scholarly journals Explanation Supports Hypothesis Generation in Learning

2020 ◽  
Author(s):  
Erik Brockbank ◽  
Caren Walker
Keyword(s):  
Prior Knowledge ◽  
Learning Process ◽  
Category Learning ◽  
Large Body ◽  
Learning Task ◽  
Good Explanation ◽  
Hypothesis Generation ◽  
Cognitive Resources ◽  
Correct Hypothesis ◽  
Hypothesis Evaluation

A large body of research has shown that engaging in explanation improves learning across a range of tasks. The act of explaining has been proposed to draw attention and cognitive resources toward evidence that will support a good explanation—information that is broad, abstract, and consistent with prior knowledge—which in turn aids discovery and generalization. However, it remains unclear whether explanation acts on the learning process via improved hypothesis generation, increasing the probability that the correct hypothesis is considered in the first place, or hypothesis evaluation, the appraisal of the correct hypothesis in light of evidence. In the present study, we address this question by separating the hypothesis generation and evaluation processes in a novel category learning task and quantifying the effect of explanation on each process independently. We find that explanation supports the generation of broad and abstract hypotheses but has less effect on the evaluation of hypotheses.

Category Learning in Schoolchildren. Its Relation to Age, Academic Marks and Resolution Patterns

2019 ◽  
Vol 22 ◽  
Author(s):  
Cristina Casadevante ◽  
Miriam Romero ◽  
Tatiana Fernández-Marcos ◽  
José Manuel Hernández
Keyword(s):  
Young Children ◽  
Learning Process ◽  
Category Learning ◽  
Response Speed ◽  
Learning Task ◽  
Children's Behavior ◽  
Computerized Task ◽  
Learning Test ◽  
The Impact

Abstract The aim of this research was to study the learning process using an objective and computerized task. The performance of 466 schoolchildren aged between 6 and 11 in a category learning task, the Category Learning Test (CLT), was examined. The results showed evidence of category learning throughout the trials for the whole sample, F(7, 469) = 29.979, p <.001. In addition, categorization performance improved with age, H(2) = 48.475, p <.001. However, there were old children that struggled with the task and young children that performed very well. The ability to learn the categories was related to the children’s behavior when trying to solve the task: the response speed (r = –.217, p <.01) and the organization index (r = .247, p <.01). Nevertheless, performance in the task and academic marks were not related. We discuss the impact of these findings on the promotion and improvement of learning in schools: an intervention to promote slowness and organization might help some children to learn.

Good Things Come to Those Who Wait: Enhancement in a Rule-Based Category Learning Task After Social-Evaluative Stress

2014 ◽  
Author(s):  
Shawn Ell ◽  
Steve Hutchinson ◽  
Lauren Hawthorne ◽  
Lauren Szymula ◽  
Shannon K. McCoy
Keyword(s):  
Category Learning ◽  
Learning Task ◽  
Rule Based

The Eye Movements Differences During Category Learning Process Between High and Low Creativity Students

2010 ◽  
Vol 42 (2) ◽  
pp. 251-261 ◽  
Author(s):  
Jian-Zhong WO ◽  
Wan-Ru CHEN ◽  
Yang LIU ◽  
Chong-De LIN
Keyword(s):  
Eye Movements ◽  
Learning Process ◽  
Category Learning

More evidence on competing cognitive systems

1985 ◽  
Vol 1 (1) ◽  
pp. 47-72 ◽  
Author(s):  
Sascha W. Felix
Keyword(s):  
Second Language ◽  
Problem Solving ◽  
General Problem ◽  
Learning Process ◽  
Native Speaker ◽  
Universal Grammar ◽  
Learning Task ◽  
Competition Model ◽  
Cognitive Systems ◽  
Specific System

