Intracranial Entrainment Reveals Statistical Learning across Levels of Abstraction

Sherman, Brynn E.; Aljishi, Ayman; Graves, Kathryn N.; Quraishi, Imran H.; Sivaraju, Adithya; Damisah, Eyiyemisi; Turk‐Browne, Nicholas B.

doi:10.1162/jocn_a_02012

Cited by 6 publications

(4 citation statements)

References 55 publications

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“…I next asked whether a basic Hebbian learning model can explain periodic neural activity (e.g., Batterink, 2020; Batterink & Paller, 2017; Benjamin, Fló, Palu et al., 2023; Buiatti et al., 2009; Choi et al., 2020; Fló et al., 2022; Henin et al., 2021; Kabdebon et al., 2015; Moreau et al., 2022; Moser et al., 2021; Sherman et al., 2023), at least for the forgetting rates for which the network preferred words to part‐words. In a first analysis, I recorded the total network activation after each syllable in a word had been presented.…”

Section: Resultsmentioning

confidence: 99%

“…Strong evidence in favor of this possibility comes from electrophysiology, where rhythmic activity has been observed in response to statistically structured sequences. In the time domain, different authors have observed amplitude peaks around the boundaries of statistically defined words (e.g., Abla et al., 2008; Cunillera et al., 2006; Kudo et al., 2011; Sanders et al., 2002; Teinonen et al., 2009); in the frequency domain, a frequency response with a period of the word duration emerges as participants learn the statistical structure of the speech stream (e.g., Batterink, 2020; Batterink & Paller, 2017; Benjamin, Fló, Palu et al., 2023; Buiatti et al., 2009; Choi et al., 2020; Fló et al., 2022; Henin et al., 2021; Kabdebon et al., 2015; Moreau et al., 2022; Moser et al., 2021; Sherman et al., 2023).…”

Section: Discussionmentioning

confidence: 99%

“…If so, they should also show a neural rhythm with the same periodicity. While Buiatti et al (2009) detected such a rhythm only when words were separated by brief silences, later investigations found such rhythms in continuous sequences in adults (Batterink, 2020;Batterink & Paller, 2017; see also Moser et al, 2021 for a magneto-encephalography study and Henin et al, 2021;Sherman et al, 2023 for intracranial recordings), children (Moreau et al, 2022), infants (Choi et al, 2020;Kabdebon et al, 2015), and even newborns (Fló et al, 2022).…”

Section: Electrophysiological Correlates Of Statistical Learningmentioning

confidence: 99%

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Hebbian learning can explain rhythmic neural entrainment to statistical regularities

Endress

2024

Developmental Science

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In many domains, learners extract recurring units from continuous sequences. For example, in unknown languages, fluent speech is perceived as a continuous signal. Learners need to extract the underlying words from this continuous signal and then memorize them. One prominent candidate mechanism is statistical learning, whereby learners track how predictive syllables (or other items) are of one another. Syllables within the same word predict each other better than syllables straddling word boundaries. But does statistical learning lead to memories of the underlying words—or just to pairwise associations among syllables? Electrophysiological results provide the strongest evidence for the memory view. Electrophysiological responses can be time‐locked to statistical word boundaries (e.g., N400s) and show rhythmic activity with a periodicity of word durations. Here, I reproduce such results with a simple Hebbian network. When exposed to statistically structured syllable sequences (and when the underlying words are not excessively long), the network activation is rhythmic with the periodicity of a word duration and activation maxima on word‐final syllables. This is because word‐final syllables receive more excitation from earlier syllables with which they are associated than less predictable syllables that occur earlier in words. The network is also sensitive to information whose electrophysiological correlates were used to support the encoding of ordinal positions within words. Hebbian learning can thus explain rhythmic neural activity in statistical learning tasks without any memory representations of words. Learners might thus need to rely on cues beyond statistical associations to learn the words of their native language.Research Highlights Statistical learning may be utilized to identify recurring units in continuous sequences (e.g., words in fluent speech) but may not generate explicit memory for words. Exposure to statistically structured sequences leads to rhythmic activity with a period of the duration of the underlying units (e.g., words). I show that a memory‐less Hebbian network model can reproduce this rhythmic neural activity as well as putative encodings of ordinal positions observed in earlier research. Direct tests are needed to establish whether statistical learning leads to declarative memories for words.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Electrophysiological Correlates Of Statistical Learningmentioning

confidence: 99%

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Hebbian learning can explain rhythmic neural entrainment to statistical regularities

