Statistical learning as a window into developmental disabilities

Analyzing the Impact of Four Cognitive Constructs on Nonverbal Intelligence Test Performance: Implications for Children With Neurodevelopmental Disorders

PURPOSE

Children with neurodevelopmental disorders historically exhibit lower and more variable nonverbal intelligence (NVIQ) scores compared to their typically developing peers. We hypothesize that the intrinsic characteristics of the tests themselves, particularly the cognitive constructs they assess, may account for both the lower scores and variability across tests and over time. Using a qualitative content analysis approach, we examined the extent to which key cognitive constructs are engaged in NVIQ tests and how these constructs compare across different tests.

METHOD

Current editions of seven NVIQ tests were selected based on their relevance in clinical and research settings. Qualitative coding of constructs was developed iteratively by speech-language pathologists and researchers. The codes focused on cognitive domains most affected in highly prevalent neurodevelopmental conditions, including attention, receptive language, statistical learning, and working memory.

RESULTS

We identified multiple subfeatures for our constructs of interest. Using this coding framework, we found that NVIQ tests qualitatively differ in the extent to which these four constructs influence test performance.

CONCLUSIONS

Our findings suggest that understanding the impact of cognitive constructs on NVIQ tests can help explain why children with neurodevelopmental disorders exhibit lower and more unstable NVIQ scores compared to their peers. We provide recommendations for the use of NVIQ tests with neurodevelopmental disorder populations and encourage researchers and clinicians in speech and hearing sciences and psychology to use our results to inform test interpretation and selection.

SUPPLEMENTAL MATERIAL

https://doi.org/10.23641/asha.28886957.

Temporal dynamics of uncertainty and prediction error in musical improvisation across different periods

Human improvisational acts contain an innate individuality, derived from one's experiences based on epochal and cultural backgrounds. Musical improvisation, much like spontaneous speech, reveals intricate facets of the improviser's state of mind and emotional character. However, the specific musical components that reveal such individuality remain largely unexplored. Within the framework of human statistical learning and predictive processing, this study examined the temporal dynamics of uncertainty and surprise (prediction error) in a piece of musical improvisation. This cognitive process reconciles the raw auditory cues, such as melody and rhythm, with the musical predictive models shaped by its prior experiences. This study employed the Hierarchical Bayesian Statistical Learning (HBSL) model to analyze a corpus of 456 Jazz improvisations, spanning 1905 to 2009, from 78 distinct Jazz musicians. The results indicated distinctive temporal patterns of surprise and uncertainty, especially in pitch and pitch-rhythm sequences, revealing era-specific features from the early 20th to the 21st centuries. Conversely, rhythm sequences exhibited a consistent degree of uncertainty across eras. Further, the acoustic properties remain unchanged across different periods. These findings highlight the importance of how temporal dynamics of surprise and uncertainty in improvisational music change over periods, profoundly influencing the distinctive methodologies artists adopt for improvisation in each era. Further, it is suggested that the development of improvisational music can be attributed to the adaptive statistical learning mechanisms. This study explores the period-specific characteristics in the temporal dynamics of improvisational music, emphasizing how artists adapt their methods to resonate with the cultural and emotional contexts of their times. Such shifts in improvisational ways offer a window into understanding how artists intuitively respond and adapt their craft to resonate with the cultural zeitgeist and the emotional landscapes of their respective times.

