human visual system can spot an abnormal image, and reason about what makes it strange. This task has not received enough attention in computer vision. In this paper we study various types of atypicalities in images in a more comprehensive way than has been done before. We propose a new dataset of abnormal images showing a wide range of atypicalities. We design human subject experiments to discover a coarse taxonomy of the reasons for abnormality. Our experiments reveal three major categories of abnormality: object-centric, scene-centric, and contextual. Based on this taxonomy, we propose a comprehensive computational model that can predict all different types of abnormality in images and outperform prior arts in abnormality recognition.Comment: To appear in the Thirtieth AAAI Conference on Artificial Intelligence (AAAI 2016

Category-based induction in conceptual spaces

Category-based induction is an inferential mechanism that uses knowledge of conceptual relations in order to estimate how likely is for a property to be projected from one category to another. During the last decades, psychologists have identified several features of this mechanism, and they have proposed different formal models of it. In this article; we propose a new mathematical model for category-based induction based on distances on conceptual spaces. We show how this model can predict most of the properties of this kind of reasoning while providing a solid theoretical foundation for it. We also show that it subsumes some of the previous models proposed in the literature and that it generates new predictions

Structured computer-based training in the interpretation of neuroradiological images

Computer-based systems may be able to address a recognised need throughout the medical profession for a more structured approach to training. We describe a combined training system for neuroradiology, the MR Tutor that differs from previous approaches to computer-assisted training in radiology in that it provides case-based tuition whereby the system and user communicate in terms of a well-founded Image Description Language. The system implements a novel method of visualisation and interaction with a library of fully described cases utilising statistical models of similarity, typicality and disease categorisation of cases. We describe the rationale, knowledge representation and design of the system, and provide a formative evaluation of its usability and effectiveness

Conceptual Spaces for Cognitive Architectures: A Lingua Franca for Different Levels of Representation

During the last decades, many cognitive architectures (CAs) have been realized adopting different assumptions about the organization and the representation of their knowledge level. Some of them (e.g. SOAR [35]) adopt a classical symbolic approach, some (e.g. LEABRA[ 48]) are based on a purely connectionist model, while others (e.g. CLARION [59]) adopt a hybrid approach combining connectionist and symbolic representational levels. Additionally, some attempts (e.g. biSOAR) trying to extend the representational capacities of CAs by integrating diagrammatical representations and reasoning are also available [34]. In this paper we propose a reflection on the role that Conceptual Spaces, a framework developed by Peter G¨ardenfors [24] more than fifteen years ago, can play in the current development of the Knowledge Level in Cognitive Systems and Architectures. In particular, we claim that Conceptual Spaces offer a lingua franca that allows to unify and generalize many aspects of the symbolic, sub-symbolic and diagrammatic approaches (by overcoming some of their typical problems) and to integrate them on a common ground. In doing so we extend and detail some of the arguments explored by G¨ardenfors [23] for defending the need of a conceptual, intermediate, representation level between the symbolic and the sub-symbolic one. In particular we focus on the advantages offered by Conceptual Spaces (w.r.t. symbolic and sub-symbolic approaches) in dealing with the problem of compositionality of representations based on typicality traits. Additionally, we argue that Conceptual Spaces could offer a unifying framework for interpreting many kinds of diagrammatic and analogical representations. As a consequence, their adoption could also favor the integration of diagrammatical representation and reasoning in CAs

Context is Everything: Facilitating Fit When New Products are Ambiguous

Researchers have long believed that consumers adjust their functional expectations in accordance with a product’s physical appearance. Recently this belief has come under fire. Product categories are converging rapidly. Take modern cell phones; the physical appearance of the iPhone is only tangentially related to the breadth of its functionality. Examples like this have sparked a wealth of interest in exploring how consumers generate inferences for products with functions that span multiple categories. One important finding is that consumers tend to generate functional inferences based mainly on the knowledge of a single category. This suggests that new hybrid products are not necessarily seen as hybrid, at least not when it comes to functional expectations. Although highlighted as a major marketing challenge, very little progress has been made in explaining why single category beliefs occur, and why any one particular category is chosen above another. I seek to mend this gap by illustrating how context frames single category beliefs by inferring the manufacturer’s intent. Specifically, I demonstrate that context alters functional expectations (study 1), attribute preference (study 2), perceptions of proto-typicality (study 3), and attitude stability (study 4). When combined, the four studies offer a comprehensive extension of the literature on product categorization, and more importantly, illustrate the need to account for context when estimating how consumers will respond to new products with functions that span multiple categories