Of rodents and primates: Time-variant gain in drift-diffusion decision models

Sequential sampling models of decision-making involve evidence accumulation over time and have been successful in capturing choice behaviour. A popular model is the drift-diffusion model (DDM). To capture the finer aspects of choice reaction times (RTs), time-variant gain features representing urgency signals have been implemented in DDM that can exhibit slower error RTs than correct RTs. However, time-variant gain is often implemented on both DDM’s signal and noise features, with the assumption that increasing gain on the drift rate (due to urgency) is similar to DDM with collapsing decision bounds. Hence, it is unclear whether gain effects on just the signal or noise feature can lead to different choice behaviour. This work presents an alternative DDM variant, focusing on the implications of time-variant gain mechanisms, constrained by model parsimony. Specifically, using computational modelling of choice behaviour of rats, monkeys and humans, we systematically showed that time-variant gain only on the DDM’s noise was sufficient to produce slower error RTs, as in monkeys, while time-variant gain only on drift rate leads to faster error RTs, as in rodents. We also found minimal effects of time-variant gain in humans. By highlighting these patterns, this study underscores the utility of group-level modelling in capturing general trends and effects consistent across species. Thus, time-variant gain on DDM’s different components can lead to different choice behaviour, shed light on the underlying time-variant gain mechanisms for different species, and can be used for systematic data fitting

STILL RECORDING AFRICAN MUSIC IN THE FIELD

Field sound recordings are an indispensable source of data for ethnomusicologists. However, to my knowledge there are no standards or guidelines of how this data should be captured and managed. With the progress made in machine learning, it has become vital to record data in a way that also supports the retrieval of information about the music. This article describes a model developed for field recordings that aims to aid an objective data gathering process. This model, developed through an action research process that spanned multiple field recording sessions from 2009–2015, include recording equipment, production processes, the gathering of metadata as well as intellectual property rights. The core principles identified in this research are that field recording systems should be designed to provide accurate feedback as a means of quality control and should capture and manage metadata without relying on secondary tools. The major findings are presented in the form of a checklist that can serve as a point of departure for ethnomusicologists making field recordings

Proceedings of the Sixth Deep Brain Stimulation Think Tank Modulation of Brain Networks and Application of Advanced Neuroimaging, Neurophysiology, and Optogenetics

The annual deep brain stimulation (DBS) Think Tank aims to create an opportunity for a multidisciplinary discussion in the field of neuromodulation to examine developments, opportunities and challenges in the field. The proceedings of the Sixth Annual Think Tank recapitulate progress in applications of neurotechnology, neurophysiology, and emerging techniques for the treatment of a range of psychiatric and neurological conditions including Parkinson’s disease, essential tremor, Tourette syndrome, epilepsy, cognitive disorders, and addiction. Each section of this overview provides insight about the understanding of neuromodulation for specific disease and discusses current challenges and future directions. This year’s report addresses key issues in implementing advanced neurophysiological techniques, evolving use of novel modulation techniques to deliver DBS, ans improved neuroimaging techniques. The proceedings also offer insights into the new era of brain network neuromodulation and connectomic DBS to define and target dysfunctional brain networks. The proceedings also focused on innovations in applications and understanding of adaptive DBS (closed-loop systems), the use and applications of optogenetics in the field of neurostimulation and the need to develop databases for DBS indications. Finally, updates on neuroethical, legal, social, and policy issues relevant to DBS research are discussed

Sparks of Large Audio Models: A Survey and Outlook

This survey paper provides a comprehensive overview of the recent advancements and challenges in applying large language models to the field of audio signal processing. Audio processing, with its diverse signal representations and a wide range of sources--from human voices to musical instruments and environmental sounds--poses challenges distinct from those found in traditional Natural Language Processing scenarios. Nevertheless, \textit{Large Audio Models}, epitomized by transformer-based architectures, have shown marked efficacy in this sphere. By leveraging massive amount of data, these models have demonstrated prowess in a variety of audio tasks, spanning from Automatic Speech Recognition and Text-To-Speech to Music Generation, among others. Notably, recently these Foundational Audio Models, like SeamlessM4T, have started showing abilities to act as universal translators, supporting multiple speech tasks for up to 100 languages without any reliance on separate task-specific systems. This paper presents an in-depth analysis of state-of-the-art methodologies regarding \textit{Foundational Large Audio Models}, their performance benchmarks, and their applicability to real-world scenarios. We also highlight current limitations and provide insights into potential future research directions in the realm of \textit{Large Audio Models} with the intent to spark further discussion, thereby fostering innovation in the next generation of audio-processing systems. Furthermore, to cope with the rapid development in this area, we will consistently update the relevant repository with relevant recent articles and their open-source implementations at https://github.com/EmulationAI/awesome-large-audio-models.Comment: work in progress, Repo URL: https://github.com/EmulationAI/awesome-large-audio-model

Harnessing the Power of the Arctic: Connecting tourists to nature through dog sledging activities.

This qualitative study explores the complex pockets of co-created interaction and throwntogetherness that produce meanings and value through an ethnographic sensory investigation of dog sledging tourism in Finnmark. I draw on a multirelational and multisensorial perspective on dog sledging, which means a holistic and socially constructed way of understanding Human-Animal Bonding (HAB) (DeMello, 2012). HAB enabled me to move beyond ethology when studying how culture, learning, emotions, communication, and cognition shaped interactions between tourist-mushers and dogs in arctic landscapes. The analysis unpacks the richness of the tourist-mushers interactions with sledge dogs by showing how physical senses and the arctic landscape bring about emotions and behavioural changes. The three main themes revolved around how the tourist-musher, through dog sledging, disconnected from everyday life and were reconnected with arctic landscapes. Theme one bonding, co-creation and interaction, consisting of the sub-themes bonding, co-creation and interactions, revealed pockets of meaning and value. Theme two, rhythm, through the sub- themes of time and flow, exposed the interconnectedness of reflection. Theme three, discovery mechanisms, with the sub-themes of physical senses, emotion, and learning, identified emotions of trust and empathy as learning tools that led to memory-making and mindfulness. I conclude that dog sledging tourism is a unique symbolic practice where nothing comes closer to experiencing nature's power. My study's symmetrical agency of humans and non-humans revealed new embodied ways of knowing. This knowledge strengthened and supported an embodied tourist experience approach (Everingham et al., 2021). Through my sensory ethnography of human and non-human encounters travelling together in nature, I address a research gap going beyond the advancement of Finnmarks’ regional tourism in Norway to a global understanding of what Arctic is. Keywords Dog sledging, Embodiment, Ethnography, Arctic landscape, Human-animal bonding, Relational materialis