Motion-guided attention promotes adaptive communications during social navigation

Animals are capable of enhanced decision making through cooperation, whereby accurate decisions can occur quickly through decentralized consensus. These interactions often depend upon reliable social cues, which can result in highly coordinated activities in uncertain environments. Yet information within a crowd may be lost in translation, generating confusion and enhancing individual risk. As quantitative data detailing animal social interactions accumulate, the mechanisms enabling individuals to rapidly and accurately process competing social cues remain unresolved. Here, we model how motion-guided attention influences the exchange of visual information during social navigation. We also compare the performance of this mechanism to the hypothesis that robust social coordination requires individuals to numerically limit their attention to a set of n-nearest neighbours. While we find that such numerically limited attention does not generate robust social navigation across ecological contexts, several notable qualities arise from selective attention to motion cues. First, individuals can instantly become a local information hub when startled into action, without requiring changes in neighbour attention level. Second, individuals can circumvent speed–accuracy trade-offs by tuning their motion thresholds. In turn, these properties enable groups to collectively dampen or amplify social information. Lastly, the minority required to sway a group''s short-term directional decisions can change substantially with social context. Our findings suggest that motion-guided attention is a fundamental and efficient mechanism underlying collaborative decision making during social navigation.     

Representation of spatial goals in rat orbitofrontal cortex

The orbitofrontal cortex (OFC) is thought to participate in making and evaluating goal-directed decisions. In rodents, spatial navigation is a major mode of goal-directed behavior, and anatomical and lesion studies implicate the OFC in spatial processing, but there is little direct evidence for coding of spatial or motor variables. Here, we recorded from ventrolateral and lateral OFC in an odor-cued two-alternative choice task requiring orientation and approach to spatial goal ports. In this context, over half of OFC neurons encoded choice direction or goal port location. A subset of neurons was jointly selective for the trial outcome and port location, information useful for the selection or evaluation of spatial goals. These observations show that the rodent OFC not only encodes information relating to general motivational significance, as shown previously, but also encodes spatiomotor variables needed to define specific behavioral goals and the locomotor actions required to attain them.     

Led into Temptation? Rewarding Brand Logos Bias the Neural Encoding of Incidental Economic Decisions

Human decision-making is driven by subjective values assigned to alternative choice options. These valuations are based on reward cues. It is unknown, however, whether complex reward cues, such as brand logos, may bias the neural encoding of subjective value in unrelated decisions. In this functional magnetic resonance imaging (fMRI) study, we subliminally presented brand logos preceding intertemporal choices. We demonstrated that priming biased participants' preferences towards more immediate rewards in the subsequent temporal discounting task. This was associated with modulations of the neural encoding of subjective values of choice options in a network of brain regions, including but not restricted to medial prefrontal cortex. Our findings demonstrate the general susceptibility of the human decision making system to apparently incidental contextual information. We conclude that the brain incorporates seemingly unrelated value information that modifies decision making outside the decision-maker's awareness.     

A Model of Reward- and Effort-Based Optimal Decision Making and Motor Control

Costs (e.g. energetic expenditure) and benefits (e.g. food) are central determinants of behavior. In ecology and economics, they are combined to form a utility function which is maximized to guide choices. This principle is widely used in neuroscience as a normative model of decision and action, but current versions of this model fail to consider how decisions are actually converted into actions (i.e. the formation of trajectories). Here, we describe an approach where decision making and motor control are optimal, iterative processes derived from the maximization of the discounted, weighted difference between expected rewards and foreseeable motor efforts. The model accounts for decision making in cost/benefit situations, and detailed characteristics of control and goal tracking in realistic motor tasks. As a normative construction, the model is relevant to address the neural bases and pathological aspects of decision making and motor control.     

Functional role of the ventrolateral prefrontal cortex in decision making

To make deliberate decisions, we have to utilize detailed information about the environment and our internal states. The ventral visual pathway provides detailed information on object identity, including color and shape, to the ventrolateral prefrontal cortex (VLPFC). The VLPFC also receives motivational and emotional information from the orbitofrontal cortex and subcortical areas, and computes the behavioral significance of external events; this information can be used for elaborate decision making or design of goal-directed behavior. In this review, we discuss recent advances that are revealing the neural mechanisms that underlie the coding of behavioral significance in the VLPFC, and the functional roles of these mechanisms in decision making and action programming in the brain.     

