S-shaped learning curves

Impact of feeding habits on the development of language-specific processing of phonemes in brain: An event-related potentials study

Introduction

Infancy is a stage characterized by multiple brain and cognitive changes. In a short time, infants must consolidate a new brain network and develop two important properties for speech comprehension: phonemic normalization and categorical perception. Recent studies have described diet as an essential factor in normal language development, reporting that breastfed infants show an earlier brain maturity and thus a faster cognitive development. Few studies have described a long-term effect of diet on phonological perception.

Methods

To explore that effect, we compared the event-related potentials (ERPs) collected during an oddball paradigm (frequent /pa/80%, deviant/ba/20%) of infants fed with breast milk (BF), cow-milk-based formula (MF), and soy-based formula (SF), which were assessed at 3, 6, 9, 12, and 24 months of age [Mean across all age groups: 127 BF infants, Mean (M) 39.6 gestation weeks; 121 MF infants, M = 39.16 gestation weeks; 116 SF infants, M = 39.16 gestation weeks].

Results

Behavioral differences between dietary groups in acoustic comprehension were observed at 24-months of age. The BF group displayed greater scores than the MF and SF groups. In phonological discrimination task, the ERPs analyses showed that SF group had an electrophysiological pattern associated with difficulties in phonological-stimulus awareness [mismatch negativity (MMN)-2 latency in frontal left regions of interest (ROI) and longer MMN-2 latency in temporal right ROI] and less brain maturity than BF and MF groups. The SF group displayed more right-lateralized brain recruitment in phonological processing at 12-months old.

Discussion

We conclude that using soy-based formula in a prolonged and frequent manner might trigger a language development different from that observed in the BF or MF groups. The soy-based formula's composition might affect frontal left-brain area development, which is a nodal brain region in phonological-stimuli awareness.

Mathematical modeling of SARS-nCoV-2 virus in Tamil Nadu, South India

The purpose of this paper is to build a mathematical model for the study of the roles of lock-down, social distancing, vaccination, detection efficiency, and health care capacity planning of the COVID-19 pandemic taking into account the demographic topology of the State of Tamil Nadu, India. Two mathematical models are proposed for the evolution of the first and second wave of COVID-19 pandemic. The model for the first wave considers lock-down orders, social distancing measures, and detection efficiency. The model for the second wave considers more sub-populations and incorporates two more elements, vaccination and health care capacity. Daily reported data on the evolution of the COVID-19 pandemic are used to determine the parameter values. The dynamics produced by the mathematical model closely follow the evolution of COVID-19 in the State of Tamil Nadu. Numerical simulation shows that the lock-down effect is limited. Social distancing implementation and detection of positive cases are relatively ineffective compared with other big cities. Shortage of health care resources is one of the factors responsible for rapidly spreading in the second wave in Tamil Nadu.

Randomly fluctuating neural connections may implement a consolidation mechanism that explains classic memory laws

How can we reconcile the massive fluctuations in neural connections with a stable long-term memory? Two-photon microscopy studies have revealed that large portions of neural connections (spines, synapses) are unexpectedly active, changing unpredictably over time. This appears to invalidate the main assumption underlying the majority of memory models in cognitive neuroscience, which rely on stable connections that retain information over time. Here, we show that such random fluctuations may in fact implement a type of memory consolidation mechanism with a stable very long-term memory that offers novel explanations for several classic memory 'laws', namely Jost's Law (1897: superiority of spaced learning) and Ribot's Law (1881: loss of recent memories in retrograde amnesia), for which a common neural basis has been postulated but not established, as well as other general 'laws' of learning and forgetting. We show how these phenomena emerge naturally from massively fluctuating neural connections.

The S-Shaped Performance Curve Prevails in Practicing Juggling

The development of performance, such as learning a new motor skill, can be represented in a performance curve. The shape of the performance curve is both of theoretical and practical relevance. Here, the author studied the interday performance of juggling over a period of 17 days in 112 college students. The results showed that 60% of participants followed an S-shaped performance curve with the inflection date on the 11th day, followed by a decelerated (20%), accelerated (14%), and linear curve (6%). As expected, except on Day 1, male participants performed at least 33% better than female participants on each practice day. Also as expected, learning performance was found to depend on the type of performance curve with the best learning performance exhibited by the linear group. The results further revealed that pooling all participants’ performance together without considering the percentage of each underlying type of performance curve would lead to biased, nonrepresentative results. Given the variety of the observed performance curves and the dominance of the S-shaped performance curve among them, coaches should continuously monitor the shape of an individual’s performance curve.

Non-invasive Neurophysiology in Learning and Training: Mechanisms and a SWOT Analysis

Although many scholars deem non-invasive measures of neurophysiology to have promise in assessing learning, these measures are currently not widely applied, neither in educational settings nor in training. How can non-invasive neurophysiology provide insight into learning and how should research on this topic move forward to ensure valid applications? The current article addresses these questions by discussing the mechanisms underlying neurophysiological changes during learning followed by a SWOT (strengths, weaknesses, opportunities, and threats) analysis of non-invasive neurophysiology in learning and training. This type of analysis can provide a structured examination of factors relevant to the current state and future of a field. The findings of the SWOT analysis indicate that the field of neurophysiology in learning and training is developing rapidly. By leveraging the opportunities of neurophysiology in learning and training (while bearing in mind weaknesses, threats, and strengths) the field can move forward in promising directions. Suggestions for opportunities for future work are provided to ensure valid and effective application of non-invasive neurophysiology in a wide range of learning and training settings.

