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In behavior analysis, the most common way to train the necessary prerequisites for testing for derived relations is to use conditional discrimination procedures. These procedures can produce emergent relations, like stimulus equivalence. Stimulus equivalence is defined as responding in accordance with reflexivity, symmetry, and transitivity (Sidman, 1994; Sidman & Tailby, 1982). Three different training structures are typically used when establishing conditional relations: many-to-one (MTO), one-to-many (OTM), and linear series (LS) (e.g., K. J. Saunders, Saunders, Williams, & Spradlin, 1993). In an MTO training structure, many sample stimuli are trained to one comparison stimulus, while in OTM, one sample stimulus is trained to many comparisons. In an LS training structure, the nodal stimulus shift in one conditional relation from functioning as a comparison to a sample in the next condtional relation (Fields & Verhave, 1987). Even if the present experiment is concerned with basic research questions, knowledge about the differential effectiveness is important for teaching programs. The literature is not consistent with respect to the equivalence outcome of the different training structures. However, when using a simultaneous training and testing protocol
In behavior analysis, the most common way to train the necessary prerequisites for testing for derived relations is to use conditional discrimination procedures. These procedures can produce emergent relations, like stimulus equivalence. Stimulus equivalence is defined as responding in accordance with reflexivity, symmetry, and transitivity (Sidman, 1994; Sidman & Tailby, 1982). Three different training structures are typically used when establishing conditional relations: many-to-one (MTO), one-to-many (OTM), and linear series (LS) (e.g., K. J. Saunders, Saunders, Williams, & Spradlin, 1993). In an MTO training structure, many sample stimuli are trained to one comparison stimulus, while in OTM, one sample stimulus is trained to many comparisons. In an LS training structure, the nodal stimulus shift in one conditional relation from functioning as a comparison to a sample in the next condtional relation (Fields & Verhave, 1987). Even if the present experiment is concerned with basic research questions, knowledge about the differential effectiveness is important for teaching programs. The literature is not consistent with respect to the equivalence outcome of the different training structures. However, when using a simultaneous training and testing protocol
Some neural representations change slowly across multiple timescales. Here we argue that modeling this "drift" could help explain the spacing effect (the long-term benefit of distributed learning), whereby differences between stored and current temporal context activity patterns produce greater error-driven learning. We trained a neurobiologically realistic model of the entorhinal cortex and hippocampus to learn paired associates alongside temporal context vectors that drifted between learning episodes and/or before final retention intervals. In line with spacing effects, greater drift led to better model recall after longer retention intervals. Dissecting model mechanisms revealed that greater drift increased error-driven learning, strengthened weights in slower-drifting temporal context neurons (temporal abstraction), and improved direct cue-target associations (decontextualization). Intriguingly, these results suggest that decontextualization -- generally ascribed only to the neocortex -- can occur within the hippocampus itself. Altogether, our findings provide a mechanistic formalization for established learning concepts such as spacing effects and errors during learning.
This chapter reviews the training research literature reported over the past decade. We describe the progress in five areas of research including training theory, training needs analysis, antecedent training conditions, training methods and strategies, and posttraining conditions. Our review suggests that advancements have been made that help us understand better the design and delivery of training in organizations, with respect to theory development as well as the quality and quantity of empirical research. We have new tools for analyzing requisite knowledge and skills, and for evaluating training. We know more about factors that influence training effectiveness and transfer of training. Finally, we challenge researchers to find better ways to translate the results of training research into practice.
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