A genetically mediated bias in decision making driven by failure of amygdala control.

Genetic variation at the serotonin transporter-linked polymorphic region (5-HTTLPR) is associated with altered amygdala reactivity and lack of prefrontal regulatory control. Similar regions mediate decision-making biases driven by contextual cues and ambiguity, for example the "framing effect." We hypothesized that individuals hemozygous for the short (s) allele at the 5-HTTLPR would be more susceptible to framing. Participants, selected as homozygous for either the long (la) or s allele, performed a decision-making task where they made choices between receiving an amount of money for certain and taking a gamble. A strong bias was evident toward choosing the certain option when the option was phrased in terms of gains and toward gambling when the decision was phrased in terms of losses (the frame effect). Critically, this bias was significantly greater in the ss group compared with the lala group. In simultaneously acquired functional magnetic resonance imaging data, the ss group showed greater amygdala during choices made in accord, compared with those made counter to the frame, an effect not seen in the lala group. These differences were also mirrored by differences in anterior cingulate-amygdala coupling between the genotype groups during decision making. Specifically, lala participants showed increased coupling during choices made counter to, relative to those made in accord with, the frame, with no such effect evident in ss participants. These data suggest that genetically mediated differences in prefrontal-amygdala interactions underpin interindividual differences in economic decision making.

Sequential decision making in general state space models

Many problems in control and signal processing can be formulated as sequential decision problems for general state space models. However, except for some simple models one cannot obtain analytical solutions and has to resort to approximation. In this thesis, we have investigated problems where Sequential Monte Carlo (SMC) methods can be combined with a gradient based search to provide solutions to online optimisation problems. We summarise the main contributions of the thesis as follows. Chapter 4 focuses on solving the sensor scheduling problem when cast as a controlled Hidden Markov Model. We consider the case in which the state, observation and action spaces are continuous. This general case is important as it is the natural framework for many applications. In sensor scheduling, our aim is to minimise the variance of the estimation error of the hidden state with respect to the action sequence. We present a novel SMC method that uses a stochastic gradient algorithm to find optimal actions. This is in contrast to existing works in the literature that only solve approximations to the original problem. In Chapter 5 we presented how an SMC can be used to solve a risk sensitive control problem. We adopt the use of the Feynman-Kac representation of a controlled Markov chain flow and exploit the properties of the logarithmic Lyapunov exponent, which lead to a policy gradient solution for the parameterised problem. The resulting SMC algorithm follows a similar structure with the Recursive Maximum Likelihood(RML) algorithm for online parameter estimation. In Chapters 6, 7 and 8, dynamic Graphical models were combined with with state space models for the purpose of online decentralised inference. We have concentrated more on the distributed parameter estimation problem using two Maximum Likelihood techniques, namely Recursive Maximum Likelihood (RML) and Expectation Maximization (EM). The resulting algorithms can be interpreted as an extension of the Belief Propagation (BP) algorithm to compute likelihood gradients. In order to design an SMC algorithm, in Chapter 8 uses a nonparametric approximations for Belief Propagation. The algorithms were successfully applied to solve the sensor localisation problem for sensor networks of small and medium size.