Estimating Emotions Through Language Statistics and Embodied Cognition

Tillman, Richard; Louwerse, Max M.

doi:10.1007/s10936-017-9522-y

Cited by 13 publications

(12 citation statements)

References 19 publications

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“…Emotional information has been addressed most extensively (Bestgen & Vincze, 2012; Hollis et al, 2017; Lenci, Lebani, & Passaro, 2018; Passaro et al, 2017; Recchia & Louwerse, 2015; Tillmand & Louwerse, 2018; Westbury, Keith, Briesemeister, Hofmann, & Jacobs, 2015). Bestgen and Vincze (2012) proposed a method for predicting three affective variables, that is, valence, arousal, and dominance, of ANEW norms (Bradley & Lang, 1999).…”

Section: Related Workmentioning

confidence: 99%

“…Recchia and Louwerse (2015) also demonstrated that PMI can predict ANEW ratings as accurately as Bestgen and Vincze's (2012) method, and Hollis et al (2017) showed that the skip‐gram model predicted them better than Recchia and Louwerse's (2015) model. Tillmand and Louwerse (2018) demonstrated that language statistics discriminated between same‐emotion and different‐emotion word pairs. Language statistics also explained reaction times required for truthfulness judgment of emotion sentences, but perceptual simulation factors did not.…”

Section: Related Workmentioning

confidence: 99%

“…Despite the fact that an extensive number of studies have applied word vectors to a variety of tasks in many research fields, relatively little effort has been made to explore what type of information or knowledge is encoded in word vectors. Previous studies that have addressed this question have demonstrated that text‐based word vectors reflect perceptual (Louwerse & Connell, 2011; Riordan & Jones, 2011), emotional (Passaro, Bondielli, & Lenci, 2017; Recchia & Louwerse, 2015; Tillmand & Louwerse, 2018), and social (Hutchinson & Louwerse, 2018) information. Although these studies have focused on specific information, some recent studies (Grand, Blank, Pereira, & Fedorenko, 2018; Sommerauer & Fokkens, 2018) have compared various types of knowledge in terms of the representational ability of word vectors.…”

Section: Introductionmentioning

confidence: 99%

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Exploring What Is Encoded in Distributional Word Vectors: A Neurobiologically Motivated Analysis

Utsumi¹

2020

Cognitive Science

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The pervasive use of distributional semantic models or word embeddings for both cognitive modeling and practical application is because of their remarkable ability to represent the meanings of words. However, relatively little effort has been made to explore what types of information are encoded in distributional word vectors. Knowing the internal knowledge embedded in word vectors is important for cognitive modeling using distributional semantic models. Therefore, in this paper, we attempt to identify the knowledge encoded in word vectors by conducting a computational experiment using Binder et al.'s (2016) featural conceptual representations based on neurobiologically motivated attributes. In an experiment, these conceptual vectors are predicted from text‐based word vectors using a neural network and linear transformation, and prediction performance is compared among various types of information. The analysis demonstrates that abstract information is generally predicted more accurately by word vectors than perceptual and spatiotemporal information, and specifically, the prediction accuracy of cognitive and social information is higher. Emotional information is also found to be successfully predicted for abstract words. These results indicate that language can be a major source of knowledge about abstract attributes, and they support the recent view that emphasizes the importance of language for abstract concepts. Furthermore, we show that word vectors can capture some types of perceptual and spatiotemporal information about concrete concepts and some relevant word categories. This suggests that language statistics can encode more perceptual knowledge than often expected.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Exploring What Is Encoded in Distributional Word Vectors: A Neurobiologically Motivated Analysis

Utsumi¹

2020

Cognitive Science

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“…The fi rst level is the level of functioning of cognitions, to which psychologists refer the main psychical processes (sensation, perception, memory, attention, which, in turn, «control» the course of cognitive activity), as well as thinking and imagination, speech and dialogical interaction (Collins, 2014;Smulson, 2003;Zasekina, 2006). The second level of intelligence is the level of metacognitions (metacognitive integrators, «secondary» mental processes), among which the main ones are intellectual initiation (self-statement of the problem), refl ection, decentralization, intellectual strategies and abilities (abilities and competences), as well as metacognitive monitoring, intuition, intellectual atheist (values, meanings) (Tillman & Louwerse, 2018). The fi rst and the second levels of intelligence, in our opinion, are fully understood by the person who carries out intellectual activity.…”

Section: Introductionmentioning

confidence: 99%

Psycholinguistic Study of Functioning of Cognitions and Metacognitions on the Levels of Social Intelligence

Ivashkevych

Yatsjuryk

2019

Psycholing.

