Changes of mind in decision-making.

A decision is a commitment to a proposition or plan of action based on evidence and the expected costs and benefits associated with the outcome. Progress in a variety of fields has led to a quantitative understanding of the mechanisms that evaluate evidence and reach a decision. Several formalisms propose that a representation of noisy evidence is evaluated against a criterion to produce a decision. Without additional evidence, however, these formalisms fail to explain why a decision-maker would change their mind. Here we extend a model, developed to account for both the timing and the accuracy of the initial decision, to explain subsequent changes of mind. Subjects made decisions about a noisy visual stimulus, which they indicated by moving a handle. Although they received no additional information after initiating their movement, their hand trajectories betrayed a change of mind in some trials. We propose that noisy evidence is accumulated over time until it reaches a criterion level, or bound, which determines the initial decision, and that the brain exploits information that is in the processing pipeline when the initial decision is made to subsequently either reverse or reaffirm the initial decision. The model explains both the frequency of changes of mind as well as their dependence on both task difficulty and whether the initial decision was accurate or erroneous. The theoretical and experimental findings advance the understanding of decision-making to the highly flexible and cognitive acts of vacillation and self-correction.

Risk-sensitivity and the mean-variance trade-off: decision making in sensorimotor control.

Numerous psychophysical studies suggest that the sensorimotor system chooses actions that optimize the average cost associated with a movement. Recently, however, violations of this hypothesis have been reported in line with economic theories of decision-making that not only consider the mean payoff, but are also sensitive to risk, that is the variability of the payoff. Here, we examine the hypothesis that risk-sensitivity in sensorimotor control arises as a mean-variance trade-off in movement costs. We designed a motor task in which participants could choose between a sure motor action that resulted in a fixed amount of effort and a risky motor action that resulted in a variable amount of effort that could be either lower or higher than the fixed effort. By changing the mean effort of the risky action while experimentally fixing its variance, we determined indifference points at which participants chose equiprobably between the sure, fixed amount of effort option and the risky, variable effort option. Depending on whether participants accepted a variable effort with a mean that was higher, lower or equal to the fixed effort, they could be classified as risk-seeking, risk-averse or risk-neutral. Most subjects were risk-sensitive in our task consistent with a mean-variance trade-off in effort, thereby, underlining the importance of risk-sensitivity in computational models of sensorimotor control.

National Centers for Coastal Ocean Science Coastal Ecosystem Assessment Program: a manual of methods

Environmental managers strive to preserve natural resources for future generations but have limited decision-making tools to define ecosystem health. Many programs offer relevant broad-scale, environmental policy information on regional ecosystem health. These programs provide evidence of environmental condition and change, but lack connections between local impacts and direct effects on living resources. To address this need, the National Oceanic and Atmospheric Administration/National Ocean Service (NOAA/NOS) Cooperative Oxford Laboratory (COL), in cooperation with federal, state, and academic partners, implemented an integrated biotic ecosystem assessment on a sub-watershed 14-digit Hydrologic Unit Code (HUD) scale in Chesapeake Bay. The goals of this effort were to 1) establish a suite of bioindicators that are sensitive to ecosystem change, 2) establish the effects of varying land-use patterns on water quality and the subsequent health of living resources, 3) communicate these findings to local decision-makers, and 4) evaluate the success of management decisions in these systems. To establish indicators, three sub-watersheds were chosen based on statistical analysis of land-use patterns to represent a gradient from developed to agricultural. The Magothy (developed), Corsica (agricultural), and Rhode (reference) Rivers were identified. A random stratified design was developed based on depth (2m contour) and river mile. Sampling approaches were coordinated within this structure to allow for robust system comparisons. The sampling approach was hierarchal, with metrics chosen to represent a range from community to cellular level responses across multiple organisms. This approach allowed for the identification of sub-lethal stressors, and assessment of their impact on the organism and subsequently the population. Fish, crabs, clams, oysters, benthic organisms, and bacteria were targeted, as each occupies a separate ecological niche and may respond dissimilarly to environmental stressors. Particular attention was focused on the use of pathobiology as a tool for assessing environmental condition. By integrating the biotic component with water quality, sediment indices, and land- use information, this holistic evaluation of ecosystem health will provide management entities with information needed to inform local decision-making processes and establish benchmarks for future restoration efforts.

Statistical analyses to support guidelines for marine avian sampling: Final report. [Digital Supplements A-G attached]

Interest in development of offshore renewable energy facilities has led to a need for high-quality, statistically robust information on marine wildlife distributions. A practical approach is described to estimate the amount of sampling effort required to have sufficient statistical power to identify species specific “hotspots” and “coldspots” of marine bird abundance and occurrence in an offshore environment divided into discrete spatial units (e.g., lease blocks), where “hotspots” and “coldspots” are defined relative to a reference (e.g., regional) mean abundance and/or occurrence probability for each species of interest. For example, a location with average abundance or occurrence that is three times larger the mean (3x effect size) could be defined as a “hotspot,” and a location that is three times smaller than the mean (1/3x effect size) as a “coldspot.” The choice of the effect size used to define hot and coldspots will generally depend on a combination of ecological and regulatory considerations. A method is also developed for testing the statistical significance of possible hotspots and coldspots. Both methods are illustrated with historical seabird survey data from the USGS Avian Compendium Database.