Decision making in critically ill patients with hematologic malignancy.

Hematologic neoplasms that were previously considered fatal are now potentially curable with techniques such as bone marrow transplantation. Such therapies also carry significant morbidity and mortality. With the increasing application of these therapies, a growing number of physicians are using medical decision making regarding critical care for these patients. The process by which ethical decisions are reached for these critically ill patients may be baffling because of several factors: rapidly evolving treatments, uncertain probabilities of the cure of the malignant disorder, the relatively young age of many of these patients, and the poor prognosis with critical illness. I discuss a process to reach acceptable decisions, providing a case example of the application of the process. This process is derived from the ethical principles that drive decision making in general medicine and attempts to maximize patients'' autonomy. It involves a consideration of accurate information regarding the disease process and the prognosis, a clear delineation of the goals of the medical care, and communication with patients. Appropriate, ethical, and consistent decisions regarding the critical care of patients with hematologic malignancy can be reached when these considerations are addressed.     

Flutter discrimination: neural codes,perception, memory and decision making

Recent studies combining psychophysical and neurophysiological experiments in behaving monkeys have provided new insights into how several cortical areas integrate efforts to solve a vibrotactile discrimination task. In particular, these studies have addressed how neural codes are related to perception, working memory and decision making in this model. The primary somatosensory cortex drives higher cortical areas where past and current sensory information are combined, such that a comparison of the two evolves into a behavioural decision. These and other observations in visual tasks indicate that decisions emerge from highly-distributed processes in which the details of a scheduled motor plan are gradually specified by sensory information.     

The influence of recent decisions on future goal selection

Recent decisions about actions and goals can have effects on future choices. Several studies have shown an effect of the previous trial history on neural activity in a subsequent trial. Often, but not always, these effects originate from task requirements that make it necessary to maintain access to previous trial information to make future decisions. Maintaining the information about recent decisions and their outcomes can play an important role in both adapting to new contingencies and learning. Previous goal decisions must be distinguished from goals that are currently being planned to avoid perseveration or more general errors. Output monitoring is probably based on this separation of accomplished past goals from pending future goals that are being pursued. Behaviourally, it has been shown that the history context can influence the location, error rate and latency of successive responses. We will review the neurophysiological studies in the literature, including data from our laboratory, which support a role for the frontal lobe in tracking previous goal selections and outputs when new goals need to be accomplished.     

Food behaviour and obesity: insights from decision neuroscience

Neuroimaging allows to estimate brain activity when individuals are doing something. The location and intensity of this estimated activity provides information on the dynamics and processes that guide choice behaviour and associated actions that should be considered a complement to behavioural studies. Decision neuroscience therefore sheds new light on whether the brain evaluates and compares alternatives when decisions are made, or if other processes are at stake. This work helped to demonstrate that the situations faced by individuals (risky, uncertain, delayed in time) do not all have the same (behavioural) complexity, and are not underlined by activity in the cerebral networks. Taking into account brain dynamics of people (suffering from obesity or not) when making food consumption decisions might allow for improved strategies in public health prevention, far from the rational choice theory promoted by neoclassical economics.     

A Bayesian Attractor Model for Perceptual Decision Making

Even for simple perceptual decisions, the mechanisms that the brain employs are still under debate. Although current consensus states that the brain accumulates evidence extracted from noisy sensory information, open questions remain about how this simple model relates to other perceptual phenomena such as flexibility in decisions, decision-dependent modulation of sensory gain, or confidence about a decision. We propose a novel approach of how perceptual decisions are made by combining two influential formalisms into a new model. Specifically, we embed an attractor model of decision making into a probabilistic framework that models decision making as Bayesian inference. We show that the new model can explain decision making behaviour by fitting it to experimental data. In addition, the new model combines for the first time three important features: First, the model can update decisions in response to switches in the underlying stimulus. Second, the probabilistic formulation accounts for top-down effects that may explain recent experimental findings of decision-related gain modulation of sensory neurons. Finally, the model computes an explicit measure of confidence which we relate to recent experimental evidence for confidence computations in perceptual decision tasks.     

