Habitual isomaltulose intake reduces arterial stiffness associated with postprandial hyperglycemia in middle-aged and elderly people: a randomized controlled trial.

Endothelin-1 (ET-1), produced by vascular endothelial cells, plays a pivotal role in the regulation of vascular tone. Isomaltulose, a naturally occurring sweetener and structural isomer of sucrose, reduces postprandial hyperglycemia, but its effect on arteriosclerosis due to hyperglycemia is unknown. The effects of 12 weeks of isomaltulose administration on ET-1 levels, a peptide that regulates arterial stiffness, blood pressure, and vascular tone, were tested before and after an oral glucose tolerance test. Fifty-four healthy middle-aged and older adults (30 men and 24 women) were divided into two groups: (1) a 25 g isomaltulose jelly drink intake group (Group I, 27 participants, mean age 55 ± 1 years) and (2) a sucrose jelly drink intake group (Group S, 27 participants, mean age 55 ± 1 years), each consuming isomaltulose or sucrose daily for 12 weeks, and a randomized, controlled study was conducted. Participants visited the laboratory before the intervention and 4, 8, and 12 weeks after the intervention to measure carotid-femoral (cf) and brachial-ankle (ba) pulse wave velocity (PWV), systolic blood pressure (BP), plasma glucose (PG), insulin, and ET-1 levels before and 60 and 120 min after a 75-g OGTT. baPWV, and ET-1 levels before intervention were significantly increased after 75-g OGTT compared to before 75-g OGTT in both groups (p < 0.05). The post-intervention baPWV, and ET-1 levels were significantly increased after 75-g OGTT in Group S compared to before 75-g OGTT (p < 0.05), whereas no significant changes were observed in Group I. These results suggest that consumption of isomaltulose, which has a lower GI than sucrose, is more effective in preventing the increases in systemic arterial stiffness associated with postprandial hyperglycemia in healthy middle-aged and older adults.

Children's scale errors are a natural consequence of learning to associate objects with actions: A computational model.

Young children sometimes attempt an action on an object, which is inappropriate because of the object size-they make scale errors. Existing theories suggest that scale errors may result from immaturities in children's action planning system, which might be overpowered by increased complexity of object representations or developing teleofunctional bias. We used computational modelling to emulate children's learning to associate objects with actions and to select appropriate actions, given object shape and size. A computational Developmental Deep Model of Action and Naming (DDMAN) was built on the dual-route theory of action selection, in which actions on objects are selected via a direct (nonsemantic or visual) route or an indirect (semantic) route. As in case of children, DDMAN produced scale errors: the number of errors was high at the beginning of training and decreased linearly but did not disappear completely. Inspection of emerging object-action associations revealed that these were coarsely organized by shape, hence leading DDMAN to initially select actions based on shape rather than size. With experience, DDMAN gradually learned to use size in addition to shape when selecting actions. Overall, our simulations demonstrate that children's scale errors are a natural consequence of learning to associate objects with actions.

The effects of isomaltulose ingestion on gastric parameters and cycling performance in young men.

BACKGROUND/OBJECTIVE: Isomaltulose is a disaccharide with a low glycaemic index and plays a role in maintaining postprandial glucose. The maintenance of glucose availability during prolonged exercise has been shown to enhance exercise performance. The present study compared the effects of pre-exercise isomaltulose versus maltodextrin ingestion on gastric parameters and cycling performance in young men. METHODS: Fourteen young men (mean ±â€¯S.D., age 23 ±â€¯2 years) performed 60 min of continuous cycling at 75% of maximum heart rate followed by a 15-min exercise performance test while ingesting a 500-mL of water containing 100 mg of 13C-sodium acetate with either 50 g of isomaltulose or 50 g of maltodextrin. Gastrointestinal discomfort was assessed periodically using an 11-point visual analogue scale throughout the study. The gastric emptying rate was evaluated periodically with the 13C-sodium acetate breath test. For the exercise performance test, participants were instructed to pedal a cycle ergometer, exerting as much effort as possible at a self-selected pace. RESULTS: Plasma glucose and insulin concentrations measured at 30 min after ingestion were lower in the isomaltulose trial than in the maltodextrin trial. There were no differences in mean power output during the exercise performance test, gastric emptying rate or the subjective feelings of gastrointestinal discomfort between both trials. CONCLUSION: Under the current exercise protocol, pre-exercise ingestion of isomaltulose compared with maltodextrin provided no additional benefit relative to gastric emptying or aerobic exercise performance. Both isomaltulose and maltodextrin ingestion did not influence gastrointestinal distress during 60 min of cycling and performance test.