Endress

2024

Developmental Science

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“…While category representations can be found elsewhere in the brain (Mansouri et al, 2020), it is apparent that, across both tasks (Blank & Bayer, 2022;Mack et al, 2013) and methodologies (Sherman et al, 2023), specific and general representations of experience may form in parallel.…”

Section: Discussionmentioning

confidence: 99%

Successful generalization of conceptual knowledge after training to remember specific events

Houser,

Resnick,

Zeithamova

2023

Preprint

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Categorization involves grouping information to make inferences and support noveldecisions. In the laboratory, category training commonly involves trial-and-error whereparticipants are instructed to classify stimuli and learn through feedback. Here, wetested across two experiments whether people can acquire category knowledge in anincidental manner by associating category members with other information that itself isstructured. Subjects were trained to remember specific associations consisting ofcartoon animals paired with animal-specific background scenes. Animals presented onforest vs. mountain scenes were members of two prototype-based categories, but thiswas not conveyed to the participants. Spontaneous category learning was tested byasking participants to guess habitat (mountains, forests) for old and new cartoonanimals without feedback. The results showed that participants spontaneously acquiredcategory knowledge, showing high categorization accuracy for new animals,comparable to a group that underwent a traditional feedback-based classificationtraining with the same stimuli. Strategy analysis showed that the majority of participantsin both groups abstracted the central tendency of the categories, albeit a somewhatlarger proportion of subjects relied on memory for specific training exemplars afterpaired-associate learning than classification training. No evidence was found thatcategory learning happened at the expense of memory for specific animal-sceneassociations. Overall, the findings show that despite the goal to remember specificinformation that required differentiation of stimuli within categories, subjects canspontaneously acquire category knowledge, generalizable to novel stimuli in a waycomparable to traditional classification training. This work provides new insights intohow category learning can proceed under more naturalistic demands.

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Visual statistical learning alters low-dimensional cortical architecture

Rowchan,

Gale,

Nick

et al. 2023

Preprint

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Our ability to implicitly extract environmental regularities over time and use this information to make predictions about the world is central to human cognition and behavior. While prior work has established the role of individual brain regions in this statistical learning (SL) process, it remains poorly understood how these regions coordinate their activity with the rest of the brain to guide SL. Here, using functional MRI, we measured human brain activity during visual SL, whereby individuals implicitly learned to associate pairs of images that were covertly embedded in a larger sequence of images. By projecting patterns of cortical and subcortical functional connectivity onto a low-dimensional manifold space, we found that SL was characterized by the expansion of several cortical areas along the whole-brain manifold. This included regions in higher-order visual cortex and the dorsal attention (DAN) and default-mode networks (DMN), and reflected their increased segregation from other brain networks. Consistent with these neural changes being associated with SL, we found that once the paired image structure was interrupted, these same brain areas immediately contracted back towards their pre-learning positions along the manifold. Notably, during this phase we also found that several new areas, belonging to the DAN and DMN, exhibited significant manifold contraction, with a subset of regions actually contracting beyond their baseline, pre-learning manifold locations. This suggests a putative mechanism in higher-order cortex by which learned associations can be represented over longer time horizons. Together, these results offer a new perspective on the distributed changes in whole-brain activity during visual SL.

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Intracranial Entrainment Reveals Statistical Learning across Levels of Abstraction

Cited by 6 publications

References 55 publications

Hebbian learning can explain rhythmic neural entrainment to statistical regularities

Hebbian learning can explain rhythmic neural entrainment to statistical regularities

Successful generalization of conceptual knowledge after training to remember specific events

Visual statistical learning alters low-dimensional cortical architecture

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