Delta-band neural tracking primarily reflects rule-based chunking instead of semantic relatedness between words.

It is debated whether cortical responses matching the time scales of phrases and sentences mediate the mental construction of the syntactic chunks or are simply caused by the semantic properties of words. Here, we investigate to what extent delta-band neural responses to speech can be explained by semantic relatedness between words. To dissociate the contribution of semantic relatedness from sentential structures, participants listened to sentence sequences and paired-word sequences in which semantically related words repeated at 1 Hz. Semantic relatedness in the 2 types of sequences was quantified using a word2vec model that captured the semantic relation between words without considering sentential structure. The word2vec model predicted comparable 1-Hz responses with paired-word sequences and sentence sequences. However, empirical neural activity, recorded using magnetoencephalography, showed a weaker 1-Hz response to paired-word sequences than sentence sequences in a word-level task that did not require sentential processing. Furthermore, when listeners applied a task-related rule to parse paired-word sequences into multi-word chunks, 1-Hz response was stronger than that in word-level task on the same sequences. Our results suggest that cortical activity tracks multi-word chunks constructed by either syntactic rules or task-related rules, whereas the semantic relatedness between words contributes only in a minor way.

Delta-band neural activity primarily tracks sentences instead of semantic properties of words.

Human language is generally combinatorial: Words are combined into sentences to flexibly convey meaning. How the brain represents sentences, however, remains debated. Recently, it has been shown that delta-band cortical activity correlates with the sentential structure of speech. It remains debated, however, whether delta-band cortical tracking of sentences truly reflects mental representations of sentences or is caused by neural encoding of semantic properties of individual words. The current study investigates whether delta-band neural tracking of speech can be explained by semantic properties of individual words. Cortical activity is recorded using electroencephalography (EEG) when participants listen to sentences repeating at 1 Hz and word lists. The semantic properties of individual words, simulated using a word2vec model, predict a stronger 1 Hz response to word lists than to sentences. When listeners perform a word-monitoring task that does not require sentential processing, the 1 Hz response to word lists, however, is much weaker than the 1 Hz response to sentences, contradicting the prediction of the lexical semantics model. When listeners are explicitly asked to parse word lists into multi-word chunks, however, cortical activity can reliably track the multi-word chunks. Taken together, these results suggest that delta-band neural responses to speech cannot be fully explained by the semantic properties of single words and are potentially related to the neural representation of multi-word chunks.

θ-Band Cortical Tracking of the Speech Envelope Shows the Linear Phase Property.

When listening to speech, low-frequency cortical activity tracks the speech envelope. It remains controversial, however, whether such envelope-tracking neural activity reflects entrainment of neural oscillations or superposition of transient responses evoked by sound features. Recently, it is suggested that the phase of envelope-tracking activity can potentially distinguish entrained oscillations and evoked responses. Here, we analyze the phase of envelope-tracking in humans during passive listening, and observe that the phase lag between cortical activity and speech envelope tends to change linearly across frequency in the θ band (4-8 Hz), suggesting that the θ-band envelope-tracking activity can be readily modeled by evoked responses.

The influence of linguistic information on cortical tracking of words.

Speech is a complex sound sequence that has rich acoustic and linguistic structures. Recent studies have suggested that low-frequency cortical activity can track linguistic units in speech, such as words and phrases, on top of low-level acoustic features. Here, with an artificial word learning paradigm, we investigate how different aspects of linguistic information, e.g., phonological, semantic, and orthographic information, modulate cortical tracking of words. Participants are randomly assigned to the experimental group or the control group. Both groups listen to speech streams composed of trisyllabic artificial words or trisyllabic real words. Participants in the experimental group explicitly learn different types of linguistic information of artificial words (phonological, phonological + semantic, or phonological + orthographic information), while participants in the control group do not explicitly learn the words. Electroencephalographic (EEG) data from the control group reveal weaker cortical tracking of artificial words than real words. However, when comparing the experimental and control groups, we find that explicit learning significantly improves neural tracking of artificial words. After explicit learning, cortical tracking of artificial words is comparable to real words, regardless of the training conditions. These results suggest training facilitates neural tracking of words and emphasize the basic role phonological information played in sequential grouping.

Low-frequency neural activity reflects rule-based chunking during speech listening.

Chunking is a key mechanism for sequence processing. Studies on speech sequences have suggested low-frequency cortical activity tracks spoken phrases, that is, chunks of words defined by tacit linguistic knowledge. Here, we investigate whether low-frequency cortical activity reflects a general mechanism for sequence chunking and can track chunks defined by temporarily learned artificial rules. The experiment records magnetoencephalographic (MEG) responses to a sequence of spoken words. To dissociate word properties from the chunk structures, two tasks separately require listeners to group pairs of semantically similar or semantically dissimilar words into chunks. In the MEG spectrum, a clear response is observed at the chunk rate. More importantly, the chunk-rate response is task-dependent. It is phase locked to chunk boundaries, instead of the semantic relatedness between words. The results strongly suggest that cortical activity can track chunks constructed based on task-related rules and potentially reflects a general mechanism for chunk-level representations.