Infant's action skill dynamically modulates parental action demonstration in the dyadic interaction.

When interacting with infants, human adults modify their behaviours in an exaggerated manner. Previous studies have demonstrated that infant-directed modification affects the infant's behaviour. However, little is known about how infant-directed modification is elicited during infant-parent interaction. We investigated whether and how the infant's behaviour affects the mother's action during an interaction. We recorded three-dimensional information of cup movements while mothers demonstrated a cup-nesting task during interaction with their infants aged 11 to 13 months. Analyses revealed that spatial characteristics of the mother's task demonstration clearly changed depending on the infant's object manipulation. In particular, the variance in the distance that the cup was moved decreased after the infant's cup nesting and increased after the infant's task-irrelevant manipulation (e.g. cup banging). This pattern was not observed for mothers with 6- to 8-month-olds, who do not have the fine motor skill to perform the action. These results indicate that the infant's action skill dynamically affects the infant-directed action and suggest that the mother is sensitive to the infant's potential to learn a novel action. A video abstract of this article can be viewed at: https://www.youtube.com/watch?v=VNS2IHwLIhg&feature=youtu.be.

Emergence of integrated behaviors through direct optimization for homeostasis.

Homeostasis is a self-regulatory process, wherein an organism maintains a specific internal physiological state. Homeostatic reinforcement learning (RL) is a framework recently proposed in computational neuroscience to explain animal behavior. Homeostatic RL organizes the behaviors of autonomous embodied agents according to the demands of the internal dynamics of their bodies, coupled with the external environment. Thus, it provides a basis for real-world autonomous agents, such as robots, to continually acquire and learn integrated behaviors for survival. However, prior studies have generally explored problems pertaining to limited size, as the agent must handle observations of such coupled dynamics. To overcome this restriction, we developed an advanced method to realize scaled-up homeostatic RL using deep RL. Furthermore, several rewards for homeostasis have been proposed in the literature. We identified that the reward definition that uses the difference in drive function yields the best results. We created two benchmark environments for homeostasis and performed a behavioral analysis. The analysis showed that the trained agents in each environment changed their behavior based on their internal physiological states. Finally, we extended our method to address vision using deep convolutional neural networks. The analysis of a trained agent revealed that it has visual saliency rooted in the survival environment and internal representations resulting from multimodal input.

Early development of saliency-driven attention through object manipulation.

In the first years of life, infants progressively develop attention selection skills to gather information from visually clustered environments. As young as newborns, infants are sensitive to the distinguished differences in color, orientation, and luminance, which are the components of visual saliency. However, we know little about how saliency-driven attention emerges and develops socially through everyday free-viewing experiences. The present work assessed the saliency change in infants' egocentric scenes and investigated the impacts of manual engagements on infant object looking in the interactive context of object play. Thirty parent-infant dyads, including infants in two age groups (younger: 3- to 6-month-old; older: 9- to 12-month-old), completed a brief session of object play. Infants' looking behaviors were recorded by the head-mounted eye-tracking gear, and both parents' and infants' manual actions on objects were annotated separately for analyses. The present findings revealed distinct attention mechanisms that underlie the hand-eye coordination between parents and infants and within infants during object play: younger infants are predominantly biased toward the characteristics of the visual saliency accompanying the parent's handled actions on the objects; on the other hand, older infants gradually employed more attention to the object, regardless of the saliency in view, as they gained more self-generated manual actions. Taken together, the present work highlights the tight coordination between visual experiences and sensorimotor competence and proposes a novel dyadic pathway to sustained attention that social sensitivity to parents' hands emerges through saliency-driven attention, preparing infants to focus, follow, and steadily track moving targets in free-flow viewing activities.