Collaborative Brain-Computer Interface for Aiding Decision-Making

We look at the possibility of integrating the percepts from multiple non-communicating observers as a means of achieving better joint perception and better group decisions. Our approach involves the combination of a brain-computer interface with human behavioural responses. To test ideas in controlled conditions, we asked observers to perform a simple matching task involving the rapid sequential presentation of pairs of visual patterns and the subsequent decision as whether the two patterns in a pair were the same or different. We recorded the response times of observers as well as a neural feature which predicts incorrect decisions and, thus, indirectly indicates the confidence of the decisions made by the observers. We then built a composite neuro-behavioural feature which optimally combines the two measures. For group decisions, we uses a majority rule and three rules which weigh the decisions of each observer based on response times and our neural and neuro-behavioural features. Results indicate that the integration of behavioural responses and neural features can significantly improve accuracy when compared with the majority rule. An analysis of event-related potentials indicates that substantial differences are present in the proximity of the response for correct and incorrect trials, further corroborating the idea of using hybrids of brain-computer interfaces and traditional strategies for improving decision making.     

Process evaluation of appreciative inquiry to translate pain management evidence into pediatric nursing practice

Background

Current healthcare systems have extended the evidence-based medicine (EBM) approach to health policy and delivery decisions, such as access-to-care, healthcare funding and health program continuance, through attempts to integrate valid and reliable evidence into the decision making process. These policy decisions have major impacts on society and have high personal and financial costs associated with those decisions. Decision models such as these function under a shared assumption of rational choice and utility maximization in the decision-making process.

Discussion

We contend that health policy decision makers are generally unable to attain the basic goals of evidence-based decision making (EBDM) and evidence-based policy making (EBPM) because humans make decisions with their naturally limited, faulty, and biased decision-making processes. A cognitive information processing framework is presented to support this argument, and subtle cognitive processing mechanisms are introduced to support the focal thesis: health policy makers' decisions are influenced by the subjective manner in which they individually process decision-relevant information rather than on the objective merits of the evidence alone. As such, subsequent health policy decisions do not necessarily achieve the goals of evidence-based policy making, such as maximizing health outcomes for society based on valid and reliable research evidence.

Summary

In this era of increasing adoption of evidence-based healthcare models, the rational choice, utility maximizing assumptions in EBDM and EBPM, must be critically evaluated to ensure effective and high-quality health policy decisions. The cognitive information processing framework presented here will aid health policy decision makers by identifying how their decisions might be subtly influenced by non-rational factors. In this paper, we identify some of the biases and potential intervention points and provide some initial suggestions about how the EBDM/EBPM process can be improved.     

Always Gamble on an Empty Stomach: Hunger Is Associated with Advantageous Decision Making

Three experimental studies examined the counterintuitive hypothesis that hunger improves strategic decision making, arguing that people in a hot state are better able to make favorable decisions involving uncertain outcomes. Studies 1 and 2 demonstrated that participants with more hunger or greater appetite made more advantageous choices in the Iowa Gambling Task compared to sated participants or participants with a smaller appetite. Study 3 revealed that hungry participants were better able to appreciate future big rewards in a delay discounting task; and that, in spite of their perception of increased rewarding value of both food and monetary objects, hungry participants were not more inclined to take risks to get the object of their desire. Together, these studies for the first time provide evidence that hot states improve decision making under uncertain conditions, challenging the conventional conception of the detrimental role of impulsivity in decision making.     

Introduction. Neuroeconomics: the promise and the profit

Neuroeconomics investigates the neural mechanisms underlying decisions about rewarding or punishing outcomes ('economic' decisions). It combines the knowledge about the behavioural phenomena of economic decisions with the mechanistic explanatory power of neuroscience. Thus, it is about the neurobiological foundations of economic decision making. It is hoped that by 'opening the box' we can understand how decisions about gains and losses are directed by the brain of the individual decision maker. Perhaps we can even learn why some decisions are apparently paradoxical or pathological. The knowledge could be used to create situations that avoid suboptimal decisions and harm.     

Choosing the greater of two goods: neural currencies for valuation and decision making

To make adaptive decisions, animals must evaluate the costs and benefits of available options. The nascent field of neuroeconomics has set itself the ambitious goal of understanding the brain mechanisms that are responsible for these evaluative processes. A series of recent neurophysiological studies in monkeys has begun to address this challenge using novel methods to manipulate and measure an animal's internal valuation of competing alternatives. By emphasizing the behavioural mechanisms and neural signals that mediate decision making under conditions of uncertainty, these studies might lay the foundation for an emerging neurobiology of choice behaviour.     

