### Overlap of accessible information undermines the anchoring effect

"... Abstract According to the Selective Accessibility Model of anchoring, the comparison question in the standard anchoring paradigm activates information that is congruent with an anchor. As a consequence, this information will be more likely to become the basis for the absolute judgment which will th ..."

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Abstract According to the Selective Accessibility Model of anchoring, the comparison question in the standard anchoring paradigm activates information that is congruent with an anchor. As a consequence, this information will be more likely to become the basis for the absolute judgment which will therefore be assimilated toward the anchor. However, if the activated information overlaps with information that is elicited by the absolute judgment itself, the preceding comparative judgment should not exert an incremental effect and should fail to result in an anchoring effect. The present studies find this result when the comparative judgment refers to a general category and the absolute judgment refers to a subset of the general category that was activated by the anchor value. For example, participants comparing the average annual temperature in New York City to a high 102°F judged the average winter, but not summer temperature to be higher than participants making no comparison. On the other hand, participants comparing the annual temperature to a low -4°F judged the average summer, but not winter temperature to be lower than control participants. This pattern of results was shown also in another content domain. It is consistent with the Selective Accessibility Model but difficult to reconcile with other main explanations of the anchoring effect.

### Article 61. Additional Information:

"... If you wish to contact a Curtin researcher associated with this document, you may obtain an email address from ..."

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If you wish to contact a Curtin researcher associated with this document, you may obtain an email address from

### Probabilistic Explicit Topic Modeling

, 2013

"... Latent Dirichlet Allocation (LDA) is widely used for automatic discovery of latent topics in document corpora. However, output from analysis using an LDA topic model suers from a lack of identifiability between topics not only across corpora, but across runs of the algorithm. The output is also isol ..."

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Latent Dirichlet Allocation (LDA) is widely used for automatic discovery of latent topics in document corpora. However, output from analysis using an LDA topic model suers from a lack of identifiability between topics not only across corpora, but across runs of the algorithm. The output is also isolated from enriching information from knowledge sources such as Wikipedia and is dicult for humans to interpret due to a lack of meaningful topic labels. This thesis introduces two methods for probabilistic explicit topic modeling that address these issues: Latent Dirichlet Allocation with Static Topic-Word Distributions (LDA-STWD), and Explicit Dirichlet Allocation (EDA). LDA-STWD directly substitutes precomputed counts for LDA topic-word counts, leveraging existing Gibbs sampler inference. EDA denes an entirely new explicit topic model and derives the inference method from rst principles. Both of these methods approximate topic-word distributions a priori using word distributions from Wikipedia articles, with each article corresponding to one topic and the article title being used as a topic label. By this means, LDA-STWD and EDA overcome the nonidentiability, isolation, and unintepretability of LDA output.

### ACCOUNTING

, 2015

"... ii Copyright @ 2015 ALL RIGHTS RESERVED. No part of this paper may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, graphic, electronic mechanical, photocopying, recording, scanning, or otherwise, without the prior consent of the authors. ..."

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ii Copyright @ 2015 ALL RIGHTS RESERVED. No part of this paper may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, graphic, electronic mechanical, photocopying, recording, scanning, or otherwise, without the prior consent of the authors.

### Cross-language similarity and difference in quantity categorization of Finnish and Japanese

"... A B S T R A C T The present study investigates how listeners of Finnish and Japanese, languages with very similar contrasts in plosive quantity (short vs. long), use language-specific phonetic knowledge of acoustic attributes which covary with closure duration. A fully-crossed perceptual experiment ..."

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A B S T R A C T The present study investigates how listeners of Finnish and Japanese, languages with very similar contrasts in plosive quantity (short vs. long), use language-specific phonetic knowledge of acoustic attributes which covary with closure duration. A fully-crossed perceptual experiment on consonant quantity categorization was conducted with 22 Finnish and 20 Japanese listeners, using natural speech stimuli with systematically manipulated closure durations. Stimuli were created from Finnish and Japanese productions of both long and short plosives. In the naturally produced stimuli, the duration of the vowel preceding the target consonant was affected by languagespecific word prosody patterns. The results showed an across-the-board effect of the original quantity of the produced stimulus, with the stimuli created from words with a geminate consonant tending to be perceived as geminate, irrespective of the language of the listeners or talkers, suggesting a strong influence of cues besides closure duration that are shared by the two languages. However, Japanese listeners were more heavily affected by the acoustic cues concomitant to the quantity contrast in their native language, likely due to robust languagespecific vowel duration effects in the Japanese productions. Word prosody, besides creating subtle shifts in category boundary for both language groups, created confusions in the listener responses, especially when the language-specific word-level prosodic effect is localized in the vowel preceding the target word, and when the listener is confronted with stimuli from the other language. Thus, some aspects of the quantity contrast are remarkably similar between the two languages, and listeners from one language group are attuned to attributes found in the other language. However, other attributes appear to be language specific, and such durational differences may interfere with categorization in an unfamiliar language.

