Hebbian learning can explain rhythmic neural entrainment to statistical regularities.

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.

Memory and Proactive Interference for spatially distributed items.

Our ability to briefly retain information is often limited. Proactive Interference (PI) might contribute to these limitations (e.g., when items in recognition tests are difficult to reject after having appeared recently). In visual Working Memory (WM), spatial information might protect WM against PI, especially if encoding items together with their spatial locations makes item-location combinations less confusable than simple items without a spatial component. Here, I ask (1) if PI is observed for spatially distributed items, (2) if it arises among simple items or among item-location combinations, and (3) if spatial information affects PI at all. I show that, contrary to views that spatial information protects against PI, PI is reliably observed for spatially distributed items except when it is weak. PI mostly reflects items that appear recently or frequently as memory items, while occurrences as test items play a smaller role, presumably because their temporal context is easier to encode. Through mathematical modeling, I then show that interference occurs among simple items rather than item-location combinations. Finally, to understand the effects of spatial information, I separate the effects of (a) the presence and (b) the predictiveness of spatial information on memory and its susceptibility to PI. Memory is impaired when items are spatially distributed, but, depending on the analysis, unaffected by the predictiveness of spatial information. In contrast, the susceptibility to PI is unaffected by either manipulation. Visual memory is thus impaired by PI for spatially distributed items due to interference from recent memory items (rather than test items or item-location combinations).

Generic learning mechanisms can drive social inferences: The role of type frequency.

How do we form opinions about typical and morally acceptable behavior in other social groups despite variability in behavior? Similar learning problems arise during language acquisition, where learners need to infer grammatical rules (e.g., the walk/walk-ed past-tense) despite frequent exceptions (e.g., the go/went alternation). Such rules need to occur with many different words to be learned (i.e., they need a high type frequency). In contrast, frequent individual words do not lead to learning. Here, we ask whether similar principles govern social learning. Participants read a travel journal where a traveler observed behaviors in different imaginary cities. The behaviors were performed once by many distinct actors (high type frequency) or frequently by a single actor (low type frequency), and could be good, neutral or bad. We then asked participants how morally acceptable the behavior was (in general or for the visited city), and how widespread it was in that city. We show that an ideal observer model estimating the prevalence of behaviors is only sensitive to the behaviors' type frequency, but not to how often they are performed. Empirically, participants rated high type frequency behaviors as more morally acceptable more prevalent than low type frequency behaviors. They also rated good behaviors as more acceptable and prevalent than neutral or bad behaviors. These results suggest that generic learning mechanisms and epistemic biases constrain social learning, and that type frequency can drive inferences about groups. To combat stereotypes, high type frequency behaviors might thus be more effective than frequently appearing individual role models.

Socio-Cultural Values Are Risk Factors for COVID-19-Related Mortality.

To assess whether socio-cultural values are population-level risk factors for health, I sought to predict COVID-19-related mortality between 2 weeks and 6 months after the first COVID-19-related death in a country based on values extracted from the World Values Survey for different country sets, after controlling for various confounding variables. COVID-19-related mortality was increased in countries endorsing political participation but decreased in countries with greater trust in institutions and materialistic orientations. The values were specific to COVID-19-related mortality, did not predict general health outcomes, and values predicting increased COVID-19-related mortality predicted decreased mortality from other outcomes (e.g., environmental-related mortality).

A Simple, Biologically Plausible Feature Detector for Language Acquisition.

Language has a complex grammatical system we still have to understand computationally and biologically. However, some evolutionarily ancient mechanisms have been repurposed for grammar so that we can use insight from other taxa into possible circuit-level mechanisms of grammar. Drawing upon recent evidence for the importance of disinhibitory circuits across taxa and brain regions, I suggest a simple circuit that explains the acquisition of core grammatical rules used in 85% of the world's languages: grammatical rules based on sameness/difference relations. This circuit acts as a sameness detector. "Different" items are suppressed through inhibition, but presenting two "identical" items leads to inhibition of inhibition. The items are thus propagated for further processing. This sameness detector thus acts as a feature detector for a grammatical rule. I suggest that having a set of feature detectors for elementary grammatical rules might make language acquisition feasible based on relatively simple computational mechanisms.

Transitional probabilities count more than frequency, but might not be used for memorization.