Phonological syllables allow children with developmental dyslexia to access words

Learning to read is a middle-distance race for children worldwide. Most of them succeed in this acquisition with "normal" difficulties that ensue from the progressive (re)structuring of the phonological and orthographic systems. Evidence accumulated on reading difficulties in children with developmental dyslexia (DYS children, henceforth) shows a pervasive phonological deficit. However, the phonological deficit may not be due to degraded phonological representations but rather due to impaired access to them. This study focused on how and to what extent phonological syllables, which are essential reading units in French, were accessible to DYS children to segment and access words. We tested the assumption that DYS children did not strictly have pervasive degraded phonological representations but also have impaired access to phonological and orthographic representations. We administered a visually adapted word-spotting paradigm, engaging both sublexical processing and lexical access, with French native-speaking DYS children (N = 25; Mage in months = 121.6, SD = 3.0) compared with chronological age-matched peers (N = 25; Mage in months = 121.8, SD = 2.7; CA peers henceforth) and reading level-matched peers (N = 25; Mage in months = 94.0, SD = 4.6; RL peers henceforth). Although DYS children were slower and less accurate than CA and RL peers, we found that they used phonological syllables to access and segment words. However, they exhibit neither the classical inhibitory syllable frequency effect nor the lexical frequency effect, which is generally observed in typically developing children. Surprisingly, DYS children did not show strictly degraded phonological representations because they demonstrated phonological syllable-based segmentation abilities, particularly with high-frequency syllables. Their difficulties are rather interpreted in terms of impaired access to orthographic and phonological representations, which could be a direct effect of difficulties in generalizing and consolidating low-frequency syllables. We discuss these results regarding reading acquisition and the specificities of the French linguistic system.

Visual statistical learning in preverbal infants at a higher likelihood of autism and its association with later social communication skills

Visual statistical Learning (SL) allows infants to extract the statistical relationships embedded in a sequence of elements. SL plays a crucial role in language and communication competencies and has been found to be impacted in Autism Spectrum Disorder (ASD). This study aims to investigate visual SL in infants at higher likelihood of developing ASD (HL-ASD) and its predictive value on autistic-related traits at 24-36 months. At 6 months of age, SL was tested using a visual habituation task in HL-ASD and neurotypical (NT) infants. All infants were habituated to a visual sequence of shapes containing statistically predictable patterns. In the test phase, infants viewed the statistically structured, familiar sequence in alternation with a novel sequence that did not contain any statistical information. HL-ASD infants were then evaluated at 24-36 months to investigate the associations between visual SL and ASD-related traits. Our results showed that NT infants were able to learn the statistical structure embedded in the visual sequences, while HL-ASD infants showed different learning patterns. A regression analysis revealed that SL ability in 6-month-old HL-ASD infants was related to social communication and interaction abilities at 24-36 months of age. These findings indicate that early differences in learning visual statistical patterns might contribute to later social communication skills.

Temporal dynamics of statistical learning in children's song contributes to phase entrainment and production of novel information in multiple cultures

Statistical learning is thought to be linked to brain development. For example, statistical learning of language and music starts at an early age and is shown to play a significant role in acquiring the delta-band rhythm that is essential for language and music learning. However, it remains unclear how auditory cultural differences affect the statistical learning process and the resulting probabilistic and acoustic knowledge acquired through it. This study examined how children's songs are acquired through statistical learning. This study used a Hierarchical Bayesian statistical learning (HBSL) model, mimicking the statistical learning processes of the brain. Using this model, I conducted a simulation experiment to visualize the temporal dynamics of perception and production processes through statistical learning among different cultures. The model learned from a corpus of children's songs in MIDI format, which consists of English, German, Spanish, Japanese, and Korean songs as the training data. In this study, I investigated how the probability distribution of the model is transformed over 15 trials of learning in each song. Furthermore, using the probability distribution of each model over 15 trials of learning each song, new songs were probabilistically generated. The results suggested that, in learning processes, chunking and hierarchical knowledge increased gradually through 15 rounds of statistical learning for each piece of children's songs. In production processes, statistical learning led to the gradual increase of delta-band rhythm (1-3 Hz). Furthermore, by combining the acquired chunks and hierarchy through statistical learning, statistically novel music was generated gradually in comparison to the original songs (i.e. the training songs). These findings were observed consistently, in multiple cultures. The present study indicated that the statistical learning capacity of the brain, in multiple cultures, contributes to the acquisition and generation of delta-band rhythm, which is critical for acquiring language and music. It is suggested that cultural differences may not significantly modulate the statistical learning effects since statistical learning and slower rhythm processing are both essential functions in the human brain across cultures. Furthermore, statistical learning of children's songs leads to the acquisition of hierarchical knowledge and the ability to generate novel music. This study may provide a novel perspective on the developmental origins of creativity and the importance of statistical learning through early development.

