Event Abstract Back to Event Dynamic Bayesian network model on two opposite types of sensory adaptation Yoshiyuki Sato1* and Kazuyuki Aihara1 1 The University of Tokyo, Institute of Industrial Science, Japan We can adjust our sensory and motor systems to fit the changes in our bodies and the surrounding environment. Such adaptational phenomena have been found to play an important role in facilitating an understanding of the nervous system. In recent times, a wide variety of studies have shown that human perception and action can be regarded as optimal computations to compensate for the stochastic nature of our sensory and motor systems and the environment. Specifically, some studies have shown that adaptation can also be regarded as Bayesian inference of relevant parameters. Two opposite types of adaptational effect have been observed in psychophysical experiments. For example, after a subject is exposed to audio-visual stimuli with biased temporal difference repeatedly, the subject tends to perceive the repeated stimuli as simultaneous, and this effect is called "lag adaptation". This type of adaptational effect has been observed in a very wide range of domains. Recently, an opposite type of effect was found in tactile perception, which was called "Bayesian calibration" [1]. In the experiment, repeated stimuli were more unlikely to be judged as simultaneous. However, the mechanism and function of this new type of adaptational effect is not clear. The two types of adaptational effects, the lag adaptation type and the Bayesian calibration type, can be explained as the adaptive learning of the likelihood functions [2] and the prior distributions [1], respectively, in the Bayesian inference of the stimulus properties. Therefore, these two effects are complementary to each other from, both, the viewpoint of their phenomenal properties and that of their Bayesian models. Although we constructed a model which included changes in, both, the likelihood functions and the prior distributions [3], the model is unable to provide a concrete interpretation of the determinants of the adaptational effect. Here, we construct a dynamic Bayesian network model of perceptual adaptation to provide a unified explanation of the adaptational effects. The model includes hidden parameters that determine the mean values of the likelihood functions and the prior distribution functions, which are assumed to be Gaussian functions. We assume that the adaptational effect is the result of the estimation of these parameters. We show that this model can reproduce both types of adaptational effects depending on the model parameters. With some modification to the optimal inference in this model structure, we analytically derive the parameter condition to determine the type of adaptation. By analyzing the result, we derive an interesting relationship between the adaptational type and the stimulus presentation method. Our model predicts that if adapting stimuli are random around a constant mean value, the effect would be more like the lag adaptation type, and if adapting stimuli have a random walk nature, the effect would be more like the Bayesian calibration type.