TY - JOUR
T1 - Statistical learning is constrained to less abstract patterns in complex sensory input (but not the least)
AU - Emberson, Lauren L.
AU - Rubinstein, Dani Y.
N1 - Funding Information:
We’d like to thank Drs. Dima Amso, Rick Dale, Jordan DeLong, David Field, Thomas Farmer, Gary Lupyan, Adele Goldberg, Elika Bergelson, Toben Mintz and three anonymous Reviewers for helpful conversations and/or comments on the manuscript. We’d also like to thank Esteban Buz and Dave Kleinschmidt for their (statistical) support. Thank you to Claire Schmidt, Andrew Webb, Joey Ciufo, Mary Marchetti, Haley Weaver and Camila Rivero for help with data collection and, in particular, we thank Dr. Michael Spivey for his support during initial data collection at Cornell University. This work is supported by a Canadian Institution of Health Research post-doctoral fellowship ( 201210MFE-290131-231192 ) and an NIH K99 award ( HD076166-01A1 ) to LLE.
Publisher Copyright:
© 2016 Elsevier B.V.
PY - 2016/8
Y1 - 2016/8
N2 - The influence of statistical information on behavior (either through learning or adaptation) is quickly becoming foundational to many domains of cognitive psychology and cognitive neuroscience, from language comprehension to visual development. We investigate a central problem impacting these diverse fields: when encountering input with rich statistical information, are there any constraints on learning? This paper examines learning outcomes when adult learners are given statistical information across multiple levels of abstraction simultaneously: from abstract, semantic categories of everyday objects to individual viewpoints on these objects. After revealing statistical learning of abstract, semantic categories with scrambled individual exemplars (Exp. 1), participants viewed pictures where the categories as well as the individual objects predicted picture order (e.g., bird1—dog1, bird2—dog2). Our findings suggest that participants preferentially encode the relationships between the individual objects, even in the presence of statistical regularities linking semantic categories (Exps. 2 and 3). In a final experiment we investigate whether learners are biased towards learning object-level regularities or simply construct the most detailed model given the data (and therefore best able to predict the specifics of the upcoming stimulus) by investigating whether participants preferentially learn from the statistical regularities linking individual snapshots of objects or the relationship between the objects themselves (e.g., bird_picture1—dog_picture1, bird_picture2—dog_picture2). We find that participants fail to learn the relationships between individual snapshots, suggesting a bias towards object-level statistical regularities as opposed to merely constructing the most complete model of the input. This work moves beyond the previous existence proofs that statistical learning is possible at both very high and very low levels of abstraction (categories vs. individual objects) and suggests that, at least with the current categories and type of learner, there are biases to pick up on statistical regularities between individual objects even when robust statistical information is present at other levels of abstraction. These findings speak directly to emerging theories about how systems supporting statistical learning and prediction operate in our structure-rich environments. Moreover, the theoretical implications of the current work across multiple domains of study is already clear: statistical learning cannot be assumed to be unconstrained even if statistical learning has previously been established at a given level of abstraction when that information is presented in isolation.
AB - The influence of statistical information on behavior (either through learning or adaptation) is quickly becoming foundational to many domains of cognitive psychology and cognitive neuroscience, from language comprehension to visual development. We investigate a central problem impacting these diverse fields: when encountering input with rich statistical information, are there any constraints on learning? This paper examines learning outcomes when adult learners are given statistical information across multiple levels of abstraction simultaneously: from abstract, semantic categories of everyday objects to individual viewpoints on these objects. After revealing statistical learning of abstract, semantic categories with scrambled individual exemplars (Exp. 1), participants viewed pictures where the categories as well as the individual objects predicted picture order (e.g., bird1—dog1, bird2—dog2). Our findings suggest that participants preferentially encode the relationships between the individual objects, even in the presence of statistical regularities linking semantic categories (Exps. 2 and 3). In a final experiment we investigate whether learners are biased towards learning object-level regularities or simply construct the most detailed model given the data (and therefore best able to predict the specifics of the upcoming stimulus) by investigating whether participants preferentially learn from the statistical regularities linking individual snapshots of objects or the relationship between the objects themselves (e.g., bird_picture1—dog_picture1, bird_picture2—dog_picture2). We find that participants fail to learn the relationships between individual snapshots, suggesting a bias towards object-level statistical regularities as opposed to merely constructing the most complete model of the input. This work moves beyond the previous existence proofs that statistical learning is possible at both very high and very low levels of abstraction (categories vs. individual objects) and suggests that, at least with the current categories and type of learner, there are biases to pick up on statistical regularities between individual objects even when robust statistical information is present at other levels of abstraction. These findings speak directly to emerging theories about how systems supporting statistical learning and prediction operate in our structure-rich environments. Moreover, the theoretical implications of the current work across multiple domains of study is already clear: statistical learning cannot be assumed to be unconstrained even if statistical learning has previously been established at a given level of abstraction when that information is presented in isolation.
KW - Categorization
KW - Cognitive development
KW - Implicit learning
KW - Learning
KW - Object perception
KW - Pattern recognition
KW - Perception
KW - Perceptual learning
KW - Psychology
KW - Statistical learning
KW - Vision
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U2 - 10.1016/j.cognition.2016.04.010
DO - 10.1016/j.cognition.2016.04.010
M3 - Article
C2 - 27139779
AN - SCOPUS:85014866313
SN - 0010-0277
VL - 153
SP - 63
EP - 78
JO - Cognition
JF - Cognition
ER -