Exploring cognitive individuality and the underlying creativity in statistical learning and phase entrainment

Statistical learning starts at an early age and is intimately linked to brain development and the emergence of individuality. Through such a long period of statistical learning, the brain updates and constructs statistical models, with the model's individuality changing based on the type and degree of stimulation received. However, the detailed mechanisms underlying this process are unknown. This paper argues three main points of statistical learning, including 1) cognitive individuality based on "reliability" of prediction, 2) the construction of information "hierarchy" through chunking, and 3) the acquisition of "1-3Hz rhythm" that is essential for early language and music learning. We developed a Hierarchical Bayesian Statistical Learning (HBSL) model that takes into account both reliability and hierarchy, mimicking the statistical learning processes of the brain. Using this model, we conducted a simulation experiment to visualize the temporal dynamics of perception and production processes through statistical learning. By modulating the sensitivity to sound stimuli, we simulated three cognitive models with different reliability on bottom-up sensory stimuli relative to top-down prior prediction: hypo-sensitive, normal-sensitive, and hyper-sensitive models. We suggested that statistical learning plays a crucial role in the acquisition of 1-3 Hz rhythm. Moreover, a hyper-sensitive model quickly learned the sensory statistics but became fixated on their internal model, making it difficult to generate new information, whereas a hypo-sensitive model has lower learning efficiency but may be more likely to generate new information. Various individual characteristics may not necessarily confer an overall advantage over others, as there may be a trade-off between learning efficiency and the ease of generating new information. This study has the potential to shed light on the heterogeneous nature of statistical learning, as well as the paradoxical phenomenon in which individuals with certain cognitive traits that impede specific types of perceptual abilities exhibit superior performance in creative contexts.

Does the public know what researchers know? Perceived task difficulty impacts adults' intuitions about children's early word learning

The present study examined adults' understanding of children's early word learning. Undergraduates, non-parents, parents, and Speech-Language Pathologists (N = 535, 74% female, 56% White) completed a survey with 11 word learning principles from the perspective of a preschooler. Questions tested key principles from early word learning research. For each question, participants were prompted to select an answer based on the perspective of a preschooler. Adults demonstrated aligned intuitions for all principles except those derived from domain-general theories, regardless of experience with language development (Experiment 1). Experiment 2 revealed that perceived difficulty of a task for a preschooler impacted adults' reasoning about word learning processes. Experiment 3 ruled out level of confidence and interest as mechanisms to explain the results. These results highlight disconnects in knowledge between the cognitive development research community and the general public. Therefore, efforts must be made to communicate scientific findings to the broader non-academic community, emphasizing children's ability to excel at word learning in the face of task difficulty.

Explicit Instructions Do Not Enhance Auditory Statistical Learning in Children With Developmental Language Disorder: Evidence From Event-Related Potentials