Comprehensive intervention for reducing stigma of autism spectrum disorders: Incorporating the experience of simulated autistic perception and social contact.

Stigma of autism spectrum disorders (ASD) is a crucial factor leading to a lower quality of life for individuals with ASD. This research evaluated an intervention effect for the stigma through an experience of simulated autistic visual perception and video-based social contact. The intervention was conducted as an experiential workshop for the ASD simulator. Participants (N = 217; 156 women, 61 men; a mean age of 41.3) voluntarily attended the workshop. In the workshop, they received a short lecture on autistic perception, a simulated experience using the ASD simulator, a narrative video of individuals with ASD, and a group discussion. They completed the multidimensional attitudes scale, which was developed to measure four attitudes toward ASD: negative affect, calm, cognitions, and behaviors. The attitudes were measured three times: the period of registration with the workshop, during participation in the workshop, and six weeks after the workshop. With regard to the measure during participation in the workshop, attendees responded to the attitudes at the beginning or end of the workshop, to confirm whether attitudes changes were attributed to the effectiveness of the intervention or artifacts resulting from participation in the workshop. A significant reduction was observed in negative affective attitudes at the end of the workshop and persisted even six weeks afterward, even though not observed in calm, cognitive, and behavioral attitudes. Our findings suggest that implementation of our intervention can bring about reduction of the stigma associated with ASD. Further studies with a random sampling method are needed to validate its generalizability and elaborate the components of the intervention.

Non-autistic persons modulate their speech rhythm while talking to autistic individuals.

How non-autistic persons modulate their speech rhythm while talking to autistic (AUT) individuals remains unclear. We investigated two types of phonological characteristics: (1) the frequency power of each prosodic, syllabic, and phonetic rhythm and (2) the dynamic interaction among these rhythms using speech between AUT and neurotypical (NT) individuals. Eight adults diagnosed with AUT (all men; age range, 24-44 years) and eight age-matched non-autistic NT adults (three women, five men; age range, 23-45 years) participated in this study. Six NT and eight AUT respondents were asked by one of the two NT questioners (both men) to share their recent experiences on 12 topics. We included 87 samples of AUT-directed speech (from an NT questioner to an AUT respondent), 72 of NT-directed speech (from an NT questioner to an NT respondent), 74 of AUT speech (from an AUT respondent to an NT questioner), and 55 of NT speech (from an NT respondent to an NT questioner). We found similarities between AUT speech and AUT-directed speech, and between NT speech and NT-directed speech. Prosody and interactions between prosodic, syllabic, and phonetic rhythms were significantly weaker in AUT-directed and AUT speech than in NT-directed and NT speech, respectively. AUT speech showed weaker dynamic processing from higher to lower phonological bands (e.g. from prosody to syllable) than NT speech. Further, we found that the weaker the frequency power of prosody in NT and AUT respondents, the weaker the frequency power of prosody in NT questioners. This suggests that NT individuals spontaneously imitate speech rhythms of the NT and AUT interlocutor. Although the speech sample of questioners came from just two NT individuals, our findings may suggest the possibility that the phonological characteristics of a speaker influence those of the interlocutor.

The effects of palatinose on attention and cerebral blood flow in healthy adults: A randomized double-blind placebo-controlled crossover study.

OBJECTIVE: The primary energy source for the brain is glucose, and a continuous supply is required for the brain to work longer. This study aimed to verify the effects of palatinose on attention and cerebral blood flow in healthy adults. METHODS: This randomized, double-blind, placebo-controlled crossover study included 64 healthy Japanese adults. Participants performed the Digit Vigilance Task (DVT) 60 min pre-ingestion (14:00) and 0 (15:00), 60 (16:00), 120 (17:00), and 180 (18:00) min after ingestion of 10 g of either palatinose or glucose. Cerebral blood flow was measured using a wearable 2CH functional near-infrared spectrometer (fNIRS) during each DVT. The participants underwent the Uchida-Kraepelin (UK) test between each DVT to control for fatigue. RESULTS: DVT reaction times with palatinose intake were significantly shorter than those with glucose intake at 16:00, 17:00, and 18:00 (p = 0.0015, p < 0.001, and p < 0.001, respectively). The change in cerebral blood flow as a function of total hemoglobin level was significantly higher in the palatinose group than in the glucose group (p = 0.018). Regarding the post-UK mood questionnaire, "physically fatigued" and "annoyed" were significantly lower in the palatinose intake group compared to the glucose intake group at 17:00 (p = 0.0445 and p = 0.0318, respectively). Furthermore, "physically fatigued" was significantly lower, and "seriously" was higher in the palatinose intake compared to the glucose intake group at 18:00 (p = 0.00652 and p < 0.001, respectively). CONCLUSION: These findings suggest that 10 g of palatinose has favorable effects on attention and cerebral blood flow. TRIAL REGISTRATION: UMIN000046182.

