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We introduce an experimental paradigm for studying the cumulative cultural evolution of language. In doing so we provide the first experimental validation for the idea that cultural transmission can lead to the appearance of design without a designer. Our experiments involve the iterated learning of artificial languages by human participants. We show that languages transmitted culturally evolve in such a way as to maximize their own transmissibility: over time, the languages in our experiments become easier to learn and increasingly structured. Furthermore, this structure emerges purely as a consequence of the transmission of language over generations, without any intentional design on the part of individual language learners. Previous computational and mathematical models suggest that iterated learning provides an explanation for the structure of human language and link particular aspects of linguistic structure with particular constraints acting on language during its transmission. The experimental work presented here shows that the predictions of these models, and models of cultural evolution more generally, can be tested in the laboratory.

An increasing number of studies within digital pathology show the potential of artificial intelligence (AI) to diagnose cancer using histological whole slide images, which requires large and diverse data sets. While diversification may result in more generalizable AI-based systems, it can also introduce hidden variables. If neural networks are able to distinguish/learn hidden variables, these variables can introduce batch effects that compromise the accuracy of classification systems. The objective of the study was to analyze the learnability of an exemplary selection of hidden variables (patient age, slide preparation date, slide origin, and scanner type) that are commonly found in whole slide image data sets in digital pathology and could create batch effects. We trained four separate convolutional neural networks (CNNs) to learn four variables using a data set of digitized whole slide melanoma images from five different institutes. For robustness, each CNN training and evaluation run was repeated multiple times, and a variable was only considered learnable if the lower bound of the 95% confidence interval of its mean balanced accuracy was above 50.0%. A mean balanced accuracy above 50.0% was achieved for all four tasks, even when considering the lower bound of the 95% confidence interval. Performance between tasks showed wide variation, ranging from 56.1% (slide preparation date) to 100% (slide origin). Because all of the analyzed hidden variables are learnable, they have the potential to create batch effects in dermatopathology data sets, which negatively affect AI-based classification systems. Practitioners should be aware of these and similar pitfalls when developing and evaluating such systems and address these and potentially other batch effect variables in their data sets through sufficient data set stratification.

Learnability in Automated Driving (LiAD) is a neglected research topic, especially when considering the unpredictable and intricate ways humans learn to interact and use automated driving systems (ADS) over the sequence of time. Moreover, there is a scarcity of publications dedicated to LiAD (specifically extended learnability methods) to guide the scientific paradigm. As a result, this generates scientific discord and, thus, leaves many facets of long-term learning effects associated with automated driving in dire need of significant research courtesy. This, we believe, is a constraint to knowledge discovery on quality interaction design phenomena. In a sense, it is imperative to abstract knowledge on how long-term effects and learning effects may affect (negatively and positively) users’ learning and mental models. As well as induce changeable behavioural configurations and performances. In view of that, it may be imperative to examine operational concepts that may help researchers envision future scenarios with automation by assessing users’ learning ability, how they learn and what they learn over the sequence of time. As well as constructing a theory of effects (from micro, meso and macro perspectives), which may help profile ergonomic quality design aspects that stand the test of time. As a result, we reviewed the literature on learnability, which we mined for LiAD knowledge discovery from the experience perspective of long-term learning effects. Therefore, the paper offers the reader the resulting discussion points formulated under the Learnability Engineering Life Cycle. For instance, firstly, contextualisation of LiAD with emphasis on extended LiAD. Secondly, conceptualisation and operationalisation of the operational mechanics of LiAD as a concept in ergonomic quality engineering (with an introduction of Concepts for Applying Learnability Engineering (CALE) research based on LiAD knowledge discovery). Thirdly, the systemisation of implementable long-term research strategies towards comprehending behaviour modification associated with extended LiAD. As the vehicle industry revolutionises at a rapid pace towards automation and artificially intelligent (AI) systems, this knowledge is useful for illuminating and instructing quality interaction strategies and Quality Automated Driving (QAD).

Deep neural networks drive the success of natural language processing. A fundamental property of language is its compositional structure, allowing humans to systematically produce forms for new meanings. For humans, languages with more compositional and transparent structures are typically easier to learn than those with opaque and irregular structures. However, this learnability advantage has not yet been shown for deep neural networks, limiting their use as models for human language learning. Here, we directly test how neural networks compare to humans in learning and generalizing different languages that vary in their degree of compositional structure. We evaluate the memorization and generalization capabilities of a large language model and recurrent neural networks, and show that both deep neural networks exhibit a learnability advantage for more structured linguistic input: neural networks exposed to more compositional languages show more systematic generalization, greater agreement between different agents, and greater similarity to human learners. This study demonstrates that deep neural networks, like humans, show a learnability advantage when trained on languages with more structured linguistic input, resulting in closer alignment with human learning. This finding has important implications for both understanding human language acquisition and designing artificial language systems.

