scholarly journals Effects of "chained" study on spontaneous relational discovery

2020 ◽  
Author(s):  
Douglas Benjamin Markant
Keyword(s):  
Prior Knowledge ◽  
Temporal Dynamics ◽  
Relational Learning ◽  
Transitive Inference ◽  
Relational Structure ◽  
Successive Presentation ◽  
Relational Schema

Prior knowledge of relational structure allows people to quickly make sense of and respond to new experiences. When awareness of such structure is not necessary to support learning, however, it is unclear when and why individuals “spontaneously discover” an underlying relational schema. The present study examines the determinants of such discovery in discrimination-based transitive inference (TI), whereby people learn about a hierarchy of interrelated premises and are tested on their ability to draw inferences that bridge studied associations. Experiencing “chained” sequences of overlapping premises during training was predicted to facilitate the discovery of relational structure. Among individuals without prior knowledge of the hierarchy, chaining improved relational learning and was most likely to result in explicit awareness of the underlying relations between items. These findings add to growing evidence that the temporal dynamics of training, including successive presentation of overlapping associations, are key to understanding spontaneous relational discovery during learning.

GaME Design for Intuitive Concept Knowledge

2009 ◽  
pp. 1104-1126 ◽  
Author(s):  
Debbie Denise Reese
Keyword(s):  
Mental Models ◽  
Prior Knowledge ◽  
Game Design ◽  
Relational Structure ◽  
Learning Science ◽  
Learning Tools ◽  
Instructional Games ◽  
Concept Knowledge ◽  
Powerful Learning ◽  
Intuitive Concept

Game-based, metaphor-enhanced (GaME) design is a process for engineering instructional games to prepare learners with the prior knowledge they need to learn later, more complex science concepts. The key step in the method is specifying a domain’s relational structure and then developing a game world based upon that structure. Reviewing relevant game design, cognitive science, and learning science theories, the author argues: (a) the need for GaME design; (b) that game worlds, complex concepts, and mental models are analogous systems; (c) how game-based technologies can provide a pragmatic and embodied context for making complex, introductory concepts intuitive; and (d) that the pragmatic, physical, and procedural aspects of games make them powerful learning tools that must be carefully designed. The author illustrates GaME design using Selene: A Lunar Creation GaME. Rigorous methods for design of instructional games will enhance control over learning outcomes.

Old ideas, new mistakes: All learning is relational

1997 ◽  
Vol 20 (1) ◽  
pp. 79-80
Author(s):  
Stellan Ohlsson
Keyword(s):  
Prior Knowledge ◽  
Relational Learning ◽  
Human Learning ◽  
Input Output ◽  
Learning Tasks ◽  
Sequence Effects

Learning is the acquisition of knowledge, not of input/output mappings. The distinction between statistical and relational learning, as Clark & Thornton define those terms, is not useful because all human learning is relational. However, prior knowledge does influence later learning and the sequence in which learning tasks are encountered is indeed crucial. Simulations of sequence effects would be interesting.

scholarly journals Asymmetric learning facilitates human inference of transitive relations

2021 ◽  
Author(s):  
Simon Ciranka ◽  
Juan Linde-Domingo ◽  
Ivan Padezhki ◽  
Clara Wicharz ◽  
Charley M Wu ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Learning Strategy ◽  
Cognitive Distortions ◽  
Relational Learning ◽  
Empirical Support ◽  
Transitive Inference ◽  
Learning Mechanisms ◽  
Previous Evidence ◽  
Asymmetric Learning ◽  
Intense Research

Humans and other animals are capable of inferring never-experienced relations (e.g., A>C) from other relational observations (e.g., A>B and B>C). The processes behind such transitive inference are subject to intense research. Here, we demonstrate a new aspect of relational learning, building on previous evidence that transitive inference can be accomplished through simple reinforcement learning mechanisms. We show in simulations that inference of novel relations benefits from an asymmetric learning policy, where observers update only their belief about the winner (or loser) in a pair. Across 4 experiments (n=145), we find substantial empirical support for such asymmetries in inferential learning. The learning policy favoured by our simulations and experiments gives rise to a compression of values which is routinely observed in psychophysics and behavioural economics. In other words, a seemingly biased learning strategy that yields well-known cognitive distortions can be beneficial for transitive inferential judgments.

GaME Design for Intuitive Concept Knowledge

2011 ◽  
pp. 474-496
Author(s):  
Debbie Denise Reese
Keyword(s):  
Mental Models ◽  
Prior Knowledge ◽  
Game Design ◽  
Relational Structure ◽  
Learning Science ◽  
Learning Tools ◽  
Instructional Games ◽  
Concept Knowledge ◽  
Powerful Learning ◽  
Intuitive Concept

Game-based, metaphor-enhanced (GaME) design is a process for engineering instructional games to prepare learners with the prior knowledge they need to learn later, more complex science concepts. The key step in the method is specifying a domain’s relational structure and then developing a game world based upon that structure. Reviewing relevant game design, cognitive science, and learning science theories, the author argues: (a) the need for GaME design; (b) that game worlds, complex concepts, and mental models are analogous systems; (c) how game-based technologies can provide a pragmatic and embodied context for making complex, introductory concepts intuitive; and (d) that the pragmatic, physical, and procedural aspects of games make them powerful learning tools that must be carefully designed. The author illustrates GaME design using Selene: A Lunar Creation GaME. Rigorous methods for design of instructional games will enhance control over learning outcomes.

scholarly journals Relational learning with and without awareness: Transitive inference using nonverbal stimuli in humans

2001 ◽  
Vol 29 (6) ◽  
pp. 893-902 ◽  
Author(s):  
Anthony J. Greene ◽  
Barbara A. Spellman ◽  
William B Levy ◽  
Jeffery A. Dusek ◽  
Howard B. Eichenbaum
Keyword(s):  
Relational Learning ◽  
Transitive Inference

Effects of Monetary Incentives on Task Switching

2012 ◽  
Vol 59 (4) ◽  
pp. 216-226 ◽  
Author(s):  
Thomas Kleinsorge ◽  
Gerhard Rinkenauer
Keyword(s):  
Task Switching ◽  
Temporal Dynamics ◽  
Reward Processing ◽  
General Level ◽  
Monetary Incentives ◽  
Switch Costs ◽  
Expectancy Effect ◽  
Reward Expectancy ◽  
Trial Basis

In two experiments, effects of incentives on task switching were investigated. Incentives were provided as a monetary bonus. In both experiments, the availability of a bonus varied on a trial-to-trial basis. The main difference between the experiments relates to the association of incentives to individual tasks. In Experiment 1, the association of incentives to individual tasks was fixed. Under these conditions, the effect of incentives was largely due to reward expectancy. Switch costs were reduced to statistical insignificance. This was true even with the task that was not associated with a bonus. In Experiment 2, there was a variable association of incentives to individual tasks. Under these conditions, the reward expectancy effect was bound to conditions with a well-established bonus-task association. In conditions in which the bonus-task association was not established in advance, enhanced performance of the bonus task was accompanied by performance decrements with the task that was not associated with a bonus. Reward expectancy affected mainly the general level of performance. The outcome of this study may also inform recently suggested neurobiological accounts about the temporal dynamics of reward processing.

Accessibility of Prior Knowledge and Susceptibility to Semantic Illusions

2012 ◽  
Author(s):  
Hillary G. Mullet ◽  
Sharda Umanath ◽  
Elizabeth J. Marsh
Keyword(s):  
Prior Knowledge
Sign in / Sign up

Export Citation Format

Share Document