Behavioral Analysis of Mammalian Sleep and Learning

BEHAVIORAL ANALYSIS OF MAMMALIAN SLEEP AND LEARNING DAVID BRYSON, M.D.* and STEPHEN SCHACHER, Af.D.f Theories ofmammalian learning and theories ofmammalian sleep have developed with scant interaction. This article states that mammalian sleep and learning are related fundamentally. Our approach is theoretical; no new experimental data are presented. Aspects ofhuman behavior which are distinctfrom thoseofmammals in generalwillnot be consideredhere.1 Synopsis Much of the mature mammal's behavior is devoid of any significant decision making, due to the familiarity ofmost ofthe inputs with which the mature mammal deals. Familiar inputs are associated with the execution ofroutine behavioral acts, and during such periods mammalian record keeping is insignificant. In contrast, the record of the mammal's activities which is kept is directly related to episodes ofdecision making. A decision is made when the mammal is confronted with an unfamiliar input, and this input, the resulting decision, and the outcome ofthe decision are recorded . For example, a hungry mammal may suspect that some unfamiliar substance is edible, may decide to sample it, and may find the taste unacceptable . This input, this decision, and this consequence are recorded. The decisional record thus grows as the waking state progresses, at a rate related to the frequency ofunfamiliar inputs. The decisional record may be accessed as soon as a decision and its consequence have occurred, enabling a mammal to learn within a single waking period. Should some * Behavior Systems Division, Westinghouse Learning Corporation, 1840 Lomas Blvd., Albuquerque, New Mexico 87106. f New England Medical Center, Boston, Massachusetts. 1 In particular, the presence ofinductive logic as a problem-solving tool available to nonsleeping adult humans necessitates a separate discussion ofhuman learning. 71 unfamiliar input recur, the current need for decision making may be reduced considerably if the previous decision for the same input had an acceptable consequence. Ifnot, the probability of invoking this decision again is decreased, and decision making yields another decision and perhaps a more acceptable consequence. Learning of this type is inadequate in one important respect. Access to the decisional record is limited to one decisional cluster (unfamiliar input, decision, consequence) at a time; decisional clusters are not themselves compared. For those inputs which do recur, the original problem of input unfamiliarity is improved empirically on a case-by-case basis. However , improved decision making for each case still fails to exploit any generic relationships which may exist between the individual decisional clusters. During sleep, the information in the decisional record is utilized for a further purpose. The individual decisional clusters are combined into informational sets, the combinatorial rule being to group those unfamiliar inputs in which the decisions and consequences were similar. For example, suppose a mammal in an unfamiliar environment has spent various portions ofthe current waking state in trying to establish reliable landmarks for navigational purposes. During sleep, those environmental features which were selected to be—and in fact turned out to be—reliable landmarks are grouped into one informational set. Inductive analysis of this set of unfamiliar inputs may reveal recurrent similarities in their characteristics, a finding with implications for the input classification (perceptual) system. Ifso, the perceptual system is revised as the mammal sleeps. Sleep thus enables the mammal to classify unfamiliar inputs having the same significance as the same perception. Thus previously unfamiliar inputs subsequently are perceived with increased familiarity. Two categories of mammalian learning are thus proposed: decisional learning, a waking activity in which output selection for the same input is improved; and perceptual learning, a sleeping activity in which the functional reorganization of input classes reduces the number of decisional involvements requiredpreviously. Decisionallearning is akin to "stimulusresponse " learning; perceptual learning is akin to "gestalt" learning. All perceptual learning is the result of recent decisional learning. When the inputs associated with a series of decisions with inadequate consequences 72 David Bryson and Stephen Schacher · Mammalian Sleep and Learning Perspectives in Biology and Medicine · Autumn 1969 can berelatedand thuslead to perceptuallearning, a considerablebehavioral advancement may occur from one waking state to the next. The first section of the article develops a model of the informational operations between initial input (attention) and final output (manifest behavior). The model describes how decisional learning and perceptual learning are...