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Super-optimality and relative distance coding in location memory
The prevailing model of landmark integration in location memory is Maximum Likelihood Estimation, which assumes that each landmark implies a target...
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Human Behavior in Suboptimal Choice Tasks: Defining Optimality
Suboptimal choice behavior, or behavior that leads to a loss of resources over time, has been observed in a laboratory setting from multiple species....
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Joint contributions of preview and task instructions on visual search strategy selection
People tend to employ suboptimal attention control strategies during visual search. Here we question why people are suboptimal, specifically...
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Investigating the foreign language effect as a mitigating influence on the ‘optimality bias’ in moral judgements
Bilinguals often display reduced emotional resonance their second language (L2) and therefore tend to be less prone to decision-making biases in...
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A statistical test for the optimality of deliberative time allocation
Whenever we make a choice, we must also decide how much time to spend making it. Many theories of decision-making crucially assume that this...
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A theoretical analysis of the reward rate optimality of collapsing decision criteria
A standard assumption of most sequential sampling models is that decision-makers rely on a decision criterion that remains constant throughout the...
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Individual optimization of risky decisions in duration and distance estimations
Many everyday decisions require an accurate perception of how much time has passed since a previous event. Although humans estimate time intervals...
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Regret, Sub-optimality, and Vagueness
This paper concerns regret, where regretting is to be understood, roughly, as mourning the loss of a forgone good. My ultimate aim is to add a new...
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Item selection methods in multidimensional computerized adaptive testing for forced-choice items using Thurstonian IRT model
Multidimensional computerized adaptive testing for forced-choice items (MFC-CAT) combines the benefits of multidimensional forced-choice (MFC) items...
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Central tendency biases must be accounted for to consistently capture Bayesian cue combination in continuous response data
Observers in perceptual tasks are often reported to combine multiple sensory cues in a weighted average that improves precision—in some studies,...
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Selection history contributes to suboptimal attention strategies
Attentional control balances the competing drives of performance maximization and effort minimization. One way the attention system minimizes effort...
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Modeling Optimal Stopping in Changing Environments: a Case Study in Mate Selection
Optimal stopping problems require people to choose from a sequence of values presented sequentially, under the constraint that it is not possible to...
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Parallel Optimal Calibration of Mixed-Format Items for Achievement Tests
When large achievement tests are conducted regularly, items need to be calibrated before being used as operational items in a test. Methods have been...
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Correctly establishing evidence for cue combination via gains in sensory precision: Why the choice of comparator matters
Studying how sensory signals from different sources (sensory cues) are integrated within or across multiple senses allows us to better understand the...
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Statistically Optimal Cue Integration During Human Spatial Navigation
In 2007, Cheng and colleagues published their influential review wherein they analyzed the literature on spatial cue interaction during navigation...
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The effect of noninstrumental information on reward learning
Investigations of information-seeking often highlight people’s tendency to forgo financial reward in return for advance information about future...
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A Shadow-Test Approach to Adaptive Item Calibration
A shadow-test approach to the calibration of field-test items embedded in adaptive testing is presented. The objective function used in the...
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Intention offloading: Domain-general versus task-specific confidence signals
Intention offloading refers to the use of external reminders to help remember delayed intentions (e.g., setting an alert to help you remember when...
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Optimizing Large-Scale Educational Assessment with a “Divide-and-Conquer” Strategy: Fast and Efficient Distributed Bayesian Inference in IRT Models
With the growing attention on large-scale educational testing and assessment, the ability to process substantial volumes of response data becomes...
