From improvisation to learning: how naturalness and systematicity shape language evolution

Silent gesture studies, in which hearing participants from different linguistic backgrounds produce gestures to communicate events, have been used to test hypotheses about the cognitive biases that govern cross-linguistic word order preferences. In particular, the differential use of SOV and SVO order to communicate, respectively, extensional events (where the direct object exists independently of the event; e.g., girl throws ball) and intensional events (where the meaning of the direct object is potentially dependent on the verb; e.g., girl thinks of ball), has been suggested to represent a natural preference, demonstrated in improvisation contexts. However, natural languages tend to prefer systematic word orders, where a single order is used regardless of the event being communicated. We present a series of studies that investigate ordering preferences for SOV and SVO orders using an online forced-choice experiment, where participants select orders for different events i) in the absence of conventions and ii) after learning event-order mappings in different frequencies in a regularisation experiment. Our results show that natural ordering preferences arise in the absence of conventions, replicating previous findings from production experiments. In addition, we show that participants regularise the input they learn in the manual modality in two ways, such that, while the preference for systematic order patterns increases through learning, it exists in competition with the natural ordering preference, that conditions order on the semantics of the event. Using our experimental data in a computational model of cultural transmission, we show that this pattern is expected to persist over generations, suggesting that we should expect to see evidence of semantically-conditioned word order variability in at least some languages.

Privacy of study participants in open-access Health and Demographic Surveillance System data: A requirements analysis for data anonymisation (Preprint)

BACKGROUND Sharing and anonymising data have become hot topics for individuals, organisations, and countries around the world. Open-access sharing of anonymised data containing sensitive information about individuals makes the most sense whenever the utility of the data can be preserved and the risk of disclosure can be kept below acceptable levels. In this case, researchers can use the data without access restrictions and limitations. OBJECTIVE The goal of this paper is to highlight solutions and requirements for sharing longitudinal health and surveillance event history data in form of open-access data. The challenges lie in the anonymisation of multiple event dates and the time-varying variables. A sequential approach that adds noise to the event dates is proposed. This approach maintains the event order and preserves the average time between events. Additionally, a nosy neighbor distance-based matching approach to estimate the risk is proposed. Regarding dealing with the key variables that change over time such as educational level or occupation, we make two proposals, one based on limiting the intermediate status of a person (e.g. on education), and the other to achieve k-anonymity in subsets of the data. The proposed approaches were applied to the Karonga Health and Demographic Surveillance System (HDSS) core dataset, which contains longitudinal data from 1995 to the end of 2016 and includes 280,381 event records with time-varying, socio-economic variables and demographic information on individuals. The proposed anonymisation strategy lowers the risk of disclosure to acceptable levels thus allowing sharing of the data. METHODS statistical disclosure control, k-anonymity, adding noise, disclosure risk measurement, event history data anonymization, longitudinal data anonymization, data utility by visual comparisons. RESULTS Anonymized version of event history data including longitudinal information on individuals over time with high data utility. CONCLUSIONS The proposed anonymisation of study participants in event history data including static and time-varying status variables, specifically applied to longitudinal health and demographic surveillance system data, led to an anonymized data set with very low disclosure risk and high data utility ready to be shared to the public in form of an open-access data set. Different level of noise for event history dates were evaluated for disclosure risk and data utility. It turned out that high utility had been achieved even with the highest level of noise. Details matters to ensure consistency/credibility. Most important, the sequential noise approach presented in this paper maintains the event order. It has been shown that not even the event order is preserved but also the time between events is well maintained in comparison to the original data. We also proposed an anonymization strategy to handle the information of time-varying status of educational, occupational level of a person, year of death, year of birth, and number of events of a person. We proposed an approach that preserves the data utility well but limit the number of educational and occupational levels of a person. Using distance-based neighborhood matching we simulated an attack under a nosy neighbor situation and by using a worst-case scenario where attackers has full information on the original data. It could be shown that the disclosure risk is very low even by assuming that the attacker’s data base and information is optimal. The HDSS and medical science research communities in LMIC settings will be the primary beneficiaries of the results and methods presented in this science article, but the results will be useful for anyone working on anonymising longitudinal datasets possibly including also time-varying information and event history data for purposes of sharing. In other words, the proposed approaches can be applied to almost any event history data, and, additionally, to event history data including static and/or status variables that changes its entries in time.

Structuring time: The hippocampus constructs sequence memories that generalize temporal relations across experiences

The hippocampal-entorhinal region supports memory for episodic details, such as temporal relations of sequential events, and mnemonic constructions combining experiences for inferential reasoning. However, it is unclear whether hippocampal event representations reflect temporal relations derived from mnemonic constructions, event order, or elapsing time, and whether they generalize temporal relations across similar sequences. Here, participants mnemonically constructed times of events from multiple sequences using infrequent cues and their experience of passing time. After learning, event representations in the anterior hippocampus reflected sequence relations based on constructed times. These event representations generalized across sequences, revealing distinct representational formats for events from the same or different sequences. Structural knowledge about time patterns, abstracted from different sequences, biased the construction of specific event times. These findings demonstrate that the hippocampus reconciles representations of specific relations with the generalization across different episodes, consistent with memory-based constructions combining episodic details and general knowledge to simulate scenarios.

