Neural signatures of economic preferences for risk and ambiguity

People often prefer the known over the unknown, sometimes sacrificing potential rewards for the sake of surety. Overcoming impulsive preferences for certainty in order to exploit uncertain but potentially lucrative options may require specialized neural mechanisms. Here, we demonstrate by functional magnetic resonance imaging (fMRI) that individuals' preferences for risk (uncertainty with known probabilities) and ambiguity (uncertainty with unknown probabilities) predict brain activation associated with decision making. Activation within the lateral prefrontal cortex was predicted by ambiguity preference and was also negatively correlated with an independent clinical measure of behavioral impulsiveness, suggesting that this region implements contextual analysis and inhibits impulsive responses. In contrast, activation of the posterior parietal cortex was predicted by risk preference. Together, this novel double dissociation indicates that decision making under ambiguity does not represent a special, more complex case of risky decision making; instead, these two forms of uncertainty are supported by distinct mechanisms.     

Need for Cognitive Closure Modulates How Perceptual Decisions Are Affected by Task Difficulty and Outcome Relevance

The aim of this study was to assess the extent to which Need for Cognitive Closure (NCC), an individual-level epistemic motivation, can explain inter-individual variability in the cognitive effort invested on a perceptual decision making task (the random motion task). High levels of NCC are manifested in a preference for clarity, order and structure and a desire for firm and stable knowledge. The study evaluated how NCC moderates the impact of two variables known to increase the amount of cognitive effort invested on a task, namely task ambiguity (i.e., the difficulty of the perceptual discrimination) and outcome relevance (i.e., the monetary gain associated with a correct discrimination). Based on previous work and current design, we assumed that reaction times (RTs) on our motion discrimination task represent a valid index of effort investment. Task ambiguity was associated with increased cognitive effort in participants with low or medium NCC but, interestingly, it did not affect the RTs of participants with high NCC. A different pattern of association was observed for outcome relevance; high outcome relevance increased cognitive effort in participants with moderate or high NCC, but did not affect the performance of low NCC participants. In summary, the performance of individuals with low NCC was affected by task difficulty but not by outcome relevance, whereas individuals with high NCC were influenced by outcome relevance but not by task difficulty; only participants with medium NCC were affected by both task difficulty and outcome relevance. These results suggest that perceptual decision making is influenced by the interaction between context and NCC.     

Relating Pupil Dilation and Metacognitive Confidence during Auditory Decision-Making

The sources of evidence contributing to metacognitive assessments of confidence in decision-making remain unclear. Previous research has shown that pupil dilation is related to the signaling of uncertainty in a variety of decision tasks. Here we ask whether pupil dilation is also related to metacognitive estimates of confidence. Specifically, we measure the relationship between pupil dilation and confidence during an auditory decision task using a general linear model approach to take into account delays in the pupillary response. We found that pupil dilation responses track the inverse of confidence before but not after a decision is made, even when controlling for stimulus difficulty. In support of an additional post-decisional contribution to the accuracy of confidence judgments, we found that participants with better metacognitive ability – that is, more accurate appraisal of their own decisions – showed a tighter relationship between post-decisional pupil dilation and confidence. Together our findings show that a physiological index of uncertainty, pupil dilation, predicts both confidence and metacognitive accuracy for auditory decisions.     

Reward and Uncertainty Favor Risky Decision-Making in Pilots: Evidence from Cardiovascular and Oculometric Measurements

In this paper we examined plan continuation error (PCE), a well known error made by pilots consisting in continuing the flight plan despite adverse meteorological conditions. Our hypothesis is that a large range of strong negative emotional consequences, including those induced by economic pressure, are associated with the decision to revise the flight plan and favor PCE. We investigated the economic hypothesis with a simplified landing task (reproduction of a real aircraft instrument) in which uncertainty and reward were manipulated. Heart rate (HR), heart rate variability (HRV) and eye tracking measurements were performed to get objective clues both on the cognitive and emotional state of the volunteers. Results showed that volunteers made more risky decisions under the influence of the financial incentive, in particular when uncertainty was high. Psychophysiological examination showed that HR increased and total HRV decreased in response to the cognitive load generated by the task. In addition, HR also increased in response to the financially motivated condition. Eventually, fixation times increased when uncertainty was high, confirming the difficulty in obtaining/interpreting information from the instrument in this condition. These results support the assumption that risky-decision making observed in pilots can be, at least partially, explained by a shift from cold to hot (emotional) decision-making in response to economic constraints and uncertainty.     