Learners often need to extract recurring items from continuous sequences, in both vision and audition. The best-known example is probably found in word-learning, where listeners have to determine where words start and end in fluent speech. This could be achieved through universal and experience-independent statistical mechanisms, for example by relying on Transitional Probabilities (TPs). Further, these mechanisms might allow learners to store items in memory. However, previous investigations have yielded conflicting evidence as to whether a sensitivity to TPs is diagnostic of the memorization of recurring items. Here, we address this issue in the visual modality. Participants were familiarized with a continuous sequence of visual items (i.e., arbitrary or everyday symbols), and then had to choose between (i) high-TP items that appeared in the sequence, (ii) high-TP items that did not appear in the sequence, and (iii) low-TP items that appeared in the sequence. Items matched in TPs but differing in (chunk) frequency were much harder to discriminate than items differing in TPs (with no significant sensitivity to chunk frequency), and learners preferred unattested high-TP items over attested low-TP items. Contrary to previous claims, these results cannot be explained on the basis of the similarity of the test items. Learners thus weigh within-item TPs higher than the frequency of the chunks, even when the TP differences are relatively subtle. We argue that these results are problematic for distributional clustering mechanisms that analyze continuous sequences, and provide supporting computational results. We suggest that the role of TPs might not be to memorize items per se, but rather to prepare learners to memorize recurring items once they are presented in subsequent learning situations with richer cues.

Learning multiple rules simultaneously: Affixes are more salient than reduplications.

Language learners encounter numerous opportunities to learn regularities, but need to decide which of these regularities to learn, because some are not productive in their native language. Here, we present an account of rule learning based on perceptual and memory primitives (Endress, Dehaene-Lambertz, & Mehler, Cognition, 105(3), 577-614, 2007; Endress, Nespor, & Mehler, Trends in Cognitive Sciences, 13(8), 348-353, 2009), suggesting that learners preferentially learn regularities that are more salient to them, and that the pattern of salience reflects the frequency of language features across languages. We contrast this view with previous artificial grammar learning research, which suggests that infants "choose" the regularities they learn based on rational, Bayesian criteria (Frank & Tenenbaum, Cognition, 120(3), 360-371, 2013; Gerken, Cognition, 98(3)B67-B74, 2006, Cognition, 115(2), 362-366, 2010). In our experiments, adult participants listened to syllable strings starting with a syllable reduplication and always ending with the same "affix" syllable, or to syllable strings starting with this "affix" syllable and ending with the "reduplication". Both affixation and reduplication are frequently used for morphological marking across languages. We find three crucial results. First, participants learned both regularities simultaneously. Second, affixation regularities seemed easier to learn than reduplication regularities. Third, regularities in sequence offsets were easier to learn than regularities at sequence onsets. We show that these results are inconsistent with previous Bayesian rule learning models, but mesh well with the perceptual or memory primitives view. Further, we show that the pattern of salience revealed in our experiments reflects the distribution of regularities across languages. Ease of acquisition might thus be one determinant of the frequency of regularities across languages.

Linguistics, cognitive psychology, and the Now-or-Never bottleneck.

Christiansen & Chater (C&C)'s key premise is that "if linguistic information is not processed rapidly, that information is lost for good" (sect. 1, para. 1). From this "Now-or-Never bottleneck" (NNB), C&C derive "wide-reaching and fundamental implications for language processing, acquisition and change as well as for the structure of language itself" (sect. 2, para. 10). We question both the premise and the consequentiality of its purported implications.

Hebbian, correlational learning provides a memory-less mechanism for Statistical Learning irrespective of implementational choices: Reply to Tovar and Westermann (2022).

Statistical learning relies on detecting the frequency of co-occurrences of items and has been proposed to be crucial for a variety of learning problems, notably to learn and memorize words from fluent speech. Endress and Johnson (2021) (hereafter EJ) recently showed that such results can be explained based on simple memory-less correlational learning mechanisms such as Hebbian Learning. Tovar and Westermann (2022) (hereafter TW) reproduced these results with a different Hebbian model. We show that the main differences between the models are whether temporal decay acts on both the connection weights and the activations (in TW) or only on the activations (in EJ), and whether interference affects weights (in TW) or activations (in EJ). Given that weights and activations are linked through the Hebbian learning rule, the networks behave similarly. However, in contrast to TW, we do not believe that neurophysiological data are relevant to adjudicate between abstract psychological models with little biological detail. Taken together, both models show that different memory-less correlational learning mechanisms provide a parsimonious account of Statistical Learning results. They are consistent with evidence that Statistical Learning might not allow learners to learn and retain words, and Statistical Learning might support predictive processing instead.

