The Natural Selection of Prediction Heuristics: Anchoring and Adjustment versus Representativeness

There are several heuristics which people use in making numerical predictions and these heuristics compete for the determination of prediction output. Some of them (e.g. representativeness) lead to excessively extreme predictions while others (e.g. anchoring and adjustment) lead to regressive (and even over-regressive) predictions. In this paper we study the competition between these two heuristics by varying the representation of predictor and outcome. The results indicate that factors which facilitate reliance on representativeness (e.g. compatibility between predictor and outcome) indeed lead to an increase in extremity, while factors that facilitate reliance on anchoring and adjustment (e.g. increased salience of a potential anchor) lead to a decrease in extremity.     

Improved Test–retest Reliability Criteria as Applied to Probability Assessments

Test–retest reliability is a common indicator of response consistency. It is argued that using regression coefficients for detecting systematic response error is less appropriate than testing for shifts in the mean (median) and variance. This procedure is exemplified using probability response data. For this data, shifts in centrality were found to be about 2.5 times more likely than shifts in variability. The shifts in centrality did not favor any particular direction; however, variability tended to decrease over time in early sessions.     

When is the Wilcoxon–Mann–Whitney procedure a test of location? Implications for effect-size measures

The Wilcoxon–Mann–Whitney procedure is invariant under monotone transformations but its use as a test of location or shift is said not to be so. It tests location only under the shift model, the assumption of parallel cumulative distribution functions (cdfs). We show that infinitely many monotone transformations of the measured variable produce parallel cdfs, so long as the original cdfs intersect nowhere or everywhere. Thus there are infinitely many effect sizes measured as shifts of medians, invalidating the notion that there is one true shift parameter and thereby rendering any single estimate dubious. Measuring effect size using the probability of superiority alleviates this difficulty.     

条件推理:“演绎”与“概率”两种实验研究范式之比较

本研究通过推理心理学研究中的“演绎”和“概率”两种实验范式设计对同一个班级的大学生参与者(实验一中N=57,实验二中N=43)进行先后两次有关条件推理的实验研究后,得出如下主要结果:(1)推理者在对不同的“纯形式条件命题本身的认可度”以及对由它们各自建构的同类型推理题的推理结果之间的作答反应模式之间的差异都很小且具有较高的一致性;(2)对由不同的“含具体内容的假言命题”本身的认可度之间以及由它们建构的同类型条件推理题的推理结果之间具有较大的差异性;(3)推理者对“演绎”和“概率”两种不同实验范式分别建构的内容近似的推进题进行推理时具有大致相同的作答反应趋势。由此可以推论推理者在“概率推理实验范式”中的作答或推理结果可以被视为只是对“演绎推理实验范式”的相应推理题给出“概率解”的心理加工过程。     

统计学习的认知神经机制及其与语言的关系

统计学习是指个体在连续刺激流中发现转移概率等统计规律的过程, 在Saffran等(1996)的经典婴儿语音切分研究中首次被提出。大量研究证实了统计学习的普遍存在, 近期学界开始关注统计学习的特异性及其对认知的影响, 尤其是从学习过程及其特异性两个方面阐述统计学习的认知神经机制并揭示其和语言的交互作用。未来应从脑和行为的多模态数据视角, 丰富统计学习结果的行为和神经指标, 考察不同类型统计学习过程的动态神经活动模式, 建立统计学习行为和脑的关联, 深化对统计学习认知神经机制的认识, 在统计学习与语言交互作用的基础上, 从成人二语学习切入结合音乐统计学习训练探讨促进语言学习的统计学习干预手段。     

Molecular analyses of the principal components of response strength

Killeen and Hall (2001) showed that a common factor called strength underlies the key dependent variables of response probability, latency, and rate, and that overall response rate is a good predictor of strength. In a search for the mechanisms that underlie those correlations, this article shows that (a) the probability of responding on a trial is a two-state Markov process; (b) latency and rate of responding can be described in terms of the probability and period of stochastic machines called clocked Bernoulli modules, and (c) one such machine, the refractory Poisson process, provides a functional relation between the probability of observing a response during any epoch and the rate of responding. This relation is one of proportionality at low rates and curvilinearity at higher rates.     