### CALCULATION OF FORENSIC LIKELIHOOD RATIOS: USE OF MONTE CARLO SIMULATIONS TO COMPARE THE OUTPUT OF SCORE- BASED APPROACHES WITH TRUE LIKELIHOOD-RATIO VALUES

, 2015

"... A group of approaches for calculating forensic likelihood ratios first calculates scores which quantify the degree of difference or the degree of similarity between pairs of samples, then converts those scores to likelihood ratios. In order for a score-based approach to produce a forensically interp ..."

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A group of approaches for calculating forensic likelihood ratios first calculates scores which quantify the degree of difference or the degree of similarity between pairs of samples, then converts those scores to likelihood ratios. In order for a score-based approach to produce a forensically interpretable likelihood ratio, however, in addition to accounting for the similarity of the questioned sample with respect to the known sample, it must also account for the typicality of the questioned sample with respect to the relevant population. The present paper explores a number of score-based approaches using different types of scores and different procedures for converting scores to likelihood ratios. Monte Carlo simulations are used to compare the output of these approaches to true likelihood-ratio values calculated on the basis of the distribution specified for a simulated population. The inadequacy of approaches based on similarity-only or difference-only scores is illustrated, and the relative performance of different approaches which take account of both similarity and typicality is assessed.

### Bayesian First Aid Pearson Correlation Coefficient Test

"... This talk will introduce BayesianFirstAid1, an R package that implements Bayesian alternatives to the most commonly used statistical tests. It is inspired by the BEST package [2] and is similarly intended both as a practical tool and as a teaching aid. A main feature of the package is that the Bayes ..."

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This talk will introduce BayesianFirstAid1, an R package that implements Bayesian alternatives to the most commonly used statistical tests. It is inspired by the BEST package [2] and is similarly intended both as a practical tool and as a teaching aid. A main feature of the package is that the Bayesian alternatives are called in the same way as the corresponding classical test functions, save for the addition of bayes. to the beginning of the function name. For example, if binom.test(x=7, n=10) runs a classical binomial test then bayes.binom.test(x=7, n=10) runs the Bayesian alternative. This makes the package easy to pick up and use, especially if you are already used to the classical *.test functions, and it also facilitates comparing the output of the different approaches. All models are implemented using the JAGS modeling language, called from R using the rjags package. The generic function model.code makes it straightforward to start modifying the models underlying the package. It takes a BayesianFirstAid object and prints out the underlying model code which is ready to be copy-n-pasted into an R script and tinkered with from there. All BayesianFirstAid objects have default plots that show the posteriors of the parameters of interest together with a display that enables a quick posterior predictive check. Below is an example of the output from the Bayesian First Aid alternative to cor.test(...). The data is the hand grip strength (in kg) and index / ring finger ratio for the male group in [1].> bayes.cor.test(digit_ratio, grip_strength)

### Bayesian Estimation in Hierarchical Models

"... Bayesian data analysis involves describing data by meaningful mathematical models, and allocating credibility to parameter values that are consistent with the data and with prior knowledge. The Bayesian approach is ideally suited for constructing hierarchical models, which are useful for data struct ..."

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Bayesian data analysis involves describing data by meaningful mathematical models, and allocating credibility to parameter values that are consistent with the data and with prior knowledge. The Bayesian approach is ideally suited for constructing hierarchical models, which are useful for data structures with multiple levels, such as data from individuals who are members of groups which in turn are in higher-level organizations. Hierarchical models have parameters that meaningfully describe the data at their multiple levels and connect information within and across levels. Bayesian methods are very flexible and straightforward for estimating parameters of complex hierarchical models (and simpler models too). We provide an introduction to the ideas of hierarchical models and to the Bayesian estimation of their parameters, illustrated with two extended examples. One example considers baseball batting averages of individual players grouped by fielding position. A second example uses a hierarchical extension of a cognitive process model to examine individual differences in attention allocation of people who have eating disorders. We conclude by discussing Bayesian model comparison as a case of hierarchical modeling.

### Journal of Mathematical Psychology ( ) – Contents lists available at ScienceDirect Journal of Mathematical Psychology

"... journal homepage: www.elsevier.com/locate/jmp Bayesian alternatives to null-hypothesis significance testing for ..."

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journal homepage: www.elsevier.com/locate/jmp Bayesian alternatives to null-hypothesis significance testing for