Memory representations are flexibly adapted to orthographic systems: A comparison of English and Hebrew.

Across languages, speech unfolds in the same temporal order, constrained by the forward flow of time. But the way phonology is spatially mapped onto orthography is language-specific, ranging from left-to-right, right-to-left, and top-to-bottom, among others. While the direction of writing systems influences how known words are visually processed, it is unclear whether it influences learning and memory for novel orthographic regularities. The present study tested English and Hebrew speakers on an orthographic word-referent mapping task in their native orthographies (written left-to-right and right-to-left, respectively), where the onsets and offsets of words were equally informative cues to word identity. While all individuals learned orthographic word-referent mappings significantly above chance, the parts of the word that were most strongly represented varied. English monolinguals false alarmed most to competing foils that began with the same bigram as the target, representing word onsets most strongly. However, Hebrew bilinguals trained on their native orthography showed no difference between false alarm rates to onset and offset competitors, representing the beginning and ends of words equally strongly. Importantly, Hebrew bilinguals tested on English words displayed a more English-like false alarm pattern (although not a full switch) suggesting that memory biases adapt to the opposite directionality of encountered text while retaining traces of native language biases. These findings demonstrate that experience with different writing systems influences how individuals represent novel orthographic words, starting in the earliest stages of learning.

Statistical Learning of Language: A Meta-Analysis Into 25 Years of Research.

Statistical learning is a key concept in our understanding of language acquisition. Ample work has highlighted its role in numerous linguistic functions-yet statistical learning is not a unitary construct, and its consistency across different language properties remains unclear. In a meta-analysis of auditory-linguistic statistical learning research spanning the last 25 years, we evaluated how learning varies across different language properties in infants, children, and adults and surveyed the methodological trends in the literature. We found robust learning across stimuli (syllables, words, etc.) in infants, and across stimuli and structures (adjacent dependencies, non-adjacent dependencies, etc.) in adults, with larger effect sizes when multiple cues were present. However, the analysis also showed significant publication bias and revealed a tendency toward using a narrow range of simplified language properties, including in the strength of the transitional probabilities used during training. Bayes factor analyses revealed prevalent data insensitivity of moderators commonly hypothesized to impact learning, such as the amount of exposure and transitional probability strength, which contradict core theoretical assumptions in the field. Methodological factors, such as the tasks used at test, also significantly impacted effect sizes in adults and children, suggesting that choice of task may critically constrain current theories of how statistical learning operates. Collectively, our results suggest that auditory-linguistic statistical learning has the kind of robustness needed to play a foundational role in language acquisition, but that more research is warranted to reveal its full potential.

Statistically based chunking of nonadjacent dependencies.

How individuals learn complex regularities in the environment and generalize them to new instances is a key question in cognitive science. Although previous investigations have advocated the idea that learning and generalizing depend upon separate processes, the same basic learning mechanisms may account for both. In language learning experiments, these mechanisms have typically been studied in isolation of broader cognitive phenomena such as memory, perception, and attention. Here, we show how learning and generalization in language is embedded in these broader theories by testing learners on their ability to chunk nonadjacent dependencies-a key structure in language but a challenge to theories that posit learning through the memorization of structure. In two studies, adult participants were trained and tested on an artificial language containing nonadjacent syllable dependencies, using a novel chunking-based serial recall task involving verbal repetition of target sequences (formed from learned strings) and scrambled foils. Participants recalled significantly more syllables, bigrams, trigrams, and nonadjacent dependencies from sequences conforming to the language's statistics (both learned and generalized sequences). They also encoded and generalized specific nonadjacent chunk information. These results suggest that participants chunk remote dependencies and rapidly generalize this information to novel structures. The results thus provide further support for learning-based approaches to language acquisition, and link statistical learning to broader cognitive mechanisms of memory. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Individual differences in artificial and natural language statistical learning.

Statistical learning (SL) is considered a cornerstone of cognition. While decades of research have unveiled the remarkable breadth of structures that participants can learn from statistical patterns in experimental contexts, how this ability interfaces with real-world cognitive phenomena remains inconclusive. These mixed results may arise from the fact that SL is often treated as a general ability that operates uniformly across all domains, typically assuming that sensitivity to one kind of regularity implies equal sensitivity to others. In a preregistered study, we sought to clarify the link between SL and language by aligning the type of structure being processed in each task. We focused on the learning of trigram patterns using artificial and natural language statistics, to evaluate whether SL predicts sensitivity to comparable structures in natural speech. Adults were trained and tested on an artificial language incorporating statistically-defined syllable trigrams. We then evaluated their sensitivity to similar statistical structures in natural language using a multiword chunking task, which examines serial recall of high-frequency word trigrams-one of the building blocks of language. Participants' aptitude in learning artificial syllable trigrams positively correlated with their sensitivity to high-frequency word trigrams in natural language, suggesting that similar computations span learning across both tasks. Short-term SL taps into key aspects of long-term language acquisition when the statistical structures-and the computations used to process them-are comparable. Better aligning the specific statistical patterning across tasks may therefore provide an important steppingstone toward elucidating the relationship between SL and cognition at large.

