JUDGES: A descriptive group decision support system for the ranking of items

A set of tools for group decision support are presented. Decision problems involving several decision makers, here-after called judges, that have to rank several alternatives, are considered. The toolbox is called JUDGES. It includes the four following procedures:

• - a hierarchical representation of the judges allows to display the existing conflicts between groups of judges,

• - enhanced box-plots representations of the alternatives are generated in order to detect those that are responsible for the major conflicts,

• - specific advice is issued to each judge in order to reach more easily a consensus,

• - a general framework for a pairwise group preference structure is proposed, and can be used to finalise the decision.

These procedures are embedded in an interactive software, implemented on micro-computer, which currently simulates the use on a network. Actual network implementation is foreseen in the near future. Several applications are presented and future developments are discussed.

A connectionist account of asymmetric category learning in early infancy.

Young infants show unexplained asymmetries in the exclusivity of categories formed on the basis of visually presented stimuli. A connectionist model is described that shows similar exclusivity asymmetries when categorizing the same stimuli presented to infants. The asymmetries can be explained in terms of an associative learning mechanism, distributed internal representations, and the statistics of the feature distributions in the stimuli. The model was used to explore the robustness of this asymmetry. The model predicts that the asymmetry will persist when a category is acquired in the presence of mixed category exemplars. An experiment with 3–4-month-olds showed that asymmetric exclusivity persisted in the presence of mixed-exemplar familiarization, thereby confirming the model's prediction. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Action selection in complex routinized sequential behaviors.

We report two experiments in which errors and interaction latencies were recorded during routinization of hierarchically structured computer-based tasks. Experiment 1 demonstrates that action selection is slowed at subtask transitions, especially when selecting lower frequency actions. This frequency effect is compounded by concurrent performance of a secondary, attentionally demanding, task. Experiment 2 replicates these results in a more complex task and further demonstrates that the effects are reduced by experience. Several other factors were also found to affect latencies, including the availability of an external disambiguation cue and the temporal distance over which task context needs to be internally maintained. The results support a “dual-systems” account of action selection in which a “routine” system, sensitive to frequency, context, and experience, is selectively modulated by an attentionally demanding “nonroutine” system. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

TRACX: A recognition-based connectionist framework for sequence segmentation and chunk extraction.

Individuals of all ages extract structure from the sequences of patterns they encounter in their environment, an ability that is at the very heart of cognition. Exactly what underlies this ability has been the subject of much debate over the years. A novel mechanism, implicit chunk recognition (ICR), is proposed for sequence segmentation and chunk extraction. The mechanism relies on the recognition of previously encountered subsequences (chunks) in the input rather than on the prediction of upcoming items in the input sequence. A connectionist autoassociator model of ICR, truncated recursive autoassociative chunk extractor (TRACX), is presented in which chunks are extracted by means of truncated recursion. The performance and robustness of the model is demonstrated in a series of 9 simulations of empirical data, covering a wide range of phenomena from the infant statistical learning and adult implicit learning literatures, as well as 2 simulations demonstrating the model's ability to generalize to new input and to develop internal representations whose structure reflects that of the items in the input sequence. TRACX outperforms PARSER (Perruchet & Vintner, 1998) and the simple recurrent network (SRN, Cleeremans & McClelland, 1991) in matching human sequence segmentation on existing data. A new study is presented exploring 8-month-olds' use of backward transitional probabilities to segment auditory sequences. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Steady-State Flow Experiments to Visualise the Stress Field and Potential Crack Trajectories in 2D Elastic-Brittle Cracked Media in Uniaxial Compression

An experimental method to visualise crack-induced perturbations of the stress field in two-dimensional elastic cracked media is presented. The method is based on the formal equivalence between the steady-state velocity field of an incompressible Newtonian fluid and the displacement field in an incompressible elastic solid in equilibrium. Two-dimensional steady-state flow experiments of an incompressible Newtonian fluid were conducted in a Hele Shaw cell. Thin rubber inclusions were inserted in the cell to model pre-existing cracks. The streamlines around the obstacles were visualised by injection of methylen blue dye into the flow. Several experiments were conducted in which different configurations of cracks were modeled: (1) one single crack inclined to the flow direction; (2) left-stepping en échelon cracks with different step-overs; and (3) randomly distributed cracks. The results compare well with the available experimental data on crack growth in elastic plates under uniaxial compression and demonstrate the ability of the method to accurately account both for cracks induced perturbations and potential crack trajectories in elastic media. Different mechanisms of crack interaction such as screening or enhancing effects are illustrated by the experiments. This revised version was published online in August 2006 with corrections to the Cover Date.     

How computational models help explain the origins of reasoning

Developmental psychology is ready to blossom into a modern science that focuses on causal mechanistic explanations of development rather than just describing and classifying the skills that children show at different ages. Computational models of cognitive development are formal systems that track the changes in information processing taking place as a behavior is acquired. Models are generally implemented as psychologically constrained computer simulations that learn tasks such as reasoning, categorization, and language. Their principal use is as tools for exploring mechanisms of transition (development) from one level of competence to the next during the course of cognitive development. They have been used to probe questions such as the extent of 'pre-programmed' or innate knowledge that exists in the infant mind, and how the sophistication of reasoning can increase with age and experience.