While questionnaires for assessing digital literacy exist, there is still a need for an easy-to-use and implementable questionnaire for assessing digital readiness in a broader sense. Additionally, learnability should be assessed to identify those patients who need additional training to use digital tools in a health care setting. The aim of the development of the Digital Health Readiness Questionnaire (DHRQ) was to create a short, usable, and freely accessible questionnaire that was designed from a clinical practice perspective. It was a prospective single-center survey study conducted in Jessa Hospital Hasselt in Belgium. The questionnaire was developed with a panel of field experts with questions in following 5 categories: digital usage, digital skills, digital literacy, digital health literacy, and digital learnability. All participants who were visiting the cardiology department as patients between February 1, 2022, and June 1, 2022, were eligible for participation. Cronbach α and confirmatory factor analysis were performed. A total number of 315 participants were included in this survey study, of which 118 (37.5%) were female. The mean age of the participants was 62.6 (SD 15.1) years. Cronbach α analysis yielded a score of >.7 in all domains of the DHRQ, which indicates acceptable internal consistency. The fit indices of the confirmatory factor analysis showed a reasonably good fit: standardized root-mean-square residual=0.065, root-mean-square error of approximation=0.098 (95% CI 0.09-0.106), Tucker-Lewis fit index=0.895, and comparative fit index=0.912. The DHRQ was developed as an easy-to-use, short questionnaire to assess the digital readiness of patients in a routine clinical setting. Initial validation demonstrates good internal consistency, and future research will be needed to externally validate the questionnaire. The DHRQ has the potential to be implemented as a useful tool to gain insight into the patients who are treated in a care pathway, tailor digital care pathways to different patient populations, and offer those with low digital readiness but high learnability appropriate education programs in order to let them take part in the digital pathways.

Humans and other animals often infer spurious associations among unrelated events. However, such superstitious learning is usually accounted for by conditioned associations, raising the question of whether an animal could develop more complex cognitive structures independent of reinforcement. Here, we tasked monkeys with discovering the serial order of two pictorial sets: a “learnable” set in which the stimuli were implicitly ordered and monkeys were rewarded for choosing the higher-rank stimulus and an “unlearnable” set in which stimuli were unordered and feedback was random regardless of the choice. We replicated prior results that monkeys reliably learned the implicit order of the learnable set. Surprisingly, the monkeys behaved as though some ordering also existed in the unlearnable set, showing consistent choice preference that transferred to novel untrained pairs in this set, even under a preference-discouraging reward schedule that gave rewards more frequently to the stimulus that was selected less often. In simulations, a model-free reinforcement learning algorithm (Q-learning) displayed a degree of consistent ordering among the unlearnable set but, unlike the monkeys, failed to do so under the preference-discouraging reward schedule. Our results suggest that monkeys infer abstract structures from objectively random events using heuristics that extend beyond stimulus–outcome conditional learning to more cognitive model-based learning mechanisms.

This paper performs an investigation of Kleinberg’s axioms (from both an intuitive and formal standpoint) as they relate to the well-known k -mean clustering method. The axioms, as well as a novel variations thereof, are analyzed in Euclidean space. A few natural properties are proposed, resulting in k -means satisfying the intuition behind Kleinberg’s axioms (or, rather, a small, and natural variation on that intuition). In particular, two variations of Kleinberg’s consistency property are proposed, called centric consistency and motion consistency. It is shown that these variations of consistency are satisfied by k-means.

COVID-19 has disrupted educational institutes across the world. Teachers and students are now forced to teach and study online for an unidentified period, which severely hampers the learning capacity as well the student’s intention toward entrepreneurship. This study compared the impact of traditional teaching and teaching through online management simulation games on student learning performance and further leads to entrepreneurial intention. To further understand the desirability of business simulation games, we used the technology acceptance model (TAM) and extended it by employing knowledge sharing, knowledge application, learnability, perceived pleasure, and self-efficacy as exogenous variables. For this purpose, time-lagged data were collected from 277 students enrolled in entrepreneurship courses in public sector universities. To deal with homogeneity and generalizability issues, students from different collaborative universities were asked to participate in the study. Structural equation modeling was employed for analysis, where the results depict that the students learning performance was enhanced after using simulation games compared to regular theoretical online lectures, which further encouraged them to be entrepreneurs. We also concluded that simulation games are novel and effective online teaching methodology for students during a time of crisis. The study concludes with its theoretical, practical implications, and directions for future researchers.

When speaking to infants, adults often produce speech that differs systematically from that directed to other adults. To quantify the acoustic properties of this speech style across a wide variety of languages and cultures, we extracted results from empirical studies on the acoustic features of infant-directed speech. We analysed data from 88 unique studies (734 effect sizes) on the following five acoustic parameters that have been systematically examined in the literature: fundamental frequency ( f 0 ), f 0 variability, vowel space area, articulation rate and vowel duration. Moderator analyses were conducted in hierarchical Bayesian robust regression models to examine how these features change with infant age and differ across languages, experimental tasks and recording environments. The moderator analyses indicated that f 0 , articulation rate and vowel duration became more similar to adult-directed speech over time, whereas f 0 variability and vowel space area exhibited stability throughout development. These results point the way for future research to disentangle different accounts of the functions and learnability of infant-directed speech by conducting theory-driven comparisons among different languages and using computational models to formulate testable predictions. This meta-analysis examines different features of infant-directed speech across languages and infant ages. The results suggest that there are cross-linguistic tendencies and that caregivers adjust the properties of infant-directed speech to suit infants’ changing needs.