Neural processes underlying statistical learning for speech segmentation in dogs

Animal cognition: Dogs build semantic expectations between spoken words and objects

A recent study has used scalp-recorded electroencephalography to obtain evidence of semantic processing of human speech and objects by domesticated dogs. The results suggest that dogs do comprehend the meaning of familiar spoken words, in that a word can evoke the mental representation of the object to which it refers.

Neural evidence for referential understanding of object words in dogs

Using words to refer to objects in the environment is a core feature of the human language faculty. Referential understanding assumes the formation of mental representations of these words.1,2 Such understanding of object words has not yet been demonstrated as a general capacity in any non-human species,3 despite multiple behavior-based case reports.4,5,6,7,8,9,10 In human event-related potential (ERP) studies, object word knowledge is typically tested using the semantic violation paradigm, where words are presented either with their referent (match) or another object (mismatch).11,12 Such mismatch elicits an N400 effect, a well-established neural correlate of semantic processing.12,13 Reports of preverbal infant N400 evoked by semantic violations14 assert the use of this paradigm to probe mental representations of object words in nonverbal populations. Here, measuring dogs' (Canis familiaris) ERPs to objects primed with matching or mismatching object words, we found a mismatch effect at a frontal electrode, with a latency (206-606 ms) comparable to the human N400. A greater difference for words that dogs knew better, according to owner reports, further supported a semantic interpretation of this effect. Semantic expectations emerged irrespective of vocabulary size, demonstrating the prevalence of referential understanding in dogs. These results provide the first neural evidence for object word knowledge in a non-human animal. VIDEO ABSTRACT.

Long-Horizon Associative Learning Explains Human Sensitivity to Statistical and Network Structures in Auditory Sequences

Networks are a useful mathematical tool for capturing the complexity of the world. In a previous behavioral study, we showed that human adults were sensitive to the high-level network structure underlying auditory sequences, even when presented with incomplete information. Their performance was best explained by a mathematical model compatible with associative learning principles, based on the integration of the transition probabilities between adjacent and nonadjacent elements with a memory decay. In the present study, we explored the neural correlates of this hypothesis via magnetoencephalography (MEG). Participants (N = 23, 16 females) passively listened to sequences of tones organized in a sparse community network structure comprising two communities. An early difference (∼150 ms) was observed in the brain responses to tone transitions with similar transition probability but occurring either within or between communities. This result implies a rapid and automatic encoding of the sequence structure. Using time-resolved decoding, we estimated the duration and overlap of the representation of each tone. The decoding performance exhibited exponential decay, resulting in a significant overlap between the representations of successive tones. Based on this extended decay profile, we estimated a long-horizon associative learning novelty index for each transition and found a correlation of this measure with the MEG signal. Overall, our study sheds light on the neural mechanisms underlying human sensitivity to network structures and highlights the potential role of Hebbian-like mechanisms in supporting learning at various temporal scales.

Rhythmically Modulating Neural Entrainment during Exposure to Regularities Influences Statistical Learning

The ability to discover regularities in the environment, such as syllable patterns in speech, is known as statistical learning. Previous studies have shown that statistical learning is accompanied by neural entrainment, in which neural activity temporally aligns with repeating patterns over time. However, it is unclear whether these rhythmic neural dynamics play a functional role in statistical learning or whether they largely reflect the downstream consequences of learning, such as the enhanced perception of learned words in speech. To better understand this issue, we manipulated participants' neural entrainment during statistical learning using continuous rhythmic visual stimulation. Participants were exposed to a speech stream of repeating nonsense words while viewing either (1) a visual stimulus with a "congruent" rhythm that aligned with the word structure, (2) a visual stimulus with an incongruent rhythm, or (3) a static visual stimulus. Statistical learning was subsequently measured using both an explicit and implicit test. Participants in the congruent condition showed a significant increase in neural entrainment over auditory regions at the relevant word frequency, over and above effects of passive volume conduction, indicating that visual stimulation successfully altered neural entrainment within relevant neural substrates. Critically, during the subsequent implicit test, participants in the congruent condition showed an enhanced ability to predict upcoming syllables and stronger neural phase synchronization to component words, suggesting that they had gained greater sensitivity to the statistical structure of the speech stream relative to the incongruent and static groups. This learning benefit could not be attributed to strategic processes, as participants were largely unaware of the contingencies between the visual stimulation and embedded words. These results indicate that manipulating neural entrainment during exposure to regularities influences statistical learning outcomes, suggesting that neural entrainment may functionally contribute to statistical learning. Our findings encourage future studies using non-invasive brain stimulation methods to further understand the role of entrainment in statistical learning.

Of words and whistles: Statistical learning operates similarly for identical sounds perceived as speech and non-speech

Statistical learning is an ability that allows individuals to effortlessly extract patterns from the environment, such as sound patterns in speech. Some prior evidence suggests that statistical learning operates more robustly for speech compared to non-speech stimuli, supporting the idea that humans are predisposed to learn language. However, any apparent statistical learning advantage for speech could be driven by signal acoustics, rather than the subjective perception per se of sounds as speech. To resolve this issue, the current study assessed whether there is a statistical learning advantage for ambiguous sounds that are subjectively perceived as speech-like compared to the same sounds perceived as non-speech, thereby controlling for acoustic features. We first induced participants to perceive sine-wave speech (SWS)-a degraded form of speech not immediately perceptible as speech-as either speech or non-speech. After this induction phase, participants were exposed to a continuous stream of repeating trisyllabic nonsense words, composed of SWS syllables, and then completed an explicit familiarity rating task and an implicit target detection task to assess learning. Critically, participants showed robust and equivalent performance on both measures, regardless of their subjective speech perception. In contrast, participants who perceived the SWS syllables as more speech-like showed better detection of individual syllables embedded in speech streams. These results suggest that speech perception facilitates processing of individual sounds, but not the ability to extract patterns across sounds. Our findings suggest that statistical learning is not influenced by the perceived linguistic relevance of sounds, and that it may be conceptualized largely as an automatic, stimulus-driven mechanism.

