THEOLOGY,EVOLUTION, AND THE HUMAN MIND: HOW MUCH CAN BIOLOGY EXPLAIN?

Evolutionary biology contributes much to our present understanding of life, and it promises also to deepen our understanding of human intelligence, ethics, and even religion. For some scientific thinkers, however, Darwin's science seems so impressive that it now supplants theology altogether by providing the ultimate explanation of all manifestations of life, not only biologically but also metaphysically. By focusing on human intelligence as an emergent aspect of nature this essay examines the question of whether theology can still have an explanatory role to play alongside biology in attempts to understand mind.     

On approximate confidence intervals for measures of concordance

The use ofU-statistics based on rank correlation coefficients in estimating the strength of concordance among a group of rankers is examined for cases where the null hypothesis of random rankings is not tenable. The studentizedU-statistics is asymptotically distribution-free, and the Student-t approximation is used for small and moderate sized samples. An approximate confidence interval is constructed for the strength of concordance. Monte Carlo results indicate that the Student-t approximation can be improved by estimating the degrees of freedom.Research partially supported on ONR Contract N00014-82-K-0207.     

Probability-of-coverage tolerance intervals for the beta binomial

True score tolerance intervals, which are designed to cover a chosen proportion of the conditional distribution of true scores given an observed score, are suggested as alternatives to true score confidence intervals. Using large sample theory, a tolerance interval estimator for the beta binomial is derived. An example indicates that with moderate sample sizes, tolerance intervals with high probability of coverage will not be much wider than when the two beta true score parameters are known.The author acknowledges valuable comments from Richard Sawyer.Most work was completed while the author was at the American College Testing Program.     

Contrast effects in multiple fixed-interval reinforcement schedules

Pigeons were exposed to a multiple fixed-interval one-minute fixed-interval three-minute schedule of reinforcement following training on either a multiple fixed-interval one-minute fixed-interval one-minute schedule or a multiple fixed-interval three-minute fixed-interval three-minute schedule. For all birds, large negative local contrast effects developed during the first of four three-minute intervals in a component; response rate was depressed and postreinforcement pause lengthened in this interval. Positive local contrast effects were evident during the first of 12 one-minute intervals in a component for five of six birds; at asymptote, the pause was very short and response rate slightly elevated during this interval. Overall positive contrast was generally transient and varied considerably across subjects, while overall negative contrast effects, if they occurred, appeared only after a large number of sessions.     

The effects of interval duration on temporal tracking and alternation learning

On cyclic-interval reinforcement schedules, animals typically show a postreinforcement pause that is a function of the immediately preceding time interval (temporal tracking). Animals, however, do not track single-alternation schedules-when two different intervals are presented in strict alternation on successive trials. In this experiment, pigeons were first trained with a cyclic schedule consisting of alternating blocks of 12 short intervals (5 s or 30 s) and 12 long intervals (180 s), followed by three different single-alternation interval schedules: (a) 30 s and 180 s, (b) 5 s and 180 s, and (c) 5 s and 30 s. Pigeons tracked both schedules with alternating blocks of 12 intervals. With the single-alternation schedules, when the short interval duration was 5 s, regardless of the duration of the longer interval, pigeons learned the alternation pattern, and their pause anticipated the upcoming interval. When the shorter interval was 30 s, even when the ratio of short to long intervals was kept at 6:1, pigeons did not initially show anticipatory pausing-a violation of the principle of timescale invariance.     

On the sampling interpretation of confidence intervals and hypothesis tests in the context of conditional maximum likelihood estimation

In the context ofconditional maximum likelihood (CML) estimation, confidence intervals can be interpreted in three different ways, depending on the sampling distribution under which these confidence intervals contain the true parameter value with a certain probability. These sampling distributions are (a) the distribution of the data given theincidental parameters, (b) the marginal distribution of the data (i.e., with the incidental parameters integrated out), and (c) the conditional distribution of the data given the sufficient statistics for the incidental parameters. Results on the asymptotic distribution of CML estimates under sampling scheme (c) can be used to construct asymptotic confidence intervals using only the CML estimates. This is not possible for the results on the asymptotic distribution under sampling schemes (a) and (b). However, it is shown that theconditional asymptotic confidence intervals are also valid under the other two sampling schemes. I am indebted to Theo Eggen, Norman Verhelst and one of Psychometrika's reviewers for their helpful comments.     

Reply to William R. Ferrell's paper "Calibration of sensory and cognitive judgments: A single model for both"

William R. Ferrell's paper is a reply to our article "Calibration of sensory and cognitive judgments: Two different accounts", 1993, 34, 135–148 in this journal. Ferrell's critique centres around three issues: (1) The model of sensory discrimination proposed by Björkman et al. (1993) and discussed in Winman and Juslin (1993) is not new, (2) this model does not predict underconfidence, (3) there is no need for separate models of calibration of sensory and cognitive judgments. Below we respond to each of the three issues raised by Ferrell.     

Moment of Truth: Why Aha! Experiences are Correct

Having a sudden insight is often associated with inherent confidence, enough for Archimedes to run naked through the streets shouting “Eureka!”. Recent evidence demonstrates that public displays of enthusiasm, such as the ancient polymath's, are actually supported by a higher likelihood of being correct.     

特征归纳的关联相似性模型

作者提出特征归纳的关联相似性模型,用以描述以归纳特征的关联特征相似性为基础的归纳推理,把归纳推理中相似性和关联知识统一整合起来。该模型认为归纳强度主要取决于归纳特征与关联特征的关联强度和关联特征的相似性程度的乘积,归纳信心主要取决于关联强度,从而分离归纳信心和归纳强度。以大学生为被试的两个实验的主要结果支持关联相似性模型的主要预测。关联相似性模型能够描述解释以关联特征相似性为基础的归纳推理现象,比以往的归纳理论具有更大的解释能力和解释范围