How We Choose One over Another: Predicting Trial-by-Trial Preference Decision

Preference formation is a complex problem as it is subjective, involves emotion, is led by implicit processes, and changes depending on the context even within the same individual. Thus, scientific attempts to predict preference are challenging, yet quite important for basic understanding of human decision making mechanisms, but prediction in a group-average sense has only a limited significance. In this study, we predicted preferential decisions on a trial by trial basis based on brain responses occurring before the individuals made their decisions explicit. Participants made a binary preference decision of approachability based on faces while their electrophysiological responses were recorded. An artificial neural network based pattern-classifier was used with time-frequency resolved patterns of a functional connectivity measure as features for the classifier. We were able to predict preference decisions with a mean accuracy of 74.3±2.79% at participant-independent level and of 91.4±3.8% at participant-dependent level. Further, we revealed a causal role of the first impression on final decision and demonstrated the temporal trajectory of preference decision formation.     

Resolution of uncertainty in prefrontal cortex

Making optimal decisions in the face of uncertain or incomplete information arises as a common problem in everyday behavior, but the neural processes underlying this ability remain poorly understood. A typical case is navigation, in which a subject has to search for a known goal from an unknown location. Navigating under uncertain conditions requires making decisions on the basis of the current belief about location and updating that belief based on incoming information. Here, we use functional magnetic resonance imaging during a maze navigation task to study neural activity relating to the resolution of uncertainty as subjects make sequential decisions to reach a goal. We show that distinct regions of prefrontal cortex are engaged in specific computational functions that are well described by a Bayesian model of decision making. This permits efficient goal-oriented navigation and provides new insights into decision making by humans.     

Spatio-temporal credit assignment in neuronal population learning

In learning from trial and error, animals need to relate behavioral decisions to environmental reinforcement even though it may be difficult to assign credit to a particular decision when outcomes are uncertain or subject to delays. When considering the biophysical basis of learning, the credit-assignment problem is compounded because the behavioral decisions themselves result from the spatio-temporal aggregation of many synaptic releases. We present a model of plasticity induction for reinforcement learning in a population of leaky integrate and fire neurons which is based on a cascade of synaptic memory traces. Each synaptic cascade correlates presynaptic input first with postsynaptic events, next with the behavioral decisions and finally with external reinforcement. For operant conditioning, learning succeeds even when reinforcement is delivered with a delay so large that temporal contiguity between decision and pertinent reward is lost due to intervening decisions which are themselves subject to delayed reinforcement. This shows that the model provides a viable mechanism for temporal credit assignment. Further, learning speeds up with increasing population size, so the plasticity cascade simultaneously addresses the spatial problem of assigning credit to synapses in different population neurons. Simulations on other tasks, such as sequential decision making, serve to contrast the performance of the proposed scheme to that of temporal difference-based learning. We argue that, due to their comparative robustness, synaptic plasticity cascades are attractive basic models of reinforcement learning in the brain.     

How to Find Decision Makers in Neural Networks

Nervous systems often face the problem of classifying stimuli and making decisions based on these classifications. The neurons involved in these tasks can be characterized as sensory or motor, according to their correlation with sensory stimulus or motor response. In this study we define a third class of neurons responsible for making perceptual decisions. Our mathematical formalism enables the weighting of neuronal units according to their contribution to decision making, thus narrowing the field for more detailed studies of underlying mechanisms. We develop two definitions of a contribution to decision making. The first definition states that decision making activity can be found at the points of emergence for behavioral correlations in the system. The second definition involves the study of propagation of noise in the network. The latter definition is shown to be equivalent to the first one in the cases when they can be compared. Our results suggest a new approach to analyzing decision making networks An erratum to this article can be found at     

Social Influences in Sequential Decision Making

People often make decisions in a social environment. The present work examines social influence on people’s decisions in a sequential decision-making situation. In the first experimental study, we implemented an information cascade paradigm, illustrating that people infer information from decisions of others and use this information to make their own decisions. We followed a cognitive modeling approach to elicit the weight people give to social as compared to private individual information. The proposed social influence model shows that participants overweight their own private information relative to social information, contrary to the normative Bayesian account. In our second study, we embedded the abstract decision problem of Study 1 in a medical decision-making problem. We examined whether in a medical situation people also take others’ authority into account in addition to the information that their decisions convey. The social influence model illustrates that people weight social information differentially according to the authority of other decision makers. The influence of authority was strongest when an authority''s decision contrasted with private information. Both studies illustrate how the social environment provides sources of information that people integrate differently for their decisions.     