Non-invasive neurophysiological measures of learning: A meta-analysis

In a meta-analysis of 113 experiments we examined neurophysiological outcomes of learning, and the relationship between neurophysiological and behavioral outcomes of learning. Findings showed neurophysiology yielding large effect sizes, with the majority of studies examining electroencephalography and eye-related outcome measures. Effect sizes on neurophysiological outcomes were smaller than effect sizes on behavioral outcomes, however. Neurophysiological outcomes were, but behavioral outcomes were not, influenced by several modulating factors. These factors included the sensory system in which learning took place, number of learning days, whether feedback on performance was provided, and age of participants. Controlling for these factors resulted in the effect size differences between behavior and neurophysiology to disappear. The findings of the current meta-analysis demonstrate that neurophysiology is an appropriate measure in assessing learning, particularly when taking into account factors that could have an influence on neurophysiology. We propose a first model to aid further studies that are needed to examine the exact interplay between learning, neurophysiology, behavior, individual differences, and task-related aspects.

Semantic Similarity to Known Second Language Words Impacts Learning of New Meanings

Second language (L2) learners need to continually learn new L2 words as well as additional meanings of previously learned L2 words. The present study investigated the influence of semantic similarity on the growth curve of learning of artificially paired new meanings of previously known L2 words in Chinese–English bilinguals. The results of a translation recognition task showed that related meanings are learned faster and more accurately than unrelated meanings. The advantage of learning related new meaning persisted and increased for a week after learning the new meanings. These results suggest that semantic similarities impact the learning of new meanings for known L2 words, and that the shared features between previously known and new meanings of a word facilitate the process of incorporating the related new meaning into the lexical semantic network. Our results are discussed under the framework of the connectionist model.

Computational modeling of epiphany learning

Models of reinforcement learning (RL) are prevalent in the decision-making literature, but not all behavior seems to conform to the gradual convergence that is a central feature of RL. In some cases learning seems to happen all at once. Limited prior research on these "epiphanies" has shown evidence of sudden changes in behavior, but it remains unclear how such epiphanies occur. We propose a sequential-sampling model of epiphany learning (EL) and test it using an eye-tracking experiment. In the experiment, subjects repeatedly play a strategic game that has an optimal strategy. Subjects can learn over time from feedback but are also allowed to commit to a strategy at any time, eliminating all other options and opportunities to learn. We find that the EL model is consistent with the choices, eye movements, and pupillary responses of subjects who commit to the optimal strategy (correct epiphany) but not always of those who commit to a suboptimal strategy or who do not commit at all. Our findings suggest that EL is driven by a latent evidence accumulation process that can be revealed with eye-tracking data.

Two years of unintended consequences: introducing an electronic health record system in a hospice in Scotland

AIMS AND OBJECTIVES

To explore the impact of implementing an electronic health record system on staff at a Scottish hospice.

BACKGROUND

Electronic health records are broadly considered preferable to paper-based systems. However, changing from one system to the other is difficult. This study analysed the impact of this change in a Scottish hospice.

DESIGN

Naturalistic prospective repeated-measures mixed-methods approach.

METHODS

Data on the usability of the system, staff engagement and staff experience were obtained at four time points spanning 30 months from inception. Quantitative data were obtained from surveys, and qualitative from concurrent analysis of free-text comments and focus group. Participants were all 150 employees of a single hospice in Scotland.

RESULTS

Both system usability and staff engagement scores decreased for the first two years before recovering at 30 months. Staff experience data pointed to two main challenges: (1) Technical issues, with subthemes of accessibility and usability. (2) Cultural issues, with subthemes of time, teamwork, care provision and perception of change.

CONCLUSIONS

It took 30 months for system usability and staff engagement scores to rise, after falling significantly for the first two years. The unintended outcomes of implementation included challenges to the way the patient story was both recorded and communicated. Nevertheless, this process of change was found to be consistent with the 'J-curve' theory of organisational change, and as such, it is both predictable and manageable for other organisations.

RELEVANCE TO CLINICAL PRACTICE

It is known that implementing an electronic health record system is complex. This paper puts parameters on this complexity by defining both the nature of the complexity ('J' curve) and the time taken for the organisation to begin recovery from the challenges (two years). Understanding these parameters will help health organisations across the world plan more strategically.

Do Resit Exams Promote Lower Investments of Study Time? Theory and Data from a Laboratory Study

Although many educational institutions allow students to resit exams, a recently proposed mathematical model suggests that this could lead to a dramatic reduction in study-time investment, especially in rational students. In the current study, we present a modification of this model in which we included some well-justified assumptions about learning and performance on multiple-choice tests, and we tested its predictions in two experiments in which participants were asked to invest fictional study time for a fictional exam. Consistent with our model, the prospect of a resit exam was found to promote lower investments of study time for a first exam and this effect was stronger for participants scoring higher on the cognitive reflection test. We also found that the negative effect of resit exams on study-time investment was attenuated when access to the resit was made uncertain by making it probabilistic or dependent on obtaining a minimal, non-passing grade for the first attempt. Taken together, these results suggest that offering students resit exams may compromise the achievement of learning goals, and they raise the more general implication that second chances promote risky behavior.