To describe phenomena that occur at different time scales, computational models of the brain must incorporate different levels of abstraction. At time scales of approximately 1/3 of a second, orienting movements of the body play a crucial role in cognition and form a useful computational level embodiment level,” the constraints of the physical system determine the nature of cognitive operations. The key synergy is that at time scales of about 1/3 of a second, the natural sequentiality of body movements can (...) be matched to the natural computational economies of sequential decision systems through a system of implicit reference called deictic in which pointing movements are used to bind objects in the world to cognitive programs. This target article focuses on how deictic bindings make it possible to perform natural tasks. Deictic computation provides a mechanism for representing the essential features that link external sensory data with internal cognitive programs and motor actions. One of the central features of cognition, working memory, can be related to moment-by-moment dispositions of body features such as eye movements and hand movements. (shrink)

This paper offers an explanation of how collaboration leads to abstract and flexible problem solving. We asked the individual and paired subjects to indicate 3/4 of 2/3 of the area of a square sheet of paper and found that (1) they primarily folded or partitioned the paper rather than algorithmically calculating the answer, (2) they strongly tendened to backtrack and confirm their proto‐plans on externalized traces such as creases on the paper, and (3) only the paired subjects shifted to the (...) mathematical strategy in their second trials. Based on these results, we propose that two factors, individuals' activeness in choosing and confirming the initial strategies and the frequent role exchange between task‐doing and monitoring in collaborative situations, interact in collaboration to generate various solutions differing in the degree of abstraction, which are then reflected upon by the participants to lead them to abstraction. (shrink)

Can the output of human cognition be predicted from the assumption that it is an optimal response to the information-processing demands of the environment? A methodology called rational analysis is described for deriving predictions about cognitive phenomena using optimization assumptions. The predictions flow from the statistical structure of the environment and not the assumed structure of the mind. Bayesian inference is used, assuming that people start with a weak prior model of the world which they integrate with experience to develop (...) stronger models of specific aspects of the world. Cognitive performance maximizes the difference between the expected gain and cost of mental effort. Memory performance can be predicted on the assumption that retrieval seeks a maximal trade-off between the probability of finding the relevant memories and the effort required to do so; in categorization performance there is a similar trade-off between accuracy in predicting object features and the cost of hypothesis formation; in casual inference the trade-off is between accuracy in predicting future events and the cost of hypothesis formation; and in problem solving it is between the probability of achieving goals and the cost of both external and mental problem-solving search. The implemention of these rational prescriptions in neurally plausible architecture is also discussed. (shrink)

Understanding the nature of errors in a simple, rule‐based task—programming a VCR—required analyzing the interactions among human cognition, the artifact, and the task. This analysis was guided by least‐effort principles and yielded a control structure that combined a rule hierarchy task‐to‐device with display‐based difference‐reduction. A model based on this analysis was used to trace action protocols collected from participants as they programmed a simulated VCR. Trials that ended without success (the show was not correctly programmed) were interrogated to yield insights (...) regarding problems in acquiring the control structure. For successful trials (the show was correctly programmed), steps that the model would make were categorized as matches to the model; steps that the model would not make were violations of the model. The model was able to trace the vast majority of correct keystrokes and yielded a business‐as‐usual account of the detection and correction of errors. Violations of the model fell into one of two fundamental categories. The model provided insights into certain subcategories of errors; whereas, regularities within other subcategories of error suggested limitations to the model. Although errors were rare when compared to the total number of correct actions, they were important. Errors were made on 4% of the keypresses that, if not detected, would have prevented two‐thirds of the shows from being successfully recorded. A misprogrammed show is a minor annoyance to the user. However, devices with the approximate complexity of a VCR are ubiquitous and have found their way into emergency rooms, airplane cockpits, power plants, and so on. Errors of ignorance may be reduced by training; however, errors in the routine performance of skilled users can only be reduced by design. (shrink)