A mouse model of autism implicates endosome pH in the regulation of presynaptic calcium entry.

Psychoactive compounds such as chloroquine and amphetamine act by dissipating the pH gradient across intracellular membranes, but the physiological mechanisms that normally regulate organelle pH remain poorly understood. Interestingly, recent human genetic studies have implicated the endosomal Na+/H+ exchanger NHE9 in both autism spectrum disorders (ASD) and attention deficit hyperactivity disorder (ADHD). Plasma membrane NHEs regulate cytosolic pH, but the role of intracellular isoforms has remained unclear. We now find that inactivation of NHE9 in mice reproduces behavioral features of ASD including impaired social interaction, repetitive behaviors, and altered sensory processing. Physiological characterization reveals hyperacidic endosomes, a cell-autonomous defect in glutamate receptor expression and impaired neurotransmitter release due to a defect in presynaptic Ca2+ entry. Acute inhibition of synaptic vesicle acidification rescues release but without affecting the primary defect due to loss of NHE9

Optimal team composition for tool‐based problem solving

In this paper, we construct a framework for modeling teams of agents who apply techniques or procedures (tools) to solve problems. In our framework, tools differ in their likelihood of solving the problem at hand; agents, who may be of different types, vary in their skill at using tools. We establish baseline hiring rules when a manager can dictate tool choice and then derive results for strategic tool choice by team members. We highlight three main findings: First, that cognitively diverse teams are more likely to solve problems in both settings. Second, that teams consisting of types that master diverse tools have an indirect strategic advantage because tool diversity facilitates coordination. Third, that strategic tool choice creates counterintuitive optimal hiring practices. For example, optimal teams may exclude the highest ability types and can include dominated types. In addition, optimal groups need not increase setwise. Our framework extends to cover teamwork on decomposable problems, to cases where individuals apply multiple tools, and to teams facing a flow or set of problems.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/152035/1/jems12295.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/152035/2/jems12295_am.pd

ASPIRATION-BASED REINFORCEMENT LEARNING IN REPEATED INTERACTION GAMES: AN OVERVIEW

In models of aspiration-based reinforcement learning, agents adapt by comparing payoffs achieved from actions chosen in the past with an aspiration level. Though such models are well-established in behavioural psychology, only recently have they begun to receive attention in game theory and its applications to economics and politics. This paper provides an informal overview of a range of such theories applied to repeated interaction games. We describe different models of aspiration formation: where (1) aspirations are fixed but required to be consistent with longrun average payoffs; (2) aspirations evolve based on past personal experience or of previous generations of players; and (3) aspirations are based on the experience of peers. Convergence to non-Nash outcomes may result in either of these formulations. Indeed, cooperative behaviour can emerge and survive in the long run, even though it may be a strictly dominated strategy in the stage game, and despite the myopic adaptation of stage game strategies. Differences between reinforcement learning and evolutionary game theory are also discussed. </jats:p