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      The learning curve: Implications of a quantitative analysis

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      Proceedings of the National Academy of Sciences
      Proceedings of the National Academy of Sciences

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          Abstract

          The negatively accelerated, gradually increasing learning curve is an artifact of group averaging in several commonly used basic learning paradigms (pigeon autoshaping, delay- and trace-eye-blink conditioning in the rabbit and rat, autoshaped hopper entry in the rat, plus maze performance in the rat, and water maze performance in the mouse). The learning curves for individual subjects show an abrupt, often step-like increase from the untrained level of responding to the level seen in the well trained subject. The rise is at least as abrupt as that commonly seen in psychometric functions in stimulus detection experiments. It may indicate that the appearance of conditioned behavior is mediated by an evidence-based decision process, as in stimulus detection experiments. If the appearance of conditioned behavior is taken instead to reflect the increase in an underlying associative strength, then a negligible portion of the function relating associative strength to amount of experience is behaviorally visible. Consequently, rate of learning cannot be estimated from the group-average curve; the best measure is latency to the onset of responding, determined for each subject individually.

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          The problem of inference from curves based on group data.

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            Toward a method of selecting among computational models of cognition.

            The question of how one should decide among competing explanations of data is at the heart of the scientific enterprise. Computational models of cognition are increasingly being advanced as explanations of behavior. The success of this line of inquiry depends on the development of robust methods to guide the evaluation and selection of these models. This article introduces a method of selecting among mathematical models of cognition known as minimum description length, which provides an intuitive and theoretically well-grounded understanding of why one model should be chosen. A central but elusive concept in model selection, complexity, can also be derived with the method. The adequacy of the method is demonstrated in 3 areas of cognitive modeling: psychophysics, information integration, and categorization.
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              The rat approximates an ideal detector of changes in rates of reward: implications for the law of effect.

              Rats responded on 2 levers delivering brain stimulation reward on concurrent variable interval schedules. Following many successive sessions with unchanging relative rates of reward, subjects adjusted to an eventual change slowly and showed spontaneous reversions at the beginning of subsequent sessions. When changes in rates of reward occurred between and within every session, subjects adjusted to them about as rapidly as they could in principle do so, as shown by comparison to a Bayesian model of an ideal detector. This and other features of the adjustments to frequent changes imply that the behavioral effect of reinforcement depends on the subject's perception of incomes and changes in incomes rather than on the strengthening and weakening of behaviors in accord with their past effects or expected results. Models for the process by which perceived incomes determine stay durations and for the process that detects changes in rates are developed.
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                Author and article information

                Journal
                Proceedings of the National Academy of Sciences
                Proceedings of the National Academy of Sciences
                Proceedings of the National Academy of Sciences
                0027-8424
                1091-6490
                September 07 2004
                September 07 2004
                August 26 2004
                September 07 2004
                : 101
                : 36
                : 13124-13131
                Article
                10.1073/pnas.0404965101
                516535
                15331782
                ae114551-481f-46b2-9a7b-ffe4f538c5f8
                © 2004
                History

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