Dual Process for Intentional and Reactive Decisions

Efficient cognitive decisions should be adjustable to incoming novel information. However, most current models of decision making have so far neglected any potential interaction between intentional and stimulus-driven decisions. We report here behavioral results and a new model on the interaction between a perceptual decision and non-predictable novel information. We asked participants to anticipate their response to an external stimulus and presented this stimulus with variable delay. Participants were clearly able to adjust their initial decision to the new stimulus if this latter appeared sufficiently early. To account for these results, we present a two-stage model in which two systems, an intentional and a stimulus-driven, interact only in the second stage. In the first stage of the model, the intentional and stimulus-driven processes race independently to reach a transition threshold between the two stages. The model can also account for results of a second experiment where a response bias is introduced. Our model is consistent with some physiological results that indicate that both parallel and interactive processing take place between intentional and stimulus-driven information. It emphasizes that in natural conditions, both types of processing are important and it helps pinpoint the transition between parallel and interactive processing.     

Science,Uncertainty, and Values in Ecological Restoration: A Case Study in Structured Decision‐Making and Adaptive Management

This article demonstrates a structured and collaborative approach to decision‐making in the context of adaptive management experiments, using a case study involving the restoration of a hydrological regime in a regulated river in western Canada. It provides a framework based on principles of decision analysis for structuring difficult multi‐attribute decisions and building the trust and technical capacity needed to implement them. Participants included ecologists and fisheries biologists, government regulators, electric utility employees, and representatives of aboriginal communities. The case study demonstrates a values‐based approach to implementing adaptive management that addresses some of the long‐standing difficulties associated with integrating adaptive management into restoration decisions. It highlights practical methods for incorporating participants' values concerned with learning, cultural quality, and stewardship as part of developing a decision‐making and monitoring framework for restoration initiatives. It also provides an example of how to implement principles of meaningful consultation in a restoration context, with emphasis on ensuring that all voices and concerns are heard and meaningfully incorporated. Participants have adopted the framework as a model to guide future collaborative decision‐making processes involving Aboriginal communities, regulatory agencies, and other parties.     

Using and communicating uncertainty for the effective control of invasive non-native species

Estimates of quantities needed to plan invasive species control, such as population size, are always uncertain; this is an issue that can become a problem when mishandled in ecological science and its communication. The complexities of incorporating uncertainty into sophisticated decision-support tools may be a barrier to their use by decision makers, leading to decisions being made without due regard to uncertainty and risking misplaced certainty of predicted outcomes. We summarise ways in which uncertainty has been incorporated into and used to advise decisions on the management of invasive non-native species and other problem species, and offer a simple conceptual model for accommodating and using uncertainty at the planning stage. We also demonstrate how frequently uncertainty has been misused and miscommunicated in the wildlife management literature. We contend that uncertainty in estimates of natural quantities must be acknowledged, can inform decisions and can be made to derive decisions, and should not be ignored if invasive species policy is to be delivered effectively. Uncertainty must be communicated thoroughly and correctly by scientists if decision makers are to understand its consequences for planning and resourcing control programmes.     

Modelling large herbivore movement decisions: Beyond food availability as a predictor of ranging patterns

The ability of animals to adapt to their changing environment will depend in part on shifts in their ranging patterns, but when and why individuals choose to move requires detailed understanding of their decision‐making processes. We develop a simple decision‐making model accounting for resource availability in habitually used ranges. We suggest that disparities between model predictions and animal tracking data indicate additional factors influencing movement decisions, which may be identified given detailed system‐specific knowledge. The model was evaluated using movement data from satellite‐tracked elephants (Loxodonta africana) inhabiting the Amboseli basin in Kenya, moving from savannah areas with low quality but constant resource availability, to areas with temporally constrained higher nutrient availability. Overall, the model fits the data well: There was a good correlation between predicted and observed locations for the combined data from all elephants, but variation between individuals in how well the model fits. For those elephants where model predictions were less successful, additional factors likely to affect movement decisions, including reproduction, anthropogenic threats, memory and perception are suggested. This protocol for building and testing decision‐making models should contribute to success in attempts to preserve sufficient space for large herbivores in their increasingly human‐dominated ecosystems.     