Pre-exercise isomaltulose intake affects carbohydrate oxidation reduction during endurance exercise and maximal power output in the subsequent Wingate test.

BACKGROUND: Ingestion of low-glycemic index (GI) isomaltulose (ISO) not only suppresses subsequent carbohydrate (CHO) oxidation but also inversely retains more CHO after prolonged endurance exercise. Therefore, ISO intake may affect anaerobic power output after prolonged endurance exercise. This study aimed to clarify the time course of CHO utilization during endurance exercise after a single intake of ISO or sucrose (SUC) and the anaerobic power output at the end of endurance exercise. METHODS: After an intake of either ISO or SUC, 13 athletes were kept at rest for 60 min. Thereafter, they performed a 90-min of treadmill running at their individual target level of % [Formula: see text]max. During the experimental session, the expired gas was recorded, and the energy expenditure (EE) and CHO oxidation rate were estimated. Immediately after 90 min of running, a 30-s Wingate test was performed, and the maximal anaerobic power output was compared between the ISO and SUC conditions. RESULTS: The percentage of CHO-derived EE increased rapidly after CHO intake and then decreased gradually throughout the experiment. The slopes of the regression lines calculated from the time course in the CHO-derived EE were significantly (negatively) larger in the SUC condition (-19.4 ± 9.6 [%/h]) than in the ISO condition (-13.3 ± 7.5 [%/h]). Furthermore, the maximal power output in the Wingate test immediately after the endurance exercise was significantly higher in the ISO condition than in the SUC condition (peak power: 12.0 ± 0.6 vs. 11.5 ± 0.9 [W/kg]). CONCLUSION: Compared with SUC intake, ISO intake does not produce an abrupt decline in the percentage of CHO-derived EE during prolonged endurance exercise; it remains relatively high until the final exercise phase. Additionally, anaerobic power output at the end of the exercise, largely contributed by anaerobic glycolysis, was greater after ISO intake than after SUC intake.

Simulating Developmental and Individual Differences of Drawing Behavior in Children Using a Predictive Coding Model.

Predictive coding has recently been proposed as a mechanistic approach to explain human perception and behavior based on the integration of perceptual stimuli (bottom-up information) and the predictions about the world based on previous experience (top-down information). However, the gap between the computational accounts of cognition and evidence of behavioral studies remains large. In this study, we used a computational model of drawing based on the mechanisms of predictive coding to systematically investigate the effects of the precision of top-down and bottom-up information when performing a drawing completion task. The results indicated that sufficient precision of both signals was required for the successful completion of the stimuli and that a reduced precision in either sensory or prediction (i.e., prior) information led to different types of atypical drawing behavior. We compared the drawings produced by our model to a dataset of drawings created by children aged between 2 and 8 years old who drew on incomplete drawings. This comparison revealed that a gradual increase in children's precision of top-down and bottom-up information as they develop effectively explains the observed change of drawing style from scribbling toward representational drawing. Furthermore, individual differences that are prevalent in children's drawings, might arise from different developmental pathways regarding the precision of these two signals. Based on these findings we propose a theory of how both general and individual development of drawing could be explained in a unified manner within the framework of predictive coding.

Quantifying developmental and individual differences in spontaneous drawing completion among children.

