Neurons of organisms with complex and flexible behavior, especially humans, must precisely control protein localization and activity to support higher brain functions such as learning and memory. In contrast, simpler organisms generally have simpler individual neurons, less complex nervous systems and display more limited behaviors. Strikingly, however, many key neuronal proteins are conserved between organisms that have very different degrees of behavioral complexity. Here we discuss a possible mechanism by which conserved neuronal proteins acquired new attributes that were crucial in (...) the evolution of complexity of nervous system structure and function. Specifically, we hypothesize that vertebrate‐specific post‐translational palmitoylation sites and PDZ‐binding protein‐protein interaction motifs act as gain‐of‐function mutations, increasing the regulatory potential of conserved proteins without affecting their core functions. We further hypothesize that the additional regulation of neurotransmitter receptors and other membrane proteins made possible by these sites and motifs is critical for the function of complex nervous systems. (shrink)

This article provides a simple decision theoretic model in which elements of the world successively enter the decision maker’s scope and the state space expands over time, which is intended to be the closest correspondence to the standard subjective expected utility theory. We propose a dynamic consistency condition that after any expansion of the scope, the preference ranking should remain unchanged over acts to which the expansion is irrelevant. Together with other natural axioms, it characterizes a model in which the (...) decision maker’s belief extends over time in order that the marginal distribution of the new belief induced over the old state space coincides with the old belief. It is extended to encompass both expansion of scope and learning events, and we characterize the model with an additional property that the decision maker’s belief updating follows Bayes’ rule when she learns events. (shrink)

This article provides an experimental analysis of attitude toward imprecise and variable information. Imprecise information is provided in the form of a set of possible probability values, such that it is virtually impossible for the subjects to guess or estimate, which one in the set is true or more likely to be true. We investigate how geometric features of such information pieces affect choices. We find that the subjects care about more features than the pairs of best-case and worst-case, which (...) is a counter-evidence to the well-known models, maximin and α-maximin. We find that presence of nonextreme points in the set affects choice, which suggests that attitude toward imprecision is ‘nonlinear.’ We also obtain an observation, though not significant, that information pieces have a complementarity that may not be explained by the Bayesian view. (shrink)

We examine if and to what extent choice dispositions can allow dependence on contexts and maintain consistency over time, in a dynamic environment under uncertainty. We focus on one of the context dependence properties, opportunity dependence because of being affected by anticipated regret, where the consequentialist choice framework is maintained. There are two sources of potential inconsistency: one is arrival of information, and the other is changing opportunities. First, we go over the general method of resolution of potential inconsistency, by (...) taking any kinds of inconsistency as given constraints. Second, we characterize a class of choice dispositions that are consistent to information arrival, but may be inconsistent to changing opportunities. Finally, we consider the overall requirement of dynamic consistency and show that it necessarily implies each of consistency to information arrival and independence of choice opportunities. The last result states that the two kinds of potential inconsistency cannot “compensate” each other to recover dynamic consistency overall. (shrink)