Age-related hearing loss associated with altered response efficiency and variability on a visual sustained attention task

ABSTRACT

This study investigated the association between age-related hearing loss (ARHL) and differences in response efficiency and variability on a sustained attention task. The study population comprised 32 participants in a hearing loss group (HLG) and 34 controls without hearing loss (CG). Mean reaction time (RT) and accuracy were recorded to assess response efficiency. RT variability was decomposed to examine temporal aspects of variability associated with neural arousal and top-down executive control of vigilant attention. The HLG had a significantly longer mean RT, possibly reflecting a strategic approach to maintain accuracy. The HLG also demonstrated altered variability (indicative of greater decline in neural arousal) but maintained executive control that was significantly predictive of poorer response efficiency. Adults with ARHL may rely on higher-order attention networks to compensate for decline in both peripheral sensory function and in subcortical arousal systems which mediate lower-order automatic neurocognitive processes.     

Does emotion influence visual perception? Depends on how you look at it

ABSTRACT

One of the biggest challenges in the study of emotion–cognition interaction is addressing the question of whether and how emotions influence processes of perception as distinct from other higher-level cognitive processes. Most theories of emotion agree that an emotion episode begins with a sensory experience – such as a visual percept – that elicits a cascade of affective, cognitive, physiological, and/or behavioural responses (the ordering and inclusion of those latter components being forever debated). However, for decades, a subset of philosophers and scientists have suggested that the presumed perception → emotion relationship is in fact bidirectional, with emotion also altering the perceptual process. In the present review we reflect on the history and empirical support (or, some might argue, lack thereof) for the notion that emotion influences visual perception. We examine ways in which researchers have attempted to test the question, and the ways in which this pursuit is so difficult. As is the case with the ongoing debate about the cognitive penetrability of perception, we conclude that nothing is conclusive in the debate about the emotional penetrability of perception. We nonetheless don rose-coloured glasses as we look forward to the future of this research topic.     

Monotone Quantifiers Emerge via Iterated Learning

Natural languages exhibit many semantic universals, that is, properties of meaning shared across all languages. In this paper, we develop an explanation of one very prominent semantic universal, the monotonicity universal. While the existing work has shown that quantifiers satisfying the monotonicity universal are easier to learn, we provide a more complete explanation by considering the emergence of quantifiers from the perspective of cultural evolution. In particular, we show that quantifiers satisfy the monotonicity universal evolve reliably in an iterated learning paradigm with neural networks as agents.     

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.