From digital personal assistants like Siri and Alexa to customer service chatbots, computers are slowly learning to talk to us. But as anyone who has interacted with them will appreciate, the results are often imperfect. Each time we speak or write, we use grammatical rules to combine words in a specific order. These rules enable us to produce new sentences that we have never seen or heard before, and to understand the sentences of others. But computer scientists adopt a different strategy when training computers to use language. Instead of grammar, they provide the computers with vast numbers of example sentences and phrases. The computers then use this input to calculate how likely for one word to follow another in a given context. "The sky is blue" is more common than "the sky is green", for example. But is it possible that the human brain also uses this approach? When we listen to speech, the brain shows patterns of activity that correspond to units such as sentences. But previous research has been unable to tell whether the brain is using grammatical rules to recognise sentences, or whether it relies on a probability-based approach like a computer. Using a simple artificial language, Jin et al. have now managed to tease apart these alternatives. Healthy volunteers listened to lists of words while lying inside a brain scanner. The volunteers had to group the words into pairs, otherwise known as chunks, by following various rules that simulated the grammatical rules present in natural languages. Crucially, the volunteers' brain activity tracked the chunks ­ which differed depending on which rule had been applied ­ rather than the individual words. This suggests that the brain processes speech using abstract rules instead of word probabilities. While computers are now much better at processing language, they still perform worse than people. Understanding how the human brain solves this task could ultimately help to improve the performance of personal digital assistants.

[The neural encoding of continuous speech - recent advances in EEG and MEG studies].

Speech comprehension is a central cognitive function of the human brain. In cognitive neuroscience, a fundamental question is to understand how neural activity encodes the acoustic properties of a continuous speech stream and resolves multiple levels of linguistic structures at the same time. This paper reviews the recently developed research paradigms that employ electroencephalography (EEG) or magnetoencephalography (MEG) to capture neural tracking of acoustic features or linguistic structures of continuous speech. This review focuses on two questions in speech processing: (1) The encoding of continuously changing acoustic properties of speech; (2) The representation of hierarchical linguistic units, including syllables, words, phrases and sentences. Studies have found that the low-frequency cortical activity tracks the speech envelope. In addition, the cortical activities on different time scales track multiple levels of linguistic units and constitute a representation of hierarchically organized linguistic units. The article reviewed these studies, which provided new insights into the processes of continuous speech in the human brain.

Auditory and language contributions to neural encoding of speech features in noisy environments.

Recognizing speech in noisy environments is a challenging task that involves both auditory and language mechanisms. Previous studies have demonstrated human auditory cortex can reliably track the temporal envelope of speech in noisy environments, which provides a plausible neural basis for noise-robust speech recognition. The current study aimed at teasing apart auditory and language contributions to noise-robust envelope tracking by comparing the neural responses of 2 groups of listeners, i.e., native listeners and foreign listeners who did not understand the testing language. In the experiment, speech signals were mixed with spectrally matched stationary noise at 4 intensity levels and listeners' neural responses were recorded using electroencephalography (EEG). When the noise intensity increased, the neural response gain increased in both groups of listeners, demonstrating auditory gain control. Language comprehension generally reduced the response gain and envelope-tracking precision, and modulated the spatial and temporal profile of envelope-tracking activity. Based on the spatio-temporal dynamics of envelope-tracking activity, a linear classifier can jointly decode the 2 listener groups and 4 levels of noise intensity. Altogether, the results showed that without feedback from language processing, auditory mechanisms such as gain control can lead to a noise-robust speech representation. High-level language processing modulated the spatio-temporal profile of the neural representation of speech envelope, instead of generally enhancing the envelope representation.

Eye activity tracks task-relevant structures during speech and auditory sequence perception.

The sensory and motor systems jointly contribute to complex behaviors, but whether motor systems are involved in high-order perceptual tasks such as speech and auditory comprehension remain debated. Here, we show that ocular muscle activity is synchronized to mentally constructed sentences during speech listening, in the absence of any sentence-related visual or prosodic cue. Ocular tracking of sentences is observed in the vertical electrooculogram (EOG), whether the eyes are open or closed, and in eye blinks measured by eyetracking. Critically, the phase of sentence-tracking ocular activity is strongly modulated by temporal attention, i.e., which word in a sentence is attended. Ocular activity also tracks high-level structures in non-linguistic auditory and visual sequences, and captures rapid fluctuations in temporal attention. Ocular tracking of non-visual rhythms possibly reflects global neural entrainment to task-relevant temporal structures across sensory and motor areas, which could serve to implement temporal attention and coordinate cortical networks.

Circular-polarization-sensitive metamaterial based on triple-quantum-dot molecules.

We propose a new type of chiral metamaterial based on an ensemble of artificial molecules formed by three identical quantum dots in a triangular arrangement. A static magnetic field oriented perpendicular to the plane breaks mirror symmetry, rendering the molecules sensitive to the circular polarization of light. By varying the orientation and magnitude of the magnetic field one can control the polarization and frequency of the emission spectrum. We identify a threshold frequency Ω, above which we find strong birefringence. In addition, Kerr rotation and circular-polarized lasing action can be implemented. We investigate the single-molecule lasing properties for different energy-level arrangements and demonstrate the possibility of circular-polarization conversion. Finally, we analyze the effect of weak stray electric fields or deviations from the equilateral triangular geometry.

Strong coupling of spin qubits to a transmission line resonator.

We propose a mechanism for coupling spin qubits formed in double quantum dots to a superconducting transmission line resonator. Coupling the resonator to the gate controlling the interdot tunneling creates a spin qubit-resonator interaction with a strength of tens of MHz. This mechanism allows operating the system at a symmetry point where decoherence due to charge noise is minimized. The transmission line can serve as the shuttle, allowing for fast two-qubit operations including the generation of qubit-qubit entanglement and the implementation of a controlled-phase gate.

Tunnelling spin current and spin diode behaviour in a bilayer system.

The coherent tunnelling spin current in the bilayer system with spin-orbit coupling is investigated. Based on the continuity-like equations, we discuss the definition of the tunnelling current and show that the overlaps between wavefunctions for different layers contribute to the tunnelling current. We study the linear response of the tunnelling spin current to an in-plane electric field in the presence of nonmagnetic impurities. The tunnelling spin conductivity we obtained presents a feature asymmetrical with respect to the gate voltage when the strengths of impurity potentials are different in each layer.