Brendan T. Johns

The Role of Semantic Diversity in Lexical Organization

Recent research has challenged the notion that word frequency is the organizing principle underlying lexical access, pointing instead to the number of contexts that a word occurs in (Adelman, Brown, & Quesada, 2006). Counting contexts gives a better quantitative fit to human lexical decision and naming data than counting raw occurrences of words. However, this approach ignores the information redundancy of the contexts in which the word occurs, a factor we refer to as semantic diversity. Using both a corpus-based study and a controlled artificial language experiment, we demonstrate the importance of contextual redundancy in lexical access, suggesting that contextual repetitions in language only increase a words memory strength if the repetitions are accompanied by a modulation in semantic context. We introduce a cognitive process mechanism to explain the pattern of behaviour by encoding the words context relative to the information redundancy between the current context and the words current memory representation. The model gives a better account of identification latency data than models based on either raw frequency or document count, and also produces a better-organized space to simulate semantic similarity.

Perceptual inference through global lexical similarity.

The literature contains a disconnect between accounts of how humans learn lexical semantic representations for words. Theories generally propose that lexical semantics are learned either through perceptual experience or through exposure to regularities in language. We propose here a model to integrate these two information sources. Specifically, the model uses the global structure of memory to exploit the redundancy between language and perception in order to generate inferred perceptual representations for words with which the model has no perceptual experience. We test the model on a variety of different datasets from grounded cognition experiments and demonstrate that this diverse set of results can be explained as perceptual simulation (cf. Barsalou, Simmons, Barbey, & Wilson, 2003) within a global memory model.

Evaluating the random representation assumption of lexical semantics in cognitive models

A common assumption implicit in cognitive models is that lexical semantics can be approximated by using randomly generated representations to stand in for word meaning. However, the use of random representations contains the hidden assumption that semantic similarity is symmetrically distributed across randomly selected words or between instances within a semantic category. We evaluated this assumption by computing similarity distributions for randomly selected words from a number of well-known semantic measures and comparing them with the distributions from random representations commonly used in cognitive models. The similarity distributions from all semantic measures were positively skewed compared with the symmetric normal distributions assumed by random representations. We discuss potential consequences that this false assumption may have for conclusions drawn from process models that use random representations.

Effects of word frequency, contextual diversity, and semantic distinctiveness on spoken word recognition

The relative abilities of word frequency, contextual diversity, and semantic distinctiveness to predict accuracy of spoken word recognition in noise were compared using two data sets. Word frequency is the number of times a word appears in a corpus of text. Contextual diversity is the number of different documents in which the word appears in that corpus. Semantic distinctiveness takes into account the number of different semantic contexts in which the word appears. Semantic distinctiveness and contextual diversity were both able to explain variance above and beyond that explained by word frequency, which by itself explained little unique variance.

The influence of contextual diversity on word learning.

In a series of analyses over mega datasets, Jones, Johns, and Recchia (Canadian Journal of Experimental Psychology, 66(2), 115–124, 2012) and Johns et al. (Journal of the Acoustical Society of America, 132:2, EL74-EL80, 2012) found that a measure of contextual diversity that takes into account the semantic variability of a word’s contexts provided a better fit to both visual and spoken word recognition data than traditional measures, such as word frequency or raw context counts. This measure was empirically validated with an artificial language experiment (Jones et al.). The present study extends the empirical results with a unique natural language learning paradigm, which allows for an examination of the semantic representations that are acquired as semantic diversity is varied. Subjects were incidentally exposed to novel words as they rated short selections from articles, books, and newspapers. When novel words were encountered across distinct discourse contexts, subjects were both faster and more accurate at recognizing them than when they were seen in redundant contexts. However, learning across redundant contexts promoted the development of more stable semantic representations. These findings are predicted by a distributional learning model trained on the same materials as our subjects.

A Synchronization Account of False Recognition.