Early conceptual and linguistic processes operate in independent channels.

Language and concepts are intimately linked, but how do they interact? In the study reported here, we probed the relation between conceptual and linguistic processing at the earliest processing stages. We presented observers with sequences of visual scenes lasting 200 or 250 ms per picture. Results showed that observers understood and remembered the scenes' abstract gist and, therefore, their conceptual meaning. However, observers remembered the scenes at least as well when they simultaneously performed a linguistic secondary task (i.e., reading and retaining sentences); in contrast, a nonlinguistic secondary task (equated for difficulty with the linguistic task) impaired scene recognition. Further, encoding scenes interfered with performance on the nonlinguistic task and vice versa, but scene processing and performing the linguistic task did not affect each other. At the earliest stages of conceptual processing, the extraction of meaning from visually presented linguistic stimuli and the extraction of conceptual information from the world take place in remarkably independent channels.

Syntax-induced pattern deafness.

Perceptual systems often force systematically biased interpretations upon sensory input. These interpretations are obligatory, inaccessible to conscious control, and prevent observers from perceiving alternative percepts. Here we report a similarly impenetrable phenomenon in the domain of language, where the syntactic system prevents listeners from detecting a simple perceptual pattern. Healthy human adults listened to three-word sequences conforming to patterns readily learned even by honeybees, rats, and sleeping human neonates. Specifically, sequences either started or ended with two words from the same syntactic category (e.g., noun-noun-verb or verb-verb-noun). Although participants readily processed the categories and learned repetition patterns over nonsyntactic categories (e.g., animal-animal-clothes), they failed to learn the repetition pattern over syntactic categories, even when explicitly instructed to look for it. Further experiments revealed that participants successfully learned the repetition patterns only when they were consistent with syntactically possible structures, irrespective of whether these structures were attested in English or in other languages unknown to the participants. When the repetition patterns did not match such syntactically possible structures, participants failed to learn them. Our results suggest that when human adults hear a string of nouns and verbs, their syntactic system obligatorily attempts an interpretation (e.g., in terms of subjects, objects, and predicates). As a result, subjects fail to perceive the simpler pattern of repetitions--a form of syntax-induced pattern deafness that is reminiscent of how other perceptual systems force specific interpretations upon sensory input.

From movements to actions: two mechanisms for learning action sequences.

When other individuals move, we interpret their movements as discrete, hierarchically-organized, goal-directed actions. However, the mechanisms that integrate visible movement features into actions are poorly understood. Here, we consider two sequence learning mechanisms - transitional probability-based (TP) and position-based encoding computations - that have been studied extensively in the domain of language learning, and investigate their potential for integrating movements into actions. If these learning mechanisms integrate movements into actions, then they should create memory units that contain (i) movement information, (ii) information about the order in which movements occurred, and (iii) information allowing actions to be recognized from different viewpoints. We show that both mechanisms retain movement information. However, only the position-based mechanism creates movement representations that are view-invariant and contain order information. The TP-based mechanism creates movement representations that are view-dependent and contain no order information. We therefore suggest that the TP-based mechanism is unlikely to play an important role for integrating movements into actions. In contrast, the position-based mechanism retains some of the types of information needed to represent goal-directed actions, which makes it an attractive target for further research to explore what, if any, role it plays in the perception of goal-directed actions.

When forgetting fosters learning: A neural network model for statistical learning.

Learning often requires splitting continuous signals into recurring units, such as the discrete words constituting fluent speech; these units then need to be encoded in memory. A prominent candidate mechanism involves statistical learning of co-occurrence statistics like transitional probabilities (TPs), reflecting the idea that items from the same unit (e.g., syllables within a word) predict each other better than items from different units. TP computations are surprisingly flexible and sophisticated. Humans are sensitive to forward and backward TPs, compute TPs between adjacent items and longer-distance items, and even recognize TPs in novel units. We explain these hallmarks of statistical learning with a simple model with tunable, Hebbian excitatory connections and inhibitory interactions controlling the overall activation. With weak forgetting, activations are long-lasting, yielding associations among all items; with strong forgetting, no associations ensue as activations do not outlast stimuli; with intermediate forgetting, the network reproduces the hallmarks above. Forgetting thus is a key determinant of these sophisticated learning abilities. Further, in line with earlier dissociations between statistical learning and memory encoding, our model reproduces the hallmarks of statistical learning in the absence of a memory store in which items could be placed.