Explaining distortions in metacognition with an attractor network model of decision uncertainty

Metacognition is the ability to reflect on, and evaluate, our cognition and behaviour. Distortions in metacognition are common in mental health disorders, though the neural underpinnings of such dysfunction are unknown. One reason for this is that models of key components of metacognition, such as decision confidence, are generally specified at an algorithmic or process level. While such models can be used to relate brain function to psychopathology, they are difficult to map to a neurobiological mechanism. Here, we develop a biologically-plausible model of decision uncertainty in an attempt to bridge this gap. We first relate the model’s uncertainty in perceptual decisions to standard metrics of metacognition, namely mean confidence level (bias) and the accuracy of metacognitive judgments (sensitivity). We show that dissociable shifts in metacognition are associated with isolated disturbances at higher-order levels of a circuit associated with self-monitoring, akin to neuropsychological findings that highlight the detrimental effect of prefrontal brain lesions on metacognitive performance. Notably, we are able to account for empirical confidence judgements by fitting the parameters of our biophysical model to first-order performance data, specifically choice and response times. Lastly, in a reanalysis of existing data we show that self-reported mental health symptoms relate to disturbances in an uncertainty-monitoring component of the network. By bridging a gap between a biologically-plausible model of confidence formation and observed disturbances of metacognition in mental health disorders we provide a first step towards mapping theoretical constructs of metacognition onto dynamical models of decision uncertainty. In doing so, we provide a computational framework for modelling metacognitive performance in settings where access to explicit confidence reports is not possible.     

Robustness and power of the unified model in the analysis of quantitative measurements.

The resolution between skewness in the distribution of a quantitative trait and segregation of a major gene is a difficult issue in family studies. Quantitative data were simulated on six-member nuclear families in order to study the behavior of the unified model under these circumstances. Replicates of 100 nuclear families were generated assuming a multifactorial model with skewness. In the range where a major gene was falsely detected in 80%-100% of the simulations analyzed under the transmission probability or mixed models, use of the unified model reduces the frequency of false inference to between 10% and 40%. This protection against a false conclusion requires estimation of the three transmission probabilities and testing hypotheses of Mendelian transmission and equal transmission probabilities. Alternatively, it was shown that use of a transformation to remove skewness induced by a major gene leads to a decrease of power of approximately 55%. These results suggest that the unified model may obviate the need to compare analyses performed on transformed and untransformed data, particularly when skewness is low (less than 0.2) or high (greater than 0.4). For intermediate skewness (0.2-0.4), estimating segregation parameters under the mixed model simultaneously with a transformation to remove residual skewness can be considered as an alternative method.     

Intrinsic Valuation of Information in Decision Making under Uncertainty

In a dynamic world, an accurate model of the environment is vital for survival, and agents ought regularly to seek out new information with which to update their world models. This aspect of behaviour is not captured well by classical theories of decision making, and the cognitive mechanisms of information seeking are poorly understood. In particular, it is not known whether information is valued only for its instrumental use, or whether humans also assign it a non-instrumental intrinsic value. To address this question, the present study assessed preference for non-instrumental information among 80 healthy participants in two experiments. Participants performed a novel information preference task in which they could choose to pay a monetary cost to receive advance information about the outcome of a monetary lottery. Importantly, acquiring information did not alter lottery outcome probabilities. We found that participants were willing to incur considerable monetary costs to acquire payoff-irrelevant information about the lottery outcome. This behaviour was well explained by a computational cognitive model in which information preference resulted from aversion to temporally prolonged uncertainty. These results strongly suggest that humans assign an intrinsic value to information in a manner inconsistent with normative accounts of decision making under uncertainty. This intrinsic value may be associated with adaptive behaviour in real-world environments by producing a bias towards exploratory and information-seeking behaviour.     

Robust optimization for resource-constrained project scheduling with uncertain activity durations

The purpose of this paper is to propose models for project scheduling when there is considerable uncertainty in the activity durations, to the extent that the decision maker cannot with confidence associate probabilities with the possible outcomes of a decision. Our modeling techniques stem from robust discrete optimization, which is a theoretical framework that enables the decision maker to produce solutions that will have a reasonably good objective value under any likely input data scenario. We develop and implement a scenario-relaxation algorithm and a scenario-relaxation-based heuristic. The first algorithm produces optimal solutions but requires excessive running times even for medium-sized instances; the second algorithm produces high-quality solutions for medium-sized instances and outperforms two benchmark heuristics.     

