Temporal control in rats: analysis of nonlocalized effects from short interfood intervals.

The present experiment analyzed temporal control of postreinforcement pause duration during within-session changes in the criterion for reinforcement (interfood interval, IFI). Analysis of interval-by-interval changes in the pause revealed localized and nonlocalized effects from short intervals that caused specific changes in performance. In Phase 1, rats were presented with five consecutive 15-s IFIs intercalated into a series of 60-s IFIs. The 15-s set decreased the pause in adjacent and more remote 60-s intervals. In Phase 2, two sets of 15-s intervals were intercalated. The spacing between the two sets varied so that 0, 5, 10, or 15 60-s IFIs separated the sets. The postreinforcement pause tracked all changes in the IFI duration, and the localized effect from a short set extended beyond the next interval to the next few 60-s IFIs. Effects from one set, however, did not combine with a second set: Changes in the pause after two sets were the same regardless of the spacing between sets.     

Concurrent second-order schedules of reinforcement

Responses on one key (the main key) of a two-key chamber produced food according to a second-order variable-interval schedule with fixed-interval schedule components. A response on a second key (the changeover key) alternated colors on the main key and provided a second independent second-order variable-interval schedule with fixed-interval components. The fixed-interval component on one variable-interval schedule was held constant at 8 sec, while the fixed interval on the other variable-interval schedule was varied from 0 to 32 sec. Under some conditions, a brief stimulus terminated each fixed interval and generated fixed-interval patterns; in other conditions, the brief stimulus was omitted. Relative response rate and relative time deviated substantially from scheduled relative reinforcement rate and, to a lesser extent, from obtained relative reinforcement rate under both brief-stimulus and no-stimulus conditions. Matching was observed with equal components on both schedules; with unequal components, increasingly greater proportions of time and responses than the matching relation would predict were spent on the variable-interval schedule containing the shorter component. Preference for the shorter fixed interval was typically more extreme under brief-stimulus than under no-stimulus schedules. The results limit the extension of the matching relation typically observed under simple concurrent variable-interval schedules to concurrent second-order variable-interval schedules.     

Effects of signaling on temporal control of behavior in response‐initiated fixed intervals

Behavior and events distributed in time can serve as markers that signal delays to future events. The majority of timing research has focused on how behavior changes as the time to some event, usually food availability, decreases. The primary objective of the two experiments presented here was to assess how behavior changes as time passes between two time markers when the first time marker was manipulated but the second, food delivery, was held constant. Pigeons were exposed to fixed‐interval, response‐initiated fixed‐interval, and signaled response‐initiated fixed‐interval 15‐ and 30‐s schedules of reinforcement. In Experiment 1, first‐response latencies were systematically shorter in the signaled response‐initiated schedules than response‐initiated schedules, suggesting that the first response was a more effective time marker when it was signaled. In Experiment 2, responding in no‐food (i.e. “peak”) trials indicated that timing accuracy was equivalent in the three schedule types. Compared to fixed interval schedules, timing precision was reduced in the signaled response‐initiated schedules and was lowest in response‐initiated schedules. Results from Experiments 1 and 2 coupled with previous research suggest that the overall “informativeness” of a time marker relative to other events and behaviors in the environment may determine its efficacy.     

A Comparative Investigation of Confidence Intervals for IndependentVariables in Linear Regression

In linear regression, the most appropriate standardized effect size for individual independent variables having an arbitrary metric remains open to debate, despite researchers typically reporting a standardized regression coefficient. Alternative standardized measures include the semipartial correlation, the improvement in the squared multiple correlation, and the squared partial correlation. No arguments based on either theoretical or statistical grounds for preferring one of these standardized measures have been mounted in the literature. Using a Monte Carlo simulation, the performance of interval estimators for these effect-size measures was compared in a 5-way factorial design. Formal statistical design methods assessed both the accuracy and robustness of the four interval estimators. The coverage probability of a large-sample confidence interval for the semipartial correlation coefficient derived from Aloe and Becker was highly accurate and robust in 98% of instances. It was better in small samples than the Yuan-Chan large-sample confidence interval for a standardized regression coefficient. It was also consistently better than both a bootstrap confidence interval for the improvement in the squared multiple correlation and a noncentral interval for the squared partial correlation.     

Dealing with Multiple Solutions in Structural Vector Autoregressive Models

Structural vector autoregressive models (VARs) hold great potential for psychological science, particularly for time series data analysis. They capture the magnitude, direction of influence, and temporal (lagged and contemporaneous) nature of relations among variables. Unified structural equation modeling (uSEM) is an optimal structural VAR instantiation, according to large-scale simulation studies, and it is implemented within an SEM framework. However, little is known about the uniqueness of uSEM results. Thus, the goal of this study was to investigate whether multiple solutions result from uSEM analysis and, if so, to demonstrate ways to select an optimal solution. This was accomplished with two simulated data sets, an empirical data set concerning children's dyadic play, and modifications to the group iterative multiple model estimation (GIMME) program, which implements uSEMs with group- and individual-level relations in a data-driven manner. Results revealed multiple solutions when there were large contemporaneous relations among variables. Results also verified several ways to select the correct solution when the complete solution set was generated, such as the use of cross-validation, maximum standardized residuals, and information criteria. This work has immediate and direct implications for the analysis of time series data and for the inferences drawn from those data concerning human behavior.     

