Reproducing affective norms with lexical co-occurrence statistics:Predicting valence, arousal, and dominance

Human ratings of valence, arousal, and dominance are frequently used to study the cognitive mechanisms of emotional attention, word recognition, and numerous other phenomena in which emotions are hypothesized to play an important role. Collecting such norms from human raters is expensive and time consuming. As a result, affective norms are available for only a small number of English words, are not available for proper nouns in English, and are sparse in other languages. This paper investigated whether affective ratings can be predicted from length, contextual diversity, co-occurrences with words of known valence, and orthographic similarity to words of known valence, providing an algorithm for estimating affective ratings for larger and different datasets. Our bootstrapped ratings achieved correlations with human ratings on valence, arousal, and dominance that are on par with previously reported correlations across gender, age, education and language boundaries. We release these bootstrapped norms for 23,495 English words

Grounding the ungrounded: Estimating locations of unknown place names from linguistic associations and grounded representations

Abstract Spatial locations can be extracted from language statistics, based on the idea that nearby locations are mentioned in similar linguistic contexts, akin to Tobler's first law of geography. However, the performance of language-based estimates is inferior to human estimates, raising questions about whether human spatial representations can actually be informed by such (inferior) statistics. We show that alternative methods of computing co-occurrence statistics improve language-based estimates, illustrating that simple linguisti

Effect Size Matters: The Role of Language Statistics and Perceptual Simulation in Conceptual Processing

The cognitive science literature increasingly demonstrates that perceptual representations are activated during conceptual processing. Such findings suggest that the debate on whether conceptual processing is predominantly symbolic or perceptual has been resolved. However, studies too frequently provide evidence for perceptual simulations without addressing whether other factors explain dependent variables as well, and if so, to what extent. The current paper examines effect sizes computed from 137 experiments in 52 published embodied cognition studies to clarify the conditions under which perceptual simulations are most important. Results showed that effects of perceptual simulation tend to be as large as those of language statistics. Moreover, factors that can be associated with immediate processing (button press, word processing) tend to reduce the effect size of perceptual simulation. These findings are considered in respect to the Symbol Interdependency Hypothesis, which argues that language encodes perceptual information, with language statistics explaining quick, good-enough representations and perceptual simulation explaining more effortful, detailed representations

Predicting the good guy and the bad guy: Attitudes are encoded in language statistics

Various studies have provided evidence that people activate introspective simulations when making valence judgments. Such evidence is in line with an embodied cognition account that argues that cognition is fundamentally embodied, with perceptual simulation rather than language statistics being the source of lexical semantics. Recently, demonstrations that conceptual knowledge is encoded in language have been used to argue that semantic processing involves both language statistics and perceptual simulation, with linguistic cues allowing meaning to be bootstrapped with minimal symbol grounding. Whether language also encodes attitudes towards concepts is unclear. In three studies, negative-valence words were found to be more closely associated in language with individuals commonly considered villains, and positivevalence words with heroes (both fictional and historical). These results suggest that attitudes toward persons can be inferred from lexical associations