Adherence to social norms is compromised in a variety of neuropsychiatric conditions. Functional neuroimaging studies have investigated social norm compliance in healthy individuals, leading to the identification of a network of fronto-subcortical regions that underpins this ability. However, there is a lack of corroborative evidence from human lesion models investigating the structural anatomy of norm compliance across this fronto-subcortical network. To address this, we developed a neuroeconomic task to investigate social norm compliance in a neurodegenerative lesion model: behavioural variant frontotemporal dementia, a condition characterized by gross social dysfunction. The task assessed norm compliance across three behaviours that are well-studied in the neuroeconomics literature: fairness, prosocial and punishing behaviours. We administered our novel version of the Ultimatum Game in 22 patients with behavioural variant frontotemporal dementia and 22 age-matched controls, to assess how decision-making behaviour was modulated in response to (i) fairness of monetary offers; and (ii) social context of monetary offers designed to produce either prosocial or punishing behaviours. Voxel-based morphometry was used to characterize patterns of grey matter atrophy associated with task performance. Acceptance rates between patients and controls were equivalent when only fairness was manipulated. However, patients were impaired in modulating their decisions in response to social contextual information. Patients' performance in the punishment condition was consistent with a reduced tendency to engage in punishment; this was associated with decreased grey matter volume in the anterior cingulate, orbitofrontal cortex, left dorsolateral prefrontal cortex and right inferior frontal gyrus. In the prosocial condition, patients' performance suggested a reduced expression of prosocial behaviour, associated with decreased grey matter in the anterior insula, lateral orbitofrontal cortex, anterior cingulate and dorsal striatum. Acceptance rates in the Ultimatum Game were also significantly related to impairments in the everyday expression of empathic concern. In conclusion, we demonstrate that compliance to basic social norms (fairness) can be maintained in behavioural variant frontotemporal dementia; however, more complex normative behaviours (prosociality, punishment) that require integration of social contextual information are disrupted in association with atrophy in key fronto-striatal regions. These results suggest that the integration of social contextual information to guide normative behaviour is uniquely impaired in behavioural variant frontotemporal dementia, and may explain other common features of the condition including gullibility and impaired empathy. Our findings also converge with previous functional neuroimaging investigations in healthy individuals and provide the first description of the structural anatomy of social norm compliance in a neurodegenerative lesion model.

For decisions in the wild, time is of the essence. Available decision time is often cut short through natural or artificial constraints, or is impinged upon by the opportunity cost of time. Experimental economists have only recently begun to conduct experiments with time constraints and to analyze response time (RT) data, in contrast to experimental psychologists. RT analysis has proven valuable for the identification of individual and strategic decision processes including identification of social preferences in the latter case, model comparison/selection, and the investigation of heuristics that combine speed and performance by exploiting environmental regularities. Here we focus on the benefits, challenges, and desiderata of RT analysis in strategic decision making. We argue that unlocking the potential of RT analysis requires the adoption of process-based models instead of outcome-based models, and discuss how RT in the wild can be captured by time-constrained experiments in the lab. We conclude that RT analysis holds considerable potential for experimental economics, deserves greater attention as a methodological tool, and promises important insights on strategic decision making in naturally occurring environments.

For decisions in the wild, time is of the essence. Available decision time is often cut short through natural or artificial constraints, or is impinged upon by the opportunity cost of time. Experimental economists have only recently begun to conduct experiments with time constraints and to analyze response time (RT) data, in contrast to experimental psychologists. RT analysis has proven valuable for the identification of individual and strategic decision processes including identification of social preferences in the latter case, model comparison/selection, and the investigation of heuristics that combine speed and performance by exploiting environmental regularities. Here we focus on the benefits, challenges, and desiderata of RT analysis in strategic decision making. We argue that unlocking the potential of RT analysis requires the adoption of process-based models instead of outcomebased models, and discuss how RT in the wild can be captured by time-constrained experiments in the lab. We conclude that RT analysis holds considerable potential for experimental economics, deserves greater attention as a methodological tool, and promises important insights on strategic decision making in naturally occurring environments. Keywords Response time Á Time constraints Á Experimental economics Á Procedural rationality Á Games Á Strategic decision making BCD refers to Benefits, Challenges, and Desiderata.

The ecological rationality of heuristics has been extensively investigated in the domain of individual decision making. In strategic decision making, however, the focus has been on repeated games, and there is a lack of research on 1-shot games, where opponents and the game itself can vary from one interaction to another. Mapping the performance of simple versus more complex decision policies (or strategies) from the experimental game theory literature is an important first step in this direction. We investigate how 10 policies fare conditional on strategic properties of the games and 2 classes of uncertainty. The strategic properties are the complexity (number of actions) and the degree of harmony (competitiveness) of the games. The first class of uncertainty is environmental (or payoff) uncertainty, arising from missing payoff values. The second class is strategic uncertainty about the type of opponent a player is facing. Policies' performance was measured by 3 criteria: a mean criterion averaging over the whole set of opponent policies, a maxmin criterion capturing the worst-case scenario and another criterion measuring robustness to different distributions of opponent policies. Heuristics performed well and were more robust than complex policies such as pure-strategy Nash equilibria, while simultaneously requiring significantly less information and fewer computational resources. Our ranking of the decision policies' performance was closely aligned to their prevalence in experimental studies of games. In particular, the Level-1 policy, which completely ignores an opponent's payoffs and uses equal weighting to determine the expected payoffs of different actions, exhibited a robust beauty.