Pitches that Wire Together Fire Together: Scale Degree Associations Across Time Predict Melodic Expectations

Verosky, Niels; Morgan, Emily

doi:10.1111/cogs.13037

Cited by 10 publications

(15 citation statements)

References 46 publications

(97 reference statements)

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“…We used an “off-the-shelf” implementation in which the model was pre-trained on a large corpus of Western melodies with the following parameters: learning rate = 0.001, batch size = 128, number of layers = 2 × 512 nodes, attention length = 40, dropout rate = 0.5. Internal model weights were optimized during training to maximize the probability mass assigned to the note occurring at t n+1 given the pitch and duration of previous notes from t 1:n .We used the “Attention” configuration of melodyRNN ( 34, 68 ), which enables the model to learn long-term dependencies characteristic of music that traditional Markov-based approaches fail to capture ( 69 ). For all phrases in the current stimulus set, melodyRNN was used to calculate the surprisal of each note, defined as the negative log probability of the note e that occurred at position i , given the MIDI pitch and duration (quantized to the nearest 16 th note) of preceding notes in the melody: We also computed the uncertainty of the melody at each timestep, which is defined as the entropy of the probability distribution over an alphabet of K possible notes: Surprisal and uncertainty are complementary measures in that the former indicates the extent to which an event deviated from preexisting expectations, while the latter conveys the specificity of those expectations in anticipation of the event.…”

Section: Methodsmentioning

confidence: 99%

Encoding of melody in the human auditory cortex

Sankaran,

Leonard,

Theunissen

et al. 2023

Preprint

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Melody is a core component of music in which discrete pitches are serially arranged to convey emotion and meaning. Perception of melody varies along several pitch-based dimensions: (1) the absolute pitch of notes, (2) the difference in pitch between successive notes, and (3) the higher-order statistical expectation of each note conditioned on its prior context. While humans readily perceive melody, how these dimensions are collectively represented in the brain and whether their encoding is specialized for music remains unknown. Here, we recorded high-density neurophysiological activity directly from the surface of human auditory cortex while Western participants listened to Western musical phrases. Pitch, pitch-change, and expectation were selectively encoded at different cortical sites, indicating a spatial code for representing distinct dimensions of melody. The same participants listened to spoken English, and we compared evoked responses to music and speech. Cortical sites selective for music were systematically driven by the encoding of expectation. In contrast, sites that encoded pitch and pitch-change used the same neural code to represent equivalent properties of speech. These findings reveal the multidimensional nature of melody encoding, consisting of both music-specific and domain-general sound representations in auditory cortex.

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Section: Methodsmentioning

confidence: 99%

Encoding of melody in the human auditory cortex

Sankaran,

Leonard,

Theunissen

et al. 2023

Preprint

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“…In contrast, statistical learning might well be important for predicting events across time (e.g., Endress & de Seyssel, under review; Morgan et al, 2019;Sherman & Turk-Browne, 2020;Turk-Browne et al, 2010;Verosky & Morgan, 2021) and space (Theeuwes et al, 2022), an ability that is clearly critical for mature language processing (e.g., Levy, 2008;Trueswell et al, 1999) as well as many other processes (e.g., Clark, 2013;Friston, 2010;Keller & Mrsic-Flogel, 2018).…”

Section: Discussionmentioning

confidence: 99%

“…In contrast, statistical learning might well be important for predicting events across time (e.g., Endress & de Seyssel, under review; Morgan et al., 2019; Sherman & Turk‐Browne, 2020; Turk‐Browne et al., 2010; Verosky & Morgan, 2021) and space (Theeuwes et al., 2022), an ability that is clearly critical for mature language processing (e.g., Levy, 2008; Trueswell et al., 1999) as well as many other processes (e.g., Clark, 2013; Friston, 2010; Keller & Mrsic‐Flogel, 2018). This suggests that predictive processing might also be crucial for word learning, but it is an important topic for further research to find out how predictive processing is used during language acquisition and which mechanisms are used for word segmentation.…”

Section: Discussionmentioning

confidence: 99%

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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“…In contrast, statistical learning might well be important for predicting events across time (e.g., Endress & de Seyssel, under review;Morgan, Fogel, Nair, & Patel, 2019;Sherman & Turk-Browne, 2020;Turk-Browne, Scholl, Johnson, & Chun, 2010;Verosky & Morgan, 2021) and space (Theeuwes, Bogaerts, & van Moorselaar, 2022), an ability that is clearly critical for mature language processing (e.g., Levy, 2008;Trueswell, Sekerina, Hill, & Logrip, 1999) (as well as many other processes; Clark, 2013;Friston, 2010;Keller & Mrsic-Flogel, 2018). This suggests that predictive processing might also be crucial for word learning, but it is an important topic for further research to find out how predictive processing is used during language acquisition.…”

Section: Discussionmentioning

confidence: 99%

Hebbian learning can explain rhythmic neural entrainment to statistical regularities

Endress¹

2023

Preprint

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In many domains, learners need to extract recurring units from continuous sequences composed of discrete units. For example, fluent speech is perceived as a continuous signal (at least in unknown languages). Learners need to extract the underlying words from this continuous signal and then memorize them. One prominent candidate mechanism tracks how predictive syllables(or other items) are of one another. Syllables within the same word are more predictive of one another than syllables straddling word boundaries. Behaviorally, however, it is controversial whether such statistical learning abilities lead to memories of the underlying units beyond pairwise associations among syllables. The strongest evidence for this possibility comes from evoked electrophysiological responses time-locked to word boundaries (e.g., N400’s) and rhythmic neural activity with a periodicity of word durations. Here, I reproduce such results with a simple Hebbian network. When exposed to statistically structured syllable sequences, the network activation is rhythmic with a periodicity of the word duration and an activation maximum on word-final syllables. This is because word-final syllables receive more excitatory input from earlier syllables with which they are associated than syllables that occur earlier in words (and are less predictable). Hebbian learning can thus explain rhythmic neural activity in statistical learning tasks without any memory representations of words. N400’s time-locked to word boundaries might thus index the reduced predictability of word-initial syllables rather than word-onsets. Learners might thus need to rely on other cues to learn the words of their native language.

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Pitches that Wire Together Fire Together: Scale Degree Associations Across Time Predict Melodic Expectations

Cited by 10 publications

References 46 publications

Encoding of melody in the human auditory cortex

Encoding of melody in the human auditory cortex

Hebbian learning can explain rhythmic neural entrainment to statistical regularities

Hebbian learning can explain rhythmic neural entrainment to statistical regularities

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