Entailment with Near Surety of Scaled Assertions of High Conditional Probability

An assertion of high conditional probability or, more briefly, an HCP assertion is a statement of the type: The conditional probability of B given A is close to one. The goal of this paper is to construct logics of HCP assertions whose conclusions are highly likely to be correct rather than certain to be correct. Such logics would allow useful conclusions to be drawn when the premises are not strong enough to allow conclusions to be reached with certainty. This goal is achieved by taking Adams" (1966) logic, changing its intended application from conditionals to HCP assertions, and then weakening its criterion for entailment. According to the weakened entailment criterion, called the Criterion of Near Surety and which may be loosely interpreted as a Bayesian criterion, a conclusion is entailed if and only if nearly every model of the premises is a model of the conclusion. The resulting logic, called NSL, is nonmonotonic. Entailment in this logic, although not as strict as entailment in Adams" logic, is more strict than entailment in the propositional logic of material conditionals. Next, NSL was modified by requiring that each HCP assertion be scaled; this means that to each HCP assertion was associated a bound on the deviation from 1 of the conditional probability that is the subject of the assertion. Scaling of HCP assertions is useful for breaking entailment deadlocks. For example, it it is known that the conditional probabilities of C given A and of ¬ C given B are both close to one but the bound on the former"s deviation from 1 is much smaller than the latter"s, then it may be concluded that in all likelihood the conditional probability of C given A B is close to one. The resulting logic, called NSL-S, is also nonmonotonic. Despite great differences in their definitions of entailment, entailment in NSL is equivalent to Lehmann and Magidor"s rational closure and, disregarding minor differences concerning which premise sets are considered consistent, entailment in NSL-S is equivalent to entailment in Goldszmidt and Pearl"s System-Z ⁺. Bacchus, Grove, Halpern, and Koller proposed two methods of developing a predicate calculus based on the Criterion of Near Surety. In their random-structures method, which assumed a prior distribution similar to that of NSL, it appears possible to define an entailment relation equivalent to that of NSL. In their random-worlds method, which assumed a prior distribution dramatically different from that of NSL, it is known that the entailment relation is different from that of NSL.     

On the effects of signaling reinforcer probability and magnitude in delayed matching to sample

Two experiments examined whether postsample signals of reinforcer probability or magnitude affected the accuracy of delayed matching to sample in pigeons. On each trial, red or green choice responses that matched red or green stimuli seen shortly before a variable retention interval were reinforced with wheat access. In Experiment 1, the reinforcer probability was either 0.2 or 1.0 for both red and green responses. Reinforcer probability was signaled by line or cross symbols that appeared after the sample had been presented. In Experiment 2, all correct responses were reinforced, and the signaled reinforcer durations were 1.0 s and 4.5 s. Matching was more accurate when larger or more probable reinforcers were signaled, independently of retention interval duration. Because signals were presented postsample, the effects were not the result of differential attention to the sample.     

An evidential support accumulation model of subjective probability

A model of cue-based probability judgment is developed within the framework of support theory. Cue diagnosticity is evaluated from experience as represented by error-free frequency counts. When presented with a pattern of cues, the diagnostic implications of each cue are assessed independently and then summed to arrive at an assessment of the support for a hypothesis, with greater weight placed on present than on absent cues. The model can also accommodate adjustment of support in light of the baserate or prior probability of a hypothesis. Support for alternatives packed together in a "residual" hypothesis is discounted; fewer cues are consulted in assessing support for alternatives as support for the focal hypothesis increases. Results of fitting this and several alternative models to data from four new multiple-cue probability learning experiments are reported.