We describe a computational model to explain a variety of results in both standard and false recognition. A key attribute of the model is that it uses plausible semantic representations for words, built through exposure to a linguistic corpus. A study list is encoded in the model as a gist trace, similar to the proposal of fuzzy trace theory (Brainerd & Reyna, 2002), but based on realistically structured semantic representations of the component words. The model uses a decision process based on the principles of neural synchronization and information accumulation. The decision process operates by synchronizing a probe with the gist trace of a study context, allowing information to be accumulated about whether the word did or did not occur on the study list, and the efficiency of synchronization determines recognition. We demonstrate that the model is capable of accounting for standard recognition results that are challenging for classic global memory models, and can also explain a wide variety of false recognition effects and make item-specific predictions for critical lures. The model demonstrates that both standard and false recognition results may be explained within a single formal framework by integrating realistic representation assumptions with a simple processing mechanism.

Applying the permutation test to factorial designs

The permutation test follows directly from the procedure in a comparative experiment, does not depend on a known distribution for error, and is sometimes more sensitive to real effects than are the corresponding parametric tests. Despite its advantages, the permutation test is seldom (if ever) applied to factorial designs because of the computational load that they impose. We propose two methods to limit the computation load. We show, first, that orthogonal contrasts limit the computational load and, second, that when combined with Gill’s (2007) algorithm, the factorial permutation test is both practical and efficient. For within-subjects designs, the factorial permutation test is equivalent to an ANOVA when the latter’s assumptions have been met. For between-subjects designs, the factorial test is conservative. Code to execute the routines described in this article may be downloaded from http://brm.psychonomic-journals.org/content/supplemental.

Generating Structure From Experience: A Retrieval-Based Model of Language Processing

Standard theories of language generally assume that some abstraction of linguistic input is necessary to create higher level representations of linguistic structures (e.g., a grammar). However, the importance of individual experiences with language has recently been emphasized by both usage-based theories (Tomasello, 2003) and grounded and situated theories (e.g., Zwaan & Madden, 2005). Following the usage-based approach, we present a formal exemplar model that stores instances of sentences across a natural language corpus, applying recent advances from models of semantic memory. In this model, an exemplar memory is used to generate expectations about the future structure of sentences, using a mechanism for prediction in language processing (Altmann & Mirković, 2009). The model successfully captures a broad range of behavioral effects-reduced relative clause processing (Reali & Christiansen, 2007), the role of contextual constraint (Rayner & Well, 1996), and event knowledge activation (Ferretti, Kutas, & McRae, 2007), among others. We further demonstrate how perceptual knowledge could be integrated into this exemplar-based framework, with the goal of grounding language processing in perception. Finally, we illustrate how an exemplar memory system could have been used in the cultural evolution of language. The model provides evidence that an impressive amount of language processing may be bottom-up in nature, built on the storage and retrieval of individual linguistic experiences.

Randomization tests and the unequal-N/unequal-variance problem

When both the variance and the N are unequal in a two-group design, the probability of a Type I error shifts from the nominal 5% error rate. The probability is too liberal when the small cell has the larger variance and too conservative when the large cell has the larger variance. We present an algorithm to circumvent the problem when the smaller group has the larger variance and show, by simulation, that the algorithm brings the error rate back to the nominal value without sacrificing the ability to detect true effects.

The Role of Semantic Diversity in Word Recognition across Aging and Bilingualism.

Frequency effects are pervasive in studies of language, with higher frequency words being recognized faster than lower frequency words. However, the exact nature of frequency effects has recently been questioned, with some studies finding that contextual information provides a better fit to lexical decision and naming data than word frequency (Adelman et al., 2006). Recent work has cemented the importance of these results by demonstrating that a measure of the semantic diversity of the contexts that a word occurs in provides a powerful measure to account for variability in word recognition latency (Johns et al., 2012, 2015; Jones et al., 2012). The goal of the current study is to extend this measure to examine bilingualism and aging, where multiple theories use frequency of occurrence of linguistic constructs as central to accounting for empirical results (Gollan et al., 2008; Ramscar et al., 2014). A lexical decision experiment was conducted with four groups of subjects: younger and older monolinguals and bilinguals. Consistent with past results, a semantic diversity variable accounted for the greatest amount of variance in the latency data. In addition, the pattern of fits of semantic diversity across multiple corpora suggests that bilinguals and older adults are more sensitive to semantic diversity information than younger monolinguals.