Measuring children's auditory statistical learning via serial recall.

Statistical learning (SL) has been a prominent focus of research in developmental and adult populations, guided by the assumption that it is a fundamental component of learning underlying higher-order cognition. In developmental populations, however, there have been recent concerns regarding the degree to which many current tasks reliably measure SL, particularly in younger children. In the current article, we present the results of two studies that measured auditory statistical learning (ASL) of linguistic stimuli in children aged 5-8 years. Children listened to 6 min of continuous syllables comprising four trisyllabic pseudowords. Following the familiarization phase, children completed (a) a two-alternative forced-choice task and (b) a serial recall task in which they repeated either target sequences embedded during familiarization or foils, manipulated for sequence length. Results showed that, although both measures consistently revealed learning at the group level, the recall task better captured learning across the full range of abilities and was more reliable at the individual level. We conclude that, as has also been demonstrated in adults, the method holds promise for future studies of individual differences in ASL of linguistic stimuli.

Affective Arousal Links Sound to Meaning.

Prior investigations have demonstrated that people tend to link pseudowords such as bouba to rounded shapes and kiki to spiky shapes, but the cognitive processes underlying this matching bias have remained controversial. Here, we present three experiments underscoring the fundamental role of emotional mediation in this sound-shape mapping. Using stimuli from key previous studies, we found that kiki-like pseudowords and spiky shapes, compared with bouba-like pseudowords and rounded shapes, consistently elicit higher levels of affective arousal, which we assessed through both subjective ratings (Experiment 1, N = 52) and acoustic models implemented on the basis of pseudoword material (Experiment 2, N = 70). Crucially, the mediating effect of arousal generalizes to novel pseudowords (Experiment 3, N = 64, which was preregistered). These findings highlight the role that human emotion may play in language development and evolution by grounding associations between abstract concepts (e.g., shapes) and linguistic signs (e.g., words) in the affective system.

Statistically Induced Chunking Recall: A Memory-Based Approach to Statistical Learning.

The computations involved in statistical learning have long been debated. Here, we build on work suggesting that a basic memory process, chunking, may account for the processing of statistical regularities into larger units. Drawing on methods from the memory literature, we developed a novel paradigm to test statistical learning by leveraging a robust phenomenon observed in serial recall tasks: that short-term memory is fundamentally shaped by long-term distributional learning. In the statistically induced chunking recall (SICR) task, participants are exposed to an artificial language, using a standard statistical learning exposure phase. Afterward, they recall strings of syllables that either follow the statistics of the artificial language or comprise the same syllables presented in a random order. We hypothesized that if individuals had chunked the artificial language into word-like units, then the statistically structured items would be more accurately recalled relative to the random controls. Our results demonstrate that SICR effectively captures learning in both the auditory and visual modalities, with participants displaying significantly improved recall of the statistically structured items, and even recall specific trigram chunks from the input. SICR also exhibits greater test-retest reliability in the auditory modality and sensitivity to individual differences in both modalities than the standard two-alternative forced-choice task. These results thereby provide key empirical support to the chunking account of statistical learning and contribute a valuable new tool to the literature.

Chunk-Based Memory Constraints on the Cultural Evolution of Language.

In the fields of linguistics and cognitive science, considerable attention has been devoted to the question of how linguistic structure emerged over evolutionary time. Here, we highlight the contribution of a fundamental constraint on processing, the Now-or-Never bottleneck. Language takes place in the here and now, with the transience of acoustic speech signals and our exceedingly limited memory for sound sequences requiring immediate processing. To overcome this bottleneck, the cognitive system employs basic chunking mechanisms to rapidly compress and recode incoming linguistic input into increasingly abstract levels of representation, thereby prolonging its retention in memory. Our suggestion is that these chunk-based memory processes influence linguistic structure across multiple time scales. Chunk-based memory constraints govern language acquisition and processing on the level of the individual. Through usage, linguistic structures that are more easily chunked will tend to proliferate, thus shaping the cultural evolution of language across generations of language users. This results in a selection of learnable structures, from individual words to multiword sequences that are optimally "chunkable," so as to better squeeze through the Now-or-Never bottleneck. From this perspective, language can be thought of as an adaptive system that culturally evolves to fit learners' cognitive capabilities, thereby resulting in the structure it bears today.