The fear factor : effects of implicit and explicit processing on the emotional enhancement of memory

This thesis examines whether implicit and explicit processing of emotional facial expressions affects the emotional enhancement of memory (EEM). On the basis that explicit processing is associated with relative reductions in amygdala activation and arousal, I predicted that fearful faces, in particular, would lead to a robust EEM effect following encoding with implicit, but not explicit processing. Participants were shown a series of facial expressions (happy, fearful, angry, and neutral) in an "indirect" and a "direct" task designed to elicit implicit and explicit processing, respectively. Later they underwent a recognition memory test using the Remember-Know paradigm. Fearful faces exhibited a unique pattern whereby indirect encoding led to an enhanced subjective sense of recollection, whereas direct encoding prevented an increase in recollection that was observed for all other emotions. These findings may reflect interactions among amygdalar/arousal thresholds and levels of processing (LOP) effects on recognition memory.

The fear factor : effects of implicit and explicit processing on the emotional enhancement of memory

This thesis examines whether implicit and explicit processing of emotional facial expressions affects the emotional enhancement of memory (EEM). On the basis that explicit processing is associated with relative reductions in amygdala activation and arousal, I predicted that fearful faces, in particular, would lead to a robust EEM effect following encoding with implicit, but not explicit processing. Participants were shown a series of facial expressions (happy, fearful, angry, and neutral) in an "indirect" and a "direct" task designed to elicit implicit and explicit processing, respectively. Later they underwent a recognition memory test using the Remember-Know paradigm. Fearful faces exhibited a unique pattern whereby indirect encoding led to an enhanced subjective sense of recollection, whereas direct encoding prevented an increase in recollection that was observed for all other emotions. These findings may reflect interactions among amygdalar/arousal thresholds and levels of processing (LOP) effects on recognition memory.

A Novel Biologically Inspired Developmental Indirect Encoding for the Evolution of Neural Network Controllers for Autonomous Agents

Evolutionary algorithms provide the ability to automatically design robot controllers, but their wider use is hampered by a number of problems, including the difficulty of obtaining complex behaviors. This paper proposes a biologically inspired indirect encoding method for developing neural networks that control autonomous agents. The model is divided into three stages, the first two stages determine the structure of the network – the positions of the neurons and the network connectivity, and the third stage, occurring during the lifetime of the agent, determines the strength of connections based on the network activity. The model was tested experimentally by simulating an agent in an artificial environment, and the results of these simulations show that the method successfully evolved agents, capable of distinguishing between several types of objects, collecting some while avoiding others, without the use of a complex fitness function.

Collective Event Detection by a Distributed Low-Cost Smart Camera Network

The current surveillance systems must identify the continuous human behaviors to detect various events from video streams. To enhance the performance of event recognition, in this chapter, we propose a distributed low-cost smart cameras system, together with a machine learning technique to detect abnormal events through analyzing the sequential behaviors of a group of people. Our system mainly includes a simple but efficient strategy to organize the behavior sequence, a new indirect encoding scheme to represent a group of people with relatively few features, and a multi-camera collaboration strategy to perform collective decision making for event recognition. Experiments have been conducted and the results confirm the reliability and stability of the proposed system in event recognition.

Collective Event Detection by a Distributed Low-Cost Smart Camera Network

The current surveillance systems must identify the continuous human behaviors to detect various events from video streams. To enhance the performance of event recognition, in this chapter, we propose a distributed low-cost smart cameras system, together with a machine learning technique to detect abnormal events through analyzing the sequential behaviors of a group of people. Our system mainly includes a simple but efficient strategy to organize the behavior sequence, a new indirect encoding scheme to represent a group of people with relatively few features, and a multi-camera collaboration strategy to perform collective decision making for event recognition. Experiments have been conducted and the results confirm the reliability and stability of the proposed system in event recognition.

Indirectly Encoding Running and Jumping Sodarace Creatures for Artificial Life

This article presents a lightweight platform for evolving two-dimensional artificial creatures. The aim of providing such a platform is to reduce the barrier to entry for researchers interested in evolving creatures for artificial life experiments. In effect the novel platform, which is inspired by the Sodarace construction set, makes it easy to set up creative scenarios that test the abilities of Sodarace-like creatures made of masses and springs. In this way it allows the researcher to focus on evolutionary algorithms and dynamics. The new indirectly encoded Sodarace (IESoR) system introduced in this article extends the original Sodarace by enabling the evolution of significantly more complex and regular creature morphologies. These morphologies are themselves encoded by compositional pattern-producing networks (CPPNs), an indirect encoding previously shown effective at encoding regularities and symmetries in structure. The capability of this lightweight system to facilitate research in artificial life is then demonstrated through both walking and jumping domains, in which IESoR discovers a wide breadth of strategies through novelty search with local competition.