Differentiating normal and problem gambling: a grounded theory approach.

A previous study (Ricketts &amp; Macaskill, 2003) delineated a theory of problem gambling based on the experiences of treatment seeking male gamblers and allowed predictions to be made regarding the processes that differentiate between normal and problem gamblers. These predictions are the focus of the present study, which also utilised a grounded theory approach, but with a sample of male high frequency normal gamblers. The findings suggest that there are common aspects of gambling associated with arousal and a sense of achievement. The use of gambling to manage negative emotional states differentiated normal and problem gambling. Perceived self-efficacy , emotion management skills and perceived likelihood of winning money back were intervening variables differentiating problem and normal gamblers.</p

A hybrid neural network and virtual relality system for spatial language processing

This paper describes a neural network model for the study of spatial language. It deals with both geometric and functional variables, which have been shown to play an important role in the comprehension of spatial prepositions. The network is integrated with a virtual reality interface for the direct manipulation of geometric and functional factors. The training uses experimental stimuli and data. Results show that the networks reach low training and generalization errors. Cluster analyses of hidden activation show that stimuli primarily group according to extrageometrical variables

Object representation-by-fragments in the visual system: A neurocomputational model

The paper presents a model of visual object representation by fragment views, rather than canonically-oriented whole-object views used in Chorus systems. Following recent results (Sheinberg and Logothetis, 2001) on object representation in inferotemporal cells during free viewing, we implemented a simplified attentional system which yields fragment views of objects, which are then used to train object-tuned modules. Each object is represented by a complete RBF module, instantiating a representation space. We show that such a system can produce distributed representations, like Chorus of views systems, and that dissociating objects from retinotopy enables a fuller model of scene geometry analysis to be advanced

On the foundations of perceptial symbol systems: Specifying embodied representations via connectionism

Embodied theories of cognition propose that symbol systems are analogue (e.g. Barsalou, 1999; Glenberg, 1997), as opposed to the classicist view that they are amodal e.g. Newell and Simon (1976), Fodor (1998). The fundamental problem of symbol grounding (Harnad, 1990) is resolved in embodied theories by admitting only theories of symbolic representation that are grounded in the perceptual system’s representation (rather than by reference or mapping of amodal symbols through the sensory systems of the agent). These are often called analogical representations (Mandler, 1998). Barsalou’s (1999) proposal for perceptual symbol systems (PSS) provides just such a framework for how analogue symbols might come into being, but remains agnostic on the implementation of these PSSs. In this paper, we advance an implementation of PSSs which might fill this explanatory gap. We provide descriptions, an implementation and results from a model and its consequences for Barsalou’s theory and embodied representations generally. We constrain our model to the visual modality, but without loss of generality

Consistency in successive spatial utterances

Vorwerg C. Consistency in successive spatial utterances. In: Coventry KR, Tenbrink T, Bateman JA, eds. Spatial language and dialogue. Oxford: Oxford University Press; 2009: 40-55

Connectionist Modeling of Linguistic Quantifiers

This paper presents a new connectionist model of the grounding of linguistic quantifiers in perception that takes into consideration the contextual factors affecting the use of vague quantifiers. A preliminary validation of the model is presented through the training and testing of the model with experimental data on the rating of quantifiers. The model is able to perform the “psychological” counting of objects (fish) in visual scenes and to select the quantifier that best describes the scene, as in psychological experiments

Spatial prepositions and vague quantifiers: Implementing the functional geometric framework

There is much empirical evidence showing that factors other than the relative positions of objects in Euclidean space are important in the comprehension of a wide range of spatial prepositions in English and other languages. We first the overview the functional geometric framework [11] which puts “what” and “where” information together to underpin the situation specific meaning of spatial terms. We then outline an implementation of this framework. The computational model for the processing of visual scenes and the identification of the appropriate spatial preposition consists of three main modules: (1) Vision Processing, (2) Elman Network, (3) Dual-Route Network. Mirroring data from experiments with human participants, we show that the model is both able to predict what will happen to objects in a scene, and use these judgements to influence the appropriateness of over/under/above/below to describe where objects are located in the scene. Extensions of the model to other prepositions and quantifiers are discussed