scholarly journals What drives individual differences in statistical learning? The role of perceptual fluency and familiarity

2020 ◽  
Author(s):  
Andrew Perfors ◽  
Evan Kidd
Keyword(s):  
Individual Differences ◽  
Statistical Learning ◽  
Learning Task ◽  
Perceptual Processing ◽  
Perceptual Fluency ◽  
Learning Performance ◽  
Visual Modality ◽  
Stimulus Familiarity ◽  
Different Levels

Humans have the ability to learn surprisingly complicated statistical information in avariety of modalities and situations, often based on relatively little input. These statistical learning (SL) skills appear to underlie many kinds of learning, but despite their ubiquity, we still do not fully understand precisely what SL is and what individual differences on SL tasks reflect. Here we present experimental work suggesting that at least some individual differences arise from variation in perceptual fluency — the ability to rapidly or efficiently code and remember the stimuli that statistical learning occurs over – and that perceptual fluency is driven at least in part by stimulus familiarity: performance on a standard SL task varies substantially within the same (visual) modality as a function of whether the stimuli involved are familiar or not, independent of stimulus complexity. Moreover, we find that test-retest correlations of performance in a statistical learning task using stimuli of the same level of familiarity (but distinct items) are stronger than correlations across the same task with stimuli of different levels of familiarity. Finally, we demonstrate that statistical learning performance is predicted by an independent measure of stimulus-specific perceptual fluency that contains no statistical learning component at all. Our results suggest that a key component of statistical learning performance may be related to stimulus-specific perceptual processing and familiarity.

scholarly journals What drives individual differences in statistical learning? The role of perceptual fluency and familiarity

2018 ◽  
Author(s):  
Amy Perfors ◽  
Evan Kidd
Keyword(s):  
Individual Differences ◽  
Statistical Learning ◽  
Learning Task ◽  
Perceptual Fluency ◽  
Learning Performance ◽  
Visual Modality ◽  
Learning Skills ◽  
Domain Specific ◽  
Different Levels

Humans have the ability to learn surprisingly complicated statistical information in a variety of modalities and situations, often based on relatively little input. These statistical learning (SL) skills appear to underlie many kinds of learning, but despite their ubiquity, we still do not fully understand precisely what SL is and what individual differences on SL tasks reflect. Here we present experimental work suggesting that at least some individual differences arise from variation in perceptual fluency — the ability to rapidly or efficiently code and remember the stimuli that statistical learning occurs over. We show that performance on a standard SL task varies substantially within the same (visual) modality as a function of whether the stimuli involved are familiar or not, independent of stimulus complexity. Moreover, we find that test-retest correlations of performance in a statistical learning task using stimuli of the same level of familiarity (but distinct items) are stronger than correlations across the same task with different levels of familiarity. Finally, we demonstrate that statistical learning performance is predicted by an independent measure of stimulus-specific perceptual fluency which contains no statistical learning component at all. Our results suggest that a key component of SL performance may be unrelated to either domain-specific statistical learning skills or modality-specific perceptual processing.

scholarly journals Statistical learning in domestic chicks is modulated by strain and sex

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Chiara Santolin ◽  
Orsola Rosa-Salva ◽  
Bastien S. Lemaire ◽  
Lucia Regolin ◽  
Giorgio Vallortigara
Keyword(s):  
Sex Differences ◽  
Statistical Learning ◽  
Comparative Research ◽  
Learning Task ◽  
Human Infants ◽  
Sensory Inputs ◽  
Automated Tracking ◽  
Domestic Chicks ◽  
Continuous Stream

Abstract Statistical learning is a key mechanism for detecting regularities from a variety of sensory inputs. Precocial newborn domestic chicks provide an excellent model for (1) exploring unsupervised forms of statistical learning in a comparative perspective, and (2) elucidating the ecological function of statistical learning using imprinting procedures. Here we investigated the role of the sex of the chicks in modulating the direction of preference (for familiarity or novelty) in a visual statistical learning task already employed with chicks and human infants. Using both automated tracking and direct human coding, we confirmed chicks’ capacity to recognize the presence of a statistically defined structure underlying a continuous stream of shapes. Using a different chicken strain than previous studies, we were also able to highlight sex differences in chicks’ propensity to approach the familiar or novel sequence. This could also explain a previous failure to reveal statistical learning in chicks which sex was however not determined. Our study confirms chicks’ ability to track visual statistics. The pivotal role of sex in determining familiarity or novelty preferences in this species and the interaction with the animals’ strain highlight the importance to contextualize comparative research within the ecology of each species.

