Emotions, Simulation, and Abstract Art

2021 ◽  
pp. 1-33
Author(s):  
P. N. Johnson-Laird ◽  
Keith Oatley
Keyword(s):  
Mental Models ◽  
Mental Model ◽  
Simulation Theory ◽  
Abstract Art ◽  
Emotional States ◽  
Mental Processes ◽  
Basic Emotions ◽  
Complex Emotions ◽  
Access To Knowledge ◽  
Do So

Abstract Some people feel emotions when they look at abstract art. This article presents a ‘simulation’ theory that predicts which emotions they will experience, including those based on their aesthetic reactions. It also explains the mental processes underlying these emotions. This new theory embodies two precursors: an account of how mental models represent perceptions, descriptions, and self-reflections, and an account of the communicative nature of emotions, which distinguishes between basic emotions that can be experienced without knowledge of their objects or causes, and complex emotions that are founded on basic ones, but that include propositional contents. The resulting simulation theory predicts that abstract paintings can evoke the basic emotions of happiness, sadness, anger, and anxiety, and that they do so in several ways. In mimesis, models simulate the actions and gestures of people in emotional states, elicited from cues in the surface of paintings, and that in turn evoke basic emotions. Other basic emotions depend on synaesthesia, and both association and projection can yield complex emotions. Underlying viewers’ awareness of looking at a painting is a mental model of themselves in that relation with the painting. This self-reflective model has access to knowledge, enabling people to evaluate the work, and to experience an aesthetic emotion, such as awe or revulsion. The comments of artists and critics, and experimental results support the theory.

scholarly journals Mental Models of a Mobile Shoe Rack

2021 ◽  
Vol 10 (2) ◽  
pp. 1-36
Author(s):  
Matthew Rueben ◽  
Jeffrey Klow ◽  
Madelyn Duer ◽  
Eric Zimmerman ◽  
Jennifer Piacentini ◽  
...  
Keyword(s):  
Mental Models ◽  
Mental Model ◽  
Human Robot Interaction ◽  
Direct Access ◽  
Future Research ◽  
Yoga Class ◽  
In The Wild ◽  
Access To Knowledge ◽  
Model Formation

Most people do not have direct access to knowledge about the inner workings of robots. Instead, they must develop mental models of the robot, a process that is not well understood. This article presents findings from a long-term, in-the-wild, qualitative, hypothesis-generating study of the mental model formation process. The focus was on how (qualitatively) users form mental models of the robot—specifically its perceptual capabilities, rules of behavior, and communication with other humans. Participants of diverse ages had multiple interactions with the robot over six weeks in a non-laboratory setting. The robot’s rules of behavior were changed every two weeks. A novel, non-anthropomorphic robot was created for the study with a realistic use case: storing people’s shoes during a yoga class. This article reports findings from a case study analysis of 28 interviews conducted over six weeks with six participants. These findings are organized into six topics: (1) variability in the rate at which mental models are updated to be more predictive, (2) types of reasoning and hypothesizing about the robot, (3) borrowing from existing mental models and use of imagination, (4) attributing sensing capabilities where there are no visible sensors, (5) judgments about whether the robot is autonomous or teleoperated, and (6) experimenting with the robot. Specific suggestions for future research are given throughout, culminating in a set of study design recommendations. This work demonstrates the fruitfulness of long-term, in-the-wild studies of human-robot interaction, of which mental model formation is a foundational aspect.

scholarly journals Context-Sensitive Control of Adaptation: Self-Modeling Networks for Human Mental Processes Using Mental Models

2021 ◽  
Author(s):  
Raj Bhalwankar ◽  
Laila van Ments ◽  
Jan Treur
Keyword(s):  
Machine Learning ◽  
Mental Models ◽  
Mental Model ◽  
Mental Processes ◽  
Computational Approaches ◽  
Context Sensitive ◽  
Computational Architecture ◽  
The World ◽  
Learning And Adaptation ◽  
Adaptation Processes

Within their mental and social processes, humans often learn, adapt and apply specific mental models of processes in the world or other persons, as a kind of blueprints. In this paper, it is discussed how analysis of this provides useful inspiration for the development of new computational approaches from a Machine Learning and Network-Oriented Modeling perspective. Three main elements are: applying the mental model by internal simulation, developing and revising a mental model by some form of adaptation, and exerting control over this adaptation in a context-sensitive manner. This concept of controlled adaptation relates to the Plasticity Versus Stability Conundrum from neuroscience. The presented analysis has led to a three-level computational architecture for controlled adaptation. It is discussed and illustrated by examples of applications how this three-level computational architecture can be specified based on a self-modeling network and used to model controlled learning and adaptation processes based on mental models in a context-sensitive manner.

