About this Research Topic
While lots of research has uncovered behavioral and neural mechanisms underlying decision making, most of the studies have focused heavily on static decisions. This neglects the majority of real decisions, which occur in dynamic environments where conditions and decisions change over time. Recently, a diverse set of researchers have been tackling different aspects of dynamic decision making.
This Frontiers Research Topic on dynamic decision making (DDM) seeks to combine contributions from researchers that discuss the neural computations underlying DDM and adaptive behavior. DDM raises a number of new problems that have been neglected by the traditional decision making research including:
1.) How decisions adapt to changing environmental conditions? Changing environment necessitates modifying strategies, and determining when to quit a strategy that is no longer appropriate. A critical ability that underlies this adaptive strategy is to be able to rapidly detect environmental changes. Recent studies have revealed sophisticated mechanisms for tracking the statistics of the environment and the ability to convert changes in these statistics into changes in decision making behavior.
2.) How is value information integrated in real time? In typical static decision problems, to evaluate the alternative options requires assigning and integrating values along a multitude of dimensions. To solve this problem requires finding a common currency to allow integration of disparate value dimensions. In dynamic decisions, this multi-dimensional integration must be updated across time. Models like drift diffusion are limited to single dimensions and conditions where values and options are static. Recently, a new theory was introduced to model dynamic integration of value information from disparate sources. Recent studies have also revealed sophisticated mechanisms on how good- and action- values are updated online.
3.) How do decisions unfold in real time? In dynamic environments, new opportunities are constantly presented, and hence subjects may have to change their decisions while acting. A series of studies aim to understand how a decision evolves in real time. Recent findings suggest that when people are faced with more than one potential goal, they generate concurrent action plans that compete for selection, and accumulate information to bias the competition, until a single goal is pursued. How this competition is encoded in the brain and how decision factors bias this competition are currently topics of many studies.
4.) Should I stay or should I go? Deciding when to leave a depleting resource and move to another is a fundamental problem in DDM. Recent studies explore the mechanisms underlying this stay-or-go problem and suggest that animals evaluate continuously the benefit and the cost for staying or leaving a patch. When the reward received from a current location diminishes to the average reward of other locations, they decide to leave a patch and explore new environments.
Overall dynamic decision making forms a critical set of problems that will increasingly form the research horizon in the neuroscience of decision making. This Frontiers Research Topic will help to identify the common core in these problems, and set the stage for rapid advances in the field.
This Research Topic is cross-listed in Frontiers in Computational Neuroscience
This Frontiers Research Topic on dynamic decision making (DDM) seeks to combine contributions from researchers that discuss the neural computations underlying DDM and adaptive behavior. DDM raises a number of new problems that have been neglected by the traditional decision making research including:
1.) How decisions adapt to changing environmental conditions? Changing environment necessitates modifying strategies, and determining when to quit a strategy that is no longer appropriate. A critical ability that underlies this adaptive strategy is to be able to rapidly detect environmental changes. Recent studies have revealed sophisticated mechanisms for tracking the statistics of the environment and the ability to convert changes in these statistics into changes in decision making behavior.
2.) How is value information integrated in real time? In typical static decision problems, to evaluate the alternative options requires assigning and integrating values along a multitude of dimensions. To solve this problem requires finding a common currency to allow integration of disparate value dimensions. In dynamic decisions, this multi-dimensional integration must be updated across time. Models like drift diffusion are limited to single dimensions and conditions where values and options are static. Recently, a new theory was introduced to model dynamic integration of value information from disparate sources. Recent studies have also revealed sophisticated mechanisms on how good- and action- values are updated online.
3.) How do decisions unfold in real time? In dynamic environments, new opportunities are constantly presented, and hence subjects may have to change their decisions while acting. A series of studies aim to understand how a decision evolves in real time. Recent findings suggest that when people are faced with more than one potential goal, they generate concurrent action plans that compete for selection, and accumulate information to bias the competition, until a single goal is pursued. How this competition is encoded in the brain and how decision factors bias this competition are currently topics of many studies.
4.) Should I stay or should I go? Deciding when to leave a depleting resource and move to another is a fundamental problem in DDM. Recent studies explore the mechanisms underlying this stay-or-go problem and suggest that animals evaluate continuously the benefit and the cost for staying or leaving a patch. When the reward received from a current location diminishes to the average reward of other locations, they decide to leave a patch and explore new environments.
Overall dynamic decision making forms a critical set of problems that will increasingly form the research horizon in the neuroscience of decision making. This Frontiers Research Topic will help to identify the common core in these problems, and set the stage for rapid advances in the field.
This Research Topic is cross-listed in Frontiers in Computational Neuroscience
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