The neural systems that mediate human perceptual decision making

Perceptual decision making is the act of choosing one option or course of action from a set of alternatives on the basis of available sensory evidence. Thus, when we make such decisions, sensory information must be interpreted and translated into behaviour. Neurophysiological work in monkeys performing sensory discriminations, combined with computational modelling, has paved the way for neuroimaging studies that are aimed at understanding decision-related processes in the human brain. Here we review findings from human neuroimaging studies in conjunction with data analysis methods that can directly link decisions and signals in the human brain on a trial-by-trial basis. This leads to a new view about the neural basis of human perceptual decision-making processes.     

Is Payoff Necessarily Weighted by Probability When Making a Risky Choice? Evidence from Functional Connectivity Analysis

How people make decisions under risk remains an as-yet-unresolved but fundamental question. Mainstream theories about risky decision making assume that the core processes involved in reaching a risky decision include weighting each payoff or reward magnitude by its probability and then summing the outcomes. However, recently developed theories question whether payoffs are necessarily weighted by probability when making a risky choice. Using functional connectivity analysis, we aimed to provide neural evidence to answer whether this key assumption of computing expectations holds when making a risky choice. We contrasted a trade-off instruction choice that required participants to integrate probability and payoff information with a preferential choice that did not. Based on the functional connectivity patterns between regions in which activity was detected during both of the decision-making tasks, we classified the regions into two networks. One network includes primarily the left and right lateral prefrontal cortices and posterior parietal cortices, which were found to be related to probability in previous reports, and the other network is composed of the bilateral basal ganglia, which have been implicated in payoff. We also found that connectivity between the payoff network and some regions in the probability network (including the left lateral prefrontal cortices and bilateral inferior parietal lobes) were stronger during the trade-off instruction choice task than during the preferential choice task. This indicates that the functional integration between the probability and payoff networks during preferential choice was not as strong as the integration during trade-off instruction choice. Our results provide neural evidence that the weighting process uniformly predicted by the mainstream theory is unnecessary during preferential choice. Thus, our functional integration findings can provide a new direction for the investigation of the principles of risky decision making.     

An Evaluation of Sources of Uncertainty in a Dredged Material Assessment

The U.S. Army Corps of Engineers and the U.S. Environmental Protection Agency use a four tiered evaluation process to assess the potential for significant impacts from open water disposal of dredged material to the aquatic environment. This tiered approach requires only the appropriate level of analysis to estimate potential chemical and biological effects. Uncertainty is inherent in each tier and can lead to delayed, costly, and potentially inappropriate decisions. This paper discusses sources of uncertainty in the tiered approach with the goal of improving dredged material management decisions. These potential uncertainty sources are common to many dredging projects but might not be applicable to all projects. Although not all uncertainty sources can be quantified, even using the simple scoring procedure described here, they can still contribute significantly to uncertainty in predictions of adverse effects. Of the sources that could be scored and ranked, those identified as most uncertain include trophic transfer, chronic bioassay interpretation, fate and transport model parameter uncertainty, toxicity endpoints based on body burdens, human dose-response models, toxicity of complex mixtures, and estimation of population-level effects. Research directed at these sources of uncertainty will result in improved decision making.     

Explicit neural signals reflecting reward uncertainty

The acknowledged importance of uncertainty in economic decision making has stimulated the search for neural signals that could influence learning and inform decision mechanisms. Current views distinguish two forms of uncertainty, namely risk and ambiguity, depending on whether the probability distributions of outcomes are known or unknown. Behavioural neurophysiological studies on dopamine neurons revealed a risk signal, which covaried with the standard deviation or variance of the magnitude of juice rewards and occurred separately from reward value coding. Human imaging studies identified similarly distinct risk signals for monetary rewards in the striatum and orbitofrontal cortex (OFC), thus fulfilling a requirement for the mean variance approach of economic decision theory. The orbitofrontal risk signal covaried with individual risk attitudes, possibly explaining individual differences in risk perception and risky decision making. Ambiguous gambles with incomplete probabilistic information induced stronger brain signals than risky gambles in OFC and amygdala, suggesting that the brain's reward system signals the partial lack of information. The brain can use the uncertainty signals to assess the uncertainty of rewards, influence learning, modulate the value of uncertain rewards and make appropriate behavioural choices between only partly known options.