In recent years, simple GO/NOGO behavioural tasks have become popular due to the relative ease with which they can be combined with technologies such as in vivo multiphoton imaging. To date, it has been assumed that behavioural performance can be captured by the average performance across a session, however this neglects the effect of motivation on behaviour within individual sessions. We investigated the effect of motivation on mice performing a GO/NOGO visual discrimination task. Performance within a session tended to follow a stereotypical trajectory on a Receiver Operating Characteristic (ROC) chart, beginning with an over-motivated state with many false positives, and transitioning through a more or less optimal regime to end with a low hit rate after satiation. Our observations are reproduced by a new model, the Motivated Actor-Critic, introduced here. Our results suggest that standard measures of discriminability, obtained by averaging across a session, may significantly underestimate behavioural performance.

In recent years, simple GO/NOGO behavioural tasks have become popular due to the relative ease with which they can be combined with technologies such as in vivo multiphoton imaging. To date, it has been assumed that behavioural performance can be captured by the average performance across a session, however this neglects the effect of motivation on behaviour within individual sessions. We investigated the effect of motivation on mice performing a GO/NOGO visual discrimination task. Performance within a session tended to follow a stereotypical trajectory on a Receiver Operating Characteristic (ROC) chart, beginning with an over-motivated state with many false positives, and transitioning through a more or less optimal regime to end with a low hit rate after satiation. Our observations are reproduced by a new model, the Motivated Actor-Critic, introduced here. Our results suggest that standard measures of discriminability, obtained by averaging across a session, may significantly underestimate behavioural performance.What is the impact of motivation on behaviour? Reinforcement learning theory assumes that an animal optimises behaviour according to the value placed on the goal of an action under different levels of deprivation 1,2 . Beyond the value placed on the goal (directional effect), motivational state is also critically important in regulating the overall effort and rate of activity (activating effect) an animal engages in 3,4 . The combination of behavioural training with sophisticated electrophysiological and imaging techniques is beginning to provide unprecedented insight into the functioning of neural circuits underpinning perceptually-driven decision making 5,6 . A commonly employed paradigm for studying perceptual decisions is the two-category GO/NOGO task, where the animal performs a response to obtain a reward during a 'Go' stimulus, and needs to withhold the response for the 'NoGo' stimulus. This paradigm has been commonly used in recent years to draw inferences about sensory computations 7-12 . Mice are readily able to perform such tasks, however natural biases 13 can interfere with performance. Motivational influences on behaviour have also been documented both in rodents 3,14 and other species 15 . These factors increase the difficulty of devising appropriate training protocols and can impact on the interpretation of results 6 . The typical timeline for observation during simple decision-making tasks in modern neuroscience extends to hundreds of trials, thus capturing a range of motivational levels throughout a single behaviour session. While there have been mentions of changes in motivation within individual sessions previously 16,17 , these effects are often ignored 3,13 or factored out of analyses 18 . Here, we have analysed the effect of motivation on a GO/NOGO visual discrimination task at the single session level. To our knowledge, this is the first detailed exploration of these factors within the context of the GO/NOGO paradigm, although previously published work 14 can be re-examined to s...

Introduction Citizen Science (CS) is intertwined with public policy in multiple ways, and the question of how CS can be a resource for decision-making is increasingly debated among those who organise projects as well as among politicians. Around the world, CS is considered relevant at various levels of governance from multilateral programmes, such as the United Nations Environment Programme, to supra-national institutions (the European Union) and individual member states exploring the value of CS for environmental reporting, education, and decision-making (POST 2014; Science Communication Unit 2013). Environmental protection agencies are recognising CS by issuing recommendations, cost-benefit analyses, and decision support for when to use CS to support implementation of regulatory environmental policy (NACEPT 2016; Pocock et al. 2014; Vohland et al. 2016). Increasing openness towards CS is also spreading beyond environmental and biodiversity monitoring to include health and food security, disaster response, and research policy (Schade et al. 2017). Policy publications that mention CS typically highlight its potential for multiple fields and implementations (McElfish, Pendergrass, and Fox 2016) as well as data management (Schade and Tsinaraki 2016). State-sponsored capacity building projects or consultations to develop national strategies for CS have been run in Germany (Bonn et al. 2016), France (Houllier and Merilhou-Goudard 2016), and Spain (Fundacion Ibercivis 2017). Finally, the CS practitioner community is starting to connect with decision makers to demonstrate the validity and benefits of CS (Hecker et al. 2018). Existing studies focus on how CS supports policy development, barriers, and regulatory support (e.g., Chapman and Hodges 2016). Haklay (2015) points out that policy dimensions of CS arise from geography, policy application area, and type of engagement. Existing empirical studies analyse CS projects along dimensions such as standards (Ottinger 2010), place (Newman et al. 2017), participation of stakeholders (Gobel, Martin, and Ramirez-Andreotta 2017), and data practices (Gabrys, Pritchard, and Barratt 2016). The literature typically identifies two roles for CS in policy contexts: As a data source for the development, implementation, or monitoring of regulation and as one of the targets for science policy. This idea rests on a set of basic assumptions: Politicians and formal political institutions are considered as central actors. "Science" and "politics" are understood as separate spheres, and policymaking is seen as a linear process where policy makers determine rules that CS feeds into.

This report aims to enhance our understanding of stakeholder mapping for co-created citizen science initiatives. It presents and discusses findings from an international two-day stakeholder mapping workshop with researchers, event organizers, communication experts, and artists realizing citizen science activities. Participants identified examples of co-creation in their work and mapped stakeholders for three co-creation initiatives from the "Doing It Together Science" project. For each case, we provide an overview of the stakeholder groups involved and the lessons derived from identifying actual and potential stakeholders in different phases of each activity and using different ways for mapping them. We demonstrate that not only stakeholder mapping can be diverse, but it may take different angles depending on the characteristics and project timescales, nevertheless adding significant value to any project. We argue that a better understanding of stakeholder involvement may contribute to more effective stakeholder communication, more successful implementation, and a greater impact for citizen science initiatives.