A current issue in psycholinguistic research is whether the language difficulties exhibited by children with developmental language disorder [DLD, previously labeled specific language impairment (SLI)] are due to deficits in their abilities to pick up patterns in the sensory environment, an ability known as statistical learning (SL), and the extent to which explicit learning mechanisms can be used to compensate for those deficits. Studies designed to test the compensatory role of explicit learning mechanisms in children with DLD are, however, scarce, and the few conducted so far have led to inconsistent results. This work aimed to provide new insights into the role that explicit learning mechanisms might play on implicit learning deficits in children with DLD by resorting to a new approach. This approach involved not only the collection of event-related potentials (ERPs), while preschool children with DLD [relative to typical language developmental (TLD) controls] were exposed to a continuous auditory stream made of the repetition of three-syllable nonsense words but, importantly, the collection of ERPs when the same children performed analogous versions of the same auditory SL task first under incidental (implicit) and afterward under intentional (explicit) conditions. In each of these tasks, the level of predictability of the three-syllable nonsense words embedded in the speech streams was also manipulated (high vs. low) to mimic natural languages closely. At the end of both tasks' exposure phase, children performed a two-alternative forced-choice (2-AFC) task from which behavioral evidence of SL was obtained. Results from the 2-AFC tasks failed to show reliable signs of SL in both groups of children. The ERPs data showed, however, significant modulations in the N100 and N400 components, taken as neural signatures of word segmentation in the brain, even though a detailed analysis of the neural responses revealed that only children from the TLD group seem to have taken advantage of the previous knowledge to enhance SL functioning. These results suggest that children with DLD showed deficits both in implicit and explicit learning mechanisms, casting doubts on the efficiency of the interventions relying on explicit instructions to help children with DLD to overcome their language difficulties.

Sequential learning of emotional faces is statistical at 12 months of age

Infants are capable of extracting statistical regularities from continuous streams of elements, which helps them structuring their surrounding environment. The current study examines 12-month-olds' capacity to extract statistical information from a sequence of emotional faces. Using a familiarization procedure, infants were presented with videos of two actresses expressing the same facial emotion, and subsequently turning toward or away from each other. Videos displayed different emotions (i.e., anger, happiness, fear, sadness, surprise, amusement, disgust, and exasperation) and were organized sequentially, so that the transitional probabilities between videos were highly predictable in some cases, and less predictable in others. At test, infants discriminated highly predictable from low predictable transitional probabilities, suggesting that they extracted statistical regularities from the sequence of emotional faces. However, when examining the looking toward and the looking away conditions separately, infants showed evidence of statistical learning in the looking toward condition only. Together, these findings suggest that 12-month-old infants rely on statistical learning to segment a continuous sequence of emotional faces, although this ability can be modulated by the nature of the stimuli. The contribution of statistical learning to structure infants' social environment is discussed.

Visual Implicit Learning Abilities in Infants at Familial Risk for Language and Learning Impairments

The ability of infants to track transitional probabilities (Statistical Learning—SL) and to extract and generalize high-order rules (Rule Learning—RL) from sequences of items have been proposed as being pivotal for the acquisition of language and reading skills. Although there is ample evidence of specific associations between SL and RL abilities and, respectively, vocabulary and grammar skills, research exploring SL and RL as early markers of language and learning (dis)abilities is still scarce. Here we investigated the efficiency of visual SL and RL skills in typically developing (TD) seven-month-old infants and in seven-month-old infants at high risk (HR) for language learning impairment. Infants were tested in two visual-habituation tasks aimed to measure their ability to extract transitional probabilities (SL task) or high-order, repetition-based rules (RL task) from sequences of visual shapes. Post-habituation looking time preferences revealed that both TD and HR infants succeeded in learning the statistical structure (SL task), while only TD infants, but not HR infants, were able to learn and generalize the high-order rule (RL task). These findings suggest that SL and RL may contribute differently to the emergence of language learning impairment and support the hypothesis that a mechanism linked to the extraction of grammar structures may contribute to the disorder.

Unpredictability of the “when” influences prediction error processing of the “what” and “where”

The capability to establish accurate predictions is an integral part of learning. Whether predictions about different dimensions of a stimulus interact with each other, and whether such an interaction affects learning, has remained elusive. We conducted a statistical learning study with EEG (electroencephalography), where a stream of consecutive sound triplets was presented with deviants that were either: (a) statistical, depending on the triplet ending probability, (b) physical, due to a change in sound location or (c) double deviants, i.e. a combination of the two. We manipulated the predictability of stimulus-onset by using random stimulus-onset asynchronies. Temporal unpredictability due to random onsets reduced the neurophysiological responses to statistical and location deviants, as indexed by the statistical mismatch negativity (sMMN) and the location MMN. Our results demonstrate that the predictability of one stimulus attribute influences the processing of prediction error signals of other stimulus attributes, and thus also learning of those attributes.

