The Learning Procedure

2018 ◽  
pp. 5-36
Keyword(s):  
Learning Procedure

scholarly journals Observational Fear Learning in Rats: Role of Trait Anxiety and Ultrasonic Vocalization

2021 ◽  
Vol 11 (4) ◽  
pp. 423
Author(s):  
Markus Fendt ◽  
Claudia Paulina Gonzalez-Guerrero ◽  
Evelyn Kahl
Keyword(s):  
Trait Anxiety ◽  
Conditioned Fear ◽  
Ultrasonic Vocalization ◽  
Fear Learning ◽  
Anxiety State ◽  
Learning Procedure ◽  
The Social ◽  
Medium Level ◽  
Male Wistar Rats

Rats can acquire fear by observing conspecifics that express fear in the presence of conditioned fear stimuli. This process is called observational fear learning and is based on the social transmission of the demonstrator rat’s emotion and the induction of an empathy-like or anxiety state in the observer. The aim of the present study was to investigate the role of trait anxiety and ultrasonic vocalization in observational fear learning. Two experiments with male Wistar rats were performed. In the first experiment, trait anxiety was assessed in a light–dark box test before the rats were submitted to the observational fear learning procedure. In the second experiment, ultrasonic vocalization was recorded throughout the whole observational fear learning procedure, and 22 kHz and 50 kHz calls were analyzed. The results of our study show that trait anxiety differently affects direct fear learning and observational fear learning. Direct fear learning was more pronounced with higher trait anxiety, while observational fear learning was the best with a medium-level of trait anxiety. There were no indications in the present study that ultrasonic vocalization, especially emission of 22 kHz calls, but also 50 kHz calls, are critical for observational fear learning.

Observational Effects on the Food Preferences of Children With Autism Spectrum Disorder

2020 ◽  
pp. 108835762095436
Author(s):  
Joseph H. Cihon ◽  
Mary Jane Weiss ◽  
Julia L. Ferguson ◽  
Justin B. Leaf ◽  
Thomas Zane ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Observational Learning ◽  
Food Preferences ◽  
Autism Spectrum ◽  
Food Selectivity ◽  
Spectrum Disorder ◽  
Simple Task ◽  
Learning Procedure ◽  
Reversal Design ◽  
Selection Of

Research addressing food selectivity has involved intrusive techniques such as escape extinction. It is possible that observational learning methods employed in previous studies could provide the desired results with respect to food selectivity without the need for invasive physical interventions. The purpose of this study was to evaluate the effectiveness of an observational learning procedure on the selection of food items of three children diagnosed with autism spectrum disorder. Baseline consisted of a simple task after which a choice was presented between high- and low-preferred foods. The intervention consisted of observing an adult engage in the same task and selecting the low-preferred food while making favorable comments and engaging with the food in novel ways. The results of a reversal design demonstrated that selection of the low-preferred food only occurred following the introduction of the intervention, and all three participants engaged in flexible responding as a result of the intervention.

scholarly journals Boosted Mixture of Experts: An Ensemble Learning Scheme

1999 ◽  
Vol 11 (2) ◽  
pp. 483-497 ◽  
Author(s):  
Ran Avnimelech ◽  
Nathan Intrator
Keyword(s):  
Recognition Task ◽  
Mixture Of Experts ◽  
Data Set ◽  
Combination Model ◽  
Digit Recognition ◽  
Learning Procedure ◽  
Nist Database ◽  
Boosting Algorithm ◽  
Regression Problems ◽  
Classification And Regression

We present a new supervised learning procedure for ensemble machines, in which outputs of predictors, trained on different distributions, are combined by a dynamic classifier combination model. This procedure may be viewed as either a version of mixture of experts (Jacobs, Jordan, Nowlan, & Hinton, 1991), applied to classification, or a variant of the boosting algorithm (Schapire, 1990). As a variant of the mixture of experts, it can be made appropriate for general classification and regression problems by initializing the partition of the data set to different experts in a boostlike manner. If viewed as a variant of the boosting algorithm, its main gain is the use of a dynamic combination model for the outputs of the networks. Results are demonstrated on a synthetic example and a digit recognition task from the NIST database and compared with classical ensemble approaches.

scholarly journals How long was the nineteenth century?

