Geoff Hollis

The Blue-Collar Brain

Much effort has gone into elucidating control of the body by the brain, less so the role of the body in controlling the brain. This essay develops the idea that the brain does a great deal of work in the service of behavior that is controlled by the body, a blue-collar role compared to the white-collar control exercised by the body. The argument that supports a blue-collar role for the brain is also consistent with recent discoveries clarifying the white-collar role of synergies across the body’s tensegrity structure, and the evidence of critical phenomena in brain and behavior.

7 Origins of Order in Cognitive Activity

Origins of Order in Cognitive Activity Most cognitive scientists have run across The War of the Ghosts, a Native American story used by Bartlett (1932) in his classic studies of remembering. British college students read the story twice and recalled it in detail after 15 minutes, hours, days, months, or years “as opportunity offered” (p. 65). The compelling finding was that participants reinterpreted parts of the story, in addition to omitting details. The mystical story was reorganized and changed in the retelling to fit cultural norms of the British participants. In other words, errors in retelling the story were neither random nor arbitrary but fit together within a larger created narrative. The memory errors illustrate the ordinary constructive performance of cognition and the creation of orderly and sensible thought. Despite perpetually moving eyes, swaying body, and ambiguous stimuli, people perceive coherent and orderly objects. Despite the lack of explicit links between events, higher-order cognition fits thought and behavior within larger coherent narratives. However, the origin of such order remains a mystery. What is the basis of orderly thought, memory, speech, and other cognitive abilities? The origin of order in cognition is the topic of this chapter. We begin with a discussion of how order is explained within a traditional approach of information processing. Taking the shortcomings of this account seriously, we then turn to other disciplines – those that have framed the question of order more successfully.

Now you see it, now you don't: on emotion, context, and the algorithmic prediction of human imageability judgments.

Many studies have shown that behavioral measures are affected by manipulating the imageability of words. Though imageability is usually measured by human judgment, little is known about what factors underlie those judgments. We demonstrate that imageability judgments can be largely or entirely accounted for by two computable measures that have previously been associated with imageability, the size and density of a words context and the emotional associations of the word. We outline an algorithmic method for predicting imageability judgments using co-occurrence distances in a large corpus. Our computed judgments account for 58% of the variance in a set of nearly two thousand imageability judgments, for words that span the entire range of imageability. The two factors account for 43% of the variance in lexical decision reaction times (LDRTs) that is attributable to imageability in a large database of 3697 LDRTs spanning the range of imageability. We document variances in the distribution of our measures across the range of imageability that suggest that they will account for more variance at the extremes, from which most imageability-manipulating stimulus sets are drawn. The two predictors account for 100% of the variance that is attributable to imageability in newly-collected LDRTs using a previously-published stimulus set of 100 items. We argue that our model of imageability is neurobiologically plausible by showing it is consistent with brain imaging data. The evidence we present suggests that behavioral effects in the lexical decision task that are usually attributed to the abstract/concrete distinction between words can be wholly explained by objective characteristics of the word that are not directly related to the semantic distinction. We provide computed imageability estimates for over 29,000 words.

Connected text reading and differences in text reading fluency in adult readers

The process of connected text reading has received very little attention in contemporary cognitive psychology. This lack of attention is in parts due to a research tradition that emphasizes the role of basic lexical constituents, which can be studied in isolated words or sentences. However, this lack of attention is in parts also due to the lack of statistical analysis techniques, which accommodate interdependent time series. In this study, we investigate text reading performance with traditional and nonlinear analysis techniques and show how outcomes from multiple analyses can used to create a more detailed picture of the process of text reading. Specifically, we investigate reading performance of groups of literate adult readers that differ in reading fluency during a self-paced text reading task. Our results indicate that classical metrics of reading (such as word frequency) do not capture text reading very well, and that classical measures of reading fluency (such as average reading time) distinguish relatively poorly between participant groups. Nonlinear analyses of distribution tails and reading time fluctuations provide more fine-grained information about the reading process and reading fluency.

Extrapolating human judgments from skip-gram vector representations of word meaning

There is a growing body of research in psychology that attempts to extrapolate human lexical judgments from computational models of semantics. This research can be used to help develop comprehensive norm sets for experimental research, it has applications to large-scale statistical modelling of lexical access and has broad value within natural language processing and sentiment analysis. However, the value of extrapolated human judgments has recently been questioned within psychological research. Of primary concern is the fact that extrapolated judgments may not share the same pattern of statistical relationship with lexical and semantic variables as do actual human judgments; often the error component in extrapolated judgments is not psychologically inert, making such judgments problematic to use for psychological research. We present a new methodology for extrapolating human judgments that partially addresses prior concerns of validity. We use this methodology to extrapolate human judgments of valence, arousal, dominance, and concreteness for 78,286 words. We also provide resources for users to extrapolate these human judgments for three million English words and short phrases. Applications for large sets of extrapolated human judgments are demonstrated and discussed.