Perceptual and memory constraints on language acquisition.

A wide variety of organisms employ specialized mechanisms to cope with the demands of their environment. We suggest that the same is true for humans when acquiring artificial grammars, and at least some basic properties of natural grammars. We show that two basic mechanisms can explain many results in artificial grammar learning experiments, and different linguistic regularities ranging from stress assignment to interfaces between different components of grammar. One mechanism is sensitive to identity relations, whereas the other uses sequence edges as anchor points for extracting positional regularities. This piecemeal approach to mental computations helps to explain otherwise perplexing data, and offers a working hypothesis on how statistical and symbolic accounts of cognitive processes could be bridged.

The apes' edge: positional learning in chimpanzees and humans.

A wide variety of organisms produce actions and signals in particular temporal sequences, including the motor actions recruited during tool-mediated foraging, the arrangement of notes in the songs of birds, whales and gibbons, and the patterning of words in human speech. To accurately reproduce such events, the elements that comprise such sequences must be memorized. Both memory and artificial language learning studies have revealed at least two mechanisms for memorizing sequences, one tracking co-occurrence statistics among items in sequences (i.e., transitional probabilities) and the other one tracking the positions of items in sequences, in particular those of items in sequence-edges. The latter mechanism seems to dominate the encoding of sequences after limited exposure, and to be recruited by a wide array of grammatical phenomena. To assess whether humans differ from other species in their reliance on one mechanism over the other after limited exposure, we presented chimpanzees (Pan troglodytes) and human adults with brief exposure to six items, auditory sequences. Each sequence consisted of three distinct sound types (X, A, B), arranged according to two simple temporal rules: the A item always preceded the B item, and the sequence-edges were always occupied by the X item. In line with previous results with human adults, both species primarily encoded positional information from the sequences; that is, they kept track of the items that occurred in the sequence-edges. In contrast, the sensitivity to co-occurrence statistics was much weaker. Our results suggest that a mechanism to spontaneously encode positional information from sequences is present in both chimpanzees and humans and may represent the default in the absence of training and with brief exposure. As many grammatical regularities exhibit properties of this mechanism, it may be recruited by language and constrain the form that certain grammatical regularities take.

Word segmentation with universal prosodic cues.

When listening to speech from one's native language, words seem to be well separated from one another, like beads on a string. When listening to a foreign language, in contrast, words seem almost impossible to extract, as if there was only one bead on the same string. This contrast reveals that there are language-specific cues to segmentation. The puzzle, however, is that infants must be endowed with a language-independent mechanism for segmentation, as they ultimately solve the segmentation problem for any native language. Here, we approach the acquisition problem by asking whether there are language-independent cues to segmentation that might be available to even adult learners who have already acquired a native language. We show that adult learners recognize words in connected speech when only prosodic cues to word-boundaries are given from languages unfamiliar to the participants. In both artificial and natural speech, adult English speakers, with no prior exposure to the test languages, readily recognized words in natural languages with critically different prosodic patterns, including French, Turkish and Hungarian. We suggest that, even though languages differ in their sound structures, they carry universal prosodic characteristics. Further, these language-invariant prosodic cues provide a universally accessible mechanism for finding words in connected speech. These cues may enable infants to start acquiring words in any language even before they are fine-tuned to the sound structure of their native language.

Sequential Presentation Protects Working Memory From Catastrophic Interference.

