Yves Lacouture

SIMSTAT: Bootstrap computer simulation and statistical program for IBM personal computers

SIMSTAT is a computer program that provides bootstrap confidence intervals and estimates distribution parameters for 27 univariate and bivariate statistics. Because it permits variation of the bootstrap sample size, it is a convenient tool for research planning, allowing comparison of power estimates and estimator precision for various estimators and/or sample sizes. SIMSTAT also is a useful tool for studying the robustness of various statistics on real-world data and may be helpful in teaching statistical principles.

A Connectionist Model of Choice and Reaction Time in Absolute Identification

A connectionist architecture is developed that can be used for modeling choice probabilities and reaction times in identification tasks. The architecture consists of a feedforward network and a decoding module, and learning is by mean-variance back-propagation, an extension of the standard back-propagation learning algorithm. We suggest that the new learning procedure leads to a better model of human learning in simple identification tasks than does standard back-propagation. Choice probabilities are modeled by the input-output relations of the network and reaction times are modeled by the time taken for the network, particularly the decoding module, to achieve a stable state. In this paper, the model is applied to the identification of unidimensional stimuli; applications to the identification of multidimensional stimuli—visual displays and words—is mentioned and presented in more detail in other papers. The strengths and weaknesses of this connectionist approach vis-a-vis other approaches are discussed

Choice and response time processes in the identification and categorization of unidimensional stimuli

Lacouture and Marley (1991, 1995, 2001) have successfully modeled the probabilities of correct responses and the mean correct response times (RTs) in unidimensional absolute identification tasks for various stimulus ranges and stimulus/response set sizes, for individual and group data. These fits include those to a set of phenomena often referred to asend-anchor effects. A revised model, with the independent accumulator decision process replaced by aleaky competing accumulator decision process, fits the probabilities of correct responses and the full distributions of RTs in unidimensional absolute identification. The revised model is also applied successfully to a particular class of unidimensional categorization tasks. We discuss possible extensions for handling sequential effects in unidimensional absolute identification, and other extensions of the given class of categorization tasks that are of potential empirical and theoretical importance as a supplement to the study of multidimensional absolute identification tasks.

The Roles of Stimulus and Response Set Size in the Identification and Categorisation of Unidimensional Stimuli

Abstract Dual mapping theories (e.g., Atkinson, Bower, & Crothers, 1965; Lacouture & Marley, 1991, 1995) propose that psychophysical judgment involves two component processes: stimulus representation and response selection. This empirical and theoretical paper studies the relative contributions of these two component processes in determining performance in the identification and categorisation of unidimensional stimuli. We extend Lacouture and Marleys (1995) mapping model of absolute identification to categorisation, and demonstrate that the extension predicts that the number of response categories has a much larger effect than the number of stimuli in determining various aspects of the relevant data, including the so-called bow effect. This prediction is supported by the results of three experiments in which the number of stimuli and/or the number of response categories was manipulated.

Stimulus modality and stimulus-response compatibility in absolute identification.

Accuracy and response time (RT) were measured in the absolute identification (AI) of 10 unidimensional perfectly pairwise discriminable stimuli. One group of 20 subjects performed a visual AI task involving line segments of variable length. A second group of 20 subjects participated in an auditory task with the stimuli composed of pure tones of variable intensity. Subjects performed the task under two conditions: a spatially compatible and a spatially incompatible stimulus-response mapping. Results showed greater accuracy for the visual modality and longer RT for the incompatible mapping. The experimental factors did not substantially alter the bowing observed when performance was plotted according to the ordinal position of the stimuli. The data do not support the hypothesis that the bow effect is attributable to motor programming or motor adjustment stages.

EXPANDING MEL RESPONSE BOX TO ACCOMMODATE UP TO 16 EXTERNAL BUTTONS

Micro Experimental Laboratory (MEL) is a widely used software package developed for the automation of psychological experiments. This report presents a simple way to implement and use an expanded MEL response box that accommodates up to 16 external switches. Hardware and software considerations are reviewed, and relevant MEL code is provided. This includes a simple algorithm that permits the reading of multiple responses within a single trial.

Développement d'une épreuve franco-québécoise de lecture labiale

To date, there has been no validated speech-reading test available for the French-Canadian population. In the context of rehabilitative services for persons with noise-induced hearing loss, it was felt necessary to develop and validate a speech-reading screening test. Thirty young adults having normal hearing bilaterally and good vision underwent the test. It involved a familiarization list and two lists of 25 independent sentences each. The validation concerned mainly the internal consistency and the equivalence of the lists. Two lists satisfying these criteria are proposed.

Mean-variance backpropagation: a connectionist learning algorithm with a selective attention mechanism

A modified version of the backpropagation learning algorithm called mean-variance backpropagation (MV-BP) is presented. It uses gradient descent to minimize a weighted mixture of the overall mean and variance of the squared-errors computed across the stimulus set. Applied on a network with enough resources, the MV-BP learning algorithm yields learning curves similar to those observed with the standard backpropagation learning algorithm but with faster learning. When the new learning algorithm is used on a network with limited resources, learning is still faster, but performance asymptotes at a higher level of mean-square error. The proposed MV-BP learning algorithm might not find the best solution, but it is probably more adequate for modeling human cognitive learning since it allocates the resources in such a way that performance tends to be similar on all stimuli.<<ETX>>