Inhibitory mechanisms in single negative priming from ignored and briefly flashed primes: The key role of the inter-stimulus interval

The influence of interstimulus intervals (ISIs) on priming effects was investigated using a single-prime negative priming (NP) paradigm. In all experiments, a brief (16 ms), centrally displayed prime (a Chinese character, to be ignored) appeared, followed by a pattern mask and then a centrally displayed target (another semantically related or unrelated Chinese character); the task required semantic categorization (animate/inanimate) of the target. An ISI could occur either between prime and mask (Experiments 1 and 5) or between mask and target (Experiments 2–4). The results revealed NP when a 470 ms ISI occurred between prime and mask (Experiments 1 and 5) and when a 700 ms ISI occurred between mask and target (Experiments 3 and 4). In contrast, when a long prime-target SOA was maintained but the mask-target ISI was shortened, NP disappeared (Experiment 4). The results indicated that a persisting mask/distractor (without ISI) located in the same position as the following target interfered with the buildup of inhibition, but an ISI between prime and mask or mask and target eliminated this interference, and that inhibition processes induced by an ignore instruction were implemented faster with an ISI placed between prime and mask than with an ISI placed between mask and target.     

Reminders supporting spontaneous remembering in prospective memory tasks1

Abstract: The objective of this study was to identify the reminders that facilitate prospective memory. A total of 64 participants were either provided with three types of reminders for a prospective memory task (to circle a question number when prescribed target words appear) during the retention interval, or not reminded of the task. In addition, an articulatory suppression task was imposed halfway through the test phase to examine whether verbal rehearsal is related to the effects of the reminders. The reminders that referred to the intended activity (to circle the number) did not improve prospective memory. However, both of the reminders that notified that there was something to do and the reminders that referred to the target events (the prescribed words) improved prospective memory. Imposing the suppression task abolished the benefit of the former, but not that of the latter. This result suggests that only the effect of the reminders that referred to the existence of intention is mediated by verbal rehearsal. These data imply that there are some differences in the functions of these reminders.     

情景预见对跨期决策的影响机制

为了探究情景预见对跨期决策的影响机制, 研究采用延迟折扣任务范式, 检验了在情景预见对跨期决策的影响中延迟时间知觉起到的中介作用。两个实验分别操纵了情景预见中未来事件与自我有关的信息和未来事件的情绪特征, 结果均发现情景预见通过改变个体对延迟等待时间的时距知觉影响了跨期决策。想象与自己、与母亲有关的未来事件, 想象积极、中性情绪效价的未来事件都使得被试将延迟等待时间知觉为较短的时间段, 更倾向于选择延迟奖励。想象消极情绪效价的未来事件使得被试将延迟等待时间知觉为较长的时间段, 更加偏好即时奖励。本研究有助于增进人们对情景预见影响跨期决策现象的理解。     

Bootstrap confidence intervals for principal covariates regression

Principal covariate regression (PCOVR) is a method for regressing a set of criterion variables with respect to a set of predictor variables when the latter are many in number and/or collinear. This is done by extracting a limited number of components that simultaneously synthesize the predictor variables and predict the criterion ones. So far, no procedure has been offered for estimating statistical uncertainties of the obtained PCOVR parameter estimates. The present paper shows how this goal can be achieved, conditionally on the model specification, by means of the bootstrap approach. Four strategies for estimating bootstrap confidence intervals are derived and their statistical behaviour in terms of coverage is assessed by means of a simulation experiment. Such strategies are distinguished by the use of the varimax and quartimin procedures and by the use of Procrustes rotations of bootstrap solutions towards the sample solution. In general, the four strategies showed appropriate statistical behaviour, with coverage tending to the desired level for increasing sample sizes. The main exception involved strategies based on the quartimin procedure in cases characterized by complex underlying structures of the components. The appropriateness of the statistical behaviour was higher when the proper number of components were extracted.     

Preference as a function of active interresponse times: a test of the active time model

In this article, we describe a test of the active time model for concurrent variable interval (VI) choice. The active time model (ATM) suggests that the time since the most recent response is one of the variables controlling choice in concurrent VI VI schedules of reinforcement. In our experiment, pigeons were trained in a multiple concurrent similar to that employed by Belke (1992), with VI 20-s and VI 40-s schedules in one component, and VI 40-s and VI 80-s schedules in the other component. However, rather than use a free-operant design, we used a discrete-trial procedure that restricted interresponse times to a range of 0.5-9.0 s. After 45 sessions of training, unreinforced probe periods were mixed with reinforced training periods. These probes paired the two stimuli associated with the VI 40-s schedules. Further, the probes were defined such that during their occurrence, interresponse times were either "short" (0.5-3.0 s) or "long" (7.5-9.0 s). All pigeons showed a preference for the stimulus associated with the relatively rich VI 40-s schedule--a result mirroring that of Belke. We also observed, though, that this preference was more extreme during long probes than during short probes--a result predicted by ATM.     

