What was noted by E. J. hanger (1978) remains true today: that much of contemporary psychological research is based on the assumption that people are consciously and systematically processing incoming information in order to construe and interpret their world and to plan and engage in courses of action. As did E. J. hanger, the authors question this assumption. First, they review evidence that the ability to exercise such conscious, intentional control is actually quite limited, so that most of moment-to-moment psychological life must occur through nonconscious means if it is to occur at all. The authors then describe the different possible mechanisms that produce automatic, environmental control over these various phenomena and review evidence establishing both the existence of these mechanisms as well as their consequences for judgments, emotions, and

The authors outline a cognitive and computational account of causal learning in children. They propose that children use specialized cognitive systems that allow them to recover an accurate “causal map ” of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously understood in terms of the formalism of directed graphical causal models, or Bayes nets. Children’s causal learning and inference may involve computations similar to those for learning causal Bayes nets and for predicting with them. Experimental results suggest that 2to 4-year-old children construct new causal maps and that their learning is consistent with the Bayes net formalism.

People approach pleasure and avoid pain. To discover the true nature of approach-avoidance motivation, psychologists need to move beyond this hedonic principle to the principles that underlie the different ways that it operates. One such principle is regulatory focus, which distinguishes self-regulation with a promotion focus (accomplishments and aspirations)from self-regulation with a prevention focus (safety and responsibilities). This principle is used to reconsider the fundamental nature of approach-avoidance, expectancy-value relations, and emotional and evaluative sensitivities. Both types of regulatory focus are applied to phenonomena that have been treated in terms of either promotion (e.g., well-being) or prevention (e.g., cognitive dissonance). Then, regulatory focus is distinguished from regulatory anticipation and regulatory reference, 2 other principles underlying the different ways that people approach pleasure and avoid pain. It seems that our entire psychical activity is bent upon procuring pleasure and avoiding pain, that it is automatically regulated by the PLEASURE-PRINCIPLE. (Freud, 1920/1952, p. 365) People are motivated to approach pleasure and avoid pain. From the ancient Greeks, through 17th- and 18thcentury British philosophers, to 20th-century psychologists, this hedonic or pleasure principle has dominated scholars ' understanding of people's motivation. It is the basic motivational assumption of theories across all areas of psychology, including theories of emotion in psychobiology (e.g., Gray, 1982), conditioning in animal learning

We present a purely vision-based scheme for learning a parsimonious representation of an open environment. Using simple exploration behaviours, our system constructs a graph of appropriately chosen views. To navigate between views connected in the graph, we employ a homing strategy inspired by findings of insect ethology. Simulations and robot experiments demonstrate the feasibility of the proposed approach. Introduction 1 To survive in unpredictable and sometimes hostile environments animals have developed powerful strategies to find back to their shelter or to a previously visited food source. Successful navigation can already be achieved using simple mechanisms such as association of landmarks with movements (Wehner et al. 1996) or tracking of environmental features (Collett 1996). To understand more complex forms of spatial behaviour like finding shortcuts, however, we have to go beyond reactive control strategies, towards systems with internal states. In as far as they ...

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.

for Correspondence An agent should adopt different control modes in different situations. Depending on the predictability of its environment and the constraint imposed by its goals, the agent should modulate its sensitivity to run-time events and its commitment to specific actions. We propose an opportunistic control model that supports this flexibility. 3 Abstract Given its multiple goals, limited resources, and dynamic environment, an intelligent agent must decide which of many possible actions to execute at each point in time. Planning and reactive models embody two different modes of control. By contrast, we characterize a two-dimensional space of control modes, each of which maximizes the quality of run-time behavior in the corresponding region of a two-dimensional space of control situations. The situation space is defined by dimensions representing the predictability of the agent's task environment and the constraint imposed by its goals. The space of control modes is defined ...

this paper is to present such a reformulation. The cell assembly provides the cognitive system with flexibility far beyond the simple activation of concepts. Instead of viewing the assembly as simply active or latent we see the activation of the assembly as coming in a series of phases. Each phase of activity serves a different purpose, giving the theory the power and flexibility to handle a wide range of psychological data.

This paper adds a new viewpoint to the Anticipatory Classifier System (ACS). It approaches the system from a psychological perspective and thus provides new insights to the current system. The main learning mechanism in the ACS, the Anticipatory Learning Process (ALP), evolved out of the psychological learning theory of anticipatory behavioral control. The paper compares the ALP directly to this theory and reveals the similarities. Moreover, it investigates the behavior of the ACS. By simulating previously published rat experiments, the paper compares the behavior of the ACS with the behavior of the rats. Finally, two further cognitive mechanisms are introduced to the ACS. These two mechanisms result in an animal-like behavior of the ACS in the simulations. Furthermore, they prove the usability of the internal environmental model for reward-learning tasks for the first time.

Research in animal learning and behavioral neuroscience has distinguished between two forms of action control: a habit-based form, which relies on stored action values, and a goal-directed form, which forecasts and compares action outcomes based on a model of the environment. While habit-based control has been the subject of extensive computational research, the computational principles underlying goal-directed control in animals have so far received less attention. In the present paper, we advance a computational framework for goal-directed control in animals and humans. We take three empirically motivated points as founding premises: (1) Neurons in dorsolateral prefrontal cortex represent action policies, (2) Neurons in orbitofrontal cortex represent rewards, and (3) Neural computation, across domains, can be appropriately understood as performing structured probabilistic inference. On a purely computational level, the resulting account relates closely to previous work using Bayesian