Ansgar D. Endress’s research while affiliated with University of London and other places

Statistical learning is a mechanism for detecting associations among co-occurring elements in many domains and species. A key controversy is whether it leads to memory for discrete chunks composed of these associated elements, or merely to pairwise associations among elements. Critical evidence for the mere-association view comes from the “phantom-word” phenomenon, where learners recognize statistically coherent but unattested items better than actually presented items with weaker internal associations, suggesting that they prioritize pairwise associations over memories for discrete units. However, this phenomenon has only been demonstrated for sequentially presented stimuli, but not for simultaneously presented visual shapes, where learners might prioritize discrete units over pairwise associations. Here, I ask whether the phantom-word phenomenon can be observed with simultaneously presented visual shapes. Learners were familiarized with scenes combining two triplets of visual shapes (hereafter “words”). They were then tested on their recognition of these words vs. part-words (attested items with weaker internal associations), of phantom-words (unattested items with strong internal associations) vs. part-words, and of words vs. phantom-words. Learners preferred both words and phantom-words over part-words and showed no preference for words over phantom-words. This suggests that, as for sequentially input, statistical learning in simultaneously presented shapes leads primarily to pairwise associations rather than to memories for discrete chunks. However, as, in some analyses, the preference for words over part-words was slightly higher than for phantom-words over part-words, the results do not rule out that, for simultaneous presented items, learners might have some limited sensitivity to frequency of occurrence.

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.

Is formal simplicity a guide to learning in humans, as simplicity is said to be a guide to the acceptability of theories in science? Does simplicity determine the difficulty of various learning tasks? I argue that, similarly to how scientists sometimes preferred complex theories when this facilitated calculations, results from perception, learning and reasoning suggest that formal complexity is generally unrelated to what is easy to learn and process by humans, and depends on assumptions about available representational and processing primitives. “Simpler” hypotheses are preferred only when they are also easier to process. Historically, “simpler”, easier‐to‐process, scientific theories might also be preferred if they are transmitted preferentially. Empirically viable complexity measures should build on the representational and processing primitives of actual learners, even if explanations of their behaviour become formally more complex.

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.

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.

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).

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).

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.

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.