2000 ◽  
Vol 16 (spe) ◽  
pp. 81-98
Author(s):  
David LIGHTFOOT
Keyword(s):  
Nineteenth Century ◽  
Twentieth Century ◽  
Language Change ◽  
Learning Procedure ◽  
The Way

This paper reviews the problems of the deterministic and predictive view of language change initiated by nineteenth century linguists and shows that such a view is still present in many analyses proposed by twentieth century linguists. As an alternative to such a view, the paper discusses an approach along the lines of Niyogi and Berwick (1997), which takes the explanation for long-term tendencies to be a function of the architecture of UG and the learning procedure and of the way in which populations of speakers behave.

scholarly journals Implementing Project-Based Experiential Learning in Post-Graduate Studies in Greece: A Case Study

2016 ◽  
Vol 7 (1) ◽  
pp. 31
Author(s):  
Elissavet Karageorgou ◽  
Konstantina Koutrouba
Keyword(s):  
Relevant Information ◽  
Project Based Learning ◽  
Learning Material ◽  
Academic Needs ◽  
Graduate Studies ◽  
Innovative Teaching ◽  
University Teachers ◽  
Self Confidence ◽  
Learning Procedure ◽  
Postgraduate Studies

The present questionnaire-based study examines the outcomes of project-based learning procedures in Greek University postgraduate classes, where the project entitled “Traffic Signs” takes place. Master in Education students at Harokopio University provided relevant information by answering a set of close-ended questions specifically designed for the research. Data elaboration and statistical analysis were performed. The results of the study showed that, according to MEd students, the teachers’ role during the carrying-out of the project remains crucial, since s/he establishes the rules of communication, defines the objectives, simplifies the learning material and intervenes in a supportive way to strengthen students’ cognitive background and self-confidence, to overcome setbacks and facilitate constructive cooperation. The research also showed that as long as projects’ implementation during postgraduate studies are well-designed, attractive and demanding regarding high-ranked cognitive and socio-affective abilities, they meet satisfactorily students’ academic needs and expectations and refresh, deepen and expand the positive outcomes of the learning procedure even in scientific domains where very often University teachers tend to avoid the use of more innovative teaching methods.

Generalization and Selection of Examples in Feedforward Neural Networks

2000 ◽  
Vol 12 (10) ◽  
pp. 2405-2426 ◽  
Author(s):  
Leonardo Franco ◽  
Sergio A. Cannas
Keyword(s):  
Analytical Calculation ◽  
Feedforward Neural Networks ◽  
Global Learning ◽  
Parity Problem ◽  
Learning Procedure ◽  
Minimum Number ◽  
Training Examples ◽  
General Architecture ◽  
Independent Criterion ◽  
Selection Of

In this work, we study how the selection of examples affects the learning procedure in a boolean neural network and its relationship with the complexity of the function under study and its architecture. We analyze the generalization capacity for different target functions with particular architectures through an analytical calculation of the minimum number of examples needed to obtain full generalization (i.e., zero generalization error). The analysis of the training sets associated with such parameter leads us to propose a general architecture-independent criterion for selection of training examples. The criterion was checked through numerical simulations for various particular target functions with particular architectures, as well as for random target functions in a nonoverlapping receptive field perceptron. In all cases, the selection sampling criterion lead to an improvement in the generalization capacity compared with a pure random sampling. We also show that for the parity problem, one of the most used problems for testing learning algorithms, only the use of the whole set of examples ensures global learning in a depth two architecture. We show that this difficulty can be overcome by considering a tree-structured network of depth 2 log2(N) – 1.

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