What is learned from experience in a probabilistic environment?

Three experiments explored what is learned from experience in a probabilistic environment. The task was a simulated medical decision‐making task with each patient having one of two test results and one of two diseases. The test result was highly predictive of the disease for all participants. The base rate of the test result was varied between participants to produce different inverse conditional probabilities of the test result given the disease across conditions. Participants trained using feedback to predict a patient's disease from a test result showed the classic confusion of the inverse error, substituting the forward conditional probability for the inverse conditional probability when tested on it. Additional training on the base rate of the test result did little to improve performance. Training on the joint probabilities, however, produced good performance on either conditional probability. The pattern of results demonstrated that experience with the environment is not always sufficient for good performance. That natural sampling leads to good performance was not supported. Further, because participants not trained on joint probabilities did, however, know them but still committed the confusion of the inverse error, the hypothesis that having joint probabilities would facilitate performance was not supported. The pattern of results supported the conclusion that people learn all the necessary information from experience in a probabilistic environment, but depending upon what the experience was, it may interfere with their ability to recall to memory the appropriate sample set necessary for estimating or using the inverse conditional probability. Copyright © 2004 John Wiley & Sons, Ltd.     

Infants’ statistical word segmentation in an artificial language is linked to both parental speech input and reported production abilities

Individual variability in infant's language processing is partly explained by environmental factors, like the quantity of parental speech input, as well as by infant‐specific factors, like speech production. Here, we explore how these factors affect infant word segmentation. We used an artificial language to ensure that only statistical regularities (like transitional probabilities between syllables) could cue word boundaries, and then asked how the quantity of parental speech input and infants’ babbling repertoire predict infants’ abilities to use these statistical cues. We replicated prior reports showing that 8‐month‐old infants use statistical cues to segment words, with a preference for part‐words over words (a novelty effect). Crucially, 8‐month‐olds with larger novelty effects had received more speech input at 4 months and had greater production abilities at 8 months. These findings establish for the first time that the ability to extract statistical information from speech correlates with individual factors in infancy, like early speech experience and language production. Implications of these findings for understanding individual variability in early language acquisition are discussed.     

转换概率和词长期待对语音统计学习的影响

语音统计学习指个体在加工人工语言过程中, 可以追踪音节间的转换概率实现切分语流、提取词(语)的过程。本研究采用2(转换概率：高转换概率、低转换概率) × 2(词长期待：两音节、三音节)的混合实验设计来考察转换概率和词长期待对语音统计学习的影响, 转换概率是被试间变量, 词长期待是被试内变量。事后检验发现, 仅在低转换概率人工语言的三音节迫选条件下, 被试没有表现出显著的学习效果。事先对比发现, 在学习低转换概率的人工语言后, 被试完成三音节迫选试次的成绩显著低于两音节迫选试次; 在三音节迫选试次中, 学习低转换概率人工语言被试的成绩也显著低于学习高转换概率被试的成绩。以上结果说明, 转换概率和词长期待共同影响个体语音统计学习的效果。     

More than 50% or Less than 70% Chance: Pragmatic Implications of Single‐Bound Probability Estimates

Probability estimates can be given as ranges or uncertainty intervals, where often only one of the interval bounds (lower or upper) is specified. For instance, a climate forecast can describe La Niña as having “more than 70% chance” or “less than 90% chance” of occurring. In three experiments, we studied how research participants perceived climate‐related forecasts expressed with lower‐bound (“over X% chance”) or upper‐bound (“under Y% chance”) probability statements. Results indicate that such single‐bound statements give pragmatic information in addition to the numeric probabilities they convey. First, the studies show that these statements are directional, leading the listeners' attention in opposite directions. “Over” statements guide attention towards the possible occurrence of the event and are explained by reasons for why it might happen, while “under” statements direct attention to its possible non‐occurrence and are more often explained by reasons for why the target event might not appear, corresponding to positive (it is possible) versus negative (it is uncertain) verbal probabilities. Second, boundaries were found to reveal the forecaster's beliefs and could be perceived as indicative of an increasing or a decreasing trend. Single‐bound probability estimates are therefore not neutral communications of probability level but might “leak” information about the speaker's expectations and about past and future developments of the forecast. Copyright © 2017 John Wiley & Sons, Ltd.     

Note on lexicographic‐order preservation and stochastic dominance

This note concerns two issues left unresolved in our study of lexicographic‐order preservation and stochastic dominance in settings where preferences are represented by utility vectors, ordered lexicographically, and judgements emerge as matrices that premultiply utility vectors in expected utility sums. First, a generalization of the ‘Conjecture Σ’, which implied transitivity of a stochastic dominance relation under non‐vacuous resolution‐level information, is proved. Second, this paper comments on using resolution‐level information in higher as well as in first degree stochastic dominance analysis. Copyright © 1999 John Wiley & Sons, Ltd.     

Probability judgment accuracy for general knowledge. Cross-national differences and assessment methods

In this study we compare the probability judgment accuracy of subjects from the United States and Turkey. Three different response modes were employed — numerical probabilities, pie diagrams, and odds. The questions employed in the study were restricted to two-alternative, general-knowledge items. The observed pattern of differences in the components of probability judgment accuracy paralleled those of studies that have compared Western and Asian subjects. In particular, Turkish subjects exhibited better discrimination but worse calibration than their US counterparts. This result persisted across all three response modes. These findings lend support to previous assertions that observed cross-national differences arise from socioeconomic rather than Asian versus Western cultural differences. However, the consistency of the observed differences across response modes refutes a previous assertion that observed cultural differences are merely the result of response bias.     

The Quantitative/Qualitative Watershed for Rules of Uncertain Inference

We chart the ways in which closure properties of consequence relations for uncertain inference take on different forms according to whether the relations are generated in a quantitative or a qualitative manner. Among the main themes are: the identification of watershed conditions between probabilistically and qualitatively sound rules; failsafe and classicality transforms of qualitatively sound rules; non-Horn conditions satisfied by probabilistic consequence; representation and completeness problems; and threshold-sensitive conditions such as ‘preface’ and ‘lottery’ rules. Special Issue Formal Epistemology I. Edited by Branden Fitelson     

Reasoning with conditionals

This paper reviews the psychological investigation of reasoning with conditionals, putting an emphasis on recent work. In the first part, a few methodological remarks are presented. In the second part, the main theories of deductive reasoning (mental rules, mental models, and the probabilistic approach) are considered in turn; their content is summarised and the semantics they assume for if and the way they explain formal conditional reasoning are discussed, in particular in the light of experimental work on the probability of conditionals. The last part presents the recent shift of interest towards the study of conditional reasoning in context, that is, with large knowledge bases and uncertain premises.