352,827 research outputs found

    A system-level neural model of the brain mechanisms underlying instrumental devaluation in rats

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    Goal-directed behaviours are defined by the presence of two kinds of effect on instrumental learning. First, degrading the contingencies between produced actions and desired outcomes diminishes the number of instrumental responses; second, devaluing a reward results in a lower production of instrumental actions to obtain it. We present a computational model of the neural processes underlying instrumental devaluation in rats. The model reproduces the interaction between the basolateral complex of the amygdala (BLA) and the limbic, associative and somatosensory striato-cortical loops. Firing-rate units are used to abstract the activity features of neural populations. Learning is reproduced through the use of dopamine-dependent simple and differential hebbian rules. Constraints from anatomy of the projections between neural systems are taken into account. The central hypothesis implemented in the model is that pavlovian associations learned within the BLA between manipulanda and rewards modulate goal selection through the activation of the nucleus accumbens core (NaccCo). Selection processes happening in the limbic basal ganglia with the activation of the NaccCo decide which outcome is choosen as a goal within the prelimbic cortex (PL). Connections between the BLA and the NaccCo are learned through hebbian associations mediated by feedbacks from the PL to the NaccCo. Information about selected goals from the limbic striato-cortical loop influences action selection in the sensorimotor loop both through cortico-cortical projections and through a striato-nigro-striatal dopaminergic pathway passing through the associative striato-cortical loop. The model is tested as part of the control system of a simulated rat. Instrumental devaluation tasks are reproduced. Simulated lesions of the BLA, the NaccCo, the PL and the dorsomedial striatum (DMS) both before and after training reproduce the behavioural effect of lesions in real rats. The model provides predictions about the effects of still undocumented lesions

    Instructional strategies and tactics for the design of introductory computer programming courses in high school

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    This article offers an examination of instructional strategies and tactics for the design of introductory computer programming courses in high school. We distinguish the Expert, Spiral and Reading approach as groups of instructional strategies that mainly differ in their general design plan to control students' processing load. In order, they emphasize topdown program design, incremental learning, and program modification and amplification. In contrast, tactics are specific design plans that prescribe methods to reach desired learning outcomes under given circumstances. Based on ACT* (Anderson, 1983) and relevant research, we distinguish between declarative and procedural instruction and present six tactics which can be used both to design courses and to evaluate strategies. Three tactics for declarative instruction involve concrete computer models, programming plans and design diagrams; three tactics for procedural instruction involve worked-out examples, practice of basic cognitive skills and task variation. In our evaluation of groups of instructional strategies, the Reading approach has been found to be superior to the Expert and Spiral approaches

    Toward the automated assessment of entity-relationship diagrams

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    The need to interpret imprecise diagrams (those with malformed, missing or extraneous features) occurs in the automated assessment of diagrams. We outline our proposal for an architecture to enable the interpretation of imprecise diagrams. We discuss our preliminary work on an assessment tool, developed within this architecture, for automatically grading answers to a computer architecture examination question. Early indications are that performance is similar to that of human markers. We will be using Entity-Relationship Diagrams (ERDs) as the primary application area for our investigation of automated assessment. This paper will detail our reasons for choosing this area and outline the work ahead

    Directional Decision Lists

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    In this paper we introduce a novel family of decision lists consisting of highly interpretable models which can be learned efficiently in a greedy manner. The defining property is that all rules are oriented in the same direction. Particular examples of this family are decision lists with monotonically decreasing (or increasing) probabilities. On simulated data we empirically confirm that the proposed model family is easier to train than general decision lists. We exemplify the practical usability of our approach by identifying problem symptoms in a manufacturing process.Comment: IEEE Big Data for Advanced Manufacturin
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