This study investigated how children's drawings can provide insights into their cognitive development. It can be challenging to quantify the diversity of children's drawings across their developmental stages as well as between individuals. This study observed children's representational drawing ability by conducting a completion task where children could freely draw on partially drawn objects, and quantitatively analyzed differences in children's drawing tendencies across age and between individuals. First, we conducted preregistered analyses, based on crowd-sourced adult ratings, to investigate the differences of drawing style with the age and autistic traits of the children, where the latter was inspired by reports of atypical drawing among children with autism spectrum disorder (ASD). Additionally, the drawings were quantified using feature representations extracted with a deep convolutional neural network (CNN), which allowed an analysis of the drawings at different perceptual levels (i.e., local or global). Findings revealed a decrease in scribbling and an increase in completion behavior with increasing age. However, no correlation between drawing behavior and autistic traits was found. The network analysis demonstrated that older children adapted to the presented stimuli in a more adult-like manner than younger children. Furthermore, ways to quantify individual differences in how children adapt to the presented stimuli are explored. Based on the predictive coding theory as a unified theory of how perception and behavior might emerge from integrating sensations and predictions, we suggest that our analyses may open up new possibilities for investigating children's cognitive development.

Atypical Auditory Perception Caused by Environmental Stimuli in Autism Spectrum Disorder: A Systematic Approach to the Evaluation of Self-Reports.

Recent studies have revealed that atypical sensory perception is common in individuals with autism spectrum disorder (ASD) and is considered a potential cause of social difficulties. Self-reports by individuals with ASD have provided great insights into atypical perception from the first-person point of view and indicated its dependence on the environment. This study aimed to investigate the patterns and environmental causes of atypical auditory perception in individuals with ASD. Qualitative data from subject reports are inappropriate for statistical analysis, and reporting subjective sensory experiences is not easy for every individual. To cope with such challenges, we employed audio signal processing methods to simulate the potential patterns of atypical auditory perception. The participants in our experiment were able to select and adjust the strength of the processing methods to manipulate the sounds in the videos to match their experiences. Thus, the strength of atypical perception was recorded quantitatively and then analyzed to assess its correlation with the audio-visual stimuli contained in the videos the participants observed. In total, 22 participants with ASD and 22 typically developed (TD) participants were recruited for the experiment. The results revealed several common patterns of atypical auditory perception: Louder sounds perceived in a quiet environment, noise perception induced by intense and unsteady audio-visual stimuli, and echo perception correlated with movement and variation in sound level. The ASD group reported atypical perceptions more frequently than the control group. However, similar environmental causes were shared by the ASD and TD groups. The results help us infer the potential neural and physiological mechanisms of sensory processing in ASD.

Imitation and mirror systems in robots through Deep Modality Blending Networks.

Learning to interact with the environment not only empowers the agent with manipulation capability but also generates information to facilitate building of action understanding and imitation capabilities. This seems to be a strategy adopted by biological systems, in particular primates, as evidenced by the existence of mirror neurons that seem to be involved in multi-modal action understanding. How to benefit from the interaction experience of the robots to enable understanding actions and goals of other agents is still a challenging question. In this study, we propose a novel method, deep modality blending networks (DMBN), that creates a common latent space from multi-modal experience of a robot by blending multi-modal signals with a stochastic weighting mechanism. We show for the first time that deep learning, when combined with a novel modality blending scheme, can facilitate action recognition and produce structures to sustain anatomical and effect-based imitation capabilities. Our proposed system, which is based on conditional neural processes, can be conditioned on any desired sensory/motor value at any time step, and can generate a complete multi-modal trajectory consistent with the desired conditioning in one-shot by querying the network for all the sampled time points in parallel avoiding the accumulation of prediction errors. Based on simulation experiments with an arm-gripper robot and an RGB camera, we showed that DMBN could make accurate predictions about any missing modality (camera or joint angles) given the available ones outperforming recent multimodal variational autoencoder models in terms of long-horizon high-dimensional trajectory predictions. We further showed that given desired images from different perspectives, i.e. images generated by the observation of other robots placed on different sides of the table, our system could generate image and joint angle sequences that correspond to either anatomical or effect-based imitation behavior. To achieve this mirror-like behavior, our system does not perform a pixel-based template matching but rather benefits from and relies on the common latent space constructed by using both joint and image modalities, as shown by additional experiments. Moreover, we showed that mirror learning (in our system) does not only depend on visual experience and cannot be achieved without proprioceptive experience. Our experiments showed that out of ten training scenarios with different initial configurations, the proposed DMBN model could achieve mirror learning in all of the cases where the model that only uses visual information failed in half of them. Overall, the proposed DMBN architecture not only serves as a computational model for sustaining mirror neuron-like capabilities, but also stands as a powerful machine learning architecture for high-dimensional multi-modal temporal data with robust retrieval capabilities operating with partial information in one or multiple modalities.

