What to do, when to do it, how long to do it for: a normative microscopic approach to the labour leisure tradeoff

Dividing limited time between work and leisure is a common, everyday choice. Given the option, humans and other animals elect to distribute their time be- tween work and leisure, rather than choosing all of one and none of the other. Traditional accounts of the allocation of time have characterised behaviour on a macroscopic timescale, reporting and studying average times spent in work or leisure. We develop a novel, normative, microscopic framework in which subjects approximately maximise their expected returns by making momentary commit- ments to one or other activity. This generic theoretical framework is applied to the work-leisure tradeo . We determine the microscopic utility of leisure { an animal's innate preference irrespective of all other rewards and costs. We re- port our analyses of data from our collaboration with experimentalists who use brain stimulation reward (electrically stimulating reward circuits in the brain) { a powerful reward that does not satiate unlike food, and is not secondary, unlike money, on rat subjects. We show that in all subjects, this utility of leisure is non- linear. Subjects either prefer long leisure bouts all at one go, or many short ones, but are not indi erent to the division of leisure durations. We also develop new normative, microscopic models of how fatigue and satiation may impact decision- making, and make predictions about their e ect on the temporal distribution of choices. We then derive macroscopic utilities from microscopic ones and show how macro- scopic facets such as imperfect substitutability can arise. We show that by inte- grating our microscopic choices we can build macroscopic characterisations that are not only equivalent to, but richer than those a orded by previous macro- scopic characterisations. We therefore build a superset of traditional macroscopic quanti cations. Our normative, microscopic approach sheds new light on the na- ture of temporally relevant behaviour and may provide a powerful framework for understanding the psychological processes and neural computations underlying real-time cost-bene t decision-making

Opportunity cost determines free-operant action initiation latency and predicts apathy

Background Apathy, a disabling and poorly understood neuropsychiatric symptom, is characterised by impaired self-initiated behaviour. It has been hypothesised that the opportunity cost of time (OCT) may be a key computational variable linking self-initiated behaviour with motivational status. OCT represents the amount of reward which is foregone per second if no action is taken. Using a novel behavioural task and computational modelling, we investigated the relationship between OCT, self-initiation and apathy. We predicted that higher OCT would engender shorter action latencies, and that individuals with greater sensitivity to OCT would have higher behavioural apathy. Methods We modulated the OCT in a novel task called the 'Fisherman Game', Participants freely chose when to self-initiate actions to either collect rewards, or on occasion, to complete non-rewarding actions. We measured the relationship between action latencies, OCT and apathy for each participant across two independent non-clinical studies, one under laboratory conditions (n = 21) and one online (n = 90). 'Average-reward' reinforcement learning was used to model our data. We replicated our findings across both studies. Results We show that the latency of self-initiation is driven by changes in the OCT. Furthermore, we demonstrate, for the first time, that participants with higher apathy showed greater sensitivity to changes in OCT in younger adults. Our model shows that apathetic individuals experienced greatest change in subjective OCT during our task as a consequence of being more sensitive to rewards. Conclusions Our results suggest that OCT is an important variable for determining free-operant action initiation and understanding apathy

Dynamic excitatory and inhibitory gain modulation can produce flexible, robust and optimal decision-making

<div><p>Behavioural and neurophysiological studies in primates have increasingly shown the involvement of urgency signals during the temporal integration of sensory evidence in perceptual decision-making. Neuronal correlates of such signals have been found in the parietal cortex, and in separate studies, demonstrated attention-induced gain modulation of both excitatory and inhibitory neurons. Although previous computational models of decision-making have incorporated gain modulation, their abstract forms do not permit an understanding of the contribution of inhibitory gain modulation. Thus, the effects of co-modulating both excitatory and inhibitory neuronal gains on decision-making dynamics and behavioural performance remain unclear. In this work, we incorporate time-dependent co-modulation of the gains of both excitatory and inhibitory neurons into our previous biologically based decision circuit model. We base our computational study in the context of two classic motion-discrimination tasks performed in animals. Our model shows that by simultaneously increasing the gains of both excitatory and inhibitory neurons, a variety of the observed dynamic neuronal firing activities can be replicated. In particular, the model can exhibit winner-take-all decision-making behaviour with higher firing rates and within a significantly more robust model parameter range. It also exhibits short-tailed reaction time distributions even when operating near a dynamical bifurcation point. The model further shows that neuronal gain modulation can compensate for weaker recurrent excitation in a decision neural circuit, and support decision formation and storage. Higher neuronal gain is also suggested in the more cognitively demanding reaction time than in the fixed delay version of the task. Using the exact temporal delays from the animal experiments, fast recruitment of gain co-modulation is shown to maximize reward rate, with a timescale that is surprisingly near the experimentally fitted value. Our work provides insights into the simultaneous and rapid modulation of excitatory and inhibitory neuronal gains, which enables flexible, robust, and optimal decision-making.</p></div

