Deriving affective meaning from connectivity in the mental lexicon

The mental lexicon, the structure reflecting a person’s knowledge of words, contains not only information about a word’s denotation, pronunciation, and part of speech, but also about the word’s connotation or affective meaning. While there has been considerable research on the mental lexicon, most has focused on denotational and linguistic aspects of words; connotation has not received as much attention. In this dissertation, we will examine the relation between affective meaning and connectivity in the mental lexicon. In a first empirical study (Chapter 2), we use a large word association dataset to investigate whether connected words share affective attributes. We find that words tend to be connected to words of similar valence, arousal, dominance, and concreteness. Considering this, it seems reasonable to assume that we can obtain information about a word’s affective meaning from the words it is connected to. We examine this possibility in three further chapters. In Chapter 3, we outline a method to predict the valence, arousal, and dominance of words, using their connectivity towards words for which these values are already known. We find that obtained predictions show very high correlations to human ratings. In Chapter 4, we follow a similar approach to estimate the correspondence of words towards the Big Five personality dimensions: openness, conscientiousness, extraversion, agreeableness, and neuroticism. We use these estimates to code the responses of a free-format personality test, in which participants describe their own personality using any ten words. We find that the resulting personality profiles show a strong correspondence to profiles obtained by having trained psychologists code responses. Finally, in Chapter 5, we investigate the possibility of measuring brand personality, the human characteristics associated with a brand, by examining the connectivity of the associations people make towards a brand. We test this for a number of well-known brands, and find that the resulting brand personality indices show a mixed correspondence to human ratings, with correlations that are high for some dimensions but low and nonsignificant for others.nrpages: 149status: publishe

Computationally estimated affective word covariates

A number of affective word covariates estimated using word association dat

The role of temporal context in norm-based encoding of faces

Research shows that the human brain encodes faces in terms of how they relate to a prototypical face, a phenomenon referred to as norm-based encoding. The goal of this study was to examine the effect of short-term exposure on the development of the norm, independently of global, long-term exposure. We achieved this by varying the sequence of presentation of the stimuli while keeping global exposure constant. We found that a systematic manipulation of the average face in a set of 10 preceding trials can shift this norm toward that average. However, there was no effect of order or recency among these trials; thus, there was no evidence that the last faces mattered more than the first. This suggests that the position of the face norm is modified by information that is integrated across multiple recent faces.status: publishe

Estimating affective word covariates using word association data

Word ratings on affective dimensions are an important tool in psycholinguistic research. Traditionally, they are obtained by asking participants to rate words on each dimension, a time-consuming procedure. As such, there has been some interest in computationally generating norms, by extrapolating words’ affective ratings using their semantic similarity to words for which these values are already known. So far, most attempts have derived similarity from word co-occurrence in text corpora. In the current paper, we obtain similarity from word association data. We use these similarity ratings to predict the valence, arousal, and dominance of 14,000 Dutch words with the help of two extrapolation methods: Orientation towards Paradigm Words and k-Nearest Neighbors. The resulting estimates show very high correlations with human ratings when using Orientation towards Paradigm Words, and even higher correlations when using k-Nearest Neighbors. We discuss possible theoretical accounts of our results and compare our findings with previous attempts at computationally generating affective norms.status: publishe

Examining assortativity in the mental lexicon: Evidence from word association data

Words are characterized by a variety of lexical and psychological properties, such as their part of speech, word-frequency, concreteness, or affectivity. In this study, we examine how these properties relate to a word’s connectivity in the mental lexicon, the structure containing a person’s knowledge of words. In particular, we examine the extent to which these properties display assortative mixing, that is, the extent to which words in the lexicon are more likely to be connected to words that share these properties. We investigate three types of word properties: (1) subjective word covariates: valence, dominance, arousal, and concreteness, (2) lexical information: part of speech, and (3) distributional word properties: age-of-acquisition, word frequency, and contextual diversity. We assess which of these factors exhibit assortativity using a word association task, where the probability of producing a certain response to a cue is a measure of the associative strength between the cue and response in the mental lexicon. Our results show that the extent to which these aspects exhibit assortativity varies considerably, with a high cue-response correspondence on valence, dominance, arousal, concreteness, and part of speech, indicating that these factors correspond to the words people deem as related. In contrast, we find that cues and responses show only little correspondence on word frequency, contextual diversity, and age-of-acquisition, indicating that, compared to subjective and lexical word covariates, distributional properties exhibit only little assortativity in the mental lexicon. Possible theoretical accounts and implications of these findings are discussed.status: publishe