Comprehension as the construction of mental models

1981 ◽  
Vol 295 (1077) ◽  
pp. 353-374 ◽  
Keyword(s):  
Experimental Evidence ◽  
Mental Models ◽  
Mental Model ◽  
Syntactic Structure ◽  
Stage Theory ◽  
Research Support ◽  
Linguistic Code ◽  
State Of Affairs ◽  
Better Than ◽  
Do So

This paper presents a theory of how language is understood, and gives some supporting experimental evidence. Its fundamental hypothesis is that discourse is sometimes mentally represented in a form akin to that of perceived or imagined events. Skilled narrators have the power to elicit such representations so that their audiences seem to experience the events rather than merely to read or hear about them. The theory assumes that there are two main stages in comprehension. First, utterances are translated into a mental code that provides a direct linguistic representation of them. This stage concerns the identification of speech sounds, the recognition of words, and the recovery of superficial syntactic structure. Secondly, the linguistic code may be used as part of the basis for the inferential construction of a mental model of the state of affairs that the utterances describe. On some occasions, listeners go no further than the first stage of interpretation. Several lines of research support the two-stage theory. If people construct a mental model of a discourse, then, for example, their memory for its gist is better than if they have failed to do so, but their recall of verbatim detail is poor. If people do not construct a mental model of a discourse but rely solely on the linguistic code, then they tend to remember the overall import of the passage poorly, but they are often able to recall verbatim detail. Such contrasting results were obtained by comparing determinate descriptions with indeterminate ones that could not be accurately represented by a single mental model. The paper presents a number of other phenomena concerning the coherence of discourse that corroborate the theory.

Design and Group Creativity

2019 ◽  
pp. 305-318
Author(s):  
Arthur B. Markman ◽  
Jonathan Cagan
Keyword(s):  
Practical Reason ◽  
Group Processes ◽  
Theoretical Studies ◽  
Design Development ◽  
Group Creativity ◽  
Mental Processes ◽  
Creative Solutions ◽  
End Products ◽  
Computer Based ◽  
Do So

Design communities in engineering and other disciplines have a practical reason for caring about group creativity. People employed in these areas have to generate creative solutions routinely, and they often must do so in a group. As a result, research in these areas has focused on processes to improve group creativity. This chapter explores techniques for generating problem statements and solutions in groups that have emerged from this literature. It also examines computer-based methods of problem solving that groups can use to enhance the ideas that arise from these group processes. This work has expanded the range of elements explored in studies of group creativity. Although theoretical studies of creativity can be useful in uncovering underlying mental processes, design development requires useful end products. The focus of this research on techniques that enhance creativity in design provides an opportunity to link this literature with the broader literature on individual and group creativity.

Where is the body in the mental model for a story?

1997 ◽  
Vol 20 (1) ◽  
pp. 25-25 ◽  
Author(s):  
Arthur C. Graesser
Keyword(s):  
Mental Models ◽  
Mental Model ◽  
The Body ◽  
Discourse Processing ◽  
On Line

Researchers in the field of discourse processing have investigated how mental models are constructed when adults comprehend stories. They have explored the process of encoding various classes of inferences “on-line” when these mental microworlds are constructed during comprehension. This commentary addresses the extent to which these inferences and mental microworlds are “embodied.”

