How predictability affects habituation to novelty

Ueda, Kazutaka; Sekoguchi, Takahiro; Yanagisawa, Hideyoshi

doi:10.1371/journal.pone.0237278

Cited by 11 publications

(9 citation statements)

References 37 publications

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“…10). Our previous study showed that the former represents novelty as a source of arousal potential (Yanagisawa et al, 2019;Ueda et al, 2021). I confirmed that the term also means expectation incongruity and unfamiliarity.…”

Section: Discussionsupporting

confidence: 64%

“…In our previous study (Yanagisawa et al, 2019), we defined Bayesian surprise as information gain because it represents information content gained from incoming sensory data. I considered information gain to be an index of novelty and experimentally verified that it corresponds to human surprise (i.e., high arousal emotion) induced by unexpected and novel stimuli (Sekoguchi et al, 2019;Yanagisawa et al, 2019;Miyamoto and Yanagisawa, 2021;Ueda et al, 2021). Information gain refers to information content elicited by incongruity between prior expectation and posterior perception (Friston et al, 2015).…”

Section: Novelty and Uncertaintymentioning

confidence: 99%

“…The mathematical formulation of arousal provides a precise prediction of the acceptance condition and the effect of certain factors of arousal in a general manner. Yanagisawa et al formalized arousal using the Kullback-Leibler (KL) divergence (Kullback and Leibler, 1951) of the Bayesian posterior, termed information gain, and confirmed that it corresponds to human surprise via participants' responses using event-related potential, P300, and subjective reports of surprise to novel stimuli (Yanagisawa et al, 2019;Ueda et al, 2021). The information gain is equivalent to Bayesian surprise representing visual salience (Itti and Baldi, 2009).…”

Section: Introductionmentioning

confidence: 99%

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Free-Energy Model of Emotion Potential: Modeling Arousal Potential as Information Content Induced by Complexity and Novelty

Yanagisawa

2021

Front. Comput. Neurosci.

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Appropriate levels of arousal potential induce hedonic responses (i.e., emotional valence). However, the relationship between arousal potential and its factors (e.g., novelty, complexity, and uncertainty) have not been formalized. This paper proposes a mathematical model that explains emotional arousal using minimized free energy to represent information content processed in the brain after sensory stimuli are perceived and recognized (i.e., sensory surprisal). This work mathematically demonstrates that sensory surprisal represents the summation of information from novelty and uncertainty, and that the uncertainty converges to perceived complexity with sufficient sampling from a stimulus source. Novelty, uncertainty, and complexity all act as collative properties that form arousal potential. Analysis using a Gaussian generative model shows that the free energy is formed as a quadratic function of prediction errors based on the difference between prior expectation and peak of likelihood. The model predicts two interaction effects on free energy: that between prediction error and prior uncertainty (i.e., prior variance) and that between prediction error and sensory variance. A discussion on the potential of free energy as a mathematical principle is presented to explain emotion initiators. The model provides a general mathematical framework for understanding and predicting the emotions caused by novelty, uncertainty, and complexity. The mathematical model of arousal can help predict acceptable novelty and complexity based on a target population under different uncertainty levels mitigated by prior knowledge and experience.

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Section: Discussionsupporting

confidence: 64%

Section: Novelty and Uncertaintymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Free-Energy Model of Emotion Potential: Modeling Arousal Potential as Information Content Induced by Complexity and Novelty

Yanagisawa

2021

Front. Comput. Neurosci.

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“…Experimental studies have shown that collative variables form the Wudnt curve in a variety of fields such as art (Marin et al, 2016;Miyamoto & Yanagisawa, 2021;Silvia, 2005), design (Hekkert et al, 2003;Hung & Chen, 2012), and food science (Giacalone et al, 2014). Yanagisawa et al formulated the emotion arousal caused by collative variables using information-theoretic quantities and the Wudnt curve function as a summation of reward and aversion functions of information quantities (Miyamoto & Yanagisawa, 2021;Ueda et al, 2021;Yanagisawa, 2021;Yanagisawa et al, 2019). Yanagisawa recently suggested that information-theoretic free energy represents a general form of arousal induced by collative variables (Yanagisawa, 2021).…”

Section: Introductionmentioning

confidence: 99%

Free energy model of emotional valence in dual-process perceptions

Yanagisawa

Ueda

et al. 2023

Neural Networks

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“…The habituation effect has also been modelled within multiarmed bandits, designed for the optimisation of online marketing content and interactive advertisement delivery 43 . Recent studies and models are focused on predictability and novelty 44 , visual learning 45 , or a mathematical model of emotional habituation 46 , to mention a few.…”

Section: Introductionmentioning

confidence: 99%

Habituation effect in social networks as a potential factor silently crushing influence maximisation efforts

Jankowski

2021

Sci Rep

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Information spreading processes are a key phenomenon observed within real and digital social networks. Network members are often under pressure from incoming information with different sources, such as informative campaigns for increasing awareness, viral marketing, rumours, fake news, or the results of other activities. Messages are often repeated, and such repetition can improve performance in the form of cumulative influence. Repeated messages may also be ignored due to a limited ability to process information. Learning processes are leading to the repeated messages being ignored, as their content has already been absorbed. In such cases, responsiveness decreases with repetition, and the habituation effect can be observed. Here, we analyse spreading processes while considering the habituation effect and performance drop along with an increased number of contacts. The ability to recover when reducing the number of messages is also considered. The results show that even low habituation and a decrease in propagation probability may substantially impact network coverage. This can lead to a significant reduction in the potential for a seed set selected with an influence maximisation method. Apart from the impact of the habituation effect on spreading processes, we show how it can be reduced with the use of the sequential seeding approach. This shows that sequential seeding is less sensitive to the habituation effect than single-stage seeding, and that it can be used to limit the negative impact on users overloaded with incoming messages.

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How predictability affects habituation to novelty

Cited by 11 publications

References 37 publications

Free-Energy Model of Emotion Potential: Modeling Arousal Potential as Information Content Induced by Complexity and Novelty

Free-Energy Model of Emotion Potential: Modeling Arousal Potential as Information Content Induced by Complexity and Novelty

Free energy model of emotional valence in dual-process perceptions

Habituation effect in social networks as a potential factor silently crushing influence maximisation efforts

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