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1. How fuzzy-trace theory predicts development of risky decision making, with novel extensions to culture and reward sensitivity

   https://doi.org/10.1016/j.dr.2021.100986  

   Edelson, Sarah M. ; Reyna, Valerie F. ( December 2021 , Developmental Review)
2. Reward-driven changes in striatal pathway competition shape evidence evaluation in decision-making

   https://doi.org/10.1371/journal.pcbi.1006998  

   Dunovan, Kyle ; Vich, Catalina ; Clapp, Matthew ; Verstynen, Timothy ; Rubin, Jonathan ; Bogacz, Rafal ( May 2019 , PLOS Computational Biology)
3. Reward/Punishment‐Based Decision Making in Rodents

   https://doi.org/10.1002/cpns.100  

   Orsini, Caitlin A. ; Simon, Nicholas W. ( July 2020 , Current Protocols in Neuroscience)

   Deficits in decision making are at the heart of many psychiatric diseases, such as substance abuse disorders and attention deficit hyperactivity disorder. Consequently, rodent models of decision making are germane to understanding the neural mechanisms underlying adaptive choice behavior and how such mechanisms can become compromised in pathological conditions. A critical factor that must be integrated with reward value to ensure optimal decision making is the occurrence of consequences, which can differ based on probability (risk of punishment) and temporal contiguity (delayed punishment). This article will focus on two models of decision making that involve explicit punishment, both of which recapitulate different aspects of consequences during human decision making. We will discuss each behavioral protocol, the parameters to consider when designing an experiment, and finally how such animal models can be utilized in studies of psychiatric disease. © 2020 Wiley Periodicals LLC.

   Basic Protocol 1: Behavioral training

   Support Protocol: Equipment testing

   Alternate Protocol: Reward discrimination

   Basic Protocol 2: Risky decision‐making task (RDT)

   Basic Protocol 3: Delayed punishment decision‐making task (DPDT)

    

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4. Tuning the speed-accuracy trade-off to maximize reward rate in multisensory decision-making

   https://doi.org/10.7554/eLife.06678  

   Drugowitsch, Jan ; DeAngelis, Gregory C ; Angelaki, Dora E ; Pouget, Alexandre ( June 2015 , eLife)

   For decisions made under time pressure, effective decision making based on uncertain or ambiguous evidence requires efficient accumulation of evidence over time, as well as appropriately balancing speed and accuracy, known as the speed/accuracy trade-off. For simple unimodal stimuli, previous studies have shown that human subjects set their speed/accuracy trade-off to maximize reward rate. We extend this analysis to situations in which information is provided by multiple sensory modalities. Analyzing previously collected data (Drugowitsch et al., 2014), we show that human subjects adjust their speed/accuracy trade-off to produce near-optimal reward rates. This trade-off can change rapidly across trials according to the sensory modalities involved, suggesting that it is represented by neural population codes rather than implemented by slow neuronal mechanisms such as gradual changes in synaptic weights. Furthermore, we show that deviations from the optimal speed/accuracy trade-off can be explained by assuming an incomplete gradient-based learning of these trade-offs.

    

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5. Crystalline: Lowering the Cost for Developers to Collect and Organize Information for Decision Making

   https://doi.org/10.1145/3491102.3501968  

   Liu, Michael Xieyang ; Kittur, Aniket ; Myers, Brad A. ( April 2022 , Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI '22).)