Watch For Failing Objects: What Inappropriate Compliance Reveals About Shared Mental Models In Autonomous Cars

2021 ◽  
Vol 65 (1) ◽  
pp. 643-647
Author(s):  
Yosef S. Razin ◽  
Jack Gale ◽  
Jiaojiao Fan ◽  
Jaznae’ Smith ◽  
Karen M. Feigh
Keyword(s):  
Mental Models ◽  
Mental Model ◽  
False Positive Rate ◽  
Ground Truth ◽  
True Positive Rate ◽  
Shared Mental Models ◽  
Shared Mental Model ◽  
Autonomous Cars ◽  
Positive Rate ◽  
Dispositional Factors

This paper evaluates Banks et al.’s Human-AI Shared Mental Model theory by examining how a self-driving vehicle’s hazard assessment facilitates shared mental models. Participants were asked to affirm the vehicle’s assessment of road objects as either hazards or mistakes in real-time as behavioral and subjective measures were collected. The baseline performance of the AI was purposefully low (<50%) to examine how the human’s shared mental model might lead to inappropriate compliance. Results indicated that while the participant true positive rate was high, overall performance was reduced by the large false positive rate, indicating that participants were indeed being influenced by the Al’s faulty assessments, despite full transparency as to the ground-truth. Both performance and compliance were directly affected by frustration, mental, and even physical demands. Dispositional factors such as faith in other people’s cooperativeness and in technology companies were also significant. Thus, our findings strongly supported the theory that shared mental models play a measurable role in performance and compliance, in a complex interplay with trust.

scholarly journals Mental models accurately predict emotion transitions

2017 ◽  
Vol 114 (23) ◽  
pp. 5982-5987 ◽  
Author(s):  
Mark A. Thornton ◽  
Diana I. Tamir
Keyword(s):  
Social Interactions ◽  
Mental Models ◽  
Social Impact ◽  
Human Mind ◽  
High Accuracy ◽  
Accurate Information ◽  
Emotional States ◽  
State Representation ◽  
Emotion Dynamics ◽  
Accuracy Study

Successful social interactions depend on people’s ability to predict others’ future actions and emotions. People possess many mechanisms for perceiving others’ current emotional states, but how might they use this information to predict others’ future states? We hypothesized that people might capitalize on an overlooked aspect of affective experience: current emotions predict future emotions. By attending to regularities in emotion transitions, perceivers might develop accurate mental models of others’ emotional dynamics. People could then use these mental models of emotion transitions to predict others’ future emotions from currently observable emotions. To test this hypothesis, studies 1–3 used data from three extant experience-sampling datasets to establish the actual rates of emotional transitions. We then collected three parallel datasets in which participants rated the transition likelihoods between the same set of emotions. Participants’ ratings of emotion transitions predicted others’ experienced transitional likelihoods with high accuracy. Study 4 demonstrated that four conceptual dimensions of mental state representation—valence, social impact, rationality, and human mind—inform participants’ mental models. Study 5 used 2 million emotion reports on the Experience Project to replicate both of these findings: again people reported accurate models of emotion transitions, and these models were informed by the same four conceptual dimensions. Importantly, neither these conceptual dimensions nor holistic similarity could fully explain participants’ accuracy, suggesting that their mental models contain accurate information about emotion dynamics above and beyond what might be predicted by static emotion knowledge alone.

scholarly journals Classification of complex emotions using EEG and virtual environment: proof of concept and therapeutic implication

2020 ◽  
Author(s):  
Eleonora De Filippi ◽  
Mara Wolter ◽  
Bruno Melo ◽  
Carlos J. Tierra-Criollo ◽  
Tiago Bortolini ◽  
...  
Keyword(s):  
Real Time ◽  
Cross Validation ◽  
Support Vector ◽  
Spectral Features ◽  
Emotional States ◽  
Proof Of Concept ◽  
Neurofeedback Training ◽  
Concept Study ◽  
Complex Emotions