Top-down, bottom-up, and history-driven processing of multisensory attentional cues in intellectual disability: An experimental study in virtual reality

Models of attention demonstrated the existence of top-down, bottom-up, and history-driven attentional mechanisms, controlled by partially segregated networks of brain areas. However, few studies have examined the specific deficits in those attentional mechanisms in intellectual disability within the same experimental setting. The aim of the current study was to specify the attentional deficits in intellectual disability in top-down, bottom-up, and history-driven processing of multisensory stimuli, and gain insight into effective attentional cues that could be utilized in cognitive training programs for intellectual disability. The performance of adults with mild to moderate intellectual disability (n = 20) was compared with that of typically developing controls (n = 20) in a virtual reality visual search task. The type of a spatial cue that could aid search performance was manipulated to be either endogenous or exogenous in different sensory modalities (visual, auditory, tactile). The results identified that attentional deficits in intellectual disability are overall more pronounced in top-down rather than in bottom-up processing, but with different magnitudes across cue types: The auditory or tactile endogenous cues were much less effective than the visual endogenous cue in the intellectual disability group. Moreover, the history-driven processing in intellectual disability was altered, such that a reversed priming effect was observed for immediate repetitions of the same cue type. These results suggest that the impact of intellectual disability on attentional processing is specific to attentional mechanisms and cue types, which has theoretical as well as practical implications for developing effective cognitive training programs for the target population.

Neurophysiological tracking of speech-structure learning in typical and dyslexic readers

Statistical learning, or the ability to extract statistical regularities from the sensory environment, plays a critical role in language acquisition and reading development. Here we employed electroencephalography (EEG) with frequency-tagging measures to track the temporal evolution of speech-structure learning in individuals with reading difficulties due to developmental dyslexia and in typical readers. We measured EEG while participants listened to (a) a structured stream of repeated tri-syllabic pseudowords, (b) a random stream of the same isochronous syllables, and (c) a series of tri-syllabic real Dutch words. Participants' behavioral learning outcome (pseudoword recognition) was measured after training. We found that syllable-rate tracking was comparable between the two groups and stable across both the random and structured streams of syllables. More importantly, we observed a gradual emergence of the tracking of tri-syllabic pseudoword structures in both groups. Compared to the typical readers, however, in the dyslexic readers this implicit speech structure learning seemed to build up at a slower pace. A brain-behavioral correlation analysis showed that slower learners (i.e., participants who were slower in establishing the neural tracking of pseudowords) were less skilled in phonological awareness. Moreover, those who showed stronger neural tracking of real words tended to be less fluent in the visual-verbal conversion of linguistic symbols. Taken together, our study provides an online neurophysiological approach to track the progression of implicit learning processes and gives insights into the learning difficulties associated with dyslexia from a dynamic perspective.

Statistical Properties of Musical Creativity: Roles of Hierarchy and Uncertainty in Statistical Learning

Creativity is part of human nature and is commonly understood as a phenomenon whereby something original and worthwhile is formed. Owing to this ability, humans can produce innovative information that often facilitates growth in our society. Creativity also contributes to esthetic and artistic productions, such as music and art. However, the mechanism by which creativity emerges in the brain remains debatable. Recently, a growing body of evidence has suggested that statistical learning contributes to creativity. Statistical learning is an innate and implicit function of the human brain and is considered essential for brain development. Through statistical learning, humans can produce and comprehend structured information, such as music. It is thought that creativity is linked to acquired knowledge, but so-called "eureka" moments often occur unexpectedly under subconscious conditions, without the intention to use the acquired knowledge. Given that a creative moment is intrinsically implicit, we postulate that some types of creativity can be linked to implicit statistical knowledge in the brain. This article reviews neural and computational studies on how creativity emerges within the framework of statistical learning in the brain (i.e., statistical creativity). Here, we propose a hierarchical model of statistical learning: statistically chunking into a unit (hereafter and shallow statistical learning) and combining several units (hereafter and deep statistical learning). We suggest that deep statistical learning contributes dominantly to statistical creativity in music. Furthermore, the temporal dynamics of perceptual uncertainty can be another potential causal factor in statistical creativity. Considering that statistical learning is fundamental to brain development, we also discuss how typical versus atypical brain development modulates hierarchical statistical learning and statistical creativity. We believe that this review will shed light on the key roles of statistical learning in musical creativity and facilitate further investigation of how creativity emerges in the brain.

