Kentaro Katahira

Computational models have been used to analyze the data from behavioral experiments. One objective of the use of computational models is to estimate model parameters or internal variables for individual subjects from behavioral data. The estimates are often correlated with other variables that characterize subjects in order to investigate which computational processes are associated with specific personal or physiological traits. Although the accuracy of the estimates is important for these purp

Reinforcement learning (RL) models have been widely used to analyze the choice behavior of humans and other animals in a broad range of fields, including psychology and neuroscience. Linear regression-based models that explicitly represent how reward and choice history influences future choices have also been used to model choice behavior. While both approaches have been used independently, the relation between the two models has not been explicitly described. The aim of the present study is to

Reinforcement learning (RL) models have been broadly used in modeling the choice behavior of humans and other animals. In standard RL models, the action values are assumed to be updated according to the reward prediction error (RPE), i.e., the difference between the obtained reward and the expected reward. Numerous studies have noted that the magnitude of the update is biased depending on the sign of the RPE. The bias is represented in RL models by differential learning rates for positive and ne

Complex sequencing rules observed in birdsongs provide an opportunity to investigate the neural mechanism for generating complex sequential behaviors. To relate the findings from studying birdsongs to other sequential behaviors such as human speech and musical performance, it is crucial to characterize the statistical properties of the sequencing rules in birdsongs. However, the properties of the sequencing rules in birdsongs have not yet been fully addressed. In this study, we investigate the s

The learning rate is a key parameter in reinforcement learning that determines the extent to which novel information (outcome) is incorporated in guiding subsequent actions. Numerous studies have reported that the magnitude of the learning rate in human reinforcement learning is biased depending on the sign of the reward prediction error. However, this asymmetry can be observed as a statistical bias if the fitted model ignores the choice autocorrelation (perseverance), which is independent of th

The Variational Bayes (VB) method is widely used as an approximation of the Bayesian method. Because the VB method is a gradient algorithm, it is often trapped by poor local optimal solutions. We introduce deterministic annealing to the VB method to overcome such a local optimal problem. A temperature parameter is introduced to the free energy for controlling the annealing process deterministically. Applying the method to a mixture of Gaussian models and hidden Markov models, we show that it can

The emotional outcome of a choice affects subsequent decision making. While the relationship between decision making and emotion has attracted attention, studies on emotion and decision making have been independently developed. In this study, we investigated how the emotional valence of pictures, which was stochastically contingent on participants' choices, influenced subsequent decision making. In contrast to traditional value-based decision-making studies that used money or food as a reward, t

The songs of Bengalese finches (Lonchura striata var. domestica) have complex syntax and provide an opportunity to investigate how complex sequential behaviour emerges via the evolutionary process. In this study, we suggest that a simple mechanism, i.e. many-to-one mapping from internal states onto syllables, may underlie the emergence of apparent complex syllable sequences that have higher order history dependencies. We analysed the songs of Bengalese finches and of their wild ancestor, the whi

Emotional events resulting from a choice influence an individual's subsequent decision making. Although the relationship between emotion and decision making has been widely discussed, previous studies have mainly investigated decision outcomes that can easily be mapped to reward and punishment, including monetary gain/loss, gustatory stimuli, and pain. These studies regard emotion as a modulator of decision making that can be made rationally in the absence of emotions. In our daily lives, howeve

One of the major goals of basic studies in psychiatry is to find etiological mechanisms or biomarkers of mental disorders. A standard research strategy to pursue this goal is to compare observations of potential factors from patients with those from healthy controls. Classifications of individuals into patient and control groups are generally based on a diagnostic system, such as the <i>Diagnostic and Statistical Manual of Mental Disorders (DSM)</i> or the <i>International Classification of Dise

Computational modeling has been applied for data analysis in psychology, neuroscience, and psychiatry. One of its important uses is to infer the latent variables underlying behavior by which researchers can evaluate corresponding neural, physiological, or behavioral measures. This feature is especially crucial for computational psychiatry, in which altered computational processes underlying mental disorders are of interest. For instance, several studies employing model-based fMRI-a method for id