scholarly journals Cross-Situational Learning Is Supported by Propose-but-Verify Hypothesis Testing

2018 ◽  
Vol 28 (7) ◽  
pp. 1132-1136.e5 ◽  
Author(s):  
Sam C. Berens ◽  
Jessica S. Horst ◽  
Chris M. Bird
Author(s):  
Tanja C Roembke ◽  
Bob McMurray

AbstractIt is increasingly understood that people may learn new word/object mappings in part via a form of statistical learning in which they track co-occurrences between words and objects across situations (cross-situational learning). Multiple learning processes contribute to this, thought to reflect the simultaneous influence of real-time hypothesis testing and graduate learning. It is unclear how these processes interact, and if any require explicit cognitive resources. To manipulate the availability of working memory resources for explicit processing, participants completed a dual-task paradigm in which a cross-situational word-learning task was interleaved with a short-term memory task. We then used trial-by-trial analyses to estimate how different learning processes that play out simultaneously are impacted by resource availability. Critically, we found that the effect of hypothesis testing and gradual learning effects showed a small reduction under limited resources, and that the effect of memory load was not fully mediated by these processes. This suggests that neither is purely explicit, and there may be additional resource-dependent processes at play. Consistent with a hybrid account, these findings suggest that these two aspects of learning may reflect different aspects of a single system gated by attention, rather than competing learning systems.


2019 ◽  
Author(s):  
Yung Han Khoe ◽  
Amy Perfors ◽  
Andrew T Hendrickson

What mechanisms underlie people’s ability to use cross- situational statistics to learn the meanings of words? Here we present a large-scale evaluation of two major models of cross-situational learning: associative (Kachergis, Yu, & Shiffrin, 2012a) and hypothesis testing (Trueswell, Medina, Hafri, & Gleitman, 2013). We fit each model individually to over 1500 participants across seven experiments with a wide range of conditions. We find that the associative model better captures the full range of individual differences and conditions when learning is cross-situational, although the hypothesis testing approach outperforms it when there is no referential ambiguity during training.


PsycCRITIQUES ◽  
2012 ◽  
Vol 57 (4) ◽  
Author(s):  
David J. Pittenger
Keyword(s):  

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