AbstractDuring the last decades, neurofeedback training for emotional self-regulation has received significant attention from both the scientific and clinical communities. However, most studies have focused on broader emotional states such as “negative vs. positive”, primarily due to our poor understanding of the functional anatomy of more complex emotions at the electrophysiological level. Our proof-of-concept study aims at investigating the feasibility of classifying two complex emotions that have been implicated in mental health, namely tenderness and anguish, using features extracted from the electroencephalogram (EEG) signal in healthy participants. Electrophysiological data were recorded from fourteen participants during a block-designed experiment consisting of emotional self-induction trials combined with a multimodal virtual scenario. For the within-subject classification, the linear Support Vector Machine was trained with two sets of samples: random cross-validation of the sliding windows of all trials; and 2) strategic cross-validation, assigning all the windows of one trial to the same fold. Spectral features, together with the frontal-alpha asymmetry, were extracted using Complex Morlet Wavelet analysis. Classification results with these features showed an accuracy of 79.3% on average when doing random cross-validation, and 73.3% when applying strategic cross-validation. We extracted a second set of features from the amplitude time-series correlation analysis, which significantly enhanced random cross-validation accuracy while showing similar performance to spectral features when doing strategic cross-validation. These results suggest that complex emotions show distinct electrophysiological correlates, which paves the way for future EEG-based, real-time neurofeedback training of complex emotional states.Significance statementThere is still little understanding about the correlates of high-order emotions (i.e., anguish and tenderness) in the physiological signals recorded with the EEG. Most studies have investigated emotions using functional magnetic resonance imaging (fMRI), including the real-time application in neurofeedback training. However, concerning the therapeutic application, EEG is a more suitable tool with regards to costs and practicability. Therefore, our proof-of-concept study aims at establishing a method for classifying complex emotions that can be later used for EEG-based neurofeedback on emotion regulation. We recorded EEG signals during a multimodal, near-immersive emotion-elicitation experiment. Results demonstrate that intraindividual classification of discrete emotions with features extracted from the EEG is feasible and may be implemented in real-time to enable neurofeedback.

scholarly journals Problem based learning: Students’ mental models on water conductivity concept

2020 ◽  
Vol 9 (2) ◽  
pp. 269
Author(s):  
John Rafafy Batlolona ◽  
Haryo Franky Souisa
Keyword(s):  
Science Education ◽  
Mental Models ◽  
Mental Model ◽  
Problem Based Learning ◽  
Water Conductivity ◽  
Education Change ◽  
Pbl Model

This paper tells about the mental model of prospective scholars on the topic of temperature and heat. The purpose of this research is to improve students’ mental model by using problem based learning (PBL) model. The number of samples in the study amounted to 72 students with two different classes. The results of the study showed that, (1) the improvement of mental model that studied with PBL was higher than that studied with conventional learning. (2) high-skilled student mental models that are learning with PBL are higher than those studied by conventional learning. (3) low-skilled student mental models that study with PBL are higher than students learning with conventional learning. The conclusion of this study is the improvement of students' mental models using PBL models on the topic of conductivity in water. Thus the PBL model can be recommended in improving students' mental models on temperature and heat topics. The implication in this research is to improve the students' mental model as the agent of science education change.

scholarly journals How Influential Are Mental Models on Interaction Performance? Exploring the Gap between Users’ and Designers’ Mental Models through a New Quantitative Method

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Bingjun Xie ◽  
Jia Zhou ◽  
Huilin Wang
Keyword(s):  
Mental Models ◽  
Mental Model ◽  
Quantitative Method ◽  
User Performance ◽  
Card Sorting ◽  
Information Structures ◽  
Path Diagram ◽  
New Perspective ◽  
Model Similarity ◽  
The Relationship

The objective of this study is to investigate the effect of the gap between two different mental models on interaction performance through a quantitative way. To achieve that, an index called mental model similarity and a new method called path diagram to elicit mental models were introduced. There are two kinds of similarity: directionless similarity calculated from card sorting and directional similarity calculated from path diagram. An experiment was designed to test their influence. A total of 32 college students participated and their performance was recorded. Through mathematical analysis of the results, three findings were derived. Frist, the more complex the information structures, the lower the directional similarity. Second, directional similarity (rather than directionless similarity) had significant influence on user performance, indicating that it is more effective in eliciting mental models using path diagram than card sorting. Third, the relationship between information structures and user performance was partially mediated by directional similarity. Our findings provide practitioners with a new perspective of bridging the gap between users’ and designers’ mental models.

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