16971158
Laboratory Replication of Scientific Discovery Processes
Fourteen subjects were tape-recorded while they undertook to find a law to summarize numerical doto they were given. The source of the data was not identified, nor were the variables lobeled semantically. Unknown to the subjects. the data were measurements of the distances of the planets from the sun and the periods of their revolutions obout it-equivalent to the doto used by Johonnes Kepler to discover his third low of planetary motion. Four of the 14 subjects discovered the some low as Kepler did (the period varies OS the 3/2 power of the distance), and a fifth come very close to the answer. The subiects’ protocols provide a detatled picture of the problem-solving searches they engoged in, which were mainly, but not exclusively, in the space of possible functions for fitting the data. and provide explanations OS to why some succeeded and the others foiled. The scorch heuristics used by the subjects ore similar to those embodied in the BACON program, a computer simulation of certain scientific discovery processes. The experiment demonstrates the feasibility of examining some of the processes of scientific discovery by recreating, in the laboratory, discovery situations of substontiol historical relevance. It demonstrates also. that under conditions rather similar to those of the originol discoverer, a low can be rediscovered by persons of ordinory intelligence (i.e., the intelligence needed for academic success in a good university). The data for the successful subjects reveal no “creative” processes in this kind of o discovery situation different from those that are regulorly observed in all kinds of problem-solving settings.
145761361
CaMeRa: A computational model of multiple representations
This research aims to clarify, by constructing and testing a computer simulation, the use of multiple representations in problem solving, focusing on their role in visual reasoning. The model is motivated by extensive experimental evidence in the literature for the features it incorporates, but this article focuses on the system's structure. We illustrate the model's behavior by simulating the cognitive and perceptual processes of an economics expert as he teaches some well-learned economics principles while drawing a graph on a blackboard. Data in the experimental literature and concurrent verbal protocols were used to guide construction of a linked production system and parallel network, CaMeRa (Computation with Multiple Representations), that employs a “Mind's Eye” representation for pictorial information, consisting of a bitmap and associated node-link structures. Propositional list structures are used to represent verbal information and reasoning. Small individual pieces from the different representations are linked on a sequential and temporary basis to form a reasoning and inferencing chain, using visually encoded information recalled to the Mind's Eye from long-term memory and from cues recognized on an external display. CaMeRa, like the expert, uses the diagrammatic and verbal representations to complement one another, thus exploiting the unique advantages of each.
11287797
Collaborative Discovery in a Scientific Domain
This study compares Pairs of subjects with Single subjects in a task of discovering scientific laws with the aid of experiments. Subjects solved a molecular genetics task in a computer micro-world (Dunbar, 1993). Pairs were more successful in discovery than Singles and participated more actively in explanatory activities (i.e., entertaining hypotheses and considering alternative ideas and justifications). Explanatory activities were effective for discovery only when the subjects also conducted crucial experiments. Explanatory activities were facilitated when paired subjects made requests of each other for explanation and focused on them. The study extends from individual to collaborative discovery activities the importance to the discovery process of setting goals to find hypotheses and evidence (Dunbar, 1993) and to construct explanations of phenomena and processes encountered in examples (Chi, Bassok, Lewis, & Glaser, 1989). Discovery through collaboration is a common and growing practice in science whose processes have not yet been extensively studied. We do not yet know whether discovery processes are different when people work together, what role discussion plays in collaboration, how researchers entertain hypotheses and conduct experiments when working together or how they handle alternative hypotheses and justifications that arise in discussion. These questions may be approached through historical case studies, field observation, interviews with researchers involved in collaboration, and laboratory experiments. Each approach has its advantages and limitations. This paper follows an experimental approach. We are aware that, although experiments are highly useful for detailed analyses of discovery processes, we need to be cautious in interpreting these data as reflecting actual scientists’ collaborative discovery processes. Over the long run, combining various approaches
17561186
Situated Action: A Symbolic Interpretation
The congeries of theoretical views collectively referred to as “situated action” (SA) claim that humans and their interactions with the world cannot be understood using symbol-system models and methodology, but only by observing them within real-world contexts or building nonsymbolic models of them. SA claims also that rapid, real-time interaction with a dynamically changing environment is not amenable to symbolic interpretation of the sort espoused by the cognitive science of recent decades. Planning and representation, central to symbolic theories, are claimed to be irrelevant in everyday human activity. 
 
We will contest these claims, as well as their proponents' characterizations of the symbol-system viewpoint. We will show that a number of existing symbolic systems perform well in temporally demanding tasks embedded in complex environments, whereas the systems usually regarded as exemplifying SA are thoroughly symbolic (and representational), and, to the extent that they are limited in these respects, have doubtful prospects for extension to complex tasks. As our title suggests, we propose that the goals set forth by the proponents of SA can be attained only within the framework of symbolic systems. The main body of empirical evidence supporting our view resides in the numerous symbol systems constructed in the past 35 years that have successfully simulated broad areas of human cognition.
6294288
The Processes of Scientific Discovery: The Strategy of Experimentation
Abstract Hans Krebs' discovery, in 1932, of the urea cycle was a major event in biochemistry. This article describes a program, KEKADA, which models the heuristics Hans Krebs used in this discovery. KEKADA reacts to surprises, formulates explanations, and carries out experiments in the same manner as the evidence in the form of laboratory notebooks and interviews indicates Hans Krebs did. Furthermore, we answer a number of questions about the nature of the heuristics used by Krebs, in particular: How domain-specific are the heuristics? To what extent are they idiosyncratic to Krebs? To what extent do they represent general strategies of problem-solving search? The relative generality of KEKADA allows us to view the control structure of KEKADA and its domain-independent heuristics as a model of scientific experimentation that should apply over a broad domain.
7797628
Bayesian Networks and Inner Product Spaces
In connection with two-label classification tasks over the Boolean domain, we consider the possibility to combine the key advantages of Bayesian networks and of kernel-based learning systems. This leads us to the basic question whether the class of decision functions induced by a given Bayesian network can be represented within a low-dimensional inner product space. For Bayesian networks with an explicitly given (full or reduced) parameter collection, we show that the “natural” inner product space has the smallest possible dimension up to factor 2 (even up to an additive term 1 in many cases). For a slight modification of the so-called logistic autoregressive Bayesian network with n nodes, we show that every sufficiently expressive inner product space has dimension at least 2 n/4. The main technical contribution of our work consists in uncovering combinatorial and algebraic structures within Bayesian networks such that known techniques for proving lower bounds on the dimension of inner product spaces can be brought into play.
45759268
Prospects for Cognitive Science
45719384
A Characterization of Strong Learnability in the Statistical Query Model
In this paper, we consider Kearns' [4] Statistical Query Model of learning. It is well known [3] that the number of statistical queries, needed for "weakly learning" an unknown target concept (i.e. for gaining significant advantage over random guessing) is polynomially related to the so-called Statistical Query dimension of the concept class. In this paper, we provide a similar characterization for "strong learning" where the learners final hypothesis is required to approximate the unknown target concept up to a small rate of misclassification. The quantity that characterizes strong learnability in the Statistical Query model is a surprisingly close relative of (though not identical to) the Statistical Query dimension. For the purpose of proving the main result, we provide other characterizations of strong learnability which are given in terms of covering numbers and related notions. These results might find some interest in their own right. All characterizations are purely information-theoretical and ignore computational issues.
5581562
Computer science as empirical inquiry: symbols and search
Computer science is the study of the phenomena surrounding computers. The founders of this society understood this very well when they called themselves the Association for Computing Machinery. The machine—not just the hardware, but the programmed, living machine—is the organism we study.