The world seems different in a social context: A neural network analysis of human experimental data.

Human perception and behavior are affected by the situational context, in particular during social interactions. A recent study demonstrated that humans perceive visual stimuli differently depending on whether they do the task by themselves or together with a robot. Specifically, it was found that the central tendency effect is stronger in social than in non-social task settings. The particular nature of such behavioral changes induced by social interaction, and their underlying cognitive processes in the human brain are, however, still not well understood. In this paper, we address this question by training an artificial neural network inspired by the predictive coding theory on the above behavioral data set. Using this computational model, we investigate whether the change in behavior that was caused by the situational context in the human experiment could be explained by continuous modifications of a parameter expressing how strongly sensory and prior information affect perception. We demonstrate that it is possible to replicate human behavioral data in both individual and social task settings by modifying the precision of prior and sensory signals, indicating that social and non-social task settings might in fact exist on a continuum. At the same time, an analysis of the neural activation traces of the trained networks provides evidence that information is coded in fundamentally different ways in the network in the individual and in the social conditions. Our results emphasize the importance of computational replications of behavioral data for generating hypotheses on the underlying cognitive mechanisms of shared perception and may provide inspiration for follow-up studies in the field of neuroscience.

Abnormal cortical responses elicited by audiovisual movies in patients with autism spectrum disorder with atypical sensory behavior: A magnetoencephalographic study.

BACKGROUND: Atypical sensory behavior disrupts behavioral adaptation in children with autism spectrum disorder (ASD); however, neural correlates of sensory dysfunction using magnetoencephalography (MEG) remain unclear. METHOD: We used MEG to measure the cortical activation elicited by visual (uni)/audiovisual (multisensory) movies in 46 children (7-14 years) were included in final analysis: 13 boys with atypical audiovisual behavior in ASD (AAV+), 10 without this condition, and 23 age-matched typically developing boys. RESULTS: The AAV+ group demonstrated an increase in the cortical activation in the bilateral insula in response to unisensory movies and in the left occipital, right superior temporal sulcus (rSTS), and temporal regions to multisensory movies. These increased responses were correlated with severity of the sensory impairment. Increased theta-low gamma oscillations were observed in the rSTS in AAV+. CONCLUSION: The findings suggest that AAV is attributed to atypical neural networks centered on the rSTS.

Active Inference Through Energy Minimization in Multimodal Affective Human-Robot Interaction.

During communication, humans express their emotional states using various modalities (e.g., facial expressions and gestures), and they estimate the emotional states of others by paying attention to multimodal signals. To ensure that a communication robot with limited resources can pay attention to such multimodal signals, the main challenge involves selecting the most effective modalities among those expressed. In this study, we propose an active perception method that involves selecting the most informative modalities using a criterion based on energy minimization. This energy-based model can learn the probability of the network state using energy values, whereby a lower energy value represents a higher probability of the state. A multimodal deep belief network, which is an energy-based model, was employed to represent the relationships between the emotional states and multimodal sensory signals. Compared to other active perception methods, the proposed approach demonstrated improved accuracy using limited information in several contexts associated with affective human-robot interaction. We present the differences and advantages of our method compared to other methods through mathematical formulations using, for example, information gain as a criterion. Further, we evaluate performance of our method, as pertains to active inference, which is based on the free energy principle. Consequently, we establish that our method demonstrated superior performance in tasks associated with mutually correlated multimodal information.

Statistical Properties of Musical Creativity: Roles of Hierarchy and Uncertainty in Statistical Learning.