scholarly journals Cerebellar involvement in learning to balance a cart-pole system

2019 ◽  
Author(s):  
Nicolas Ludolph ◽  
Thomas M. Ernst ◽  
Oliver M. Mueller ◽  
Sophia L. Goericke ◽  
Martin A. Giese ◽  
...  
Keyword(s):  
Motor Learning ◽  
Skill Acquisition ◽  
Learning Task ◽  
Learning Performance ◽  
Control Mechanisms ◽  
Offline Learning ◽  
Cerebellar Involvement ◽  
Lesion Symptom Mapping ◽  
Pole System

ABSTRACTThe role of the cerebellum in error-based motor adaptation is well examined. In contrast, the involvement of the cerebellum in reward-based motor learning is less clear. In this study, we examined cerebellar involvement in a reward-based motor learning task, namely learning to control a virtual cart-pole system, over five consecutive days. Subjects with focal cerebellar lesions were compared to age-matched controls in terms of learning performance and underlying control mechanisms.Based on the overall balancing performance we have identified two subgroups of patients: (1) patients with learning performance comparable to healthy controls and (2) patients with decelerated learning, unsaturated learning progress after five days and decreased inter-manual transfer. Furthermore, we found that online learning is impaired while offline learning is partly preserved in cerebellar subjects. Regarding control mechanisms, decreased control performance was associated with impairments in predictive action timing.Voxel-wise lesion symptom mapping based on the two subgroups revealed strong associations between impairments in controlling the virtual cart-pole system and lesions in intermediate and lateral parts of lobules V and VI. These results together with previous reports suggest that the ability to predict the dynamics of the cart-pole system is an important factor for the reward-based skill acquisition process.

scholarly journals Linking the neural basis of hierarchical prediction with statistical learning: The paradox of attention

2021 ◽  
Author(s):  
Julie M. Schneider ◽  
Yi-Lun Weng ◽  
Anqi Hu ◽  
Zhenghan Qi
Keyword(s):  
Statistical Learning ◽  
Learning Task ◽  
Prediction Errors ◽  
Neural Basis ◽  
Attentional Shift ◽  
Cortical Responses ◽  
Distributional Information ◽  
Segmentation Task ◽  
Dependent Learning

Statistical learning, the process of tracking distributional information and discovering embedded patterns, is traditionally regarded as a form of implicit learning. However, recent studies proposed that both implicit (attention-independent) and explicit (attention-dependent) learning systems are involved in statistical learning. To understand the role of attention in statistical learning, the current study investigates the cortical processing of prediction errors in speech based on either local or global distributional information. We then ask how these cortical responses relate to statistical learning behavior in a word segmentation task. We found ERP evidence of pre-attentive processing of both the local (mismatching negativity) and global distributional information (late discriminative negativity). However, as speech elements became less frequent and more surprising, some participants showed an involuntary attentional shift, reflected in a P3a response. Individuals who displayed attentive neural tracking of distributional information showed faster learning in a speech statistical learning task. These results provide important neural evidence elucidating the facilitatory role of attention in statistical learning.

scholarly journals When less is more: Enhanced statistical learning of non-adjacent dependencies after disruption of bilateral DLPFC

2017 ◽  
Author(s):  
Géza Gergely Ambrus ◽  
Teodóra Vékony ◽  
Karolina Janacsek ◽  
Anna B. C. Trimborn ◽  
Gyula Kovács ◽  
...  
Keyword(s):  
Statistical Learning ◽  
Learning Performance ◽  
Less Is More ◽  
Prefrontal Cortical ◽  
Model Free ◽  
Dorsolateral Prefrontal ◽  
Statistical Regularities ◽  
Left And Right ◽  
Qualitative Changes