Stop-Signaling Reduces Split Decisions without Impairing Accuracy in the Honeybee Nest-Site Selection Process

In the honeybee swarm nest-site selection process, individual bees gather information about available candidate sites and communicate the information to other bees. The swarm makes an agreement for a candidate site when the number of bees that supports the site reaches a threshold. This threshold is usually referred to as the quorum threshold and it is shown by many studies as a key parameter that is a compromise between the accuracy and speed of decisions. In the present work, we use a model of the honeybee Apis mellifera nest-site selection process to study how the quorum threshold and discovery time of candidate sites have major impact on two unfavorable situations in selecting a nest site: decision deadlock and decision split. We show that cross-inhibitory stop-signaling, delivered among bees supporting different sites, enables swarms to avoid the decision split problem in addition to avoiding the decision deadlock problem that has been previously proposed. We also show that stop-signaling improves decision speed, but compromises decision accuracy in swarms using high quorum thresholds by causing the swarms to be trapped in local optima (e.g., choosing a sub-optimal option that is encountered first). On the other hand, we demonstrate that stop-signaling can reduce split decisions without compromising decision accuracy in swarms using low quorum thresholds when it is compared to the accuracy of swarms using the same threshold values but not exhibiting stop-signaling. Based on our simulations, we suggest that swarms using low quorum thresholds (as well as swarms with large population sizes) would benefit more from exhibiting the stop-signaling activity than not exhibiting it.     

Working Memory in the Processing of the Iowa Gambling Task: An Individual Differences Approach

The Iowa Gambling Task (IGT) is a sequential learning task in which participants develop a tendency towards advantageous options arising from the outcomes associated with their previous decisions. The role of working memory in this complex task has been largely debated in the literature. On one hand, low working memory resources lead to a decrease in the number of advantageous decisions and make a significant part of participants unable to report explicitly which options are the most profitable. On the other hand, several studies have shown no contribution of working memory to the IGT decision patterns. In order to investigate this apparent incompatibility of results, we used an individual differences approach, which has proven an effective method to investigate the role of working memory in cognition. We compared the IGT decision patterns of participants as a function of their working memory capacity (WMC). As expected, contrary to low WMC participants, high WMC participants developed a tendency towards advantageous decisions. These findings lead us to discuss the role of WMC in decision making tasks.     

Focus: Bioethics: An Ethical Framework for Incorporating Digital Technology into Advance Directives: Promoting Informed Advance Decision Making in Healthcare

Despite the presumed value of advance directives, research to demonstrate impact has shown mixed results. For advance directives to serve their role promoting patient autonomy, it is important that patients be informed decision makers. The capacity to make decisions depends upon understanding, appreciation, reasoning, and communication. Advance directives are in part faulty because these elements are often limited. The present paper explores how the application of digital technology could be organized around a framework promoting these four elements. Given the state of digital advancements, there is great potential for advance directives to be meaningfully enhanced. The beneficial effects of incorporating digital technology would be maximized if they were organized around the aim of making advance directives not only documents for declaring preferences but also ethics-driven tools with decision aid functionality. Such advance directives would aid users in making decisions that involve complex factors with potentially far-reaching impact and would also elucidate the users’ thought processes to aid those tasked with interpreting and implementing decisions based on an advance directive. Such advance directives might have embedded interactive features for learning; access to content that furthers one’s ability to project oneself into possible, future scenarios; review of the logical consistency of stated preferences; and modes for effective electronic sharing. Important considerations include mitigating the introduction of bias depending on the presentation of information; optimizing interfacing with surrogate decision makers and treating clinicians; and prioritizing essential components to respect time constraints.     

A survey of unresolved problems in life cycle assessment

Background, aims, and scope  Life cycle assessment (LCA) stands as the pre-eminent tool for estimating environmental effects caused by products and processes from ‘cradle to grave’ or ‘cradle to cradle.’ It exists in multiple forms, claims a growing list of practitioners, and remains a focus of continuing research. Despite its popularity and codification by organizations such as the International Organization for Standards and the Society of Environmental Toxicology and Chemistry, life cycle assessment is a tool in need of improvement. Multiple authors have written about its individual problems, but a unified treatment of the subject is lacking. The following literature survey gathers and explains issues, problems and problematic decisions currently limiting LCA’s goal and scope definition and life cycle inventory phases. Main features  The review identifies 15 major problem areas and organizes them by the LCA phases in which each appears. This part of the review focuses on the first 7 of these problems occurring during the goal and scope definition and life cycle inventory phases. It is meant as a concise summary for practitioners interested in methodological limitations which might degrade the accuracy of their assessments. For new researchers, it provides an overview of pertinent problem areas toward which they might wish to direct their research efforts. Results and discussion  Multiple problems occur in each of LCA’s four phases and reduce the accuracy of this tool. Considering problem severity and the adequacy of current solutions, six of the 15 discussed problems are of paramount importance. In LCA’s first two phases, functional unit definition, boundary selection, and allocation are critical problems requiring particular attention. Conclusions and recommendations  Problems encountered during goal and scope definition arise from decisions about inclusion and exclusion while those in inventory analysis involve flows and transformations. Foundational decisions about the basis of comparison (functional unit), bounds of the study, and physical relationships between included processes largely dictate the representativeness and, therefore, the value of an LCA. It is for this reason that problems in functional unit definition, boundary selection, and allocation are the most critical examined in the first part of this review.