On the analysis of studies of choice

In a review of 103 sets of data from 23 different studies of choice, Baum (1979) concluded that whereas undermatching was most commonly observed for responses, the time measure generally conformed to the matching relation. A reexamination of the evidence presented by Baum concludes that undermatching is the most commonly observed finding for both measures. Use of the coefficient of determination by both Baum (1979) and de Villiers (1977) for assessing when matching occurs is criticized on statistical grounds. An alternative to the loss-in-predictability criterion used by Baum (1979) is proposed. This alternative statistic has a simple operational meaning and is related to the usual F-ratio test. It can therefore be used as a formal test of the hypothesis that matching occurs. Baum (1979) also suggests that slope values of between .90 and 1.11 can be considered good approximations to matching. It is argued that the establishment of a fixed interval as a criterion for determining when matching occurs, is inappropriate. A confidence interval based on the data from any given experiment is suggested as a more useful method of assessment.     

A technique for the nonmetric analysis of paired comparisons of psychological intervals

A nonmetric coordinate adjustment technique is developed which determines scale values for objects whose interobject intervals (differences in subjective value) have been directly compared. In Monte Carlo simulations, the degree of metric determinancy of the scale values is shown to be quite high even when the amount of error is relatively high. This robustness under high-error conditions permitted the analysis of individual subject data in experiments on the direct comparison of loudness differences and loudness ratios where only one judgment per interval comparison was obtained per subject.This research was supported by a grant from the National Research Council of Canada.     

Violation of the transitivity axiom may explain why, in empirical studies, a significant number of subjects violate GARP

Most papers using experimental data find that a significant number of agents are not utility function maximizers. Using three experimental datasets, we provide empirical evidence that these violations of utility function maximizing behavior are simply generated by a violation of the preference transitivity axiom. Moreover, we find that 97% of agents’ behavior is consistent with maximization of a generalized utility function called a variable intervals function (which corresponds to a numerical representation of complete-acyclic preferences).     

Probabilities Associated with Precise and Vague Forecasts

Forecasts of future outcomes, such as the consequences of climate change, are given with different degrees of precision. Logically, more precise forecasts (e.g., a temperature increase of 3–4°) have a smaller probability of capturing the actual outcome than less precise forecasts (e.g., a temperature increase of 2–6°). Nevertheless, people often trust precise forecasts more than vague forecasts, perhaps because precision is associated with knowledge and expertise. In five experiments, we ask whether people expect highly confident forecasts to be associated with wider or narrower outcome ranges than less confident forecasts (Experiments 1, 2, and 5), and, conversely, whether they expect precise forecasts to be issued with higher or lower confidence than vague forecasts (Experiments 3 and 4). The results revealed two distinct ways of thinking about confidence intervals, labeled distributional (wide intervals seen as more probable than narrow intervals) and associative (wide intervals seen as more uncertain than narrow intervals). Distributional responses occurred somewhat more often in within‐subjects designs, where wide and narrow prediction intervals and high and low probability estimates can be directly compared, whereas separate evaluations (in between‐subjects design) suggested associative responses to be slightly more frequent. These findings are relevant for experts communicating forecasts through confidence intervals. Copyright © 2017 John Wiley & Sons, Ltd.     

Profile-likelihood Confidence Intervals in Item Response Theory Models

Confidence intervals (CIs) are fundamental inferential devices which quantify the sampling variability of parameter estimates. In item response theory, CIs have been primarily obtained from large-sample Wald-type approaches based on standard error estimates, derived from the observed or expected information matrix, after parameters have been estimated via maximum likelihood. An alternative approach to constructing CIs is to quantify sampling variability directly from the likelihood function with a technique known as profile-likelihood confidence intervals (PL CIs). In this article, we introduce PL CIs for item response theory models, compare PL CIs to classical large-sample Wald-type CIs, and demonstrate important distinctions among these CIs. CIs are then constructed for parameters directly estimated in the specified model and for transformed parameters which are often obtained post-estimation. Monte Carlo simulation results suggest that PL CIs perform consistently better than Wald-type CIs for both non-transformed and transformed parameters.