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The article states that social intelligence in psychology is seen as the ability of a person to understand correctly his/her own behavior and the behavior of other people in the society. This ability is very necessary for a person to have an effective interpersonal interaction and successful social adaptation. Social intelligence implements the functioning of cognitive processes associated with the reflection of a person as a partner in the processes of communication and activities. The main function of social intelligence is to predict the behavior of others. The authors of the article consider the concept of “intelligence” in the broad and narrow senses. In a broad meaning of “intelligence” it is only one intelligence, the intellectual sphere of the person in general. In this sense, the intelligence of a person can be described as a hierarchical system that has several levels. The first level is the level of functioning of cognitions. The second level of intelligence is the level of metacognitions. The third level of intelligence amplifies the characteristics of both the first and the second levels, while intellectual activity is carried out mainly at an unconscious level, a level that approximates a person to use of automated skills and abilities. The next, the fourth level, is the level of meta-intellectual activity, which explodes the creative achievements of the person. It was proved that each type of intelligence contained some abilities of the person to perform a certain type of the activity. Guided by the narrow sense of understanding the word “intelligence”, it was determined: social intelligence; technical intelligence; artistic intelligence; information intelligence. It was described the empirical research which was organized in different regions of Ukraine (the participants of this research were teachers and directors from preschool educational establishments). The results having been received in this research proved that the respondents of all groups have the results which show the advantage of average indicators of their ability to understand the meanings of non-verbal expressions and the meaning of the expression, depending on a social context. This may be due to the fact that these abilities are formed in the immediate process of communication and to a large extent depend on the conditions of the life of the subjects, at the first place – on the conditions of their professional activity, means and methods of professional activity of teachers of preschool educational establishments.

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“…Embodied cognition regards the human body and the environment as significant factors in the way we think and feel (Guell, Gabrieli, & Schmahmann, 2018). This is done by processing both emotional and modality-specific systems in the brain (Barsalou et al, 2003;Niedenthal et al, 2005;Mahon, 2015;Tillman, & Louwerse, 2018), where both emotion and the sensory multi-modal specific processing of memory work together (Niedenthal, 2007;Dreyer & Pulvermüller, 2018), also known as semantic cognition (Ralph et al, 2017:2).…”

Section: Introductionmentioning

confidence: 99%

An embodied cognition classifier of human emotion and senses

2019

El Sawah, S. (Ed.) MODSIM2019, 23rd International Congress on Modelling and Simulation.

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Drawing on the idea of embodied cognition, where the human body and its environment influence the way a person thinks and feels, we test if it is possible to classify suicide attackers from their writing by using words affected by the emotional and modality-specific systems in the brain. We use their final notes and manifestos compared with normal bloggers' posts to train a machine learning classifier. We compare two support vector machine classifier models using linear and radial base function kernels. In this exploratory study, receiver operating characteristic curves show encouraging separation accuracies. These models offer an 11-13% improvement over methods using only emotion or sense categories. This study supports the idea that an embodied cognition classifier better discriminates the way a person thinks and feels rather than treating the body and mind as separate entities and may help in reflecting behavior and applying influence in online social systems.

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Estimating Emotions Through Language Statistics and Embodied Cognition

Cited by 13 publications

References 19 publications

Exploring What Is Encoded in Distributional Word Vectors: A Neurobiologically Motivated Analysis

Exploring What Is Encoded in Distributional Word Vectors: A Neurobiologically Motivated Analysis

Psycholinguistic Study of Functioning of Cognitions and Metacognitions on the Levels of Social Intelligence

An embodied cognition classifier of human emotion and senses

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