Toddlers' Ability to Leverage Statistical Information to Support Word Learning

PURPOSE

This study investigated whether the ability to utilize statistical regularities from fluent speech and map potential words to meaning at 18-months predicts vocabulary at 18- and again at 24-months.

METHOD

Eighteen-month-olds (N = 47) were exposed to an artificial language with statistical regularities within the speech stream, then participated in an object-label learning task. Learning was measured using a modified looking-while-listening eye-tracking design. Parents completed vocabulary questionnaires when their child was 18-and 24-months old.

RESULTS

Ability to learn the object-label pairing for words after exposure to the artificial language predicted productive vocabulary at 24-months and amount of vocabulary change from 18- to 24 months, independent of non-verbal cognitive ability, socio-economic status (SES) and/or object-label association performance.

CONCLUSION

Eighteen-month-olds' ability to use statistical information derived from fluent speech to identify words within the stream of speech and then to map the "words" to meaning directly predicts vocabulary size at 24-months and vocabulary change from 18 to 24 months. The findings support the hypothesis that statistical word segmentation is one of the important aspects of word learning and vocabulary acquisition in toddlers.

Dysfunctions in Infants' Statistical Learning are Related to Parental Autistic Traits

Statistical learning refers to the ability to extract the statistical relations embedded in a sequence, and it plays a crucial role in the development of communicative and social skills that are impacted in the Autism Spectrum Disorder (ASD). Here, we investigated the relationship between infants’ SL ability and autistic traits in their parents. Using a visual habituation task, we tested infant offspring of adults (non-diagnosed) who show high (HAT infants) versus low (LAT infants) autistic traits. Results demonstrated that LAT infants learned the statistical structure embedded in a visual sequence, while HAT infants failed. Moreover, infants’ SL ability was related to autistic traits in their parents, further suggesting that early dysfunctions in SL might contribute to variabilities in ASD symptoms.

Maternal stress predicts neural responses during auditory statistical learning in 26-month-old children: An event-related potential study

Exposure to high levels of early life stress have been associated with long-term difficulties in learning, behavior, and health, with particular impact evident in the language domain. While some have proposed that the increased stress of living in a low-income household mediates observed associations between socioeconomic status (SES) and child outcomes, considerable individual differences have been observed. The extent to which specific variables associated with socioeconomic status - in particular exposure to stressful life events - influence the neurocognitive mechanisms underlying language acquisition are not well understood. Auditory statistical learning, or the ability to segment a continuous auditory stream based on its statistical properties, develops during early infancy and is one mechanism thought to underlie language learning. The present study used an event-related potential (ERP) paradigm to test whether maternal stress, adjusting for socioeconomic variables (e.g., family income, maternal education) was associated with neurocognitive processes underlying statistical learning in a sample of 26-month-old children (n = 23) from predominantly low- to middle-income backgrounds. Event-related potentials were recorded while children listened to a continuous stream of tri-tone "words" in which tone elements varied in transitional probability. "Tone-words" were presented in random order, such that Tone 1 always predicted Tones 2 and 3 (transitional probability for Tone 3 = 1.0), but Tone 1 appeared randomly. A larger P2 amplitude was observed in response to Tone 3 compared to Tone 1, demonstrating that children implicitly tracked differences in transitional probabilities during passive listening. Maternal reports of stress at 26 months, adjusting for SES, were negatively associated with difference in P2 amplitude between Tones 1 and 3. These findings suggest that maternal stress, within a low-SES context, is associated with the manner in which children process statistical properties of auditory input.

