Foraging for foundations in decision neuroscience: insights from ethology

Modern decision neuroscience offers a powerful and broad account of human behaviour using computational techniques that link psychological and neuroscientific approaches to the ways that individuals can generate near-optimal choices in complex controlled environments. However, until recently, relatively little attention has been paid to the extent to which the structure of experimental environments relates to natural scenarios, and the survival problems that individuals have evolved to solve. This situation not only risks leaving decision-theoretic accounts ungrounded but also makes various aspects of the solutions, such as hard-wired or Pavlovian policies, difficult to interpret in the natural world. Here, we suggest importing concepts, paradigms and approaches from the fields of ethology and behavioural ecology, which concentrate on the contextual and functional correlates of decisions made about foraging and escape and address these lacunae

Neural encoding of socially adjusted value during competitive and hazardous foraging

In group foraging organisms, optimizing the conflicting demands of competitive food loss and safety is critical. We demonstrate that humans select competition avoidant and risk diluting strategies during foraging depending on socially adjusted value. We formulate a mathematically grounded quantification of socially adjusted value in foraging environments and show using multivariate fMRI analyses that socially adjusted value is encoded by mid-cingulate and ventromedial prefrontal cortices, regions that integrate value and action signals

Melting Pot 2.0

Multi-agent artificial intelligence research promises a path to develop intelligent technologies that are more human-like and more human-compatible than those produced by "solipsistic" approaches, which do not consider interactions between agents. Melting Pot is a research tool developed to facilitate work on multi-agent artificial intelligence, and provides an evaluation protocol that measures generalization to novel social partners in a set of canonical test scenarios. Each scenario pairs a physical environment (a "substrate") with a reference set of co-players (a "background population"), to create a social situation with substantial interdependence between the individuals involved. For instance, some scenarios were inspired by institutional-economics-based accounts of natural resource management and public-good-provision dilemmas. Others were inspired by considerations from evolutionary biology, game theory, and artificial life. Melting Pot aims to cover a maximally diverse set of interdependencies and incentives. It includes the commonly-studied extreme cases of perfectly-competitive (zero-sum) motivations and perfectly-cooperative (shared-reward) motivations, but does not stop with them. As in real-life, a clear majority of scenarios in Melting Pot have mixed incentives. They are neither purely competitive nor purely cooperative and thus demand successful agents be able to navigate the resulting ambiguity. Here we describe Melting Pot 2.0, which revises and expands on Melting Pot. We also introduce support for scenarios with asymmetric roles, and explain how to integrate them into the evaluation protocol. This report also contains: (1) details of all substrates and scenarios; (2) a complete description of all baseline algorithms and results. Our intention is for it to serve as a reference for researchers using Melting Pot 2.0.Comment: 59 pages, 54 figures. arXiv admin note: text overlap with arXiv:2107.0685

Foraging for foundations in decision neuroscience: insights from ethology

Modern decision neuroscience offers a powerful and broad account of human behaviour using computational techniques that link psychological and neuroscientific approaches to the ways that individuals can generate near-optimal choices in complex controlled environments. However, until recently, relatively little attention has been paid to the extent to which the structure of experimental environments relates to natural scenarios, and the survival problems that individuals have evolved to solve. This situation not only risks leaving decision-theoretic accounts ungrounded but also makes various aspects of the solutions, such as hard-wired or Pavlovian policies, difficult to interpret in the natural world. Here, we suggest importing concepts, paradigms and approaches from the fields of ethology and behavioural ecology, which concentrate on the contextual and functional correlates of decisions made about foraging and escape and address these lacunae

A large scale hearing loss screen reveals an extensive unexplored genetic landscape for auditory dysfunction

The developmental and physiological complexity of the auditory system is likely reflected in the underlying set of genes involved in auditory function. In humans, over 150 non-syndromic loci have been identified, and there are more than 400 human genetic syndromes with a hearing loss component. Over 100 non-syndromic hearing loss genes have been identified in mouse and human, but we remain ignorant of the full extent of the genetic landscape involved in auditory dysfunction. As part of the International Mouse Phenotyping Consortium, we undertook a hearing loss screen in a cohort of 3006 mouse knockout strains. In total, we identify 67 candidate hearing loss genes. We detect known hearing loss genes, but the vast majority, 52, of the candidate genes were novel. Our analysis reveals a large and unexplored genetic landscape involved with auditory function