http://talklab.psy.gla.ac.uk/KeepItMaximalR2.pdf

Random effects structure for confirmatory hypothesis testing: Keep it maximal

Miller (1956) summarized evidence that people can remember about seven chunks in short-term memory (STM) tasks. How- ever, that number was meant more as a rough estimate and a rhetorical device than as a real capacity limit. Others have since suggested that there is a more precise capacity limit, but that it is only three to five chunks. The present target article brings together a wide vari- ety of data on capacity limits suggesting that the smaller capacity limit is real. Capacity limits will be useful in analyses of information processing only if the boundary conditions for observing them can be carefully described. Four basic conditions in which chunks can be identified and capacity limits can accordingly be observed are: (1) when information overload limits chunks to individual stimulus items, (2) when other steps are taken specifically to block the recoding of stimulus items into larger chunks, (3) in performance discontinuities caused by the capacity limit, and (4) in various indirect effects of the capacity limit. Under these conditions, rehearsal and long-term memory cannot be used to combine stimulus items into chunks of an unknown size; nor can storage mechanisms that are not capacity- limited, such as sensory memory, allow the capacity-limited storage mechanism to be refilled during recall. A single, central capacity limit averaging about four chunks is implicated along with other, noncapacity-limited sources. The pure STM capacity limit expressed in chunks is distinguished from compound STM limits obtained when the number of separately held chunks is unclear. Reasons why pure capacity estimates fall within a narrow range are discussed and a capacity limit for the focus of attention is proposed.

/pdf/the-magical-number-4-in-short-term-memory-a-reconsideration-3704b9phuz.pdf

The magical number 4 in short-term memory: a reconsideration of mental storage capacity.

1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

Previous research uses negative word counts to measure the tone of a text. We show that word lists developed for other disciplines misclassify common words in financial text. In a large sample of 10 Ks during 1994 to 2008, almost three-fourths of the words identified as negative by the widely used Harvard Dictionary are words typically not considered negative in financial contexts. We develop an alternative negative word list, along with five other word lists, that better reflect tone in financial text. We link the word lists to 10 K filing returns, trading volume, return volatility, fraud, material weakness, and unexpected earnings.

When is a Liability not a Liability? Textual Analysis, Dictionaries, and 10-Ks

The generation of words in speech involves a number of processing stages. There is, first, a stage of conceptual preparation; this is followed by stages of lexical selection, phonological encoding, phonetic encoding and articulation. In addition, the speaker monitors the output and, if necessary, self-corrects. Major parts of the theory have been computer modelled. The paper concentrates on experimental reaction time evidence in support of the theory.

/pdf/a-theory-of-lexical-access-in-speech-production-56yj14t9eb.pdf

A theory of lexical access in speech production

Balancing Type I Error and Power in Linear Mixed Models

The analysis of experimental data with mixed-effects models requires decisions about the specification of the appropriate random-effects structure. Recently, Barr, Levy, Scheepers, and Tily, 2013 recommended fitting `maximal' models with all possible random effect components included. Estimation of maximal models, however, may not converge. We show that failure to converge typically is not due to a suboptimal estimation algorithm, but is a consequence of attempting to fit a model that is too complex to be properly supported by the data, irrespective of whether estimation is based on maximum likelihood or on Bayesian hierarchical modeling with uninformative or weakly informative priors. Importantly, even under convergence, overparameterization may lead to uninterpretable models. We provide diagnostic tools for detecting overparameterization and guiding model simplification.

/pdf/parsimonious-mixed-models-1m7ck87kby.pdf

Parsimonious Mixed Models

Reaction times (RTs) are an important source of information in experimental psychology. Classical methodological considerations pertaining to the statistical analysis of RT data are optimized for analyses of aggregated data, based on subject or item means (c.f., Forster & Dickinson, 1976). Mixed-effects modeling (see, e.g., Baayen, Davidson, & Bates, 2008) does not require prior aggregation and allows the researcher the more ambitious goal of predicting individual responses. Mixed-modeling calls for a reconsideration of the classical methodological strategies for analysing rts. In this study, we argue for empirical exibility with respect to the choice of transformation for the RTs. We advocate minimal a-priori data trimming, combined with model criticism. We also show how trial-to-trial, longitudinal dependencies between individual observations can be brought into the statistical model. These strategies are illustrated for a large dataset with a non-trivial random-effects structure. Special attention is paid to the evaluation of interactions involving fixed-effect factors that partition the levels sampled by random-effect factors.

/pdf/analyzing-reaction-times-306gmj8e9d.pdf

Analyzing Reaction Times

1. Word Frequencies. 2. Non-parametric models. 3. Parametric models. 4. Mixture distributions. 5. The Randomness Assumption. 6. Examples of Applications. A. List of Symbols. B. Solutions of the exercises. C. Software. D. Data sets. Bibliography. Index.

Word Frequency Distributions

Theoretical linguists have traditionally relied on linguistic intuitions such as grammaticality judgments for their data. But the massive growth of computer-readable texts and recordings, the availability of cheaper, more powerful computers and software, and the development of new probabilistic models for language have now made the spontaneous useoflanguageinnaturalsettingsarichandeasilyaccessiblealternativesourceofdata. Surprisingly, many linguists believe that such ‘usage data’ are irrelevant to the theory of grammar. Four problems are repeatedly brought up in the critiques of usage data: 1. Correlated factors seeming to support reductive theories; 2. Pooled data invalidating grammatical inference; 3. Syntactic choices reducing to lexical biases; and 4. Cross-corpus differences undermining corpus studies. Presenting a case study of work on the English dative alternation, we show first,that linguistic intuitions of grammaticality are deeply flawed and seriously underestimate the space of grammatical possibility, and second, that the four problems in the critique of usage data are empirical issues that can be resolved by using modern statistical theory and modeling strategies widely used in other fields. The new models allow linguistic theory to solve more difficult problems than it has in the past, and to build convergent projects with psychology, computer science, and allied fields of cognitive science.

/pdf/predicting-the-dative-alternation-1icnuaypul.pdf

R. Harald Baayen

Papers

Balancing Type I Error and Power in Linear Mixed Models

Parsimonious Mixed Models

Analyzing Reaction Times

Word Frequency Distributions

Predicting the dative alternation