Hideyoshi Yanagisawa

Hideyoshi Yanagisawa, Xiaoxiang Wu, Kazutaka Ueda et al.

An appropriate level of arousal induces positive emotions, and a high arousal potential may provoke negative emotions. To explain the effect of arousal on emotional valence, we propose a novel mathematical framework of arousal potential variations in the dual process of human cognition: automatic and controlled. A suitable mathematical formulation to explain the emotions in the dual process is still absent. Our model associates free energy with arousal potential and its variations to explain emotional valence. Decreasing and increasing free energy consequently induce positive and negative emotions, respectively. We formalize a transition from the automatic to the controlled process in the dual process as a change of Bayesian prior. Further, we model emotional valence using free energy increase (FI) when one tries changing one's Bayesian prior and its reduction (FR) when one succeeds in recognizing the same stimuli with a changed prior and define three emotions: "interest," "confusion," and "boredom" using the variations. The results of our mathematical analysis comparing various Gaussian model parameters reveals the following: 1) prediction error (PR) increases FR (representing "interest") when the first prior variance is greater than the second prior variance, 2) PR decreases FR when the first prior variance is less than the second prior variance, and 3) the distance between priors' means always increases FR. We also discuss the association of the outcomes with emotions in the controlled process. The proposed mathematical model provides a general framework for predicting and controlling emotional valence in the dual process that varies with viewpoint and stimuli, as well as for understanding the contradictions in the effects of arousal on the valence.

Hideyoshi Yanagisawa, Shimon Honda

Epistemic emotions, such as curiosity and interest, drive the inquiry process. This study proposes a novel formulation of epistemic emotions such as curiosity and interest using two types of information gain generated by the principle of free energy minimization: Kullback-Leibler divergence(KLD) from Bayesian posterior to prior, which represents free energy reduction in recognition, and Bayesian surprise (BS), which represents the expected information gain by Bayesian prior update. By applying a Gaussian generative model with an additional uniform likelihood, we found that KLD and BS form an upward-convex function of surprise (minimized free energy and prediction error), similar to Berlyne's arousal potential functions, or the Wundt curve. We consider that the alternate maximization of BS and KLD generates an ideal inquiry cycle to approach the optimal arousal level with fluctuations in surprise, and that curiosity and interest drive to facilitate the cyclic process. We exhaustively analyzed the effects of prediction uncertainty (prior variance) and observation uncertainty (likelihood variance) on the peaks of the information gain function as optimal surprises. The results show that greater prediction uncertainty, meaning an open-minded attitude, and less observational uncertainty, meaning precise observation with attention, are expected to provide greater information gains through a greater range of exploration. The proposed mathematical framework unifies the free energy principle of the brain and the arousal potential theory to explain the Wundt curve as an information gain function and suggests an ideal inquiry process driven by epistemic emotions.

Chang Li, Hideyoshi Yanagisawa

With the growing utility of today's conversational virtual assistants, the importance of user motivation in human-AI interaction is becoming more obvious. However, previous studies in this and related fields, such as human-computer interaction and human-robot interaction, scarcely discussed intrinsic motivation and its affecting factors. Those studies either treated motivation as an inseparable concept or focused on non-intrinsic motivation. The current study aims to cover intrinsic motivation by taking an affective-engineering approach. A novel motivation model is proposed, in which intrinsic motivation is affected by two factors that derive from user interactions with virtual assistants: expectation of capability and uncertainty. Experiments are conducted where these two factors are manipulated by making participants believe they are interacting with the smart speaker "Amazon Echo". Intrinsic motivation is measured both by using questionnaires and by covertly monitoring a five-minute free-choice period in the experimenter's absence, during which the participants could decide for themselves whether to interact with the virtual assistants. Results of the first experiment showed that high expectation engenders more intrinsically motivated interaction compared with low expectation. The results also suggested suppressive effects by uncertainty on intrinsic motivation, though we had not hypothesized before experiments. We then revised our hypothetical model of action selection accordingly and conducted a verification experiment of uncertainty's effects. Results of the verification experiment showed that reducing uncertainty encourages more interactions and causes the motivation behind these interactions to shift from non-intrinsic to intrinsic.

Hideyoshi Yanagisawa

This study extends the mathematical model of emotion dimensions that we previously proposed (Yanagisawa, et al. 2019, Front Comput Neurosci) to consider perceived complexity as well as novelty, as a source of arousal potential. Berlyne's hedonic function of arousal potential (or the inverse U-shaped curve, the so-called Wundt curve) is assumed. We modeled the arousal potential as information contents to be processed in the brain after sensory stimuli are perceived (or recognized), which we termed sensory surprisal. We mathematically demonstrated that sensory surprisal represents free energy, and it is equivalent to a summation of information gain (or information from novelty) and perceived complexity (or information from complexity), which are the collative variables forming the arousal potential. We demonstrated empirical evidence with visual stimuli (profile shapes of butterfly) supporting the hypothesis that the summation of perceived novelty and complexity shapes the inverse U-shaped beauty function. We discussed the potential of free energy as a mathematical principle explaining emotion initiators.

Takuma Maki, Hideyoshi Yanagisawa

Throughout the course of product experience, a user employs multiple senses, including vision, hearing, and touch. Previous cross-modal studies have shown that multiple senses interact with each other and change perceptions. In this paper, we propose a methodology for designing multisensory product experiences by applying cross-modal effect to simultaneous stimuli. In this methodology, we first obtain a model of the comprehensive cognitive structure of user's multisensory experience by applying Kansei modeling methodology and extract opportunities of cross-modal effect from the structure. Second, we conduct experiments on these cross-modal effects and formulate them by obtaining a regression curve through analysis. Finally, we find solutions to improve the product sensory experience from the regression model of the target cross-modal effects. We demonstrated the validity of the methodology with SLR cameras as a case study, which is a typical product with multisensory perceptions.

Dan Nanno, Hideyoshi Yanagisawa

Several assistive technologies for users' operations have been recently developed. A user's sense of agency (SoA) decreases with increasing system assistance, possibly resulting in a decrease in the user's sense of fulfillment. This study aims to provide a design guideline for an assistive method to maintain and improve the sense of fulfillment with SoA. We propose a mathematical model describing the mechanisms by which the assistive method affects SoA and SoA induces a sense of fulfillment. The experience in the flow state is assumed to be a sense of fulfillment. The assistance effect on the skill-challenge plane in flow theory is defined as an increase in skill and decrease in challenge. The factor that separates the two effects from attribution theory is the locus of causality, which is matched to the judgement of agency (JoA) from the two-step account of agency. We hypothesized that the assistance increases the perception of skill and sense of fulfillment is greater when the locus of causality is internal, rather than external. To verify this hypothesis, a game task experiment was conducted with assistance that varied with the ease of recognition. We hypothesized that a player's JoA is internal for hard-to-recognize assistance, resulting in a high sense of fulfillment. Experimental results supported this hypothesis.

Kazutaka Ueda, Yuki Sakai, Hideyoshi Yanagisawa

This study aimed to develop a method to evaluate the sense of discrepancy to the operation response quantitatively. We examined the availability of event-related potential (P300), which is considered to reflect attention to stimulation, to evaluate the sense of discrepancy to the product response to the user's action. In the experiment using subjective evaluation and P300 to investigate the sense of discrepancy due to the lack of operation response (sound and vibration) to the shutter operation of the mirrorless single-lens camera, it was confirmed that P300 amplitude corresponds to the degree of the subjective sense of discrepancy. Our results showed that the P300 amplitude could evaluate the sense of discrepancy to the operation response.