How is the hypothesis space represented? Evidence from young children’s active search and predictions in a multiple-cue inference task.

2021 ◽  
Vol 57 (7) ◽  
pp. 1080-1093
Author(s):  
Angela Jones ◽  
Douglas B. Markant ◽  
Thorsten Pachur ◽  
Alison Gopnik ◽  
Azzurra Ruggeri
Keyword(s):  
Active Search ◽  
Hypothesis Space ◽  
Inference Task

scholarly journals How the hypothesis space is represented. Evidence from young children’s active search and predictions in a multiple-cue inference task

2020 ◽  
Author(s):  
Azzurra Ruggeri ◽  
Angela Jones ◽  
Thorsten Pachur ◽  
alison gopnik ◽  
Douglas Benjamin Markant
Keyword(s):  
Young Children ◽  
Computational Modeling ◽  
Space Structures ◽  
Relative Speed ◽  
Memory Aids ◽  
Hypothesis Space ◽  
High Level ◽  
The Relationship ◽  
Inference Task

To successfully navigate in an uncertain world, one has to learn the relationship between cues (e.g., symptoms) and an outcome (e.g., disease). During this learning, it is sometimes possible to actively manipulate the cue values, allowing one to test hypotheses about this relationship directly. Across two studies, we investigated how 5- to 7-year-olds select cue configurations when learning cue-outcome relationships and how they are guided by representations of the hypothesis space regarding these relationships. In our task, children selected which monster pairs to see running in a race, allowing them to learn how two cues (the color and shape of monsters) predicted the relative speed of the monsters; subsequently, they made predictions about the speed of new monsters. Using computational modeling, we compared several models in their ability to capture children’s responses. We found that young children’s search was most consistent with a model that assumed reliance on a hypothesis space represented in terms of the relative speed of individual monsters. However, when memory aids were provided during search, 7-year-olds were best described by a model that assumes reliance on a more efficient, high-level representation that organizes the hypothesis space based on abstracted cue-outcome relationships. Our results highlight the guiding role of hypothesis-space representations for search during learning, suggest that young children already spontaneously abstract hypothesis-space representations, and provide the first evidence for a shift between search and test in terms of the hypothesis-space structures on which children rely when navigating in an uncertain world.

Evaluation of relational reasoning by a transitive inference task in attention-deficit/hyperactivity disorder.

2017 ◽  
Vol 31 (2) ◽  
pp. 200-208 ◽  
Author(s):  
Emiliano Brunamonti ◽  
Floriana Costanzo ◽  
Anna Mammì ◽  
Cristina Rufini ◽  
Diletta Veneziani ◽  
...  
Keyword(s):  
Attention Deficit Hyperactivity Disorder ◽  
Attention Deficit ◽  
Transitive Inference ◽  
Relational Reasoning ◽  
Hyperactivity Disorder ◽  
Inference Task

scholarly journals Extending Bayesian concept learning to deal with representational complexity and adaptation

2001 ◽  
Vol 24 (4) ◽  
pp. 685-686
Author(s):  
Michael D. Lee
Keyword(s):  
Concept Learning ◽  
Learning Model ◽  
Fundamental Issue ◽  
Complexity Measures ◽  
Stimulus Representation ◽  
Hypothesis Space ◽  
Space Approach

While Tenenbaum and Griffiths impressively consolidate and extend Shepard's research in the areas of stimulus representation and generalization, there is a need for complexity measures to be developed to control the flexibility of their “hypothesis space” approach to representation. It may also be possible to extend their concept learning model to consider the fundamental issue of representational adaptation. [Tenenbaum & Griffiths]

scholarly journals A Simpler Approach to Coefficient Regularized Support Vector Machines Regression

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Hongzhi Tong ◽  
Di-Rong Chen ◽  
Fenghong Yang
Keyword(s):  
Support Vector Machines ◽  
Convergence Analysis ◽  
Learning Rate ◽  
Support Vector ◽  
Explicit Learning ◽  
Stepping Stone ◽  
Mild Conditions ◽  
Hypothesis Space ◽  
Vector Machines

We consider a kind of support vector machines regression (SVMR) algorithms associated withlq  (1≤q<∞)coefficient-based regularization and data-dependent hypothesis space. Compared with former literature, we provide here a simpler convergence analysis for those algorithms. The novelty of our analysis lies in the estimation of the hypothesis error, which is implemented by setting a stepping stone between the coefficient regularized SVMR and the classical SVMR. An explicit learning rate is then derived under very mild conditions.

Hypothesis Space

2013 ◽  
pp. 931-931
Author(s):  
Eyke Hüllermeier ◽  
Thomas Fober ◽  
Marco Mernberger
Keyword(s):  
Hypothesis Space
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