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Many legal decisions center on the thoughts or perceptions of some idealized group of individuals, referred to variously as the “average person,” “the typical consumer,” or the “reasonable person.” Substantial concerns exist, however, regarding the subjectivity and vulnerability to biases inherent in conventional means of assessing such responses, particularly the use of self-report evidence. Here, we addressed these concerns by complementing self-report evidence with neural data to inform the mental representations in question. Using an example from intellectual property law, we demonstrate that it is possible to construct a parsimonious neural index of visual similarity that can inform the reasonable person test of trademark infringement. Moreover, when aggregated across multiple participants, this index was able to detect experimenter-induced biases in self-report surveys in a sensitive and replicable fashion. Together, these findings potentially broaden the possibilities for neuroscientific data to inform legal decision-making across a range of settings.

Real-world decisions are often open ended, with goals, choice options, or evaluation criteria conceived by decision-makers themselves. Critically, the quality of decisions may heavily rely on the generation of options, as failure to generate promising options limits, or even eliminates, the opportunity for choosing them. This core aspect of problem structuring, however, is largely absent from classical models of decision-making, thereby restricting their predictive scope. Here, we take a step toward addressing this issue by developing a neurally inspired cognitive model of a class of ill-structured decisions in which choice options must be self-generated. Specifically, using a model in which semantic memory retrieval is assumed to constrain the set of options available during valuation, we generate highly accurate out-of-sample predictions of choices across multiple categories of goods. Our model significantly and substantially outperforms models that only account for valuation or retrieval in isolation or those that make alternative mechanistic assumptions regarding their interaction. Furthermore, using neuroimaging, we confirm our core assumption regarding the engagement of, and interaction between, semantic memory retrieval and valuation processes. Together, these results provide a neurally grounded and mechanistic account of decisions with self-generated options, representing a step toward unraveling cognitive mechanisms underlying adaptive decision-making in the real world.

Legal tests invoking the viewpoint of a so-called reasonable person play an important role in many domains of the law, ranging from intellectual property to free speech. In such cases, a central question involves determining how a hypothetical individual with “an ordinary or average level of care, prudence, or knowledge” would respond. Despite the seemingly commonsensical nature of these tests, their judicial application can be controversial due to concerns about subjectivity and vulnerability to explicit or implicit biases. Here we take a step toward addressing these concerns by using neuroscientific tools to observe, without the use of self-report, the nature of mental representations central to a set of disputes invoking the reasonable person. Specifically, using an fMRI-based measure, repetition suppression, to generate a neural index of subjective visual similarity, we sought to inform the application of the reasonable person test to a class of intellectual property law that evaluates whether a trademark is so similar to another as to generate consumer confusion. We show that, by leveraging well-established neuroscientific knowledge about visual processing, it is possible to construct a parsimonious neural index of subjective similarity using signals from object-sensitive brain regions identified a priori. Moreover, this neural index, aggregated across multiple participants, is sufficiently precise to detect instances of experimenter-induced bias in behavioral reports. Together these findings shed light on the potential evidentiary value of neuroscientific data to inform questions involving the reasonable person and suggest a novel domain for the use of neuroscience in law.

The contents of working memory must be maintained in the face of distraction, but updated when appropriate. To manage these competing demands of stability and flexibility, maintained representations in working memory are complemented by distinct gating mechanisms that selectively transmit information into and out of memory stores. The operations of such dopamine-dependent gating systems in the midbrain and striatum and their complementary dopamine-dependent memory maintenance operations in the cortex may therefore be dissociable. If true, selective increases in cortical dopamine tone should preferentially enhance maintenance over gating mechanisms. To test this hypothesis, tolcapone, a catechol-O-methyltransferase inhibitor that preferentially increases cortical dopamine tone, was administered in a randomized, double-blind, placebo-controlled, within-subject fashion to 49 participants who completed a hierarchical working memory task that varied maintenance and gating demands. Tolcapone improved performance in a condition with higher maintenance requirements and reduced gating demands, reflected in a reduction in the slope of RTs across the distribution. Resting-state fMRI data demonstrated that the degree to which tolcapone improved performance in individual participants correlated with increased connectivity between a region important for stimulus response mappings (left dorsal premotor cortex) and cortical areas implicated in visual working memory, including the intraparietal sulcus and fusiform gyrus. Together, these results provide evidence that augmenting cortical dopamine tone preferentially improves working memory maintenance.