Transitivity performance, relational hierarchy knowledge and awareness: Results of an instructional framing manipulation.

The transitive inference (TI) paradigm has been widely used to examine the role of the hippocampus in generalization. Here we consider a surprising feature of experimental findings in this task: the relatively poor transitivity performance and levels of hierarchy knowledge achieved by adult human subjects. We focussed on the influence of the task instructions on participants' subsequent performance - through a single-word framing manipulation which either specified the relation between items as transitive (i.e

Direct and Relational Representation During Transitive List Linking in Pinyon Jays (\u3ci\u3eGymnorhinus cyanocephalus\u3c/i\u3e)

The authors used the list-linking procedure (Treichler & Van Tilburg, 1996) to explore the processes by which animals assemble cognitive structures from fragmentary and often contradictory data. Pinyon jays (Gymnorhinus cyanocephalus) were trained to a high level of accuracy on two implicit transitive lists. They were then given linkage training on the single pair that linked the two lists into a composite, 10-item hierarchy. Following linkage training, the birds were tested on nonadjacent probe pairs drawn both from within (B-D and 2–4) and between (D-1, E-2, B-2, C-3) each original list. Linkage training resulted in a significant transitory disruption in performance, and the adjustment to the resulting implicit hierarchy was far from instantaneous. Detailed analysis of the course of the disruption and its subsequent recovery provided important insights into the roles of direct and relational encoding in implicit hierarchies

Semantic networks

AbstractA semantic network is a graph of the structure of meaning. This article introduces semantic network systems and their importance in Artificial Intelligence, followed by I. the early background; II. a summary of the basic ideas and issues including link types, frame systems, case relations, link valence, abstraction, inheritance hierarchies and logic extensions; and III. a survey of ‘world-structuring’ systems including ontologies, causal link models, continuous models, relevance, formal dictionaries, semantic primitives and intersecting inference hierarchies. Speed and practical implementation are briefly discussed. The conclusion argues for a synthesis of relational graph theory, graph-grammar theory and order theory based on semantic primitives and multiple intersecting inference hierarchies

Psychological verbs as a vulnerable syntactic domain: A comparative study of Latin and Italian

So-called psychological verbs such as Italian temere ‘fear’ preoccupare ‘worry’ and piacere ‘like’ denote a particular state that involves an Experiencer and a second role taker that causes, initiates or is related to the psychological state. They present an extremely varied argument structure across languages that arranges these two roles in apparently inverted hierarchies and assigns them different grammatical functions (subject, direct, indirect and prepositional objects). This paper aims to provide a descriptively adequate taxonomy of psych-verbs in Latin in a comparative perspective with Italian. We individuate seven classes of psych-verbs and show that they distribute across the transitive, unergative, unaccusative pattern with the possibility of externalising either argument, therefore creating three “direct” and three “inverted” classes. The seventh class is impersonal, with no external argument. We show that the diachronic variation and apparent idiosyncrasies displayed by some verbs can be explained by the proposal that the seven classes are potentially available to all psych-roots. For this reason, psych-verbs present a high degree of vulnerability in language contact and change which results in intra-language optionality and diachronic variation

First IJCAI International Workshop on Graph Structures for Knowledge Representation and Reasoning (GKR@IJCAI'09)

International audienceThe development of effective techniques for knowledge representation and reasoning (KRR) is a crucial aspect of successful intelligent systems. Different representation paradigms, as well as their use in dedicated reasoning systems, have been extensively studied in the past. Nevertheless, new challenges, problems, and issues have emerged in the context of knowledge representation in Artificial Intelligence (AI), involving the logical manipulation of increasingly large information sets (see for example Semantic Web, BioInformatics and so on). Improvements in storage capacity and performance of computing infrastructure have also affected the nature of KRR systems, shifting their focus towards representational power and execution performance. Therefore, KRR research is faced with a challenge of developing knowledge representation structures optimized for large scale reasoning. This new generation of KRR systems includes graph-based knowledge representation formalisms such as Bayesian Networks (BNs), Semantic Networks (SNs), Conceptual Graphs (CGs), Formal Concept Analysis (FCA), CPnets, GAI-nets, all of which have been successfully used in a number of applications. The goal of this workshop is to bring together the researchers involved in the development and application of graph-based knowledge representation formalisms and reasoning techniques