Evaluation of habitat suitability index models by global sensitivity and uncertainty analyses: a case study for submerged aquatic vegetation

Habitat suitability index (HSI) models are commonly used to predict habitat quality and species distributions and are used to develop biological surveys, assess reserve and management priorities, and anticipate possible change under different management or climate change scenarios. Important management decisions may be based on model results, often without a clear understanding of the level of uncertainty associated with model outputs. We present an integrated methodology to assess the propagation of uncertainty from both inputs and structure of the HSI models on model outputs (uncertainty analysis: UA) and relative importance of uncertain model inputs and their interactions on the model output uncertainty (global sensitivity analysis: GSA). We illustrate the GSA/UA framework using simulated hydrology input data from a hydrodynamic model representing sea level changes and HSI models for two species of submerged aquatic vegetation (SAV) in southwest Everglades National Park: Vallisneria americana (tape grass) and Halodule wrightii (shoal grass). We found considerable spatial variation in uncertainty for both species, but distributions of HSI scores still allowed discrimination of sites with good versus poor conditions. Ranking of input parameter sensitivities also varied spatially for both species, with high habitat quality sites showing higher sensitivity to different parameters than low‐quality sites. HSI models may be especially useful when species distribution data are unavailable, providing means of exploiting widely available environmental datasets to model past, current, and future habitat conditions. The GSA/UA approach provides a general method for better understanding HSI model dynamics, the spatial and temporal variation in uncertainties, and the parameters that contribute most to model uncertainty. Including an uncertainty and sensitivity analysis in modeling efforts as part of the decision‐making framework will result in better‐informed, more robust decisions.     

Self-Knowledge Dim-Out: Stress Impairs Metacognitive Accuracy

Modulation of frontal lobes activity is believed to be an important pathway trough which the hypothalamic-pituitary-adrenal (HPA) axis stress response impacts cognitive and emotional functioning. Here, we investigate the effects of stress on metacognition, which is the ability to monitor and control one''s own cognition. As the frontal lobes have been shown to play a critical role in metacognition, we predicted that under activation of the HPA axis, participants should be less accurate in the assessment of their own performances in a perceptual decision task, irrespective of the effect of stress on the first order perceptual decision itself. To test this prediction, we constituted three groups of high, medium and low stress responders based on cortisol concentration in saliva in response to a standardized psycho-social stress challenge (the Trier Social Stress Test). We then assessed the accuracy of participants'' confidence judgments in a visual discrimination task. As predicted, we found that high biological reactivity to stress correlates with lower sensitivity in metacognition. In sum, participants under stress know less when they know and when they do not know.     

Suprathreshold perceptual decisions constrain models of confidence

Perceptual confidence is an important internal signal about the certainty of our decisions and there is a substantial debate on how it is computed. We highlight three confidence metric types from the literature: observers either use 1) the full probability distribution to compute probability correct (Probability metrics), 2) point estimates from the perceptual decision process to estimate uncertainty (Evidence-Strength metrics), or 3) heuristic confidence from stimulus-based cues to uncertainty (Heuristic metrics). These metrics are rarely tested against one another, so we examined models of all three types on a suprathreshold spatial discrimination task. Observers were shown a cloud of dots sampled from a dot generating distribution and judged if the mean of the distribution was left or right of centre. In addition to varying the horizontal position of the mean, there were two sensory uncertainty manipulations: the number of dots sampled and the spread of the generating distribution. After every two perceptual decisions, observers made a confidence forced-choice judgement whether they were more confident in the first or second decision. Model results showed that the majority of observers were best-fit by either: 1) the Heuristic model, which used dot cloud position, spread, and number of dots as cues; or 2) an Evidence-Strength model, which computed the distance between the sensory measurement and discrimination criterion, scaled according to sensory uncertainty. An accidental repetition of some sessions also allowed for the measurement of confidence agreement for identical pairs of stimuli. This N-pass analysis revealed that human observers were more consistent than their best-fitting model would predict, indicating there are still aspects of confidence that are not captured by our modelling. As such, we propose confidence agreement as a useful technique for computational studies of confidence. Taken together, these findings highlight the idiosyncratic nature of confidence computations for complex decision contexts and the need to consider different potential metrics and transformations in the confidence computation.     

