Rule-based evolutionary online learning systems, often referred to as Michigan-style learning classifier systems (LCSs), were proposed nearly thirty years ago (Holland, 1976; Holland, 1977) originally calling them cognitive systems. LCSs combine the strength of reinforcement learning with the generalization capabilities of genetic algorithms promising a flexible, online generalizing, solely reinforcement dependent learning system. However, despite several initial successful applications of LCSs and their interesting relations with animal learning and cognition, understanding of the systems remained somewhat obscured. Questions concerning learning complexity or convergence remained unanswered. Performance in different problem types, problem structures, concept spaces, and hypothesis spaces stayed nearly unpredictable. This thesis has the following three major objectives: (1) to establish a facetwise theory approach for LCSs that promotes system analysis, understanding, and design; (2) to analyze, evaluate, and enhance the XCS classifier system (Wilson, 1995) by the means of the facetwise approach establishing a fundamental XCS learning theory; (3) to identify both the major advantages of an LCS-based learning approach as well as the most promising potential application areas. Achieving these three objectives leads to a rigorous understanding

The explicit investigation of anticipations in relation to adaptive behavior is a recent approach. This chapter first provides psychological background that motivates and inspires the study of anticipations in the adaptive behavior field. Next, a basic framework for the study of anticipations in adaptive behavior is suggested. Different anticipatory mechanisms are identified and characterized. First fundamental distinctions are drawn between implicit anticipatory behavior, payoff anticipatory behavior, sensory anticipatory behavior, and state anticipatory behavior. A case study allows further insights into the drawn distinctions.

The extent to which memory for information content is reliable, trustworthy, and accurate is crucial in the information age. Being forced to divert attention to interrupting messages is common, however, and can cause memory loss. The memory e#ects of interrupting messages were investigated in three experiments. In Experiment 1, attending to an interrupting message decreased memory accuracy. Experiment 2, where four interrupting messages were used, replicated this result. In Experiment 3, an interrupting message was shown to be most disturbing when it was semantically very close to the main message. Drawing from a theory of long-term working memory it is argued that interrupting messages can both disrupt the active semantic elaboration of content during encoding and cause semantic interference upon retrieval. Properties of the interrupting message a#ect the extent and type of errors in remembering. Design implications are discussed.

this article. We also thank Rob McCann and Eric Ruthruff for their valuable discussions

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This chapter is meant to give a concise introduction to the topic of this book. The study of anticipatory behavior is referring to behavior that is dependent on predictions, expectations, or beliefs about future states. Hereby, behavior includes actual decision making, internal decision making, internal preparatory mechanisms, as well as learning.

This paper provides an overview of developments in a dual processing theory of automatic and controlled processing that began with the empirical and theoretical work described by Schneider and Shiffrin (1977) and Shiffrin and Schneider (1977) over a quarter century ago. A review of relevant empirical findings suggests that there is a set of core behavioral phenomena reflecting differences between controlled and automatic processing that must be addressed by a successful theory. These phenomena relate to: consistency in training, serial versus parallel processing, level of effort, robustness to stressors, degree of control, effects on long-term memory, and priority encoding. We detail a computational model of controlled processing, CAP2, that accounts for these phenomena as emergent properties of an underlying hybrid computational architecture. The model employs a large network of distributed data modules that can categorize, buffer, associate, and prioritize information. Each module is a connectionist network with input and output layers, and each module communicates with a central Control System by outputting priority and activity report signals, and by receiving control signals. The Control System is composed of five processors including a Goal Processor, an Attention Controller, an Activity Monitor, an Episodic Store, and a Gating & Report Relay. The transition from controlled to automatic processing

Abstract—Selective visual attention in a natural environment can be seen as the interaction between the external visual stimulus and task specific knowledge of the required behavior. This interaction between the bottom-up stimulus and the top-down, task-related knowledge is crucial for what is selected in the space and time within the scene. In this paper, we propose a computational model for selective attention for a visual search task. We go beyond simple saliency-based attention models to model selective attention guided by top-down visual cues, which are dynamically integrated with the bottom-up information. In this way, selection of a location is accomplished by interaction between bottom-up and top-down information. First, the general structure of our model is briefly introduced and followed by a description of the top-down processing of task-relevant cues. This is then followed by a description of the processing of the external images to give three feature maps that are combined to give an overall bottom-up map. Second, the development of the formalism for our novel interactive spiking neural network (ISNN) is given, with the interactive activation rule that calculates the integration map. The learning rule for both bottom-up and top-down weight parameters are given, together with some further analysis of the properties of the resulting ISNN. Third, the model is applied to a face detection task to search for the location of a specific face that is cued. The results show that the trajectories of attention are dramatically changed by interaction of information and variations of cues, giving an appropriate, task-relevant search pattern. Finally, we discuss ways in which these results can be seen as compatible with existing psychological evidence. Index Terms—Attention, bottom-up map, computer vision, cueguided search, top-down map.

Attention and working memory limitations set strict limits on visual representations, yet researchers have little appreciation of how these limits constrain the acquisition of information in ongoing visually guided behavior. Subjects performed a brick sorting task in a virtual environment. A change was made to 1 of the features of the brick being held on about 10 % of trials. Rates of change detection for feature changes were generally low and depended on the pick-up and put-down relevance of the feature to the sorting task. Subjects ’ sorting decision suggests that changes may be missed because of a failure to update the changed feature. The authors also explore how hand and eye behavior are coordinated for strategic acquisition and storage of visual information throughout the task.

In HCI research there is a body of work concerned with the development of systems capable of reasoning about users ’ attention and how this might be most effectively guided for specific applications. We present eight issues relevant to this endeavour: What is attention? How can attention be measured? How do graphical displays interact with attention? How do knowledge, performance and attention interact? What is working memory? How does doing two things at a time affect attention? What is the effect of artificial feedback loops on attention? Do attentional processes differ across tasks? For each issue we present design implications for developing attention–aware systems, and present a general discussion focussing on the dynamic nature of attention, tasks (number, nature and variety), level of processing, nature of the display, and validity of measures. In conclusion, we emphasise the need to adopt a dynamic view of attention and suggest that attention is a more complex phenomenon than some designers may have realised; however, embracing the multi-faceted nature of attention provides a range of design opportunities yet to be explored.