Transdisciplinarity,neuro-techno-philosophy,and the future of philosophy

Philosophy and science have always striven to make sense of the world, continuously influencing each other in the process. Their interplay paved the way for neurophilosophy, which harnesses neuroscientific insights to address traditionally philosophical questions. Given the rapid neuroscientific and technological advances in recent years, this paper argues that philosophers who wish to tackle intractable philosophical problems and influence public discourse and policies should engage in neuro-techno-philosophy. This novel type of inquiry describes the transdisciplinary endeavor of philosophers, (neuro)scientists, and others to anticipate the societal implications of the impending transformations of subjects and theorizers. While human enhancement is likely to irreversibly change what it means to be human, disruptive technologies might lead to the emergence of artificial intelligent agents and human-machine hybrids. The paper predicts that neuro-techno-philosophy will be indispensable to understanding and engaging with these game-changing innovations and thus play a pivotal role in the future of philosophy.     

Hyperbolic modeling and assessment of hypothetical health behaviors during a viral outbreak using crowdsourced samples

The COVID-19 pandemic provided an opportunity to investigate factors related to public response to public health measures, which could help better prepare implementation of similar measures for inevitable future pandemics. To understand individual and environmental factors that influence likelihood in engaging in personal and public health measures, three crowdsourced convenience samples from Amazon Mechanical Turk (MTurk) completed likelihood-discounting tasks of engaging in health behaviors given a variety of hypothetical viral outbreak scenarios. Experiment 1 assessed likelihood of mask wearing for a novel virus. Experiment 2 assessed vaccination likelihood based on efficacy and cost. Experiment 3 assessed likelihood of seeking health care based on number of symptoms and cost of treatment. Volume-based measures and three-dimensional modeling were used to analyze hypothetical decision making. Hypothetical public and personal health participation increased as viral fatality increased and generally followed a hyperbolic function. Public health participation was moderated by political orientation and trust in science, whereas treatment-seeking was only moderated by income. Analytic methods used in this cross-sectional study predicted population-level outcomes that occurred later in the pandemic and can be extended to various health behaviors.     

Correlation between performance and quantity/variability of leg exploration in a contingency learning task during infancy

Quantity and quality of motor exploration are proposed to be fundamental for infant motor development. However, it is still not clear what types of motor exploration contribute to learning. To determine whether changes in quantity of leg movement and/or variability of leg acceleration are related to performance in a contingency learning task, twenty 6–8-month-old infants with typical development participated in a contingency learning task. During this task, a robot provided reinforcement when the infant’s right leg peak acceleration was above an individualized threshold. The correlation coefficient between the infant’s performance and the change in quantity of right leg movement, linear variability, and nonlinear variability of right leg movement acceleration from baseline were calculated. Simple linear regression and multiple linear regression were calculated to explain the contribution of each variable to the performance individually and collectively. We found significant correlation between the performance and the change in quantity of right leg movement (r = 0.86, p < 0.001), linear variability (r = 0.71, p < 0.001), and nonlinear variability (r = 0.62, p = 0.004) of right leg movement acceleration, respectively. However, multiple linear regression showed that only quantity and linear variability of leg movements were significant predicting factors for the performance ratio (p < 0.001, adjusted R² = 0.94). These results indicated that the quantity of exploration and variable exploratory strategies could be critical for the motor learning process during infancy.     

Coding infant engagement in the Face-to-Face Still-Face paradigm using deep neural networks

BackgroundThe Face-to-Face Still-Face (FFSF) task is a validated and commonly used observational measure of mother-infant socio-emotional interactions. With the ascendence of deep learning-based facial emotion recognition, it is possible that common complex tasks, such as the coding of FFSF videos, could be coded with a high degree of accuracy by deep neural networks (DNNs). The primary objective of this study was to test the accuracy of four DNN image classification models against the coding of infant engagement conducted by two trained independent manual raters.Methods68 mother-infant dyads completed the FFSF task at three timepoints. Two trained independent raters undertook second-by-second manual coding of infant engagement into one of four classes: 1) positive affect, 2) neutral affect, 3) object/environment engagement, and 4) negative affect.ResultsTraining four different DNN models on 40,000 images, we achieved a maximum accuracy of 99.5% on image classification of infant frames taken from recordings of the FFSF task with a maximum inter-rater reliability (Cohen's κ-value) of 0.993.LimitationsThis study inherits all sampling and experimental limitations of the original study from which the data was taken, namely a relatively small and primarily White sample.ConclusionsBased on the extremely high classification accuracy, these findings suggest that DNNs could be used to code infant engagement in FFSF recordings. DNN image classification models may also have the potential to improve the efficiency of coding all observational tasks with applications across multiple fields of human behavior research.     

规则变型推理对远迁移问题解决的促进

为了避免多重变异样例学习和采用题海战术,并能促进规则样例学习的远迁移问题解决,开创出原样例学习后对原型规则做出变型推理的学习方法,以算术应用题的解题样例为原样例学习材料进行了4项实验研究。结果显示：四年级小学生运用该方法可有效促进远迁移问题的解决;其变型推理能力存在数学成绩等级差异;提供问题情境和推理提示能提高部分学生规则变型推理成绩和远迁移测验成绩。实验结果证明,规则变型推理是促进远迁移问题解决的有效方法之一。     

Modality-general benefit of eye-closure on the retrieval of intentionally learned information

The beneficial effect of eye-closure during retrieval was demonstrated in many studies addressing eyewitness memory or memory of episodic events. Fewer studies examined the effect concerning the intentional learning of verbal information. Furthermore, the question of whether the eye-closure effect is modality-specific, boosting visual memory only, or modality-general, boosting also other forms of memory (e.g., auditory memory), is still open. These issues were addressed in the present study. Participants (N = 129) were asked to study aurally and visually presented lists of unrelated nouns (within-subjects). During free recall, participants either kept their eyes open or closed their eyes (between-subjects). Eye-closure resulted in better free recall than keeping the eyes open. Importantly, this effect emerged for both visually and aurally presented word lists, suggesting that the effect of closing the eyes is rather modality-general. The results are discussed with respect to limitations of previous studies and practical implications.     

Not all is forgotten: Children's associative matrices for features of a word learning episode

Word learning studies traditionally examine the narrow link between words and objects, indifferent to the rich contextual information surrounding objects. This research examined whether children attend to this contextual information and construct an associative matrix of the words, objects, people, and environmental context during word learning. In Experiment 1, preschool-aged children (age: 3;2–5;11 years) were presented with novel words and objects in an animated storybook. Results revealed that children constructed associations beyond words and objects. Specifically, children attended to and had the strongest associations for features of the environmental context but failed to learn word-object associations. Experiment 2 demonstrated that children (age: 3;0–5;8 years) leveraged strong associations for the person and environmental context to support word-object mapping. This work demonstrates that children are especially sensitive to the word learning context and use associative matrices to support word mapping. Indeed, this research suggests associative matrices of the environment may be foundational for children's vocabulary development.