Neural network models of memory are notorious for catastrophic interference: Old items are forgotten as new items are memorized (French, 1999; McCloskey & Cohen, 1989). While working memory (WM) in human adults shows severe capacity limitations, these capacity limitations do not reflect neural network style catastrophic interference. However, our ability to quickly apprehend the numerosity of small sets of objects (i.e., subitizing) does show catastrophic capacity limitations, and this subitizing capacity and WM might reflect a common capacity. Accordingly, computational investigations (Knops, Piazza, Sengupta, Eger & Melcher, 2014; Sengupta, Surampudi & Melcher, 2014) suggest that mutual inhibition among neurons can explain both kinds of capacity limitations as well as why our ability to estimate the numerosity of larger sets is limited according to a Weber ratio signature. Based on simulations with a saliency map-like network and mathematical proofs, we provide three results. First, mutual inhibition among neurons leads to catastrophic interference when items are presented simultaneously. The network can remember a limited number of items, but when more items are presented, the network forgets all of them. Second, if memory items are presented sequentially rather than simultaneously, the network remembers the most recent items rather than forgetting all of them. Hence, the tendency in WM tasks to sequentially attend even to simultaneously presented items might not only reflect attentional limitations, but also an adaptive strategy to avoid catastrophic interference. Third, the mean activation level in the network can be used to estimate the number of items in small sets, but it does not accurately reflect the number of items in larger sets. Rather, we suggest that the Weber ratio signature of large number discrimination emerges naturally from the interaction between the limited precision of a numeric estimation system and a multiplicative gain control mechanism.

Statistical learning and memory.

Learners often need to identify and remember recurring units in continuous sequences, but the underlying mechanisms are debated. A particularly prominent candidate mechanism relies on distributional statistics such as Transitional Probabilities (TPs). However, it is unclear what the outputs of statistical segmentation mechanisms are, and if learners store these outputs as discrete chunks in memory. We critically review the evidence for the possibility that statistically coherent items are stored in memory and outline difficulties in interpreting past research. We use Slone and Johnson's (2018) experiments as a case study to show that it is difficult to delineate the different mechanisms learners might use to solve a learning problem. Slone and Johnson (2018) reported that 8-month-old infants learned coherent chunks of shapes in visual sequences. Here, we describe an alternate interpretation of their findings based on a multiple-cue integration perspective. First, when multiple cues to statistical structure were available, infants' looking behavior seemed to track with the strength of the strongest one - backward TPs, suggesting that infants process multiple cues simultaneously and select the strongest one. Second, like adults, infants are exquisitely sensitive to chunks, but may require multiple cues to extract them. In Slone and Johnson's (2018) experiments, these cues were provided by immediate chunk repetitions during familiarization. Accordingly, infants showed strongest evidence of chunking following familiarization sequences in which immediate repetitions were more frequent. These interpretations provide a strong argument for infants' processing of multiple cues and the potential importance of multiple cues for chunk recognition in infancy.

Evidence of an evolutionary precursor to human language affixation in a non-human primate.

Human language, and grammatical competence in particular, relies on a set of computational operations that, in its entirety, is not observed in other animals. Such uniqueness leaves open the possibility that components of our linguistic competence are shared with other animals, having evolved for non-linguistic functions. Here, we explore this problem from a comparative perspective, asking whether cotton-top tamarin monkeys (Saguinus oedipus) can spontaneously (no training) acquire an affixation rule that shares important properties with our inflectional morphology (e.g. the rule that adds -ed to create the past tense, as in the transformation of walk into walk-ed). Using playback experiments, we show that tamarins discriminate between bisyllabic items that start with a specific 'prefix' syllable and those that end with the same syllable as a 'suffix'. These results suggest that some of the computational mechanisms subserving affixation in a diversity of languages are shared with other animals, relying on basic perceptual or memory primitives that evolved for non-linguistic functions.

Duplications and domain-generality.

Although specialized, adaptive behavioral traits are ubiquitous in the animal kingdom, at least in humans, there are considerable debates on whether the mind is primarily characterized by various special-purpose, domain-specific mechanisms or by a few general-purpose, domain-general mechanisms. Drawing from research on artificial language learning, associative learning, serial learning, executive control, and formal linguistics, I argue that neither domain-specificity nor domain-generality provide satisfactory descriptions when considering how cognitive mechanisms are implemented. I suggest that some cognitive mechanisms are domain-bound-they are available in multiple domains (and thus not domain-specific), but not in other domains (and thus not domain-general). Hence, these computations can be performed in many domains but not in others, can be recruited simultaneously by multiple domains, and, across domains, individual abilities with a given computation are relatively uncorrelated. Domain-bound mechanisms have a straightforward evolutionary interpretation: Analogously to the evolution of molecular and morphological structures, cognitive mechanisms can become duplicated over evolution, with independent copies in different domains. This and previous evidence for the importance of duplications for our cognitive abilities call for a revision of the concept domain-generality, suggesting that, in many cases, mechanisms traditionally seen as domain-general might really reflect a collection of local copies of specialized mechanisms. (PsycINFO Database Record (c) 2019 APA, all rights reserved).