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      Failure of Intuition When Choosing Whether to Invest in a Single Goal or Split Resources Between Two Goals

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      Psychological Science
      SAGE Publications

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          Abstract

          In a series of related experiments, we asked people to choose whether to split their attention between two equally likely potential tasks or to prioritize one task at the expense of the other. In such a choice, when the tasks are easy, the best strategy is to prepare for both of them. As difficulty increases beyond the point at which people can perform both tasks accurately, they should switch strategy and focus on one task at the expense of the other. Across three very different tasks (target detection, throwing, and memory), none of the participants switched their strategy at the correct point. Moreover, the majority consistently failed to modify their strategy in response to changes in task difficulty. This failure may have been related to uncertainty about their own ability, because in a version of the experiment in which there was no uncertainty, participants uniformly switched at an optimal point.

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          Most cited references15

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          Bayesian integration in sensorimotor learning.

          When we learn a new motor skill, such as playing an approaching tennis ball, both our sensors and the task possess variability. Our sensors provide imperfect information about the ball's velocity, so we can only estimate it. Combining information from multiple modalities can reduce the error in this estimate. On a longer time scale, not all velocities are a priori equally probable, and over the course of a match there will be a probability distribution of velocities. According to bayesian theory, an optimal estimate results from combining information about the distribution of velocities-the prior-with evidence from sensory feedback. As uncertainty increases, when playing in fog or at dusk, the system should increasingly rely on prior knowledge. To use a bayesian strategy, the brain would need to represent the prior distribution and the level of uncertainty in the sensory feedback. Here we control the statistical variations of a new sensorimotor task and manipulate the uncertainty of the sensory feedback. We show that subjects internally represent both the statistical distribution of the task and their sensory uncertainty, combining them in a manner consistent with a performance-optimizing bayesian process. The central nervous system therefore employs probabilistic models during sensorimotor learning.
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            Optimal eye movement strategies in visual search.

            To perform visual search, humans, like many mammals, encode a large field of view with retinas having variable spatial resolution, and then use high-speed eye movements to direct the highest-resolution region, the fovea, towards potential target locations. Good search performance is essential for survival, and hence mammals may have evolved efficient strategies for selecting fixation locations. Here we address two questions: what are the optimal eye movement strategies for a foveated visual system faced with the problem of finding a target in a cluttered environment, and do humans employ optimal eye movement strategies during a search? We derive the ideal bayesian observer for search tasks in which a target is embedded at an unknown location within a random background that has the spectral characteristics of natural scenes. Our ideal searcher uses precise knowledge about the statistics of the scenes in which the target is embedded, and about its own visual system, to make eye movements that gain the most information about target location. We find that humans achieve nearly optimal search performance, even though humans integrate information poorly across fixations. Analysis of the ideal searcher reveals that there is little benefit from perfect integration across fixations--much more important is efficient processing of information on each fixation. Apparently, evolution has exploited this fact to achieve efficient eye movement strategies with minimal neural resources devoted to memory.
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              Optimal Versus Naive Diversification: How Inefficient is the 1/NPortfolio Strategy?

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                Author and article information

                Journal
                Psychological Science
                Psychol Sci
                SAGE Publications
                0956-7976
                1467-9280
                November 18 2015
                December 08 2015
                : 27
                : 1
                : 64-74
                Article
                10.1177/0956797615611933
                26646581
                3e211bf0-7cda-40a2-8027-f4e6b5c1cb57
                © 2015

                http://journals.sagepub.com/page/policies/text-and-data-mining-license

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