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| EAB 4 |
| Monday, May 31, 2004 |
| 1:30 PM–2:20 PM |
| Beacon D |
| Area: EAB |
| Chair: Chris Ninness (Stephen F. Austin State University) |
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| Conditional Discrimination or Simple Discrimination with Compound Stimuli? |
| Domain: Applied Research |
| JEAN-CLAUDE DARCHEVILLE (University of Lille III), Belkhodja Roseleyne (University of Lille III) |
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| Abstract: Adult subjects were trained to respond to compound stimuli (2 elements). From these ones new compound stimuli (2 elements) were composed by means of symmetry and transitivity. The subjects did not respond to the new stimuli if they could not split them up. But if they could, they responded. We found the same phenomenom in a conditionnal discrimination as a simple discrimination with compound stimuli. |
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| A Functional-Analytic and Neural Network Approach to Mathematical Relations |
| Domain: Applied Research |
| CHRIS NINNESS (Stephen F. Austin State University), Robin Rumph (Stephen F. Austin State University), Glen L. McCuller (Stephen F. Austin State University), Carol Harrison (Stephen F. Austin State University), Elizabeth Hancock-Akin (Stephen F. Austin State University), Ashley Capt (Stephen F. Austin State University), Sharon K. Ninness (Stephen F. Austin State University), Angela Ford (Stephen F. Austin State University) |
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| Abstract: Seven participants who were naïve with regard to algebraic and trigonometric transformations received an introductory lecture regarding the fundamentals of the rectangular coordinate system. Then, they took part in a computer-interactive matching-to-sample procedure in which they received instruction on particular formula-to-formula and formula-to-graph relations as they pertain to reflections and vertical and horizontal shifts. In training A—B, standard formulae served as samples and factored formulae served as comparisons. In training B—C, factored formulae served as samples and graphs served as comparisons. Subsequently, we assessed A—C and C—A for combinatorial mutual entailment followed by a Relational Evaluation Procedure to assess 40 novel and complex variations of the original training formulae to their graphic representations. In Experiment 1, four of seven participants demonstrated perfect or near perfect performance on novel algebraic and trigonometric formula-to-graph relations. Experiment 2 directed training at the remaining three participants. The error patterns of these three participants were classified by an artificial neural network self-organizing map (SOM), and treatment was aimed at remediating the specific types of errors identified by the SOM. Subsequently, these participants demonstrated substantially improved identification of novel formula-to-graph relations. Relational networks and applications to various mathematical functions are discussed. |
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