The PARAChute project: remote monitoring of posture and gait for fall prevention

Falls in the elderly are a major public health problem due to both their frequency and their medical and social consequences. In France alone, more than two million people aged over 65 years old fall each year, leading to more than 9 000 deaths, in particular in those over 75 years old (more than 8 000 deaths). This paper describes the PARAChute project, which aims to develop a methodology that will enable the detection of an increased risk of falling in community-dwelling elderly. The methods used for a remote noninvasive assessment for static and dynamic balance assessments and gait analysis are described. The final result of the project has been the development of an algorithm for movement detection during gait and a balance signature extracted from a force plate. A multicentre longitudinal evaluation of balance has commenced in order to validate the methodologies and technologies developed in the project

Of monkeys and men:Impatience in perceptual decision-making

For decades sequential sampling models have successfully accounted for human and monkey decision-making, relying on the standard assumption that decision makers maintain a pre-set decision standard throughout the decision process. Based on the theoretical argument of reward rate maximization, some authors have recently suggested that decision makers become increasingly impatient as time passes and therefore lower their decision standard. Indeed, a number of studies show that computational models with an impatience component provide a good fit to human and monkey decision behavior. However, many of these studies lack quantitative model comparisons and systematic manipulations of rewards. Moreover, the often-cited evidence from single-cell recordings is not unequivocal and complimentary data from human subjects is largely missing. We conclude that, despite some enthusiastic calls for the abandonment of the standard model, the idea of an impatience component has yet to be fully established; we suggest a number of recently developed tools that will help bring the debate to a conclusive settlement

Models and measurements of energy-dependent quenching

Energy-dependent quenching (qE) in photosystem II (PSII) is a pH-dependent response that enables plants to regulate light harvesting in response to rapid fluctuations in light intensity. In this review, we aim to provide a physical picture for understanding the interplay between the triggering of qE by a pH gradient across the thylakoid membrane and subsequent changes in PSII. We discuss how these changes alter the energy transfer network of chlorophyll in the grana membrane and allow it to switch between an unquenched and quenched state. Within this conceptual framework, we describe the biochemical and spectroscopic measurements and models that have been used to understand the mechanism of qE in plants with a focus on measurements of samples that perform qE in response to light. In addition, we address the outstanding questions and challenges in the field. One of the current challenges in gaining a full understanding of qE is the difficulty in simultaneously measuring both the photophysical mechanism of quenching and the physiological state of the thylakoid membrane. We suggest that new experimental and modeling efforts that can monitor the many processes that occur on multiple timescales and length scales will be important for elucidating the quantitative details of the mechanism of qE

Learning to use past evidence in a sophisticated world model

Humans and other animals are able to discover underlying statistical structure in their environments and exploit it to achieve efficient and effective performance. However, such structure is often difficult to learn and use because it is obscure, involving long-range temporal dependencies. Here, we analysed behavioural data from an extended experiment with rats, showing that the subjects learned the underlying statistical structure, albeit suffering at times from immediate inferential imperfections as to their current state within it. We accounted for their behaviour using a Hidden Markov Model, in which recent observations are integrated with evidence from the past. We found that over the course of training, subjects came to track their progress through the task more accurately, a change that our model largely attributed to improved integration of past evidence. This learning reflected the structure of the task, decreasing reliance on recent observations, which were potentially misleading