Semantic Norm Extrapolation is a Missing Data Problem

For close to 70 years psychologists have studied word meaning using a simple method: participants rate words on some theoretically motivated property (e.g. pleasantness, familiarity) using a Likert scale as the measurement instrument. Such semantic judgments serve as a means of interrogating the underlying structure of lexical semantic constructs, to select stimuli for experiments, or as covariates in models predicting brain or behaviour. Recently, there has been a surge of interest in using computational distributional semantic word representations and supervised learning to predict semantic judgments on Likert scales for words lacking empirical measurements. We call this task semantic norm extrapolation. A significant body of work has developed showing methods for semantic norm extrapolation are often highly accurate. The impressive performance of models for this task may give the appearance that non-empirical, machine learning derived estimates of semantic norms are interchangeable with empirical measurements of semantic norms. Herein, we argue that this is not the case, and that all extant methods for semantic norm extrapolation are more problematic than the literature suggests. Naive use of extrapolated semantic norms should be expected to yield biased and anti-conservative analyses. We make this argument using a mixture of 1) the principles of analysis of partially observed data, 2) simulations, and 3) a real-data example. Achieving sound inference when using semantic norm extrapolation requires a conceptual and methodological shift from treating semantic norm extrapolation as a prediction problem to treating it as a missing data problem. This shift in perspective also lays bare problems in default analytical procedures of semantic norms and megastudy data, and surprisingly suggests that semantic norm extrapolation --- when done using recommended procedures for analysis of partially observed data --- should be default methodological practice.

A cross-linguistic comparison of Spanish and English semantic norms: Looking at core features

It has been suggested that human communities that share their basic cultural foundations evince no remarkable differences concerning the characterization of core concepts. However, the small but existing differences among them reflect their sociocultural diversity. This study compares 219 concrete concepts common to both Spanish and English semantic feature norms in order to assess whether core features of concepts follow a universal or cultural language-specific pattern. Concepts were compared through a geometric technique of vector comparison in the Euclidean n-dimensional space alongside the calculation of the network’s degree of centrality. The role of cognate status was also explored by repeating the former analysis separating cognate from noncognate words. Taken together, our data show that languages are structurally similar independent of the cognate status of words, further suggesting that there are some sort of core features common to both languages.

The Lancaster Sensorimotor Norms: multidimensional measures of perceptual and action strength for 40,000 English words

Sensorimotor information plays a fundamental role in cognition. However, the existing materials that measure the sensorimotor basis of word meanings and concepts have been restricted in terms of their sample size and breadth of sensorimotor experience. Here we present norms of sensorimotor strength for 39,707 concepts across six perceptual modalities (touch, hearing, smell, taste, vision, and interoception) and five action effectors (mouth/throat, hand/arm, foot/leg, head excluding mouth/throat, and torso), gathered from a total of 3,500 individual participants using Amazon’s Mechanical Turk platform. The Lancaster Sensorimotor Norms are unique and innovative in a number of respects: They represent the largest-ever set of semantic norms for English, at 40,000 words × 11 dimensions (plus several informative cross-dimensional variables), they extend perceptual strength norming to the new modality of interoception, and they include the first norming of action strength across separate bodily effectors. In the first study, we describe the data collection procedures, provide summary descriptives of the dataset, and interpret the relations observed between sensorimotor dimensions. We then report two further studies, in which we (1) extracted an optimal single-variable composite of the 11-dimension sensorimotor profile (Minkowski 3 strength) and (2) demonstrated the utility of both perceptual and action strength in facilitating lexical decision times and accuracy in two separate datasets. These norms provide a valuable resource to researchers in diverse areas, including psycholinguistics, grounded cognition, cognitive semantics, knowledge representation, machine learning, and big-data approaches to the analysis of language and conceptual representations. The data are accessible via the Open Science Framework (http://osf.io/7emr6/) and an interactive web application (https://www.lancaster.ac.uk/psychology/lsnorms/).

The Lancaster Sensorimotor Norms: Multidimensional measures of Perceptual and Action Strength for 40,000 English words

Sensorimotor information plays a fundamental role in cognition. However, existing materials that measure the sensorimotor basis to word meanings and concepts have been restricted in sample size and breadth of sensorimotor experience. Here, we present norms of sensorimotor strength for 39,707 concepts across six perceptual modalities (touch, hearing, smell, taste, vision, and interoception) and five action effectors (mouth/throat, hand/arm, foot/leg, head excluding mouth/throat, and torso), gathered from a total of 3,500 individual participants using Amazon's Mechanical Turk platform. The Lancaster Sensorimotor Norms are unique and innovative in a number of respects: they represent the largest ever set of semantic norms for English at 40 thousand words x 11 dimensions (plus several informative cross-dimensional variables); they extend perceptual strength norming to the new modality of interoception; and they include the first norming of action strength across separate bodily effectors. In the first study, we describe the data collection procedures, provide summary descriptives of the dataset, and interpret the relations observed between sensorimotor dimensions. We then report two further studies that i) extract an optimal single-variable composite of the 11-dimension sensorimotor profile (Minkowski 3 strength), and ii) demonstrate the utility of both perceptual and action strength in facilitating lexical decision times and accuracy in two separate datasets. These norms provide a valuable resource to researchers in diverse areas including psycholinguistics, grounded cognition, cognitive semantics, knowledge representation, machine learning, and big data approaches to the analysis of language and conceptual representations. The data are accessible via the Open Science Framework (http://osf.io/7emr6/ ) and an interactive web application (https://www.lancaster.ac.uk/psychology/lsnorms/).

The Methodological Argument for Informational Teleosemantics

Teleosemantic theories are diverse, but they all endorse the claim that semantic norms, to do with correct and incorrect representation, derive in part at least from functional norms, to do with normal or proper functioning. Informational teleosemantics adds that semantic norms also derive from natural-factiveinformation. In this chapter, The author starts with the premise defended in chapter 3–– in explaining how bodies and brains operate, biologists use a notion of normal-proper function. To this the author adds that the same notion of function is used in explaining cognitive (including perceptual) capacities, and then argue that, given an information-processing approach, the norms of proper functioning are thus wedded to the aboutness of natural-factive information, so that a basic form of normative aboutness is posited. This elucidates the explanatory role of positing nonconceptual representations, establishes the scientific credentials of informational teleosemantics, and gives us good reason to try to solve its alleged problems. In the last few sections, the author argues that the main naturalistic “alternatives” to teleosemantics also have apparently ineliminableteleosemantic commitments.