AbstractBrain networks related to human learning can interact in cooperative but also competitive ways to optimize performance. The investigation of such interactive processes is rare in research on learning and memory. Previous studies have shown that manipulations reducing the engagement of prefrontal cortical areas could lead to improved statistical learning performance. However, no study has investigated how disruption of the dorsolateral prefrontal cortex (DLPFC) affects the acquisition and consolidation of non-adjacent second-order dependencies. The present study aimed to test the role of the DLPFC, more specifically, the Brodmann 9 area in implicit temporal statistical learning of non-adjacent dependencies. We applied 1 Hz inhibitory transcranial magnetic stimulation or sham stimulation over both the left and right DLPFC intermittently during the learning. The DLPFC-stimulated group showed better performance compared to the sham group after a 24-hour consolidation period. This finding suggests that the disruption of DLPFC during learning induces qualitative changes in the consolidation of non-adjacent statistical regularities. A possible mechanism behind this result is that the stimulation of the DLPFC promotes a shift to model-free learning by weakening the access to model-based processes.

scholarly journals Integrating When and What Information in the Left Parietal Lobe Allows Language Rule Generalization

2019 ◽  
Author(s):  
J Orpella ◽  
P Ripollés ◽  
M Ruzzoli ◽  
JL Amengual ◽  
A Callejas ◽  
...  
Keyword(s):  
Statistical Learning ◽  
Incidental Learning ◽  
Parietal Lobe ◽  
Rule Learning ◽  
Learning Task ◽  
Learning Performance ◽  
Directed Attention ◽  
Linguistic Rules ◽  
Abstract Rule ◽  
Statistical Relationships

AbstractA crucial aspect when learning a language is discovering the rules that govern how words are combined in order to convey meanings. Since rules are characterized by sequential co-occurrences between elements (e.g. ‘These cupcakes are unbelievable’), tracking the statistical relationships between these elements is fundamental. However, statistical learning alone cannot fully account for the ability to create abstract rule representations that can be generalized, a paramount requirement of linguistic rules. Here, we provide evidence that, after the statistical relations between words have been extracted, the engagement of goal-directed attention is key to enable rule generalization. Incidental learning performance during a rule-learning task on an artificial language revealed a progressive shift from statistical learning to goal-directed attention. In addition, and consistent with the recruitment of attention, fMRI analyses of late learning stages showed left parietal activity within a broad bilateral dorsal fronto-parietal network. Critically, rTMS on participants’ peak of activation within the left parietal cortex impaired their ability to generalize learned rules to a structurally analogous new language. No stimulation or rTMS on a non-relevant brain region did not have the same interfering effect on generalization. Performance on an additional attentional task showed that rTMS on the same parietal site hindered participants’ ability to integrate what (stimulus identity) and when (stimulus timing) information about an expected target. The present findings suggest that learning rules from speech is a two-stage process: following statistical learning, goal-directed attention –involving left parietal regions– integrates what and when stimulus information to facilitate rapid rule generalization.

scholarly journals Linguistic entrenchment: prior knowledge impacts statistical learning performance

2019 ◽  
Author(s):  
Noam Siegelman ◽  
Louisa Bogaerts ◽  
Amit Elazar ◽  
Joanne Arciuli ◽  
Ram Frost
Keyword(s):  
Prior Knowledge ◽  
Statistical Learning ◽  
Native Language ◽  
Learning Task ◽  
Individual Performance ◽  
Learning Performance ◽  
General Mechanism ◽  
Verbal Tasks ◽  
Statistical Regularities ◽  
Fourth Experiment

