TOLERANCE OF INCOHERENCE IN LAW, GRADED SPEECH ACTS AND ILLOCUTIONARY PLURALISM

One of the most difficult challenges of mature legal systems is the need to balance the conflicting demands of stability and flexibility. The demand for flexibility is at odds with the principle of impartiality, which is considered a cornerstone of the rule of law. In the present article, I explore the way in which the law copes with this dilemma by developing the idea of tolerance of incoherence. I argue that tolerance of incoherence emerges from the interplay between the inferential and lexical-semantic rules that determine the meaning of legal speech acts. I base this argument on an inferential model of speech acts, which I develop through a discussion of graded speech acts, and on the idea that the use of speech acts is governed by multiple and potentially conflicting conventions. I show how this tolerance allows the law to resolve the tension between dynamism and traditionality, and discuss its sociological and moral implications.

Probabilistic, spinally-gated control of bladder pressure and autonomous micturition by Barrington’s nucleus CRH neurons

Micturition requires precise control of bladder and urethral sphincter via parasympathetic, sympathetic and somatic motoneurons. This involves a spino-bulbospinal control circuit incorporating Barrington’s nucleus in the pons (Barr). Ponto-spinal glutamatergic neurons that express corticotrophin-releasing hormone (CRH) form one of the largest Barr cell populations. BarrCRH neurons can generate bladder contractions, but it is unknown whether they act as a simple switch or provide a high-fidelity pre-parasympathetic motor drive and whether their activation can actually trigger voids. Combined opto- and chemo-genetic manipulations along with multisite extracellular recordings in urethane anaesthetised CRHCre mice show that BarrCRH neurons provide a probabilistic drive that generates co-ordinated voids or non-voiding contractions depending on the phase of the micturition cycle. CRH itself provides negative feedback regulation of this process. These findings inform a new inferential model of autonomous micturition and emphasise the importance of the state of the spinal gating circuit in the generation of voiding.

Corticotrophin-Releasing Hormone neurons of Barrington’s nucleus: Probabilistic, spinally-gated control of bladder pressure and micturition

Micturition, the co-ordinated process of expulsion of urine from the bladder, requires precise control of bladder and urethral sphincter via parasympathetic, sympathetic and somatic motoneurons. In adult mammals this involves a spinobulbospinal control circuit incorporating Barrington’s nucleus in the pons (Barr). The largest Barr cell population is comprised of pontospinal glutamatergic neurons that express corticotrophin-releasing hormone. There is evidence that BarrCRH neurons can generate bladder contractions but it is unknown whether they act as a simple switch or a high-fidelity pre-parasympathetic motor drive and whether their activation can actually trigger voids. Combined opto- and chemo-genetic manipulations along with recordings in mice shows that BarrCRH neurons provide a probabilistic drive that generates co-ordinated voids or non-voiding contractions depending on the phase of the micturition cycle. These findings inform a new inferential model of micturition and emphasise the importance of the state of the spinal gating circuit in the generation of voiding.

A neurally plausible model for online recognition and postdiction in a dynamical environment

Humans and other animals are frequently near-optimal in their ability to integrate noisy and ambiguous sensory data to form robust percepts, which are informed both by sensory evidence and by prior experience about the causal structure of the environment. It is hypothesized that the brain establishes these structures using an internal model of how the observed patterns can be generated from relevant but unobserved causes. In dynamic environments, such integration often takes the form of postdiction, wherein later sensory evidence affects inferences about earlier percepts. As the brain must operate in current time, without the luxury of acausal propagation of information, how does such postdictive inference come about? Here, we propose a general framework for neural probabilistic inference in dynamic models based on the distributed distributional code (DDC) representation of uncertainty, naturally extending the underlying encoding to incorporate implicit probabilistic beliefs about both present and past. We show that, as in other uses of the DDC, an inferential model can be learned efficiently using samples from an internal model of the world. Applied to stimuli used in the context of psychophysics experiments, the framework provides an online and plausible mechanism for inference, including postdictive effects.