Statistical Learning and Language Impairments: Toward More Precise Theoretical Accounts

Statistical-learning (SL) theory offers an experience-based account of typical and atypical spoken and written language acquisition. Recent work has provided initial support for this view, tying individual differences in SL abilities to linguistic skills, including language impairments. In the current article, we provide a critical review of studies testing SL abilities in participants with and without developmental dyslexia and specific language impairment and discuss the directions that this field of research has taken so far. We identify substantial vagueness in the demarcation lines between different theoretical constructs (e.g., “statistical learning,” “implicit learning,” and “procedural learning”) as well as in the mappings between experimental tasks and these theoretical constructs. Moreover, we argue that current studies are not designed to contrast different theoretical approaches but rather test singular confirmatory predictions without including control tasks showing normal performance. We end by providing concrete suggestions for how to advance research on SL deficits in language impairments.

Statistical Learning in Late Talkers and Normal Peers

Background:

Late talkers (LTs) are children under three with poor vocabularies and no developmental problems. Statistical learning (SL) is defined as processing or learning patterns of environmental stimuli, for example, spoken language, music, or motor, that will unfold in time. We hypothesize if some LTs outgrow as developmental language disorder, they might be identified using SL tasks at the onset. We aimed to find any correlation between language measures and SL outcomes in LTs and normal children (NC).

Materials and Methods:

Sixteen pairs of LTs and NCs were recruited using a convenient sampling method from day-care centers and speech therapy clinics of the Comprehensive Center for Child Development in Isfahan city, Iran. Visual sequences presented using Habit software version 2.2.4. Children’s eye movements to visual sequences were monitored, and their reaction times and the number of anticipatory looks were analyzed offline. The language measures were determined in the free-play context.

Results:

Results indicated no significant correlation between SL and language measures and no difference observed in SL between the groups (P = 0.73).

Conclusions:

The results may refer to no overt correlation between SL and delayed overall linguistic measures along with inadequate samples, children’s fatigue, or insufficiency of the visual task in presenting SL.

Network architectures supporting learnability

Human learners acquire complex interconnected networks of relational knowledge. The capacity for such learning naturally depends on two factors: the architecture (or informational structure) of the knowledge network itself and the architecture of the computational unit-the brain-that encodes and processes the information. That is, learning is reliant on integrated network architectures at two levels: the epistemic and the computational, or the conceptual and the neural. Motivated by a wish to understand conventional human knowledge, here, we discuss emerging work assessing network constraints on the learnability of relational knowledge, and theories from statistical physics that instantiate the principles of thermodynamics and information theory to offer an explanatory model for such constraints. We then highlight similarities between those constraints on the learnability of relational networks, at one level, and the physical constraints on the development of interconnected patterns in neural systems, at another level, both leading to hierarchically modular networks. To support our discussion of these similarities, we employ an operational distinction between the modeller (e.g. the human brain), the model (e.g. a single human's knowledge) and the modelled (e.g. the information present in our experiences). We then turn to a philosophical discussion of whether and how we can extend our observations to a claim regarding explanation and mechanism for knowledge acquisition. What relation between hierarchical networks, at the conceptual and neural levels, best facilitate learning? Are the architectures of optimally learnable networks a topological reflection of the architectures of comparably developed neural networks? Finally, we contribute to a unified approach to hierarchies and levels in biological networks by proposing several epistemological norms for analysing the computational brain and social epistemes, and for developing pedagogical principles conducive to curious thought. This article is part of the theme issue 'Unifying the essential concepts of biological networks: biological insights and philosophical foundations'.