Creativity is part of human nature and is commonly understood as a phenomenon whereby something original and worthwhile is formed. Owing to this ability, humans can produce innovative information that often facilitates growth in our society. Creativity also contributes to esthetic and artistic productions, such as music and art. However, the mechanism by which creativity emerges in the brain remains debatable. Recently, a growing body of evidence has suggested that statistical learning contributes to creativity. Statistical learning is an innate and implicit function of the human brain and is considered essential for brain development. Through statistical learning, humans can produce and comprehend structured information, such as music. It is thought that creativity is linked to acquired knowledge, but so-called "eureka" moments often occur unexpectedly under subconscious conditions, without the intention to use the acquired knowledge. Given that a creative moment is intrinsically implicit, we postulate that some types of creativity can be linked to implicit statistical knowledge in the brain. This article reviews neural and computational studies on how creativity emerges within the framework of statistical learning in the brain (i.e., statistical creativity). Here, we propose a hierarchical model of statistical learning: statistically chunking into a unit (hereafter and shallow statistical learning) and combining several units (hereafter and deep statistical learning). We suggest that deep statistical learning contributes dominantly to statistical creativity in music. Furthermore, the temporal dynamics of perceptual uncertainty can be another potential causal factor in statistical creativity. Considering that statistical learning is fundamental to brain development, we also discuss how typical versus atypical brain development modulates hierarchical statistical learning and statistical creativity. We believe that this review will shed light on the key roles of statistical learning in musical creativity and facilitate further investigation of how creativity emerges in the brain.

World model learning and inference.

Understanding information processing in the brain-and creating general-purpose artificial intelligence-are long-standing aspirations of scientists and engineers worldwide. The distinctive features of human intelligence are high-level cognition and control in various interactions with the world including the self, which are not defined in advance and are vary over time. The challenge of building human-like intelligent machines, as well as progress in brain science and behavioural analyses, robotics, and their associated theoretical formalisations, speaks to the importance of the world-model learning and inference. In this article, after briefly surveying the history and challenges of internal model learning and probabilistic learning, we introduce the free energy principle, which provides a useful framework within which to consider neuronal computation and probabilistic world models. Next, we showcase examples of human behaviour and cognition explained under that principle. We then describe symbol emergence in the context of probabilistic modelling, as a topic at the frontiers of cognitive robotics. Lastly, we review recent progress in creating human-like intelligence by using novel probabilistic programming languages. The striking consensus that emerges from these studies is that probabilistic descriptions of learning and inference are powerful and effective ways to create human-like artificial intelligent machines and to understand intelligence in the context of how humans interact with their world.

Acute effects of difference in glucose intake on arterial stiffness in healthy subjects.

BACKGROUND: Post-prandial hyperglycemia is associated with higher cardiovascular risk, which causes arterial stiffening and impaired function. Although post-prandial increases in blood glucose are proportional to the level of intake, the acute effects of different glucose intakes on arterial stiffness have not been fully characterized. The present study aimed to determine the acute effects of differences in glucose intake on arterial stiffness. METHODS: Six healthy middle-aged and elderly individuals (mean age, 60.0 ± 12.1 years) were orally administered 15, 20, and 25 g of glucose on separate days in a randomized, controlled, cross-over fashion. Brachial-ankle pulse wave velocity, heart-brachial pulse wave velocity, cardio-ankle vascular index, brachial and ankle blood pressure, heart rate, and blood glucose and serum insulin concentrations before and 30, 60, and 90 min after glucose ingestion were measured. RESULTS: Compared to baseline, brachial-ankle pulse wave velocity was higher at 30, 60 and 90 min after ingestion of 25 g glucose, and higher at 90 min after ingestion of 20 g glucose, but at no time points after ingestion of 15 g. Cardio-ankle vascular index was higher at 60 min than at baseline after ingestion of 25 g glucose, but not after ingestion of 15 or 20 g. CONCLUSIONS: These results suggest that brachial-ankle pulse wave velocity and cardio-ankle vascular index is affected by the quantity of glucose ingested. Proposed presently is that glucose intake should be reduced at each meal to avoid increases in brachial-ankle pulse wave velocity and cardio-ankle vascular index during acute hyperglycemia.