Statistical Learning (SL) is typically considered to be a domain-general mechanism by which cognitive systems discover the underlying statistical regularities in the input. Recent findings, however, show clear differences in processing regularities across modalities and stimuli as well as low correlations between performance on visual and auditory tasks. Why does a presumably domain-general mechanism show distinct patterns of modality and stimulus specificity? Here we claim that the key to this puzzle lies in the prior knowledge brought upon by learners to the learning task. Specifically, we argue that learners’ already entrenched expectations about speech co-occurrences from their native language impacts what they learn from novel auditory verbal input. In contrast, learners are free of such entrenchment when processing sequences of visual material such as abstract shapes. We present evidence from three experiments supporting this hypothesis by showing that auditory-verbal tasks display distinct item-specific effects resulting in low correlations between test items. In contrast, non-verbal tasks – visual and auditory – show high correlations between items. Importantly, we also show that individual performance in visual and auditory SL tasks that do not implicate prior knowledge regarding co-occurrence of elements, is highly correlated. In a fourth experiment, we present further support for the entrenchment hypothesis by showing that the variance in performance between different stimuli in auditory-verbal statistical learning tasks can be traced back to their resemblance to participants' native language. We discuss the methodological and theoretical implications of these findings, focusing on models of domain generality/specificity of SL.

scholarly journals How statistical learning interacts with the socioeconomic environment to shape children’s language development

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0244954
Author(s):  
Leyla Eghbalzad ◽  
Joanne A. Deocampo ◽  
Christopher M. Conway
Keyword(s):  
Socioeconomic Status ◽  
Language Development ◽  
Statistical Learning ◽  
Event Related Potentials ◽  
Learning Task ◽  
Learning Ability ◽  
Learning Performance ◽  
Learning Abilities ◽  
Related Potentials ◽  
Social Environmental

Language is acquired in part through statistical learning abilities that encode environmental regularities. Language development is also heavily influenced by social environmental factors such as socioeconomic status. However, it is unknown to what extent statistical learning interacts with SES to affect language outcomes. We measured event-related potentials in 26 children aged 8–12 while they performed a visual statistical learning task. Regression analyses indicated that children’s learning performance moderated the relationship between socioeconomic status and both syntactic and vocabulary language comprehension scores. For children demonstrating high learning, socioeconomic status had a weaker effect on language compared to children showing low learning. These results suggest that high statistical learning ability can provide a buffer against the disadvantages associated with being raised in a lower socioeconomic status household.

scholarly journals Integrating when and what information in the left parietal lobe allows language rule generalization

PLoS Biology ◽  
2020 ◽  
Vol 18 (11) ◽  
pp. e3000895
Author(s):  
Joan Orpella ◽  
Pablo Ripollés ◽  
Manuela Ruzzoli ◽  
Julià L. Amengual ◽  
Alicia Callejas ◽  
...  
Keyword(s):  
Statistical Learning ◽  
Incidental Learning ◽  
Parietal Lobe ◽  
Repetitive Transcranial Magnetic Stimulation ◽  
Rule Learning ◽  
Learning Task ◽  
Learning Performance ◽  
Directed Attention ◽  
Frontoparietal Network ◽  
Linguistic Rules

A crucial aspect when learning a language is discovering the rules that govern how words are combined in order to convey meanings. Because rules are characterized by sequential co-occurrences between elements (e.g., “These cupcakes are unbelievable”), tracking the statistical relationships between these elements is fundamental. However, purely bottom-up statistical learning alone cannot fully account for the ability to create abstract rule representations that can be generalized, a paramount requirement of linguistic rules. Here, we provide evidence that, after the statistical relations between words have been extracted, the engagement of goal-directed attention is key to enable rule generalization. Incidental learning performance during a rule-learning task on an artificial language revealed a progressive shift from statistical learning to goal-directed attention. In addition, and consistent with the recruitment of attention, functional MRI (fMRI) analyses of late learning stages showed left parietal activity within a broad bilateral dorsal frontoparietal network. Critically, repetitive transcranial magnetic stimulation (rTMS) on participants’ peak of activation within the left parietal cortex impaired their ability to generalize learned rules to a structurally analogous new language. No stimulation or rTMS on a nonrelevant brain region did not have the same interfering effect on generalization. Performance on an additional attentional task showed that this rTMS on the parietal site hindered participants’ ability to integrate “what” (stimulus identity) and “when” (stimulus timing) information about an expected target. The present findings suggest that learning rules from speech is a two-stage process: following statistical learning, goal-directed attention—involving left parietal regions—integrates “what” and “when” stimulus information to facilitate rapid rule generalization.

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