Simple choices (e.g., eating an apple vs. orange) are made by integrating noisy evidence that is sampled over time and influenced visual attention; as a result, fluctuations in attention can affect choices. But what determines fixated when? To address this question, we model the decision process for simple choice information sampling problem, approximate optimal policy. We find it to sample from options whose value estimates both high uncertain. Furthermore, policy provides reasonable...

Human behavior emerges from planning over elaborate decompositions of tasks into goals, subgoals, and low-level actions. How are these created used? Here, we propose evaluate a normative framework for task decomposition based on the simple idea that people decompose to reduce overall cost while maintaining performance. Analyzing 11,117 distinct graph-structured tasks, find our justifies several existing heuristics makes predictions can be distinguished two alternative accounts. We report...

SignificanceMany bad decisions and their devastating consequences could be avoided if people used optimal decision strategies. Here, we introduce a principled computational approach to improving human making. The basic idea is give feedback on how they reach decisions. We develop method that leverages artificial intelligence generate this in such way quickly discover the best possible Our empirical findings suggest leads improvements decision-making competence transfer more difficult complex...

Perfectly rational decision making is almost always out of reach for people because their computational resources are limited. Instead, may rely on computationally frugal heuristics that usually yield good outcomes. Although previous research has identified many such heuristics, discovering and predicting when they will be used remains challenging. Here, we present a theoretical framework allows us to use methods from machine learning automatically derive the best heuristic in any given...

Planning is a latent cognitive process that cannot be observed directly. This makes it difficult to study how people plan. To address this problem, we propose new paradigm for studying planning provides experimenters with timecourse of participant attention information in the task environment. employs information-acquisition mechanism Mouselab paradigm, which participants click on options reveal outcome choosing those options. However, contrast original our sequential decision process, must...

Making good decisions requires thinking ahead, but the huge number of actions and outcomes one could consider makes exhaustive planning infeasible for computationally constrained agents, such as humans. How people are nevertheless able to solve novel problems when their have long-reaching consequences is thus a long-standing question in cognitive science. To address this question, we propose model resource-constrained that allows us derive optimal strategies. We find previously proposed...

When faced with a decision between several options, people rarely fully consider every alternative. Instead, we direct our attention to the most promising candidates, focusing limited cognitive resources on evaluating options that are likely choose. A growing body of empirical work has shown plays an important role in human making, but it is still unclear how choose option attend at each moment making process. In this paper, present analysis rational maker should allocate her attention. We...

Most of us have experienced moments when we could not recall some piece information but felt that it was just out reach. Research in metamemory has established such judgments are often accurate; what adaptive purpose do they serve? Here, present an optimal model how metacognitive monitoring (feeling knowing) dynamically inform control memory (the direction retrieval efforts). In two experiments, find that, consistent with the model, people report having a stronger for targets likely to and...

Abstract One of the most unique and impressive feats human mind is its ability to discover continuously refine own cognitive strategies. Elucidating underlying learning adaptation mechanisms very difficult because changes in strategies are not directly observable. important domain which studied planning. To enable researchers uncover how people learn plan, we offer a tutorial introduction recently developed process-tracing paradigm along with new computational method for measuring nature...

Perfectly rational decision-making is almost always out of reach for people because their computational resources are limited. Instead, may rely on computationally frugal heuristics that usually yield good outcomes. Although previous research has identified many such heuristics, discovering and predicting when they will be used remains challenging. Here, we present a theoretical framework allows us to use methods from machine learning automatically derive the best heuristic in any given...

The efficient use of limited computational resources is an essential ingredient intelligence. Selecting computations optimally according to rational metareasoning would achieve this, but this computationally intractable. Inspired by psychology and neuroscience, we propose the first concrete domain-general learning algorithm for approximating optimal selection computations: Bayesian metalevel policy search (BMPS). We derive general, sample-efficient a computation-selecting based on insight...

People's decisions often deviate from classical notions of rationality, incurring costs to themselves and society. One way reduce the poor is redesign decision problems people face encourage better choices. While subtle, these

How does the brain learn how to plan?We reverseengineer people's underlying learning mechanisms by combining rational process models of cognitive plasticity with recently developed empirical methods that allow us trace temporal evolution planning strategies.We find our Learned Value Computation model (LVOC) accurately captures average curve.However, there were also substantial individual differences in metacognitive are best understood terms multiple different -including strategy selection...

When making decisions, we often have more information about some options than others. Previous work has shown that people are likely to choose they look at and those confident in. But should one always prefer knows about? Intuition suggests not. Rather, how additional impacts our preferences depend critically on valuable expect the be. Here, formalize this intuition in a Bayesian sequential sampling model where attention confidence influence precision of momentary evidence. Our makes key...

Abstract The nature of eye movements during visual search has been widely studied in psychology and neuroscience. Virtual reality (VR) paradigms provide an opportunity to test whether computational models can predict naturalistic behavior. However, existing ideal observer are constrained by strong assumptions about the structure world, rendering them impractical for modeling complexity environments that be VR. To address these limitations, we frame as a problem allocating limited cognitive...

Human behavior is inherently hierarchical, resulting from the decomposition of a task into subtasks or an abstract action concrete actions. However, typically measured as sequence actions, which makes it difficult to infer its hierarchical structure. In this paper, we explore how people form hierarchically-structured plans, using experimental paradigm that representations observable: participants create programs produce sequences actions in language with explicit This lets us test two...

People's judgments and decisions often deviate from classical notions of rationality, incurring costs both to themselves society. One way reduce the poor is redesign decision problems people face encourage better choices. While subtle, these \emph{nudges} can have dramatic effects on behavior are increasingly popular in public policy, healthcare, marketing. Although nudges designed with psychological theories mind, they typically not formalized computational terms their be hard predict. As a...

There's a difference between someone instantaneously saying "Yes!" when you ask them on date compared to "...yes." Psychologists and economists have long studied how people can infer preferences from others' choices. However, these models tended focus what choose not it takes make choice. We present rational model for inferring response times, using Drift Diffusion Model characterize influence time Bayesian inference invert this relationship. test our model's predictions three experimental...