Prior distributions for random choice structures

We study various axioms of discrete probabilistic choice, measuring how restrictive they are, both alone and in the presence of other axioms, given a specific class of prior distributions over a complete collection of finite choice probabilities. We do this by using Monte Carlo simulation to compute, for a range of prior distributions, probabilities that various simple and compound axioms hold. For example, the probability of the triangle inequality is usually many orders of magnitude higher than the probability of random utility. While neither the triangle inequality nor weak stochastic transitivity imply the other, the conditional probability that one holds given the other holds is greater than the marginal probability, for all priors in the class we consider. The reciprocal of the prior probability that an axiom holds is an upper bound on the Bayes factor in favor of a restricted model, in which the axiom holds, against an unrestricted model. The relatively high prior probability of the triangle inequality limits the degree of support that data from a single decision maker can provide in its favor. The much lower probability of random utility implies that the Bayes factor in favor of it can be much higher, for suitable data.     

Solving distant analogies reduces belief-based responding in transitive inference

ABSTRACT

Previous research demonstrated that relational processing in transitive inference problems can be disrupted by belief-based content. Given the premises, The horse is larger than the goat, and The goat is larger than the elephant, accepting the valid conclusion, The horse is larger than the elephant suggests use of relational processing whereas accepting the invalid conclusion The elephant is larger than the horse indicates belief-based responding. This research examined whether solving analogies influenced participants’ reliance on belief-based or relational processing. Participants generated solutions to either distant analogies (e.g. nose:scent::antenna: ______) or near analogies (e.g. nose:scent::tongue:______) before evaluating the conclusions of transitive inference problems that were valid (V) or invalid (I) and believable (B), unbelievable (U) or neutral (N). Solving distant analogies eliminated belief-based responding (possibly reflecting improved inhibitory control) and facilitated relational responding on VU problems.     

小学生工作记忆与推理关系的实验研究

本实验以视觉同时呈现,在形近、音近、义近三种干扰下以再认方法和瑞文渐进推理测验探讨了小学生年级、性别、工作记忆与推理的关系。发现(I)小学生工作记忆与推理能力相关显著。(2)工作记忆和推理能力在小学阶段不存在性别差异。(3)年级和工作记忆是影响小学生推理能力的重要因素,但年级因素对推理能力的作用较工作记忆大。     

Smart and easy: Co-occurring activation of spontaneous trait inferences and spontaneous situational inferences

Social perceivers have been shown to draw spontaneous trait inferences (STI’s) about the behavior of an actor as well as spontaneous situational inferences (SSI’s) about the situation the actor is in. In two studies, we examined inferences about behaviors that allow for both an STI and an SSI. In Experiment 1, using a probe recognition paradigm, we found activation of both STI’s and SSI’s. In Experiment 2, using a relearning paradigm, we again found activation of both STI’s and SSI’s, regardless of temporarily activated processing goals. Results are discussed in light of three-stage models of the process of social inference.     

文本阅读中简单推理情境下的信息整合

研究采用动窗技术探讨了文本阅读中需要简单推理情境下的信息整合发生的时机。本研究通过三个实验证明了在需要简单推理情境下的文本阅读中 ,需要推理的信息整合是即时发生的 ,而不是经过追溯才发生 ,在这种情况下支持文本阅读的更新追随假设理论。同时本研究发现文本阅读中协调性整合不是以词为单位的。     

10‐month‐old infants’ inference of invisible agent: Distinction in causality between object motion and human action1

Abstract: Two habituation experiments investigated 10‐month‐old infants’ interpretation of events where a stationary object began to move without any visible causes. During habituation, infants saw that an object partly hidden by an occluder began to move away from the occluder. Then, they were tested with three test events without the occluder: the ?rst event showed a hand pushing the object, the second event showed a hand failing to touch the object, and the last event had no agent. The objects were a ball in Experiment 1, and a person in Experiment 2. The test event that the infants looked at for the shortest duration in Experiment 1 was where the hand pushed the ball, whereas they looked at the three test events almost equal amounts of time in Experiment 2. These results indicate that 10‐month‐old infants responded to the events in terms of causality and could infer the presence of the agent behind the occluder only when they saw the habituation event featuring the ball.     

Why Higher Working Memory Capacity May Help You Learn: Sampling,Search, and Degrees of Approximation

Algorithms for approximate Bayesian inference, such as those based on sampling (i.e., Monte Carlo methods), provide a natural source of models of how people may deal with uncertainty with limited cognitive resources. Here, we consider the idea that individual differences in working memory capacity (WMC) may be usefully modeled in terms of the number of samples, or “particles,” available to perform inference. To test this idea, we focus on two recent experiments that report positive associations between WMC and two distinct aspects of categorization performance: the ability to learn novel categories, and the ability to switch between different categorization strategies (“knowledge restructuring”). In favor of the idea of modeling WMC as a number of particles, we show that a single model can reproduce both experimental results by varying the number of particles—increasing the number of particles leads to both faster category learning and improved strategy‐switching. Furthermore, when we fit the model to individual participants, we found a positive association between WMC and best‐fit number of particles for strategy switching. However, no association between WMC and best‐fit number of particles was found for category learning. These results are discussed in the context of the general challenge of disentangling the contributions of different potential sources of behavioral variability.     

Do people reason rationally about causally related events? Markov violations,weak inferences,and failures of explaining away

Making judgments by relying on beliefs about the causal relationships between events is a fundamental capacity of everyday cognition. In the last decade, Causal Bayesian Networks have been proposed as a framework for modeling causal reasoning. Two experiments were conducted to provide comprehensive data sets with which to evaluate a variety of different types of judgments in comparison to the standard Bayesian networks calculations. Participants were introduced to a fictional system of three events and observed a set of learning trials that instantiated the multivariate distribution relating the three variables. We tested inferences on chains X₁ → Y → X₂, common cause structures X₁ ← Y → X₂, and common effect structures X₁ → Y ← X₂, on binary and numerical variables, and with high and intermediate causal strengths. We tested transitive inferences, inferences when one variable is irrelevant because it is blocked by an intervening variable (Markov Assumption), inferences from two variables to a middle variable, and inferences about the presence of one cause when the alternative cause was known to have occurred (the normative “explaining away” pattern). Compared to the normative account, in general, when the judgments should change, they change in the normative direction. However, we also discuss a few persistent violations of the standard normative model. In addition, we evaluate the relative success of 12 theoretical explanations for these deviations.     

A Test by Any Other Name: P Values,Bayes Factors,and Statistical Inference

Procedures used for statistical inference are receiving increased scrutiny as the scientific community studies the factors associated with insuring reproducible research. This note addresses recent negative attention directed at p values, the relationship of confidence intervals and tests, and the role of Bayesian inference and Bayes factors, with an eye toward better understanding these different strategies for statistical inference. We argue that researchers and data analysts too often resort to binary decisions (e.g., whether to reject or accept the null hypothesis) in settings where this may not be required.