Linking Detail to Temporal Structure in Naturalistic-Event Recall

Decades of memory research demonstrate the importance of temporal organization in recall dynamics, using laboratory stimuli (i.e., word lists) at seconds- to minutes-long delays. Little is known, however, about such organization in recall of richer and more remote real-world experiences, in which the focus is usually on memory content without reference to event order. Here, 119 younger and older adults freely recalled extended real-world experiences, for which the encoding sequence was controlled, after 2 days or 1 week. We paired analytical tools from the list-learning and autobiographical memory literatures to measure spontaneous contextual dynamics and details in these recall narratives. Recall dynamics were organized by temporal context (contiguity and forward asymmetry), and organization was reduced in older age, despite similar serial position effects and recall initiation across age groups. Across participants, organization was positively associated with richness of episodic detail, providing evidence for a link between reexperiencing past events and reinstating their spatiotemporal context.

Efficacy and immune-related adverse event associations in avelumab-treated patients

BackgroundAdverse events (AEs) of special interest that arise during treatment with immune checkpoint inhibitors, including immune-related AEs (irAEs), have been reported to be associated with improved clinical outcomes. We analyzed patients treated with avelumab from the JAVELIN Solid Tumor and Merkel 200 trials, examining the association between AEs and efficacy while adjusting for confounding factors such as treatment duration and event order.MethodsWe analyzed efficacy and safety data from 1783 patients treated with the programmed death ligand 1 inhibitor avelumab who were enrolled in expansion cohorts of the JAVELIN Solid Tumor and Merkel 200 trials. To analyze the association between irAEs and efficacy with regard to survival, we used a time-dependent Cox model with time-varying indicators for irAEs, as well as multistate models that accounted for competing risks and time inhomogeneity.Results295 patients (16.5%) experienced irAEs and 454 patients (25.5%) experienced infusion-related reactions. There was a reduced risk of death in patients who experienced irAEs compared with those who did not (HR 0.71, 95% CI 0.59 to 0.85) using the time-dependent Cox model. The multistate model did not suggest that the occurrence of irAEs could predict response; however, it predicted a higher chance of irAEs occurring after a response. No association was observed between response and infusion-related reactions.ConclusionsPatients who experience irAEs showed improved survival. Although irAEs are not predictors for response to immune checkpoint inhibitors, increased vigilance for irAEs is needed after treatment with avelumab.Trial registration numbersNCT01772004 and NCT02155647.

DEO: A Dynamic Event Order Strategy for t-way Sequence Covering Array Test Data Generation

Sequence covering array (SCA) generation is an active research area in recent years. Unlike the sequence-less covering arrays (CA), the order of sequence varies in the test case generation process. This paper reviews the state-of-the-art of the SCA strategies, earlier works reported that finding a minimal size of a test suite is considered as an NP-Hard problem. In addition, most of the existing strategies for SCA generation have a high order of complexity due to the generation of all combinatorial interactions by adopting one-test-at-a-time fashion. Reducing the complexity by adopting one-parameter- at-a-time for SCA generation is a challenging process. In addition, this reduction facilitates the supporting for a higher strength of coverage. Motivated by such challenge, this paper proposes a novel SCA strategy called Dynamic Event Order (DEO), in which the test case generation is done using one-parameter-at-a-time fashion. The details of the DEO are presented with a step-by-step example to demonstrate the behavior and show the correctness of the proposed strategy. In addition, this paper makes a comparison with existing computational strategies. The practical results demonstrate that the proposed DEO strategy outperforms the existing strategies in term of minimal test size in most cases. Moreover, the significance of the DEO increases as the number of sequences increases and/ or the strength of coverage increases. Furthermore, the proposed DEO strategy succeeds to generate SCAs up to t=7. Finally, the DEO strategy succeeds to find new upper bounds for SCA. In fact, the proposed strategy can act as a research vehicle for variants future implementation.

Verification Method for Accumulative Event Relation of Message Passing Behavior with Process Tree for IoT Systems

In this paper, we proposed a verification method for the message passing behavior of IoT systems by checking the accumulative event relation of process models. In an IoT system, it is hard to verify the behavior of message passing by only looking at the sequence of packet transmissions recorded in the system log. We proposed a method to extract event relations from the log and check for any minor deviations that exist in the system. Using process mining, we extracted the variation of a normal process model from the log. We checked for any deviation that is hard to be detected unless the model is accumulated and stacked over time. Message passing behavior can be verified by comparing the similarity of the process tree model, which represents the execution relation between each message passing event. As a result, we can detect minor deviations such as missing events and perturbed event order with occurrence probability as low as 3%.