This paper deals with the question of why adults, as a rule, fail to achieve native-speaker competence in a second language, whereas children appear to be generally able to acquire full command of either a first or second language. The Competition Model proposed in this paper accounts for this difference in terms of different cognitive systems or modules operating in child and adult language acquisition. It is argued that the child's learning process is guided by a language-specific module, roughly equivalent to Universal Grammar (cf. Chomsky, 1980), while adults tend to approach the learning task by utilizing a general problem-solving module which enters into competition with the language-specific system. The crucial evidence in support of the Competition Model comes from a) the availability of formal operations in different modules and b) from differences in the types of utterances produced by children and adults.

scholarly journals KoGuN: Accelerating Deep Reinforcement Learning via Integrating Human Suboptimal Knowledge

2020 ◽  
Author(s):  
Peng Zhang ◽  
Jianye Hao ◽  
Weixun Wang ◽  
Hongyao Tang ◽  
Yi Ma ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Prior Knowledge ◽  
Learning Process ◽  
Learning Algorithm ◽  
Fuzzy Rule ◽  
Policy Network ◽  
Human Knowledge ◽  
Learning Agents ◽  
The Common ◽  
Low Performance

Reinforcement learning agents usually learn from scratch, which requires a large number of interactions with the environment. This is quite different from the learning process of human. When faced with a new task, human naturally have the common sense and use the prior knowledge to derive an initial policy and guide the learning process afterwards. Although the prior knowledge may be not fully applicable to the new task, the learning process is significantly sped up since the initial policy ensures a quick-start of learning and intermediate guidance allows to avoid unnecessary exploration. Taking this inspiration, we propose knowledge guided policy network (KoGuN), a novel framework that combines human prior suboptimal knowledge with reinforcement learning. Our framework consists of a fuzzy rule controller to represent human knowledge and a refine module to finetune suboptimal prior knowledge. The proposed framework is end-to-end and can be combined with existing policy-based reinforcement learning algorithm. We conduct experiments on several control tasks. The empirical results show that our approach, which combines suboptimal human knowledge and RL, achieves significant improvement on learning efficiency of flat RL algorithms, even with very low-performance human prior knowledge.

Conditioned cough and urge-to-cough in a category learning task

2017 ◽  
Vol 129 ◽  
pp. 377
Author(s):  
Thomas Janssens ◽  
Lieven Dupont ◽  
Omer Van den Bergh
Keyword(s):  
Category Learning ◽  
Learning Task

An Examination of Different Performance Outcomes in an Analytical Procedures Task

2003 ◽  
Vol 22 (2) ◽  
pp. 219-235 ◽  
Author(s):  
Wendy J. Green ◽  
Ken T. Trotman
Keyword(s):  
Information Search ◽  
Performance Outcomes ◽  
Hypothesis Generation ◽  
Single Experiment ◽  
Search Patterns ◽  
Similar Information ◽  
Auditor Judgments ◽  
Analytical Procedures ◽  
Generation Information ◽  
Hypothesis Evaluation

In order to improve auditor judgments, it is first necessary to understand and evaluate what successful auditors do differently than those who are less successful. This study uses a computerized research instrument to examine in a single experiment the hypothesis generation, information search, hypothesis evaluation, and final judgment stages of the analytical procedures process. The inclusion of a criterion variable and the ability to search for additional evidence allows the study to examine in which of the various stages of analytical procedures auditors make less-than-optimal judgments. Of the 82 participants, 24 selected the correct cause, 19 never generated the correct cause as a hypothesis, and 39 generated the correct cause as a hypothesis but ended up not selecting it. The incorrect participants were divided into two categories: those who incorrectly selected the inherited hypothesis and those who incorrectly selected another self-generated non-error as the cause. The former group showed deficiencies in both information search and hypothesis evaluation compared to the correct group. The second incorrect group had similar information search patterns to the correct participants but had inferior hypothesis evaluation. These findings therefore lend support to the suggestion by Asare and Wright (2003) that not only is hypothesis generation important, but also information search and hypothesis evaluation are important.

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