NUANCE: Naturalistic University of Alberta Nonlinear Correlation Explorer

In this article, we describe the Naturalistic University of Alberta Nonlinear Correlation Explorer (NUANCE), a computer program for data exploration and analysis. NUANCE is specialized for finding nonlinear relations between any number of predictors and a dependent value to be predicted. It searches the space of possible relations between the predictors and the dependent value by using natural selection to evolve equations that maximize the correlation between their output and the dependent value. In this article, we introduce the program, describe how to use it, and provide illustrative examples. NUANCE is written in Java, which runs on most computer platforms. We have contributed NUANCE to the archival Web site of the Psychonomic Society (www.psychonomic.org/archive), from which it may be freely downloaded.

NUANCE 3.0: Using genetic programming to model variable relationships

Previously, we introduced a new computational tool for nonlinear curve fitting and data set exploration: the Naturalistic University of Alberta Nonlinear Correlation Explorer (NUANCE) (Hollis & Westbury, 2006). We demonstrated that NUANCE was capable of providing useful descriptions of data for two toy problems. Since then, we have extended the functionality of NUANCE in a new release (NUANCE 3.0) and fruitfully applied the tool to real psychological problems. Here, we discuss the results of two studies carried out with the aid of NUANCE 3.0. We demonstrate that NUANCE can be a useful tool to aid research in psychology in at least two ways: It can be harnessed to simplify complex models of human behavior, and it is capable of highlighting useful knowledge that might be overlooked by more traditional analytical and factorial approaches. NUANCE 3.0 can be downloaded from the Psychonomic Society Archive of Norms, Stimuli, and Data at www.psychonomic.org/archive.

Estimating the average need of semantic knowledge from distributional semantic models

Continuous bag of words (CBOW) and skip-gram are two recently developed models of lexical semantics (Mikolov, Chen, Corrado, & Dean, Advances in Neural Information Processing Systems, 26, 3111–3119, 2013). Each has been demonstrated to perform markedly better at capturing human judgments about semantic relatedness than competing models (e.g., latent semantic analysis; Landauer & Dumais, Psychological Review, 104(2), 1997 211; hyperspace analogue to language; Lund & Burgess, Behavior Research Methods, Instruments, & Computers, 28(2), 203–208, 1996). The new models were largely developed to address practical problems of meaning representation in natural language processing. Consequently, very little attention has been paid to the psychological implications of the performance of these models. We describe the relationship between the learning algorithms employed by these models and Anderson’s rational theory of memory (J. R. Anderson & Milson, Psychological Review, 96(4), 703, 1989) and argue that CBOW is learning word meanings according to Anderson’s concept of needs probability. We also demonstrate that CBOW can account for nearly all of the variation in lexical access measures typically attributable to word frequency and contextual diversity—two measures that are conceptually related to needs probability. These results suggest two conclusions: One, CBOW is a psychologically plausible model of lexical semantics. Two, word frequency and contextual diversity do not capture learning effects but rather memory retrieval effects.

Wriggly, squiffy, lummox, and boobs: What makes some words funny?

Theories of humor tend to be post hoc descriptions, suffering from insufficient operationalization and a subsequent inability to make predictions about what will be found humorous and to what extent. Here we build on the Engelthaler & Hills’ (2017) humor rating norms for 4,997 words, by analyzing the semantic, phonological, orthographic, and frequency factors that play a role in the judgments. We were able to predict the original humor rating norms and ratings for previously unrated words with greater reliability than the split half reliability in the original norms, as estimated from splitting those norms along gender or age lines. Our findings are consistent with several theories of humor, while suggesting that those theories are too narrow. In particular, they are consistent with incongruity theory, which suggests that experienced humor is proportional to the degree to which expectations are violated. We demonstrate that words are judged funnier if they are less common and have an improbable orthographic or phonological structure. We also describe and quantify the semantic attributes of words that are judged funny and show that they are partly compatible with the superiority theory of humor, which focuses on humor as scorn. Several other specific semantic attributes are also associated with humor.

When is best-worst best? A comparison of best-worst scaling, numeric estimation, and rating scales for collection of semantic norms

Large-scale semantic norms have become both prevalent and influential in recent psycholinguistic research. However, little attention has been directed towards understanding the methodological best practices of such norm collection efforts. We compared the quality of semantic norms obtained through rating scales, numeric estimation, and a less commonly used judgment format called best-worst scaling. We found that best-worst scaling usually produces norms with higher predictive validities than other response formats, and does so requiring less data to be collected overall. We also found evidence that the various response formats may be producing qualitatively, rather than just quantitatively, different data. This raises the issue of potential response format bias, which has not been addressed by previous efforts to collect semantic norms, likely because of previous reliance on a single type of response format for a single type of semantic judgment. We have made available software for creating best-worst stimuli and scoring best-worst data. We also made available new norms for age of acquisition, valence, arousal, and concreteness collected using best-worst scaling. These norms include entries for 1,040 words, of which 1,034 are also contained in the ANEW norms (Bradley & Lang, Affective norms for English words (ANEW): Instruction manual and affective ratings (pp. 1-45). Technical report C-1, the center for research in psychophysiology, University of Florida, 1999).