Kentaro Katahira

On-line assessment of statistical learning by event-related potentials

We investigated the neural processes involved in on-line statistical learning and word segmentation. Auditory event-related potentials (ERPs) were recorded while participants were exposed to continuous, nonlinguistic auditory sequences, the elements of which were organized into tritone words that were sequenced in random order, with no silent spaces between them. After listening to three 6.6-min sessions of sequences, the participants performed a behavioral choice test, in which they were instructed to indicate the most familiar tone sequence in each test trial by pressing buttons. The participants were divided into three groups (high, middle, and low learners) based on their behavioral performance. The overall mean performance was 74.4%, indicating that the tone sequence was segmented and that the participants learned the tone words statistically. Grand-averaged ERPs showed that word onset (initial tone) elicited the largest N100 and N400 in the early learning session of high learners, but in middle learners, the word-onset effect was elicited in a later session, and there was no effect in low learners. The N400 amplitudes significantly differed between the three learning sessions in the high- and middle-learner groups. The results suggest that the N400 effect indicates not only on-line word segmentation but also the degree of statistical learning. This study provides insight into the neural mechanisms underlying on-line statistical learning processes.

Sad music induces pleasant emotion

In general, sad music is thought to cause us to experience sadness, which is considered an unpleasant emotion. As a result, the question arises as to why we listen to sad music if it evokes sadness. One possible answer to this question is that we may actually feel positive emotions when we listen to sad music. This suggestion may appear to be counterintuitive; however, in this study, by dividing musical emotion into perceived emotion and felt emotion, we investigated this potential emotional response to music. We hypothesized that felt and perceived emotion may not actually coincide in this respect: sad music would be perceived as sad, but the experience of listening to sad music would evoke positive emotions. A total of 44 participants listened to musical excerpts and provided data on perceived and felt emotions by rating 62 descriptive words or phrases related to emotions on a scale that ranged from 0 (not at all) to 4 (very much). The results revealed that the sad music was perceived to be more tragic, whereas the actual experiences of the participants listening to the sad music induced them to feel more romantic, more blithe, and less tragic emotions than they actually perceived with respect to the same music. Thus, the participants experienced ambivalent emotions when they listened to the sad music. After considering the possible reasons that listeners were induced to experience emotional ambivalence by the sad music, we concluded that the formulation of a new model would be essential for examining the emotions induced by music and that this new model must entertain the possibility that what we experience when listening to music is vicarious emotion.

Complex sequencing rules of birdsong can be explained by simple hidden Markov processes.

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 statistical properties of the complex birdsong of the Bengalese finch (Lonchura striata var. domestica). Based on manual-annotated syllable labeles, we first show that there are significant higher-order context dependencies in Bengalese finch songs, that is, which syllable appears next depends on more than one previous syllable. We then analyze acoustic features of the song and show that higher-order context dependencies can be explained using first-order hidden state transition dynamics with redundant hidden states. This model corresponds to hidden Markov models (HMMs), well known statistical models with a large range of application for time series modeling. The song annotation with these models with first-order hidden state dynamics agreed well with manual annotation, the score was comparable to that of a second-order HMM, and surpassed the zeroth-order model (the Gaussian mixture model; GMM), which does not use context information. Our results imply that the hierarchical representation with hidden state dynamics may underlie the neural implementation for generating complex behavioral sequences with higher-order dependencies.

Deterministic annealing variant of variational Bayes method

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 obtain the global optimum of the free energy and discover optimal model structure.

Categorical and dimensional perceptions in decoding emotional facial expressions

We investigated whether categorical perception and dimensional perception can co-occur while decoding emotional facial expressions. In Experiment 1, facial continua with endpoints consisting of four basic emotions (i.e., happiness–fear and anger–disgust) were created by a morphing technique. Participants rated each facial stimulus using a categorical strategy and a dimensional strategy. The results show that the happiness–fear continuum was divided into two clusters based on valence, even when using the dimensional strategy. Moreover, the faces were arrayed in order of the physical changes within each cluster. In Experiment 2, we found a category boundary within other continua (i.e., surprise–sadness and excitement–disgust) with regard to the arousal and valence dimensions. These findings indicate that categorical perception and dimensional perception co-occurred when emotional facial expressions were rated using a dimensional strategy, suggesting a hybrid theory of categorical and dimensional accounts.

A neural network model for generating complex birdsong syntax

The singing behavior of songbirds has been investigated as a model of sequence learning and production. The song of the Bengalese finch, Lonchura striata var. domestica, is well described by a finite state automaton including a stochastic transition of the note sequence, which can be regarded as a higher-order Markov process. Focusing on the neural structure of songbirds, we propose a neural network model that generates higher-order Markov processes. The neurons in the robust nucleus of the archistriatum (RA) encode each note; they are activated by RA-projecting neurons in the HVC (used as a proper name). We hypothesize that the same note included in different chunks is encoded by distinct RA-projecting neuron groups. From this assumption, the output sequence of RA is a higher-order Markov process, even though the RA-projecting neurons in the HVC fire on first-order Markov processes. We developed a neural network model of the local circuits in the HVC that explains the mechanism by which RA-projecting neurons transit stochastically on first-order Markov processes. Numerical simulation showed that this model can generate first-order Markov process song sequences.

