A mixed-methods examination of autonomous sensory meridian response: Comparison to frisson

Autonomous Sensory Meridian Response (ASMR) is a pleasurable, head-oriented tingling sensation, typically induced by exposure to audiovisual triggers, producing feelings of relaxation and euphoria. This article explores the induction of ASMR experiences in a laboratory setting amongst non-specialised participants, as well as the relationship between ASMR and frisson, or ‘musical chills’. In previous work, the ASMR-15 was found to be a reliable measure of ASMR propensity, however, the predictive validity of the measure has yet to be determined. The aim of this study was to assess whether ASMR-15 scores predict greater ASMR induction in an experimental setting. To address this, N = 100 undergraduate psychology students completed the ASMR-15 and a measure of frisson, before viewing ASMR stimuli under controlled conditions. Mixed-methods analyses indicated the successful induction of ASMR amongst some participants, convergence between ASMR-15 scores and video ratings, as well as divergence between ASMR and frisson scores.     

Opportunities for learning and social interaction in infant sitting: Effects of sitting support,sitting skill,and gross motor delay

The development of independent sitting changes everyday opportunities for learning and has cascading effects on cognitive and language development. Prior to independent sitting, infants experience the sitting position with physical support from caregivers. Why does supported sitting not provide the same input for learning that is experienced in independent sitting? This question is especially relevant for infants with gross motor delay, who require support in sitting for many months after typically developing infants sit independently. We observed infants with typical development (n = 34, ages 4–7 months) and infants with gross motor delay (n = 128, ages 7–16 months) in early stages of sitting development, and their caregivers, in a dyadic play observation. We predicted that infants who required caregiver support for sitting would spend more time facing away from the caregiver and less time contacting objects than infants who could sit independently. We also predicted that caregivers of supported sitters would spend less time contacting objects because their hands would be full supporting their infants. Our first two hypotheses were confirmed; however, caregivers spent surprisingly little time using both hands to provide support, and caregivers of supported sitters spent more time contacting objects than caregivers of independent sitters. Similar patterns were seen in the group of typically developing infants and the infants with motor delay. Our findings suggest that independent sitting and supported sitting provide qualitatively distinct experiences with different implications for social interaction and learning opportunities.

Highlights

• During seated free play, supported sitters spent more time facing away from their caregivers and less time handling objects than independent sitters.
• Caregivers who spent more time supporting infants with both hands spent less time handling objects; however, caregivers mostly supported infants with one or no hands.
• A continuous measure of sitting skill did not uniquely contribute to these behaviors beyond the effect of binary sitting support (supported vs. independent sitter).
• The pattern of results was similar for typically developing infants and infants with gross motor delay, despite differences in age.

     

Applying Deutsch’s concept of good explanations to artificial intelligence and neuroscience – An initial exploration

Artificial intelligence has made great strides since the deep learning revolution, but AI systems remain incapable of learning principles and rules which allow them to extrapolate outside of their training data to new situations. For inspiration we look to the domain of science, where scientists have been able to develop theories which show remarkable ability to extrapolate and sometimes even predict the existence of phenomena which have never been observed before. According to David Deutsch, this type of extrapolation, which he calls “reach”, is due to scientific theories being hard to vary. In this work we investigate Deutsch’s hard-to-vary principle and how it relates to more formalized principles in deep learning such as the bias-variance trade-off and Occam’s razor. We distinguish internal variability, how much a model/theory can be varied internally while still yielding the same predictions, with external variability, which is how much a model must be varied to predict new, out-of-distribution data. We discuss how to measure internal variability using the notion of the Rashomon set and how to measure external variability using Kolmogorov complexity. We explore what role hard-to-vary explanations play in intelligence by looking at the human brain, the only example of highly general purpose intelligence known. We distinguish two learning systems in the brain – the first operates similar to deep learning and likely underlies most of perception while the second is a more creative system capable of generating hard-to-vary models and explanations of the world. We make contact with Popperian epistemology which suggests that the generation of scientific theories is a not an inductive process but rather an evolutionary process which proceeds through conjecture and refutation. We argue that figuring out how replicate this second system, which is capable of generating hard-to-vary explanations, is a key challenge which needs to be solved in order to realize artificial general intelligence.     

Feature centrality and property induction

A feature is central to a concept to the extent that other features depend on it. Four studies tested the hypothesis that people will project a feature from a base concept to a target concept to the extent that they believe the feature is central to the two concepts. This centrality hypothesis implies that feature projection is guided by a principle that aims to maximize the structural commonality between base and target concepts. Participants were told that a category has two or three novel features. One feature was the most central in that more properties depended on it. The extent to which the target shared the feature’s dependencies was manipulated by varying the similarity of category pairs. Participants’ ratings of the likelihood that each feature would hold in the target category support the centrality hypothesis with both natural kind and artifact categories and with both well-specified and vague dependency structures.