scholarly journals Neural systems for memory-based value judgment and decision-making

2019 ◽  
Author(s):  
Avinash R. Vaidya ◽  
David Badre

AbstractReal life choices often require that we draw inferences about the value of options based on structured, schematic knowledge about their utility for our current goals. Other times, value information may be retrieved directly from a specific prior experience with an option. In a functional magnetic resonance imaging (fMRI) experiment, we investigated the neural systems involved in retrieving and assessing information from different memory sources to support value-based choice. Participants completed a task in which items could be conferred positive or negative value based on schematic associations (i.e. schema value), or learned directly from experience via deterministic feedback (i.e. experienced value). We found that ventromedial prefrontal cortex (vmPFC) activity correlated with the influence of both experience- and schema-based values on participants’ decisions. Connectivity between vmPFC and middle temporal cortex also tracked the inferred value of items based on schematic associations on the first presentation of ingredients, prior to any feedback. In contrast, the striatum responded to participants’ willingness to bet on ingredients as a function of the unsigned strength of their memory for those options’ values. These results argue that striatum and vmPFC play distinct roles in memory-based value judgment and decision-making. Specifically, the vmPFC assesses the value of options based on information inferred from schematic knowledge and retrieved from prior direct experience, while the striatum controls a decision to act on options based on memory strength.

2020 ◽  
Vol 32 (10) ◽  
pp. 1896-1923
Author(s):  
Avinash R. Vaidya ◽  
David Badre

Real-life choices often require that we draw inferences about the value of options based on structured, schematic knowledge about their utility for our current goals. Other times, value information may be retrieved directly from a specific prior experience with an option. In an fMRI experiment, we investigated the neural systems involved in retrieving and assessing information from different memory sources to support value-based choice. Participants completed a task in which items could be conferred positive or negative value based on schematic associations (i.e., schema value) or learned directly from experience via deterministic feedback (i.e., experienced value). We found that ventromedial pFC (vmPFC) activity correlated with the influence of both experience- and schema-based values on participants' decisions. Connectivity between the vmPFC and middle temporal cortex also tracked the inferred value of items based on schematic associations on the first presentation of ingredients, before any feedback. In contrast, the striatum responded to participants' willingness to bet on ingredients as a function of the unsigned strength of their memory for those options' values. These results argue that the striatum and vmPFC play distinct roles in memory-based value judgment and decision-making. Specifically, the vmPFC assesses the value of options based on information inferred from schematic knowledge and retrieved from prior direct experience, whereas the striatum controls a decision to act on options based on memory strength.


2019 ◽  
Author(s):  
Stefan Scherbaum ◽  
Steven Lade ◽  
Thilo Gross ◽  
Stefan Siegmund ◽  
Thomas Goschke ◽  
...  

Decision-making is usually studied on a trial by trial basis and each decision is assumed to represent an isolated choice process. These assumptions are also reflected in sequential sampling models which conceive of the decision-process as an accumulation of information about the attractiveness of the options at hand. Real-life decisions however are usually embedded in a rich context of previous choices at different time scales. A fundamental yet neglected question is therefore how the dynamics of choice processes unfold on a long-term time scale across several decisions. Here, we present a neural-inspired attractor model that integrates the short-term mechanism of accumulation models with the long-term dynamics of coupled neural systems. The model represents a class of models that incorporate inherent long-term dynamics. We use the model to predict long-term patterns, namely oscillatory switching, perseveration and dependence of perseveration on the delay between decisions. Furthermore, we predict RT effects for specific trials. We validate the predictions in two new studies and a reanalysis of existing data from a novel decision game in which participants have to perform delay discounting decisions. Applying the validated reasoning to a well-established choice questionnaire, we illustrate and discuss that taking long-term choice patterns into account may be necessary to accurately analyse and model decision processes.


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