Estimation of the Coefficient of Variation with Minimum Risk: A Sequential Method for Minimizing Sampling Error and Study Cost

The coefficient of variation is an effect size measure with many potential uses in psychology and related disciplines. We propose a general theory for a sequential estimation of the population coefficient of variation that considers both the sampling error and the study cost, importantly without specific distributional assumptions. Fixed sample size planning methods, commonly used in psychology and related fields, cannot simultaneously minimize both the sampling error and the study cost. The sequential procedure we develop is the first sequential sampling procedure developed for estimating the coefficient of variation. We first present a method of planning a pilot sample size after the research goals are specified by the researcher. Then, after collecting a sample size as large as the estimated pilot sample size, a check is performed to assess whether the conditions necessary to stop the data collection have been satisfied. If not an additional observation is collected and the check is performed again. This process continues, sequentially, until a stopping rule involving a risk function is satisfied. Our method ensures that the sampling error and the study costs are considered simultaneously so that the cost is not higher than necessary for the tolerable sampling error. We also demonstrate a variety of properties of the distribution of the final sample size for five different distributions under a variety of conditions with a Monte Carlo simulation study. In addition, we provide freely available functions via the MBESS package in R to implement the methods discussed.     

关于假设检验的争议:问题的澄清与解决

对传统的假设检验作为心理学实验的数据分析工具的评价涉及两个标准:首先是它是否合法,其次是它是否有用。置于频率学派统计学框架中的传统假设检验在逻辑上实际上是合法的;但它在效用性方面则有着备择假设不可证伪以及只能提供定性结论这两方面的缺陷。置信区间能够集中地改进和弥补这些缺陷。对传统假设检验使用中错误的澄清也使得研究者们开始重视PSI问题,这使得心理学实验的设计和数据分析从关注总体转向关注个体。     

Stochastic pairwise comparative judgements and direct ratings of alternatives in the REMBRANDT system

The REMBRANDT system for multicriteria decision analysis consists of both the multiplicative variant of the AHP (which employs a method of pairwise comparative judgements by a decision maker to arrive at final impact scores for the alternatives under consideration) and SMART, the simple multiattribute rating technique (which utilizes direct rating of alternatives to achieve final impact scores). This paper examines the effect of imprecision or uncertainty in the decision maker's pairwise judgements or ratings of alternatives by expressing each pairwise judgement or rating as a probability distribution, and the structure of REMBRANDT's component models is exploited to derive interval judgements or interval ratings of the alternatives’ final impact scores. These interval judgements or interval ratings can be used to determine the probability of rank reversal amongst alternatives, i.e. to assess the stability of the final impact score vector. © 1998 John Wiley & Sons, Ltd.     

Flexible representations in visual working memory and interactions with long‐term learning: Commentary on the special issue

This special issue of the British Journal of Psychology brings together cutting edge research on a range of topics in visual working memory (VWM). In this commentary, we attempt to summarize common themes in current VWM research exemplified in this issue. The articles include several reviews of important topics as well as empirical papers covering three main themes. The first concerns the nature of mental representations of memoranda in the commonly used delayed estimation task, where both fine‐grained and broad categorical details appear to be represented, and their susceptibility to interference. The second concerns interactions between VWM representations, both those that produce individuation of representations and those that create an overarching ensemble structure. Finally, the third main topic concerns the use of VWM during visual search and in the learning of repeated configurations in search displays. The work presented here, and other work in the field, points to a rich interplay between representations in VWM but also between VWM and information in long‐term memory. Opportunities for further investigation are highlighted throughout.     

The influence of number line estimation precision and numeracy on risky financial decision making

This study examined whether different aspects of mathematical proficiency influence one's ability to make adaptive financial decisions. “Numeracy” refers to the ability to process numerical and probabilistic information and is commonly reported as an important factor which contributes to financial decision‐making ability. The precision of mental number representation (MNR), measured with the number line estimation (NLE) task has been reported to be another critical factor. This study aimed to examine the contribution of these mathematical proficiencies while controlling for the influence of fluid intelligence, math anxiety and personality factors. In our decision‐making task, participants chose between two options offering probabilistic monetary gain or loss. Sensitivity to expected value was measured as an index for the ability to discriminate between optimal versus suboptimal options. Partial correlation and hierarchical regression analyses revealed that NLE precision better explained EV sensitivity compared to numeracy, after controlling for all covariates. These results suggest that individuals with more precise MNR are capable of making more rational financial decisions. We also propose that the measurement of “numeracy,” which is commonly used interchangeably with general mathematical proficiency, should include more diverse aspects of mathematical cognition including basic understanding of number magnitude.