Valence, dominance, and arousal as organizing principles of the mental lexicon

Although there has been some research into the mental lexicon (the structure containing someone’s knowledge of words), relatively little is known about what factors influence how this structure is organized. Previous research showed that age of acquisition and valence may play an important role in determining which concepts are connected: concepts that are learned at a young age generally show higher connectivity, and concepts with a positive valence elicit a greater amount of associations and associations that are more diverse. In this study, we extend these findings by examining what role the affective dimensions valence, arousal, and dominance play in the connectivity of concepts in the lexicon. To examine this issue, we make use of word associations; association data are extremely useful when investigation the mental lexicon, as the probability of producing a certain response to a cue is considered a direct measure of the associative strength between the cue and response in the mental lexicon. We use the Dutch Association Lexicon, a very large word association dataset derived from a continued association task, to construct a semantic network, with nodes corresponding to lexicalized concepts, and links indicating semantic or lexical relations between concepts. In the first part of this study, we examine the affective similarity between cues and their corresponding associations. Combining association data from the Dutch Association Lexicon with norming data gathered with a traditional questionnaire, we examine 673,000 cue-association pairs. We find that when presented with a cue, participants tend to respond with associations of similar valence, arousal, and dominance, indicating that these dimensions play an important role in determining which concepts are connected in the mental lexicon. In the second part of this study, we expand on these results, examining whether we can use a concept’s position in a semantic network to determine its value on the dimensions valence, arousal, and dominance. For any node in the network, we calculate the cosine measure of similarity to the anchor points ‘good’ and ‘bad’ for valence, ‘strong’ and ‘weak’ for dominance, and ‘active’ and ‘passive’ for arousal. The node’s (dis)similarity to each pair of anchor points is used to create an estimate of the node’s position on the corresponding dimension. To improve the estimate, the cosine measure can include any number of the anchor points’ nearest neighbors. To verify the validity of this method, we estimated valence, arousal, and dominance for 3,762 words, and compared these estimates to norm ratings gathered using a traditional Likert type approach. We found medium to high correlations (with a higher correlation when more of the anchor points’ neighbors are included), suggesting that the association based semantic network can be used as a viable approach to estimating affective ratings. In conclusion, we find that valence, arousal, and dominance have a large effect on connectivity in the mental lexicon, and that this connectivity can be used to estimate a concept’s value on these affective dimensions. We discuss the relationship between the assortivity observed in association responses, and properties derived from the anchor point procedure.status: publishe

data from Stukken et al. (2016)

data from Understanding individual differences in representational abstraction: The role of working memory capacity L Stukken, B Van Rensbergen, W Vanpaemel, G Storms Acta Psychologica 170, 94-10

Using support vector machines to determine linear separability

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Understanding individual differences in representational abstraction: The role of working memory capacity

Several studies have reported differences in categorization strategies among participants: some learn a category by making abstraction across the category members while others use a memorization strategy. Despite the prevalence of these differences, little attention has been paid to investigating what influences some to use an abstraction strategy and others a memorization strategy. The current study had two goals: in a first experiment we investigated whether these differences were stable across time, using the parallel form method often used in psychometric research, and in a second experiment we investigated whether the individual differences in categorization strategy were related to working memory capacity. We used a modelling strategy, in which we not only focused on full abstraction and memorization strategies, but also on intermediate strategies in which some category members are abstracted and others are not. The first study revealed that the individual abstraction strategy of individual participants in two different experiments, performed at different times, correlate significantly, and second study showed that these individual differences were related to the working memory capacity of the participants.publisher: Elsevier articletitle: Understanding individual differences in representational abstraction: The role of working memory capacity journaltitle: Acta Psychologica articlelink: http://dx.doi.org/10.1016/j.actpsy.2016.06.002 content_type: article copyright: © 2016 Elsevier B.V. All rights reserved.status: publishe