This chapter explores the potential for gamesmanship in technology-assisted discovery.1 Attorneys have long embraced gamesmanship strategies in analog discovery, producing reams of irrelevant documents, delaying depositions, or interpreting requests in a hyper-technical manner.2 The new question, however, is whether machine learning technologies can transform gaming strategies. By now it is well known that technologies have reinvented the practice of civil litigation and, specifically, the extensive search for relevant documents in complex cases. Many sophisticated litigants use machine learning algorithms – under the umbrella of “Technology Assisted Review” (TAR) – to simplify the identification and production of relevant documents in discovery.3 Litigants employ TAR in cases ranging from antitrust to environmental law, civil rights, and employment disputes. But as the field becomes increasingly influenced by engineers and technologists, a string of commentators has raised questions about TAR, including lawyers’ professional role, underlying incentive structures, and the dangers of new forms of gamesmanship and abuse.4

Natural language processing techniques promise to automate an activity that lies at the core of many tasks performed by lawyers, namely the extraction and processing of information from unstructured text. The relevant methods are thought to be a key ingredient for both current and future legal tech applications. This chapter provides a non-technical overview of the current state of NLP techniques, focusing on their promise and potential pitfalls in the context of legal tech applications. It argues that, while NLP-powered legal tech can be expected to outperform humans in specific categories of tasks that play to the strengths of current ML techniques, there are severe obstacles to deploying these tools in other contexts, most importantly in tasks that require the equivalent of legal reasoning.

The medial geniculate body (MGB) of the thalamus plays a critical role in transforming the dense, high-fidelity auditory coding of the brainstem and midbrain to the sparse, abstract coding used throughout the forebrain to represent the perceptual qualities and behavioral meaning of sound. Here, we review the current state of knowledge on the connectivity, functional processing, and plasticity of interconnected neural circuits linking the MGB and the auditory cortex (ACtx). We describe new findings on the activation of corticothalamic neurons prior to expected sounds and specializations for encoding sound features that unfold on slow timescales that first emerge at the level of the MGB and ACtx. We review the literature on the development and plasticity of the MGB and ACtx, with a particular emphasis on how early auditory experience and adult learning modify sound processing at the level of thalamocortical synapses, circuits, and integrated neural systems. Despite its critical role as the root of forebrain sound processing, direct recordings from anatomically or genetically identified MGB cell types are rarely performed. We conclude by identifying several important knowns and unknowns about the distinct patterns of connectivity and functional specializations of the ventral, dorsal, and medial subdivisions of the MGB that await future investigation.

In this chapter, we discuss the hypothesis people help to regulate each other’s bodies (for better or for worse), and this is a main mechanism through which culture wires a human brain. Cultural transmission prepares the developing brain and body to meet recurrent demands within a particular cultural context, thereby supporting the development of an internal model that is sufficiently tuned to specific environments. In this way, a human brain becomes wired to run a model of the world that will control the body in an efficient, predictive manner. Our approach provides an empirically inspired account of how a human brain becomes a cultural artifact.

With world population senescence and globalization, more present-day older adults will evince cognitive aging that is influenced over a longer life span by a wide range of social practices and motivational beliefs from cultural groups across the world. Although there is no dispute that brain structure and function aggregate biological and experiential influences, a useful framework is still needed regarding the specific neural mechanisms underlying the exchange between biology and experience with age, and the effect on cognition. We introduce a predictive coding framework of the aging cognitive brain that views the older brain as making predictions about the environment based on a lifetime of experience in it. The influence of cultural experiences in shaping the aging predictive brain then reflects individual differences in processing social signals about appropriate or inappropriate behaviors and cognitive styles amid neural resources changes. We briefly annotate relevant findings on age effects and cultural differences in neurocognitive processing. We further review findings showing that cultural cognitive differences are present in children, persist in young adulthood, and are either maintained or accentuated in older adulthood. Finally, we consider that the predictive aging brain is an enculturated one, reflecting the accumulation of a lifetime of experiences that have fortified culture-specific modes of thought and neural processing in older adults.

Computational neuroscience uses formal models of brain function to characterize the mechanisms behind behavioral problems. The production of false beliefs and their behavioral consequences are a central issue in such models. Hopelessness and suicidal thoughts are examples of such false beliefs that commonly lead to suicidal behavior as a consequence. In normal everyday life, people update their beliefs based on what they perceive: bottom-up sensory inputs are compared with top-down beliefs, and mismatches are signaled as prediction errors. Neurobiological correlates of belief updating are increasingly demonstrated. Cortical activations as demonstrated in functional neuroimaging studies, such as those reported in Chapter 6, thus reflect the production of prediction errors that signal a mismatch between beliefs and perceptual information. These errors can be minimized in several ways: beliefs can be updated, or sensory input can be minimized by withdrawal into oneself or escape from this world. If something goes wrong in this process of belief updating, false beliefs may develop and persist despite perceptual proof of the opposite. This chapter will describe a predictive coding model of suicidal behavior, in which findings from neurocognitive, neuroimaging, and neurobiological studies can be integrated. This model leads to a new understanding of suicide and, consequently, to new approaches to prevention.