The idea that we might be robots is no longer the stuff of science fiction; decades of research in evolutionary biology and cognitive science have led many esteemed scientists to the conclusion that, according to the precepts of universal Darwinism, humans are merely the hosts for two replicators that have no interest in us except as conduits for replication. Richard Dawkins, for example, jolted us into realizing that we are just survival mechanisms for our own genes, sophisticated robots in service (...) of huge colonies of replicators to whom concepts of rationality, intelligence, agency, and even the human soul are irrelevant. Accepting and now forcefully responding to this decentering and disturbing idea, Keith Stanovich here provides the tools for the "robot's rebellion," a program of cognitive reform necessary to advance human interests over the limited interest of the replicators and define our own autonomous goals as individual human beings. He shows how concepts of rational thinking from cognitive science interact with the logic of evolution to create opportunities for humans to structure their behavior to serve their own ends. These evaluative activities of the brain, he argues, fulfill the need that we have to ascribe significance to human life. We may well be robots, but we are the only robots who have discovered that fact. Only by recognizing ourselves as such, argues Stanovich, can we begin to construct a concept of self based on what is truly singular about humans: that they gain control of their lives in a way unique among life forms on Earth—through rational self-determination. (shrink)

The present study discusses findings that replicate and extend the original work of Burns and Vollmeyer (2002), which showed that performance in problem solving tasks was more accurate when people were engaged in a non-specific goal than in a specific goal. The main innovation here was to examine the goal specificity effect under both observation-based and conventional action-based learning conditions. The findings show that goal specificity affects the accuracy of problem solving in the same way, both when the learning stage (...) of the task is observationbased and when it is action-based. Additionally, the findings show that, when instructions do not promote goal specificity, observation-based problem solving is as effective as action-based problem solving. (shrink)

This paper presents a skill learning model CLARION. Different from existing models of mostly high-level skill learning that use a top-down approach (that is, turning declarative knowledge into procedural knowledge through practice), we adopt a bottom-up approach toward low-level skill learning, where procedural knowledge develops first and declarative knowledge develops later. Our model is formed by integrating connectionist, reinforcement, and symbolic learning methods to perform on-line reactive learning. It adopts a two-level dual-representation framework (Sun, 1995), with a combination of localist (...) and distributed representation. We compare the model with human data in a minefield navigation task, demonstrating some match between the model and human data in several respects. (shrink)

We review the progress of naturalistic decision making in the decade since the first conference on the subject in 1989. After setting out a brief history of NDM we identify its essential characteristics and consider five of its main contributions: recognition-primed decisions, coping with uncertainty, team decision making, decision errors, and methodology. NDM helped identify important areas of inquiry previously neglected, it introduced new models, conceptualizations, and methods, and recruited applied investigators into the field. Above all, NDM contributed a new (...) perspective on how decisions are made. NDM still faces significant challenges, including improvement of the quantity and rigor of its empirical research, and confirming the validity of its prescriptive models. Copyright © 2001 John Wiley & Sons, Ltd. (shrink)

The purpose of the two studies reported here was to develop an integrated model of the scientific reasoning process. Subjects were placed in a simulated scientific discovery context by first teaching them how to use an electronic device and then asking them to discover how a hitherto unencountered function worked. To do this task, subjects had to formulate hypotheses based on their prior knowledge, conduct experiments, and evaluate the results of their experiments. In the first study, using 20 adult subjects, (...) we identified two main strategies that subjects used to generate new hypotheses. One strategy was to search memory and the other was to generalize from the results of previous experiments. We described the former group as searching an hypothesis space, and the latter as searching an experiment space. In a second study, with 10 adults, we investigated how subjects search the hypothesis space by instructing them to state all the hypotheses that they could think of prior to conducting any experiments. Following this phase, subjects were then allowed to conduct experiments. Subjects who could not think of the correct rule in the hypothesis generation phase discovered the correct rule only by generalizing from the results of experiments in the experimental phase.Both studies provide support for the view that scientific reasoning can be characterized as search in two problem spaces. By extending Simon and Lea's (1974) Generalized Rule Inducer, we present a general model of Scientific Discovery as Dual Search (SDDS) that shows how search in two problem spaces (an hypothesis space and an experiment space) shapes hypothesis generation, experimental design, and the evaluation of hypotheses. The model also shows how these processes interact with each other. Finally, we interpret earlier findings about the psychology of scientific reasoning in terms of the SDDS model. (shrink)

With the advent of computers in the experimental labs, dynamic systems have become a new tool for research on problem solving and decision making. A short review of this research is given and the main features of these systems (connectivity and dynamics) are illustrated. To allow systematic approaches to the influential variables in this area, two formal frameworks (linear structural equations and finite state automata) are presented. Besides the formal background, the article sets out how the task demands of system (...) identification and system control can be realised in these environments, and how psychometrically acceptable dependent variables can be derived. (shrink)