Decision-Making Based on Emotional Images

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, the “reward value” of the decision outcome, which guided the update of value for each choice, is unknown beforehand. To estimate the reward value of emotional pictures from participants’ choice data, we used reinforcement learning models that have successfully been used in previous studies for modeling value-based decision making. Consequently, we found that the estimated reward value was asymmetric between positive and negative pictures. The negative reward value of negative pictures (relative to neutral pictures) was larger in magnitude than the positive reward value of positive pictures. This asymmetry was not observed in valence for an individual picture, which was rated by the participants regarding the emotion experienced upon viewing it. These results suggest that there may be a difference between experienced emotion and the effect of the experienced emotion on subsequent behavior. Our experimental and computational paradigm provides a novel way for quantifying how and what aspects of emotional events affect human behavior. The present study is a first step toward relating a large amount of knowledge in emotion science and in taking computational approaches to value-based decision making.

Insular neural system controls decision-making in healthy and methamphetamine-treated rats

Significance Patients with addiction have a greater tendency to engage in risk-taking behavior. However, the neural substrates responsible for these deficits remain unknown. Here we demonstrated that chronic methamphetamine-treated rats preferred high-risk/high-reward actions and assigned higher value to high returns, indicative of altered decision-making. Pharmacological studies revealed that the insular neural system controls decision-making in both healthy and methamphetamine-treated rats. We further confirmed the role of the insular cortex in decision-making using designer receptor exclusively activated by designer drug technology. Because decision-making is a cognitive process that influences many aspects of daily living and both mental and physical health, the findings of this study have broader implications. Patients suffering from neuropsychiatric disorders such as substance-related and addictive disorders exhibit altered decision-making patterns, which may be associated with their behavioral abnormalities. However, the neuronal mechanisms underlying such impairments are largely unknown. Using a gambling test, we demonstrated that methamphetamine (METH)-treated rats chose a high-risk/high-reward option more frequently and assigned higher value to high returns than control rats, suggestive of changes in decision-making choice strategy. Immunohistochemical analysis following the gambling test revealed aberrant activation of the insular cortex (INS) and nucleus accumbens in METH-treated animals. Pharmacological studies, together with in vivo microdialysis, showed that the insular neural system played a crucial role in decision-making. Moreover, manipulation of INS activation using designer receptor exclusively activated by designer drug technology resulted in alterations to decision-making. Our findings suggest that the INS is a critical region involved in decision-making and that insular neural dysfunction results in risk-taking behaviors associated with altered decision-making.

Neural basis of decision-making guided by emotional outcomes

Emotional events resulting from a choice influence an individuals 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, however, we often encounter various emotional events that affect decisions by themselves, and mapping the events to a reward or punishment is often not straightforward. In this study, we investigated the neural substrates of how such emotional decision outcomes affect subsequent decision making. By using functional magnetic resonance imaging (fMRI), we measured brain activities of humans during a stochastic decision-making task in which various emotional pictures were presented as decision outcomes. We found that pleasant pictures differentially activated the midbrain, fusiform gyrus, and parahippocampal gyrus, whereas unpleasant pictures differentially activated the ventral striatum, compared with neutral pictures. We assumed that the emotional decision outcomes affect the subsequent decision by updating the value of the options, a process modeled by reinforcement learning models, and that the brain regions representing the prediction error that drives the reinforcement learning are involved in guiding subsequent decisions. We found that some regions of the striatum and the insula were separately correlated with the prediction error for either pleasant pictures or unpleasant pictures, whereas the precuneus was correlated with prediction errors for both pleasant and unpleasant pictures.

Individual differences in heart rate variability are associated with the avoidance of negative emotional events.

Although the emotional outcome of a choice generally affects subsequent decisions, humans can inhibit the influence of emotion. Heart rate variability (HRV) has emerged as an objective measure of individual differences in the capacity for inhibitory control. In the present study, we investigated how individual differences in HRV at rest are associated with the emotional effects of the outcome of a choice on subsequent decision making using a decision-making task in which emotional pictures appeared as decision outcomes. We used a reinforcement learning model to characterize the observed behaviors according to several parameters, namely, the learning rate and the motivational value of positive and negative pictures. Consequently, we found that individuals with a lower resting HRV exhibited a greater negative motivational value in response to negative pictures, suggesting that these individuals tend to avoid negative pictures compared with individuals with a higher resting HRV.