Swarm Intelligence in Animal Groups: When Can a Collective Out-Perform an Expert?

An important potential advantage of group-living that has been mostly neglected by life scientists is that individuals in animal groups may cope more effectively with unfamiliar situations. Social interaction can provide a solution to a cognitive problem that is not available to single individuals via two potential mechanisms: (i) individuals can aggregate information, thus augmenting their 'collective cognition', or (ii) interaction with conspecifics can allow individuals to follow specific 'leaders', those experts with information particularly relevant to the decision at hand. However, a-priori, theory-based expectations about which of these decision rules should be preferred are lacking. Using a set of simple models, we present theoretical conditions (involving group size, and diversity of individual information) under which groups should aggregate information, or follow an expert, when faced with a binary choice. We found that, in single-shot decisions, experts are almost always more accurate than the collective across a range of conditions. However, for repeated decisions - where individuals are able to consider the success of previous decision outcomes - the collective's aggregated information is almost always superior. The results improve our understanding of how social animals may process information and make decisions when accuracy is a key component of individual fitness, and provide a solid theoretical framework for future experimental tests where group size, diversity of individual information, and the repeatability of decisions can be measured and manipulated.     

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.     

MDPtoolbox: a multi‐platform toolbox to solve stochastic dynamic programming problems

Stochastic dynamic programming (SDP) or Markov decision processes (MDP) are increasingly being used in ecology to find the best decisions over time and under uncertainty so that the chance of achieving an objective is maximised. To date, few programs are available to solve SDP/MDP. We present MDPtoolbox, a multi‐platform set of functions to solve Markov decision problems (MATLAB, GNU Octave, Scilab and R). MDPtoolbox provides state‐of‐the‐art and ready to use algorithms to solve a wide range of MDPs. MDPtoolbox is easy to use, freely available and has been continuously improved since 2004. We illustrate how to use MDPtoolbox on a dynamic reserve design problem.     

Aging and consumer decision making

Research on consumer decision making and aging is especially important for fostering a better understanding of ways to maintain consumer satisfaction and high decision quality across the life span. We provide a review of extant research on the effects of normal aging on cognition and decision processes and how these age-related processes are influenced by task environment, meaningfulness of the task, and consumer expertise. We consider how research centered on these topics generates insights about changes in consumption decisions that occur with aging and identify a number of gaps and directions for future research.     

Collective Learning and Optimal Consensus Decisions in Social Animal Groups

Learning has been studied extensively in the context of isolated individuals. However, many organisms are social and consequently make decisions both individually and as part of a collective. Reaching consensus necessarily means that a single option is chosen by the group, even when there are dissenting opinions. This decision-making process decouples the otherwise direct relationship between animals'' preferences and their experiences (the outcomes of decisions). Instead, because an individual''s learned preferences influence what others experience, and therefore learn about, collective decisions couple the learning processes between social organisms. This introduces a new, and previously unexplored, dynamical relationship between preference, action, experience and learning. Here we model collective learning within animal groups that make consensus decisions. We reveal how learning as part of a collective results in behavior that is fundamentally different from that learned in isolation, allowing grouping organisms to spontaneously (and indirectly) detect correlations between group members'' observations of environmental cues, adjust strategy as a function of changing group size (even if that group size is not known to the individual), and achieve a decision accuracy that is very close to that which is provably optimal, regardless of environmental contingencies. Because these properties make minimal cognitive demands on individuals, collective learning, and the capabilities it affords, may be widespread among group-living organisms. Our work emphasizes the importance and need for theoretical and experimental work that considers the mechanism and consequences of learning in a social context.     

Collaborative Mining and Interpretation of Large-Scale Data for Biomedical Research Insights

Biomedical research becomes increasingly interdisciplinary and collaborative in nature. Researchers need to efficiently and effectively collaborate and make decisions by meaningfully assembling, mining and analyzing available large-scale volumes of complex multi-faceted data residing in different sources. In line with related research directives revealing that, in spite of the recent advances in data mining and computational analysis, humans can easily detect patterns which computer algorithms may have difficulty in finding, this paper reports on the practical use of an innovative web-based collaboration support platform in a biomedical research context. Arguing that dealing with data-intensive and cognitively complex settings is not a technical problem alone, the proposed platform adopts a hybrid approach that builds on the synergy between machine and human intelligence to facilitate the underlying sense-making and decision making processes. User experience shows that the platform enables more informed and quicker decisions, by displaying the aggregated information according to their needs, while also exploiting the associated human intelligence.