Projected status of the Pacific walrus (Odobenus rosmarus divergens) in the twenty-first century

Extensive and rapid losses of sea ice in the Arctic have raised conservation concerns for the Pacific walrus (Odobenus rosmarus divergens), a large pinniped inhabiting arctic and subarctic continental shelf waters of the Chukchi and Bering seas. We developed a Bayesian network model to integrate potential effects of changing environmental conditions and anthropogenic stressors on the future status of the Pacific walrus population at four periods through the twenty-first century. The model framework allowed for inclusion of various sources and levels of knowledge, and representation of structural and parameter uncertainties. Walrus outcome probabilities through the century reflected a clear trend of worsening conditions for the subspecies. From the current observation period to the end of century, the greatest change in walrus outcome probabilities was a progressive decrease in the outcome state of robust and a concomitant increase in the outcome state of vulnerable. The probabilities of rare and extirpated states each progressively increased but remained <10% through the end of the century. The summed probabilities of vulnerable, rare, and extirpated (P(v,r,e)) increased from a current level of 10% in 2004 to 22% by 2050 and 40% by 2095. The degree of uncertainty in walrus outcomes increased monotonically over future periods. In the model, sea ice habitat (particularly for summer/fall) and harvest levels had the greatest influence on future population outcomes. Other potential stressors had much smaller influences on walrus outcomes, mostly because of uncertainty in their future states and our current poor understanding of their mechanistic influence on walrus abundance.     

Agency Modulates the Lateral and Medial Prefrontal Cortex Responses in Belief-Based Decision Making

Many real-life decisions in complex and changing environments are guided by the decision maker’s beliefs, such as her perceived control over decision outcomes (i.e., agency), leading to phenomena like the “illusion of control”. However, the neural mechanisms underlying the “agency” effect on belief-based decisions are not well understood. Using functional imaging and a card guessing game, we revealed that the agency manipulation (i.e., either asking the subjects (SG) or the computer (CG) to guess the location of the winning card) not only affected the size of subjects’ bets, but also their “world model” regarding the outcome dependency. Functional imaging results revealed that the decision-related activation in the lateral and medial prefrontal cortex (PFC) was significantly modulated by agency and previous outcome. Specifically, these PFC regions showed stronger activation when subjects made decisions after losses than after wins under the CG condition, but the pattern was reversed under the SG condition. Furthermore, subjects with high external attribution of negative events were more affected by agency at the behavioral and neural levels. These results suggest that the prefrontal decision-making system can be modulated by abstract beliefs, and are thus vulnerable to factors such as false agency and attribution.     

Dopamine beta hydroxylase genotype identifies individuals less susceptible to bias in computer-assisted decision making

Computerized aiding systems can assist human decision makers in complex tasks but can impair performance when they provide incorrect advice that humans erroneously follow, a phenomenon known as "automation bias." The extent to which people exhibit automation bias varies significantly and may reflect inter-individual variation in the capacity of working memory and the efficiency of executive function, both of which are highly heritable and under dopaminergic and noradrenergic control in prefrontal cortex. The dopamine beta hydroxylase (DBH) gene is thought to regulate the differential availability of dopamine and norepinephrine in prefrontal cortex. We therefore examined decision-making performance under imperfect computer aiding in 100 participants performing a simulated command and control task. Based on two single nucleotide polymorphism (SNPs) of the DBH gene, -1041 C/T (rs1611115) and 444 G/A (rs1108580), participants were divided into groups of low and high DBH enzyme activity, where low enzyme activity is associated with greater dopamine relative to norepinephrine levels in cortex. Compared to those in the high DBH enzyme activity group, individuals in the low DBH enzyme activity group were more accurate and speedier in their decisions when incorrect advice was given and verified automation recommendations more frequently. These results indicate that a gene that regulates relative prefrontal cortex dopamine availability, DBH, can identify those individuals who are less susceptible to bias in using computerized decision-aiding systems.     

Role of Ecological Risk Assessment Findings in Agency Decision-Making Regarding Oyster Restoration in Chesapeake Bay

Failure of on-going management programs to restore oyster populations in Chesapeake Bay, USA, prompted state and federal agencies to consider the introduction of the non-native Asian oyster (Crassostrea ariakensis). An ecological risk assessment (ERA) of the proposed introduction was an essential element in preparation of a programmatic environmental impact statement (PEIS). The ERA had to assess risks of not only the proposed action (Asian oyster introduction) but also of the eight alternatives evaluated in the PEIS. The ERA suggested that the risk that the Asian oyster would not provide ecosystem services similar to those afforded by the native Eastern oyster was low, but there was moderate uncertainty associated with that conclusion. There was a non-zero risk of self-sustaining Asian oyster populations becoming established even if aquaculture with triploid, purportedly sterile Asian oysters were to be permitted. Nearly all of the risk conclusions had associated moderate to high uncertainty, not providing the level of proof that the agencies felt sufficient to justify proceeding with any action involving the Asian oyster. The irreversible nature of an introduction of the species bolstered that decision. Maryland and Virginia agencies have implemented numerous actions focused on the native oyster, but the outcome of these on-going actions is not yet known.     