Central nodes of canine functional brain networks are concentrated in the cingulate gyrus

Compared to the field of human fMRI, knowledge about functional networks in dogs is scarce. In this paper, we present the first anatomically-defined ROI (region of interest) based functional network map of the companion dog brain. We scanned 33 awake dogs in a "task-free condition". Our trained subjects, similarly to humans, remain willingly motionless during scanning. Our goal is to provide a reference map with a current best estimate for the organisation of the cerebral cortex as measured by functional connectivity. The findings extend a previous spatial ICA (independent component analysis) study (Szabo et al. in Sci Rep 9(1):1.25. https://doi.org/10.1038/s41598-019-51752-2 , 2019), with the current study including (1) more subjects and (2) improved scanning protocol to avoid asymmetric lateral distortions. In dogs, similarly to humans (Sacca et al. in J Neurosci Methods. https://doi.org/10.1016/j.jneumeth.2021.109084 , 2021), ageing resulted in increasing framewise displacement (i.e. head motion) in the scanner. Despite the inherently different approaches between model-free ICA and model-based ROI, the resulting functional networks show a remarkable similarity. However, in the present study, we did not detect a designated auditory network. Instead, we identified two highly connected, lateralised multi-region networks extending to non-homotropic regions (Sylvian L, Sylvian R), including the respective auditory regions, together with the associative and sensorimotor cortices and the insular cortex. The attention and control networks were not split into two fully separated, dedicated networks. Overall, in dogs, fronto-parietal networks and hubs were less dominant than in humans, with the cingulate gyrus playing a central role. The current manuscript provides the first attempt to map whole-brain functional networks in dogs via a model-based approach.

Investigating responses to object-labels in the domestic dog (Canis familiaris)

Since the dawn of comparative cognitive research, dogs were suspected to possess some capacity for responding to human spoken language. Neuroimaging studies have supported the existence of relevant mechanisms, but convincing behavioral performance is rare, with only few exceptional dogs worldwide demonstrating a lexicon of object-labels they respond to. In the present study we aimed to investigate if and how a capacity for processing verbal stimuli is expressed in dogs (N = 20), whose alleged knowledge of verbal labels is only backed-up by owner reports taken at face value, and concerning only a few words (on average 5). Dogs were tested in a two-choice paradigm with familiar objects. The experiment was divided into a cue-control condition (objects visible to the owner vs. shielded by a panel, thereby controlling the owner's ability to emit cues to the dog) and a response type condition (fetching vs. looking). Above chance performance in fetching and looking at the named object emerged on the level of the sample as a whole. Only one individual performed reliably above chance, but the group-level effect did not depend on this data point. The presence of the panel also had no influence, which supports that performance was not driven by non-verbal cues from the owners. The group-level effect suggests that in typical dogs object-label learning is an instable process, either due to the animals primarily engaging in contextual learning or possibly analogous to the early stages of implicit, statistical learning of words in humans and opposed to the rapid mapping reported in exceptional dogs with larger passive vocabulary.

Acquiring Complex Communicative Systems: Statistical Learning of Language and Emotion

During the early postnatal years, most infants rapidly learn to understand two naturally evolved communication systems: language and emotion. While these two domains include different types of content knowledge, it is possible that similar learning processes subserve their acquisition. In this review, we compare the learnable statistical regularities in language and emotion input. We then consider how domain-general learning abilities may underly the acquisition of language and emotion, and how this process may be constrained in each domain. This comparative developmental approach can advance our understanding of how humans learn to communicate with others.

Sleeping neonates track transitional probabilities in speech but only retain the first syllable of words

Extracting statistical regularities from the environment is a primary learning mechanism that might support language acquisition. While it has been shown that infants are sensitive to transition probabilities between syllables in speech, it is still not known what information they encode. Here we used electrophysiology to study how full-term neonates process an artificial language constructed by randomly concatenating four pseudo-words and what information they retain after a few minutes of exposure. Neural entrainment served as a marker of the regularities the brain was tracking during learning. Then in a post-learning phase, evoked-related potentials (ERP) to different triplets explored which information was retained. After two minutes of familiarization with the artificial language, neural entrainment at the word rate emerged, demonstrating rapid learning of the regularities. ERPs in the test phase significantly differed between triplets starting or not with the correct first syllables, but no difference was associated with subsequent violations in transition probabilities. Thus, our results revealed a two-step learning process: neonates segmented the stream based on its statistical regularities, but memory encoding targeted during the word recognition phase entangled the ordinal position of the syllables but was still incomplete at that age.

Speech segmentation: New dogs, old tricks?

A new study using electroencephalography and functional magnetic resonance imaging suggests that dogs and humans may segment speech in similar ways.