The evolution of language by sexual selection

Most accounts of the evolution of language assume that language and greater intelligence are beneficial adaptations, leading to increases in survival fitness. These accounts emphasise natural selection, with language as an adaptation to the habitat, placing less emphasis on sexual selection and reproductive fitness. An account of language evolution by natural selection alone faces problems in accounting for the prodigious power and expressivity of human language. Modern language (and its recent antecedents) would appear to offer only small incremental benefits over simpler language, which would require a smaller brain with smaller metabolic costs. Accounts by natural selection also face problems in accounting for the uniqueness of human language and intelligence. I therefore consider a hybrid account, in which both natural selection and sexual selection played a role in the evolution of language and intelligence, probably at different times. Specifically, in this account, early language was driven by natural selection to collaborate. Then later humans became subject to sexual selection for superior intelligence, with language acting as the main display mechanism for intelligence. It is hard to determine the relative roles of natural and sexual selection over the time course of the evolution of language. In the later stages, sexual selection to display intelligence drove a runaway selection process towards powerful modern language. This hybrid account retains the benefits of accounts by natural selection, while also accounting for the prodigious power of human language and intelligence, and for its uniqueness compared to other primates. Sexual selection often leads to traits which are unique to a species, and are exaggerated beyond natural needs. On this account, the capability for language may have evolved in the order: (1) pragmatics and a theory of mind; (2) using single words and constructions; (3) learning and using syntax. In this model, relevance-based pragmatics evolved before language; then, single words and constructions came into use; and later, syntax condensed out of pragmatics, as a codification of some pragmatic rules of inference. This order requires only incremental extensions of primate cognition, and agrees with the order in which children learn language.

The Thalamus as a Blackboard for Perception and Planning

It has been suggested that the thalamus acts as a blackboard, on which the computations of different cortical modules are composed, coordinated, and integrated. This article asks what blackboard role the thalamus might play, and whether that role is consistent with the neuroanatomy of the thalamus. It does so in a context of Bayesian belief updating, expressed as a Free Energy Principle. We suggest that the thalamus-as-a-blackboard offers important questions for research in spatial cognition. Several prominent features of the thalamus-including its lack of olfactory relay function, its lack of internal excitatory connections, its regular and conserved shape, its inhibitory interneurons, triadic synapses, and diffuse cortical connectivity-are consistent with a blackboard role.Different thalamic nuclei may play different blackboard roles: (1) the Pulvinar, through its reciprocal connections to posterior cortical regions, coordinates perceptual inference about "what is where" from multi-sense-data. (2) The Mediodorsal (MD) nucleus, through its connections to the prefrontal cortex, and the other thalamic nuclei linked to the motor cortex, uses the same generative model for planning and learning novel spatial movements. (3) The paraventricular nucleus may compute risk-reward trade-offs. We also propose that as any new movement is practiced a few times, cortico-thalamocortical (CTC) links entrain the corresponding cortico-cortical links, through a process akin to supervised learning. Subsequently, the movement becomes a fast unconscious habit, not requiring the MD nucleus or other thalamic nuclei, and bypassing the thalamic bottleneck.

Semantic mapping to simplify deployment of HL7 v3 Clinical Document Architecture

HL7 Version 3 was developed to provide semantically reliable healthcare interoperability but has been perceived as overly complex to implement. This paper describes how semantic mapping can simplify usage of HL7 Version 3 Clinical Document Architecture (CDA) and reports on the rationale for adopting this approach in three particular use cases and lessons learned from the practical application experience. We envision a broad application of the semantic mapping approach in clinical documents, messaging and application design modelling.

Simplifying HL7 Version 3 messages

HL7 Version 3 offers a semantically robust method for healthcare interoperability but has been criticized as overly complex to implement. This paper reviews initiatives to simplify HL7 Version 3 messaging and presents a novel approach based on semantic mapping. Based on user-defined definitions, precise transforms between simple and full messages are automatically generated. Systems can be interfaced with the simple messages and achieve interoperability with full Version 3 messages through the transforms. This reduces the costs of HL7 interfacing and will encourage better uptake of HL7 Version 3 and CDA.

Navigation by fragment fitting: a theory of hippocampal function

This paper describes a computational theory of spatial learning and navigation and its possible realization in the hippocampus. In the theory, mammals store memories of their geographical environment as a large number of independent fragments. A typical fragment denotes a few prominent landmarks in some region, their geometric relations, and their nongeometric properties, such as smells and visual cues. Navigation involves piecing together current sense data and relevant fragments to form a local map of the animal's surroundings; this is like solving a jigsaw puzzle. This computational model has been implemented in a computer program, whose performance is broadly consistent with observed levels of animal performance, and laboratory results, in spatial learning. Possible realizations of the model in animal brains are discussed. Unlike some neural net models of spatial learning, the model is strongly geometric, and uses special neural structures to store and manipulate two-dimensional vectors and bearings. A possible neural architecture is described in which the hippocampus performs the geometric operations; this has a long-term memory for fragments (somewhere in the neocortex), which can associatively recall fragments into a number of parallel fragment fitters, in the dentate gyrus and CA3 regions. These vary the positions and orientations of their fragments, to optimize the fit of the fragments to each other and to the animal's recent sense data. A local map of the animal's surroundings is stored in CA1 and subicular regions, where matching of fragment positions and attributes takes place. Mismatches are passed back via the entorhinal cortex to improve the fit during the next hippocampal theta cycle. The model offers the potential for understanding current data on spatial learning, on the neuroanatomy of the hippocampus and on place cells in a coherent framework, as well as understanding the role of the hippocampus in nonpositional memory tasks. Comparisons with experimental data are given.