How the development of executive function influences our moment-by-moment interactions with the real-world environment

Historically, the study of executive function (EF) development has relied on using experimental paradigms to assess EFs as abstract, time-invariant properties of individual brains. Here, we discuss new research that moves away from studying EFs purely as internal mental constructs, towards an approach that aims to understand how EFs are expressed through the inter-relationship between an individual’s brain and the world around them. We offer three illustrative examples of this approach. The first looks at how we learn to make predictions and anticipations based on different types of regularity in our early social and physical environment. The second looks at how we learn to correct, moment-by-moment, for changes in the outside world to maintain stability in the face of change. The third looks at how we allocate our attention on a moment-by-moment basis, in naturalistic settings. We discuss potential new therapeutic avenues for improving EFs arising from this research.

Memory for spatio-temporal contextual details during the retrieval of naturalistic episodes

Episodic memory entails the storage of events together with their spatio-temporal context and retrieval comprises the subjective experience of a link between the person who remembers and the episode itself. We used an encoding procedure with mobile-phones to generate experimentally-controlled episodes in the real world: object-images were sent to the participants' phone, with encoding durations up to 3 weeks. In other groups of participants, the same objects were encoded during the exploration of a virtual town (45 min) or using a standard laboratory paradigm, with pairs of object/place-images presented in a sequence of unrelated trials (15 min). At retrieval, we tested subjective memory for the objects (remember/familiar) and memory for the context (place and time). We found that accurate and confident context-memory increased the likelihood of “remember” responses, in all encoding contexts. We also tested the participants' ability to judge the temporal-order of the encoded episodes. Using a model of temporal similarity, we demonstrate scale-invariant properties of order-retrieval, but also highlight the contribution of non-chronological factors. We conclude that the mechanisms governing episodic memory retrieval can operate across a wide range of spatio-temporal contexts and that the multi-dimensional nature of the episodic traces contributes to the subjective experience of retrieval.

Desirable difficulties in statistical learning? How talker variability impacts artificial grammar learning

Contending with talker variability has been found to lead to processing costs but also benefits by focusing learners on invariant properties of the signal. These discrepant findings may indicate that talker variability acts as a desirable difficulty. That is, talker variability may lead to initial costs followed by long term benefits for retention and generalization. Adult participants learned an artificial grammar affording learning of multiple components by 1-, 2- or 8- talkers, tested at 3 time points. The 8-talker condition did not impact learning. The 2-talker condition negatively impacted some aspects of learning, but only under more difficult learning conditions. Across both experiments, generalization of the grammatical dependency was difficult. Taken together, we discovered that high and limited talker variability differentially impact artificial grammar learning. However, talker variability does not act as a desirable difficulty in the current paradigm, as the few evidenced costs were not related to long-term benefits.

Evaluation of Domain Generalization and Adaptation on Improving Model Robustness to Temporal Dataset Shift in Clinical Medicine

Importance: Temporal dataset shift associated with changes in healthcare over time is a barrier to deploying machine learning-based clinical decision support systems. Algorithms that learn robust models by estimating invariant properties across time periods for domain generalization (DG) and unsupervised domain adaptation (UDA) might be suitable to proactively mitigate dataset shift. Objective: To characterize the impact of temporal dataset shift on clinical prediction models and benchmark DG and UDA algorithms on improving model robustness. Design, Setting, and Participants: In this cohort study, intensive care unit patients from the MIMIC-IV database were categorized by year groups (2008-2010, 2011-2013, 2014-2016 and 2017-2019). Tasks were predicting mortality, long length of stay, sepsis and invasive ventilation. Feedforward neural networks were used as prediction models. The baseline experiment trained models using empirical risk minimization (ERM) on 2008-2010 (ERM[08-10]) and evaluated them on subsequent year groups. DG experiment trained models using algorithms that estimated invariant properties using 2008-2016 and evaluated them on 2017-2019. UDA experiment leveraged unlabelled samples from 2017-2019 for unsupervised distribution matching. DG and UDA models were compared to ERM[08-16] models trained using 2008-2016. Main Outcome(s) and Measure(s): Main performance measures were area-under-the-receiver-operating-characteristic curve (AUROC), area-under-the-precision-recall curve and absolute calibration error. Threshold-based metrics including false-positives and false-negatives were used to assess the clinical impact of temporal dataset shift and its mitigation strategies. Results: In the baseline experiments, dataset shift was most evident for sepsis prediction (maximum AUROC drop, 0.090; 95% confidence interval (CI), 0.080-0.101). Considering a scenario of 100 consecutively admitted patients showed that ERM[08-10] applied to 2017-2019 was associated with one additional false-negative among 11 patients with sepsis, when compared to the model applied to 2008-2010. When compared with ERM[08-16], DG and UDA experiments failed to produce more robust models (range of AUROC difference, -0.003-0.050). Conclusions and Relevance: DG and UDA failed to produce more robust models compared to ERM in the setting of temporal dataset shift. Alternate approaches are required to preserve model performance over time in clinical medicine.

A Systematic Study of the Cryogenic Milling of Chrysotile Asbestos

For more than 40 years, intensive research has been devoted to shedding light on the mechanisms of asbestos toxicity. Given the key role of fibre length in the mechanisms of asbestos toxicity, much work has been devoted to finding suitable comminution routes to produce fibres in desired size intervals. A promising method is cryogenic milling that, unlike other mechanical size reduction techniques, preserves the crystal–chemical properties of materials. In this study, the effect of cryogenic milling on the physical–chemical properties of commercial Russian chrysotile was studied in order to produce precise size fractions with invariant properties compared to the pristine fibres. In particular, two batches with fibres > 5 µm and <5 µm were prepared, as this limit sets their potential toxicity. The results are fundamental for future in vitro toxicity testing of this commercial product, widely used in chrysotile-friendly countries but not yet adequately studied. Results show that fibre length can be controlled by milling time under cryogenic conditions without inducing structural defects or amorphization; short fibres (95% L < 5 µm) can be obtained by cryogenic milling for 40 min, while 10 min is enough to yield long chrysotile fibres (90% L > 5 µm).

Invariance as a basis for necessity and laws

Many philosophers are baffled by necessity. Humeans, in particular, are deeply disturbed by the idea of necessary laws of nature. In this paper I offer a systematic yet down to earth explanation of necessity and laws in terms of invariance. The type of invariance I employ for this purpose generalizes an invariance used in meta-logic. The main idea is that properties and relations in general have certain degrees of invariance, and some properties/relations have a stronger degree of invariance than others. The degrees of invariance of highly-invariant properties are associated with high degrees of necessity of laws governing/describing these properties, and this explains the necessity of such laws both in logic and in science. This non-mysterious explanation has rich ramifications for both fields, including the formality of logic and mathematics, the apparent conflict between the contingency of science and the necessity of its laws, the difference between logical-mathematical, physical, and biological laws/principles, the abstract character of laws, the applicability of logic and mathematics to science, scientific realism, and logical-mathematical realism.

Geometric Invariants of Normal Curves under Conformal Transformation in $\mathbb{E}^3$

In this paper, we investigate the geometric invariant properties of a normal curve on a smooth immersed surface under conformal transformation. We obtain an invariant-sufficient condition for the conformal image of a normal curve. We also find the deviations of normal and tangential components of the normal curve under the same motion.