Structured Under‐Specification of Life Cycle Impact Assessment Data for Building Assemblies

The existence of uncertainties and variations in data represents a remaining challenge for life cycle assessment (LCA). Moreover, a full analysis may be complex, time‐consuming, and implemented mainly when a product design is already defined. Structured under‐specification, a method developed to streamline LCA, is here proposed to support the residential building design process, by quantifying environmental impact when specific information on the system under analysis cannot be available. By means of structured classifications of materials and building assemblies, it is possible to use surrogate data during the life cycle inventory phase and thus to obtain environmental impact and associated uncertainty. The bill of materials of a building assembly can be specified using minimal detail during the design process. The low‐fidelity characterization of a building assembly and the uncertainty associated with these low levels of fidelity are systematically quantified through structured under‐specification using a structured classification of materials. The analyst is able to use this classification to quantify uncertainty in results at each level of specificity. Concerning building assemblies, an average decrease of uncertainty of 25% is observed at each additional level of specificity within the data structure. This approach was used to compare different exterior wall options during the early design process. Almost 50% of the comparisons can be statistically differentiated at even the lowest level of specificity. This data structure is the foundation of a streamlined approach that can be applied not only when a complete bill of materials is available, but also when fewer details are known.     

Paradoxical Interaction between Ocular Activity,Perception, and Decision Confidence at the Threshold of Vision

In humans and some other species perceptual decision-making is complemented by the ability to make confidence judgements about the certainty of sensory evidence. While both forms of decision process have been studied empirically, the precise relationship between them remains poorly understood. We performed an experiment that combined a perceptual decision-making task (identifying the category of a faint visual stimulus) with a confidence-judgement task (wagering on the accuracy of each perceptual decision). The visual stimulation paradigm required steady fixation, so we used eye-tracking to control for stray eye movements. Our data analyses revealed an unexpected and counterintuitive interaction between the steadiness of fixation (prior to and during stimulation), perceptual decision making, and post-decision wagering: greater variability in gaze direction during fixation was associated with significantly increased visual-perceptual sensitivity, but significantly decreased reliability of confidence judgements. The latter effect could not be explained by a simple change in overall confidence (i.e. a criterion artifact), but rather was tied to a change in the degree to which high wagers predicted correct decisions (i.e. the sensitivity of the confidence judgement). We found no evidence of a differential change in pupil diameter that could account for the effect and thus our results are consistent with fixational eye movements being the relevant covariate. However, we note that small changes in pupil diameter can sometimes cause artefactual fluctuations in measured gaze direction and this possibility could not be fully ruled out. In either case, our results suggest that perceptual decisions and confidence judgements can be processed independently and point toward a new avenue of research into the relationship between them.     

Environmental Statistics and Optimal Regulation

Any organism is embedded in an environment that changes over time. The timescale for and statistics of environmental change, the precision with which the organism can detect its environment, and the costs and benefits of particular protein expression levels all will affect the suitability of different strategies–such as constitutive expression or graded response–for regulating protein levels in response to environmental inputs. We propose a general framework–here specifically applied to the enzymatic regulation of metabolism in response to changing concentrations of a basic nutrient–to predict the optimal regulatory strategy given the statistics of fluctuations in the environment and measurement apparatus, respectively, and the costs associated with enzyme production. We use this framework to address three fundamental questions: (i) when a cell should prefer thresholding to a graded response; (ii) when there is a fitness advantage to implementing a Bayesian decision rule; and (iii) when retaining memory of the past provides a selective advantage. We specifically find that: (i) relative convexity of enzyme expression cost and benefit influences the fitness of thresholding or graded responses; (ii) intermediate levels of measurement uncertainty call for a sophisticated Bayesian decision rule; and (iii) in dynamic contexts, intermediate levels of uncertainty call for retaining memory of the past. Statistical properties of the environment, such as variability and correlation times, set optimal biochemical parameters, such as thresholds and decay rates in signaling pathways. Our framework provides a theoretical basis for interpreting molecular signal processing algorithms and a classification scheme that organizes known regulatory strategies and may help conceptualize heretofore unknown ones.