Two Attempts to Formalize Counterpossible Reasoning in Deterministic Settings

Abstract. This paper motivates the study of counterpossibles (logically impossible counterfactuals) as necessary for developing a decision theory suitable for generally intelligent agents embedded within their environments. We discuss two attempts to formalize a decision theory using counterpossibles, one based on graphical models and another based on proof search

The evolution of decision rules in complex environments.

PublishedResearch Support, Non-U.S. Gov'tReviewThis is the author accepted manuscript. The final version is available from Elsevier via http://dx.doi.org/10.1016/j.tics.2013.12.012Models and experiments on adaptive decision-making typically consider highly simplified environments that bear little resemblance to the complex, heterogeneous world in which animals (including humans) have evolved. These studies reveal an array of so-called cognitive biases and puzzling features of behaviour that seem irrational in the specific situation presented to the decision-maker. Here we review an emerging body of work that highlights spatiotemporal heterogeneity and autocorrelation as key properties of most real-world environments that may help us understand why these biases evolved. Ecologically rational decision rules adapted to such environments can lead to apparently maladaptive behaviour in artificial experimental settings. We encourage researchers to consider environments with greater complexity to understand better how evolution has shaped our cognitive systems.This work was funded by the European Research Council (Advanced Grant 250209 to A.I.H.) and the Engineering and Physical Sciences Research Council (grant number EP/I032622/1 to Iain D. Gilchrist)

Dynamic mechanical properties of human brain tissue

Investigators have been studying the mechanical phenomena associated with impact to the head for many years. Several theories on the behavior of the brain during head impact have come from these studies but there has been a notable lack of information on the bulk mechanical properties of the brain which are necessary for the evaluation of these theories. This paper represents an initial attempt at providing such information.The dynamic complex shear modulus of in vitro samples of human brain have been measured. Specimens from eight brains have been subjected to a sinusoidal shear stress input under resonant conditions in an electro-mechanical test device. Tests were conducted to determine the effects of time after death, refrigeration of material and shear strain dependence. A device to measure the dynamic properties of brain in vivo is described and preliminary data on in vivo tests on Rhesus monkeys is presented.The results of the dynamic shear testing on in vitro human brain indicate that the storage modulus G' lies between 6-11 x 103 dyn/cm2, the loss modulus G" lies between 3[middle dot]5-6[middle dot]0 x 103 dyn/cm2 and the loss tangent tan [delta] is in the range 0[middle dot]40-0[middle dot]55.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/32936/1/0000319.pd

A mathematical model to determine viscoelastic behavior of in vivo primate brain

Determination of mechanical properties of the constituents of the head is very essential for the construction of various theoretical and experimental head injury models. This paper represents a mathematical model for the evaluation of viscoelastic behavior of in vivo primate brain. From a theoretical mechanics point of view, the problem being considered is that of the steady state response characteristics of a solid sphere of linear viscoelastic material whose mating surface with the rigid container is free from shear stresses. The external load is taken to be a local radial harmonic excitation. First, the response of the elastic material is determined; later the elastic response solution is converted to viscoelastic response solution through the use of the correspondence principle applicable to steady state oscillations. The paper is concluded with a discussion of a method which enables the determination of the complex dynamic shear modulus of in vivo primate brain.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/32763/1/0000134.pd

Computer simulation of glioma growth and morphology

Despite major advances in the study of glioma, the quantitative links between intra-tumor molecular/cellular properties, clinically observable properties such as morphology, and critical tumor behaviors such as growth and invasiveness remain unclear, hampering more effective coupling of tumor physical characteristics with implications for prognosis and therapy. Although molecular biology, histopathology, and radiological imaging are employed in this endeavor, studies are severely challenged by the multitude of different physical scales involved in tumor growth, i.e., from molecular nanoscale to cell microscale and finally to tissue centimeter scale. Consequently, it is often difficult to determine the underlying dynamics across dimensions. New techniques are needed to tackle these issues. Here, we address this multi-scalar problem by employing a novel predictive three-dimensional mathematical and computational model based on first-principle equations (conservation laws of physics) that describe mathematically the diffusion of cell substrates and other processes determining tumor mass growth and invasion. The model uses conserved variables to represent known determinants of glioma behavior, e.g., cell density and oxygen concentration, as well as biological functional relationships and parameters linking phenomena at different scales whose specific forms and values are hypothesized and calculated based on in vitro and in vivo experiments and from histopathology of tissue specimens from human gliomas. This model enables correlation of glioma morphology to tumor growth by quantifying interdependence of tumor mass on the microenvironment (e.g., hypoxia, tissue disruption) and on the cellular phenotypes (e.g., mitosis and apoptosis rates, cell adhesion strength). Once functional relationships between variables and associated parameter values have been informed, e.g., from histopathology or intra-operative analysis, this model can be used for disease diagnosis/prognosis, hypothesis testing, and to guide surgery and therapy. In particular, this tool identifies and quantifies the effects of vascularization and other cell-scale glioma morphological characteristics as predictors of tumor-scale growth and invasion