scholarly journals Visual Statistical Learning is Modulated by Arbitrary and Natural Categories

2020 ◽  
Author(s):  
Leeland Rogers ◽  
Su Hyoun Park ◽  
Timothy J. Vickery
Keyword(s):  
Statistical Learning ◽  
Incidental Learning ◽  
Learning Goals ◽  
Target Pair ◽  
Trial And Error ◽  
Visual Statistical Learning ◽  
Natural Categories ◽  
Statistical Regularities ◽  
Familiar Stimuli ◽  
Better Than

Visual Statistical Learning (VSL) describes the unintentional extraction of statistical regularities from visual environments across time or space, and is typically studied using novel stimuli (e.g., symbols unfamiliar to participants) and using familiarization procedures that are passive or require only basic vigilance. The natural visual world, however, is rich with a variety of complex visual stimuli, and we experience that world in the presence of goal-driven behavior including overt learning of other kinds. To examine how VSL responds to such contexts, we exposed subjects to statistical contingencies as they learned arbitrary categorical mappings of unfamiliar stimuli (fractals, experiment 1) or familiar stimuli with preexisting categorical boundaries (faces and scenes, experiment 2). In a familiarization stage, subjects learned by trial-and-error the arbitrary mappings between stimuli and one of two responses. Unbeknownst to participants, items were paired such that they always appeared together in the stream. Pairs were equally likely to be same- or different-category. In a pair recognition stage to assess VSL, subjects chose between a target pair and a foil pair. In both experiments, subjects’ VSL was shaped by arbitrary categories: same-category pairs were learned better than different-category pairs. Natural categories (Experiment 2) also played a role, with subjects learning same natural category pairs at higher rates than different-category pairs, an effect that did not interact with arbitrary mappings. We conclude that learning goals of the observer and pre-existing knowledge about the structure of the world play powerful roles in the incidental learning of novel statistical information.

scholarly journals Reconsidering the Automaticity of Visual Statistical Learning

2019 ◽  
Author(s):  
Kevin D Himberger ◽  
Amy Finn ◽  
Christopher John Honey
Keyword(s):  
Response Time ◽  
Statistical Learning ◽  
Active Engagement ◽  
Indirect Response ◽  
Visual Statistical Learning ◽  
Incidental Exposure ◽  
Statistical Regularities ◽  
Forced Choice Task ◽  
Direct Measures ◽  
Response Time Measure

Statistical learning refers to the process of extracting regularities from the world without feedback. What are the necessary conditions for statistical learning to arise? It has been argued that visual statistical learning (VSL) is “automatic”, such that subjects will passively and even unconsciously extract statistical regularities from streams of visual input as long as they attend to the stimuli. In contrast, our data indicate that simply attending to stimuli is not, on its own, sufficient for learning. In Experiments 1 & 2, we provided incidental exposure to regularities in a stream of images and observed little to zero VSL across a range of conditions. In Experiment 3, we found that explicitly instructing participants to seek regularities dramatically improved their performance on direct measures of learning, but not on an indirect response time measure. Finally, in Experiments 4 & 5, we demonstrated that a methodological confound in prior work using the indirect response time measure could account for some previous evidence of automatic and implicit VSL.Overall, we found very little evidence of learning using direct measures of VSL, and no evidence of learning using an indirect response time measure. Participants who recognized visual sequence regularities in a forced-choice task could also often recreate the sequences when explicitly probed, indicating their knowledge was not entirely implicit. We suggest that some form of active engagement with stimuli may be needed to extract sequential regularities, and that VSL does not occur automatically.

Perceptual constraints on visual statistical learning of multielement scenes

2006 ◽  
Author(s):  
Christopher M. Conway ◽  
Robert L. Goldstone ◽  
Morten H. Christiansen
Keyword(s):  
Statistical Learning ◽  
Visual Statistical Learning

Prior real-world experience influences non-linguistic statistical learning

2020 ◽  
Author(s):  
Stephen Charles Van Hedger ◽  
Ingrid Johnsrude ◽  
Laura Batterink
Keyword(s):  
Statistical Learning ◽  
Real World ◽  
Language Experience ◽  
Pitch Range ◽  
Test Items ◽  
Instrument Measuring ◽  
Statistical Regularities ◽  
Context Free ◽  
Specific Learning

Listeners are adept at extracting regularities from the environment, a process known as statistical learning (SL). SL has been generally assumed to be a form of “context-free” learning that occurs independently of prior knowledge, and SL experiments typically involve exposing participants to presumed novel regularities, such as repeating nonsense words. However, recent work has called this assumption into question, demonstrating that learners’ previous language experience can considerably influence SL performance. In the present experiment, we tested whether previous knowledge also shapes SL in a non-linguistic domain, using a paradigm that involves extracting regularities over tone sequences. Participants learned novel tone sequences, which consisted of pitch intervals not typically found in Western music. For one group of participants, the tone sequences used artificial, computerized instrument sounds. For the other group, the same tone sequences used familiar instrument sounds (piano or violin). Knowledge of the statistical regularities was assessed using both trained sounds (measuring specific learning) and sounds that differed in pitch range and/or instrument (measuring transfer learning). In a follow-up experiment, two additional testing sessions were administered to gauge retention of learning (one day and approximately one-week post-training). Compared to artificial instruments, training on sequences played by familiar instruments resulted in reduced correlations among test items, reflecting more idiosyncratic performance. Across all three testing sessions, learning of novel regularities presented with familiar instruments was worse compared to unfamiliar instruments, suggesting that prior exposure to music produced by familiar instruments interfered with new sequence learning. Overall, these results demonstrate that real-world experience influences SL in a non-linguistic domain, supporting the view that SL involves the continuous updating of existing representations, rather than the establishment of entirely novel ones.

scholarly journals Making a Tough Choice: Teacher Target-Setting and Student Achievement in a Teacher Performance System Using Student Learning Objectives

AERA Open ◽  
2021 ◽  
Vol 7 ◽  
pp. 233285842097977
Author(s):  
Allison Atteberry ◽  
Sarah E. LaCour
Keyword(s):  
Student Learning ◽  
Test Scores ◽  
Teacher Performance ◽  
The United States ◽  
Learning Objectives ◽  
Learning Goals ◽  
Target Setting ◽  
Student Learning Objectives ◽  
Performance System ◽  
Better Than

The use of student learning objectives (SLOs) as part of teacher performance systems has gained traction quickly in the United States, yet little is known about how teachers select specific students’ learning goals. When teachers are evaluated—and sometimes compensated—based on whether their students meet the very objectives the teachers set at the start of the year, there may be an incentive to set low targets. SLO systems rely on teachers’ willingness and ability to set appropriately ambitious SLOs. We describe teachers’ SLO target-setting behavior in one school-district. We document the accuracy/ambitiousness of targets and find that teachers regularly set targets that students did not meet. We also find that, within the same year, a student’s spring test scores tend to be higher on the assessments for which they received higher targets. This raises the intriguing possibility that receiving higher targets might cause students to perform better than they otherwise would have.

scholarly journals Visual statistical learning in children and young adults: how implicit?

2015 ◽  
Vol 5 ◽  
Author(s):  
Julie Bertels ◽  
Emeline Boursain ◽  
Arnaud Destrebecqz ◽  
Vinciane Gaillard
Keyword(s):  
Young Adults ◽  
Statistical Learning ◽  
Visual Statistical Learning ◽  
Children And Young Adults
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