Background Psychiatric symptoms typically cut across traditional diagnostic categories. In order to devise individually tailored treatments, there is a need to identify the basic mechanisms that underlie these symptoms. Behavioral economics provides a framework for studying these mechanisms at the behavioral level. Here, we utilized this framework to examine a widely ignored aspect of trauma-related symptomatology—individual uncertainty attitudes—in combat veterans with and without posttraumatic stress disorder (PTSD). Methods Fifty-seven combat veterans, including 30 with PTSD and 27 without PTSD, completed a risk and ambiguity decision-making task that characterizes individual uncertainty attitudes, distinguishing between attitudes toward uncertain outcomes with known (“risk”) and unknown (“ambiguity”) probabilities, and between attitudes toward uncertain gains and uncertain losses. Participants’ choices were used to estimate risk and ambiguity attitudes in the gain and loss domains. Results Veterans with PTSD were more averse to ambiguity, but not risk, compared to veterans without PTSD, when making choices between possible losses, but not gains. The degree of aversion was associated with anxious arousal (e.g., hypervigilance) symptoms, as well as with the degree of combat exposure. Moreover, ambiguity attitudes fully mediated the association between combat exposure and anxious arousal symptoms. Conclusions These results provide a foundation for prospective studies of the causal association between ambiguity attitudes and trauma-related symptoms, as well as etiologic studies of the neural underpinnings of these behavioral outcomes. More generally, these results demonstrate the potential of neuroeconomic and behavioral economic techniques for devising objective and incentive-compatible diagnostic tools, and investigating the etiology of psychiatric disorders.

An extensive literature from cognitive neuroscience examines the neural representation of value, but interpretations of these existing results are often complicated by the potential confound of saliency. At the same time, recent attempts to dissociate neural signals of value and saliency have not addressed their relationship with category information. Using a multi-category valuation task that incorporates rewards and punishments of different nature, we identify distributed neural representation of value, saliency, and category during outcome anticipation. Moreover, we reveal category encoding in multi-voxel blood-oxygen-level-dependent activity patterns of the vmPFC and the striatum that coexist with value signals. These results help clarify ambiguities regarding value and saliency encoding in the human brain and their category independence, lending strong support to the neural “common currency” hypothesis. Our results also point to potential novel mechanisms of integrating multiple aspects of decision-related information.

Although the decisions of our daily lives often occur in the context of temporal and reward structures, the impact of such regularities on decision-making strategy is poorly understood.Here, to explore how temporal and reward context modulate strategy, we trained rhesus monkeys to perform a novel perceptual decision-making task with asymmetric rewards and time-varying evidence reliability. To model the choice and response time patterns, we developed a computational framework for fitting generalized drift-diffusion models (GDDMs) which flexibly accommodates diverse evidence accumulation strategies. We found that a dynamic urgency signal and leaky integration, in combination with two independent forms of reward biases, best capture behavior. We also tested how temporal structure influences urgency by systematically manipulating the temporal structure of sensory evidence, and found that the time course of urgency was affected by temporal context. Overall, our approach identified key components of cognitive mechanisms for incorporating temporal and reward structure into decisions.One paradigm for studying perceptual decision-making computations and their neural correlates is to present dynamic sensory evidence over time (Newsome et al., 1989;Roitman and Shadlen, 2002). Evidence accumulation has been proposed as a leading strategy for decisionmaking under this paradigm, which can be formalized using the drift-diffusion model (DDM) (Ratcliff, 1978;Ratcliff et al., 2016). The DDM has been employed to capture choice and RT behavior in a range of decision-making tasks (

Real-world tasks require coordination of working memory, decision-making, and planning, yet these cognitive functions have disproportionately been studied as independent modular processes in the brain. Here, we propose that contingency representations, defined as mappings for how future behaviors depend on upcoming events, can unify working memory and planning computations. We designed a task capable of disambiguating distinct types of representations. In task-optimized recurrent neural networks, we investigated possible circuit mechanisms for contingency representations and found that these representations can explain neurophysiological observations from the prefrontal cortex during working memory tasks. Our experiments revealed that human behavior is consistent with contingency representations and not with traditional sensory models of working memory. Finally, we generated falsifiable predictions for neural data to identify contingency representations in neural data and to dissociate different models of working memory. Our findings characterize a neural representational strategy that can unify working memory, planning, and context-dependent decision-making.

Task-trained artificial recurrent neural networks (RNNs) provide a computational modeling framework of increasing interest and application in computational, systems, and cognitive neuroscience. RNNs can be trained, using deep learning methods, to perform cognitive tasks used in animal and human experiments, and can be studied to investigate potential neural representations and circuit mechanisms underlying cognitive computations and behavior. Widespread application of these approaches within neuroscience has been limited by technical barriers in use of deep learning software packages to train network models. Here we introduce PsychRNN, an accessible, flexible, and extensible Python package for training RNNs on cognitive tasks. Our package is designed for accessibility, for researchers to define tasks and train RNN models using only Python and NumPy without requiring knowledge of deep learning software. The training backend is based on TensorFlow and is readily extensible for researchers with TensorFlow knowledge to develop projects with additional customization. PsychRNN implements a number of specialized features to support applications in systems and cognitive neuroscience. Users can impose neurobiologically relevant constraints on synaptic connectivity patterns. Furthermore, specification of cognitive tasks has a modular structure, which facilitates parametric variation of task demands to examine their impact on model solutions. PsychRNN also enables task shaping during training, or curriculum learning, in which tasks are adjusted in closed-loop based on performance. Shaping is ubiquitous in training of animals in cognitive tasks, and PsychRNN allows investigation of how shaping trajectories impact learning and model solutions. Overall, the PsychRNN framework facilitates application of trained RNNs in neuroscience research.