The cross-race effect in automatic facial expression recognition violates measurement invariance.

Emotion has been a subject undergoing intensive research in psychology and cognitive neuroscience over several decades. Recently, more and more studies of emotion have adopted automatic rather than manual methods of facial emotion recognition to analyze images or videos of human faces. Compared to manual methods, these computer-vision-based, automatic methods can help objectively and rapidly analyze a large amount of data.

Unavoidable social contagion of false memory from robots to humans.

Many of us interact with voice- or text-based conversational agents daily, but these conversational agents may unintentionally retrieve misinformation from human knowledge databases, confabulate responses on their own, or purposefully spread disinformation for political purposes. Does such misinformation or disinformation become part of our memory to further misguide our decisions? If so, can we prevent humans from suffering such social contagion of false memory? Using a social contagion of memory paradigm, here, we precisely controlled a social robot as an example of these emerging conversational agents. In a series of two experiments (Σ = 120), the social robot occasionally misinformed participants prior to a recognition memory task.

Sequential processing facilitates Hebb repetition learning in visuospatial domains.

Exposure to the same information improves auditory/verbal short-term memory performance, but such improvement is not always observed in visual short-term memory. In this study, we demonstrate that sequential processing makes visuospatial repetition learning efficient in a paradigm that employs a similar design previously used for an auditory/verbal domain. When we presented sets of color patches simultaneously in Experiments 1-4, recall accuracy did not increase with repetition; however, once color patches were presented sequentially in Experiment 5, accuracy did increase rapidly with repetition, even when participants engaged in articulatory suppression.

Role of visual awareness on semantic integration of sequentially presented words: An fMRI study.

Reading comprehension requires the semantic integration of words across space and time. However, it remains unclear whether comprehension requires visual awareness for such semantic integration. Compared to earlier studies that investigated semantic integration indirectly from its priming effect, we used functional magnetic resonance imaging (fMRI) to directly examine the processes of semantic integration with or without visual awareness.

Person-identifying brainprints are stably embedded in EEG mindprints.

Electroencephalography (EEG) signals measured under fixed conditions have been exploited as biometric identifiers. However, what contributes to the uniqueness of one's brain signals remains unclear. In the present research, we conducted a multi-task and multi-week EEG study with ten pairs of monozygotic (MZ) twins to examine the nature and components of person-identifiable brain signals.

What part of the brain is involved in graphic design thinking in landscape architecture?

Graphic design thinking is a key skill for landscape architects, but little is known about the links between the design process and brain activity. Based on Goel's frontal lobe lateralization hypothesis (FLLH), we used functional magnetic resonance imaging (fMRI) to scan the brain activity of 24 designers engaging in four design processes-viewing, copy drawing, preliminary ideas, and refinement-during graphic design thinking. The captured scans produced evidence of dramatic differences between brain activity when copying an existing graphic and when engaging in graphic design thinking.

Identifying Mild Cognitive Impairment by Using Human-Robot Interactions.

Background: Mild cognitive impairment (MCI), which is common in older adults, is a risk factor for dementia. Rapidly growing health care demand associated with global population aging has spurred the development of new digital tools for the assessment of cognitive performance in older adults.

Objective: To overcome methodological drawbacks of previous studies (e.

Social Robots for Evaluating Attention State in Older Adults.

Sustained attention is essential for older adults to maintain an active lifestyle, and the deficiency of this function is often associated with health-related risks such as falling and frailty. The present study examined whether the well-established age-effect on reducing mind-wandering, the drift to internal thoughts that are seen to be detrimental to attentional control, could be replicated by using a robotic experimenter for older adults who are not as familiar with online technologies. A total of 28 younger and 22 older adults performed a Sustained Attention to Response Task (SART) by answering thought probes regarding their attention states and providing confidence ratings for their own task performances.

Personal Resilience Can Be Well Estimated from Heart Rate Variability and Paralinguistic Features during Human-Robot Conversations.

Mental health is as crucial as physical health, but it is underappreciated by mainstream biomedical research and the public. Compared to the use of AI or robots in physical healthcare, the use of AI or robots in mental healthcare is much more limited in number and scope. To date, psychological resilience-the ability to cope with a crisis and quickly return to the pre-crisis state-has been identified as an important predictor of psychological well-being but has not been commonly considered by AI systems (e.

Lithium for schizophrenia: supporting evidence from a 12-year, nationwide health insurance database and from Akt1-deficient mouse and cellular models.

Accumulating evidence suggests AKT1 and DRD2-AKT-GSK3 signaling involvement in schizophrenia. AKT1 activity is also required for lithium, a GSK3 inhibitor, to modulate mood-related behaviors. Notably, GSK3 inhibitor significantly alleviates behavioral deficits in Akt1 female mice, whereas typical/atypical antipsychotics have no effect.

Christians and Buddhists Are Comparably Happy on Twitter: A Large-Scale Linguistic Analysis of Religious Differences in Social, Cognitive, and Emotional Tendencies.

Are different religions associated with different social, cognitive, and emotional tendencies? Although major world religions are known to encourage social interactions and help regulate emotions, it is less clear to what extent adherents of various religions differ in these dimensions in daily life. We thus carried out a large-scale sociolinguistic analysis of social media messages of Christians and Buddhists living in the United States. After controlling for age and gender effects on linguistic patterns, we found that Christians used more social words and fewer cognitive words than Buddhists.

Perceiving Self, Others, and Events Through a Religious Lens: Mahayana Buddhists vs. Christians.

Are all religions essentially the same? Are believers of different religions heading in the same mental direction? To answer these questions from a sociopsychological perspective, we compared social sensitivity and causal attribution styles between Mahayana Buddhists, who practice unbiased love and compassion toward every being, and Christians, who pursue a union with God. Despite a similar cultural background, sex ratio, age distribution, socioeconomic status, and fluid intelligence level, these two religious groups in Taiwan showed opposite tendencies when inferring the mental states of others - as religiosity increased, the theory of mind ability increased in Mahayana Buddhists but decreased in Christians. Furthermore, these two religious groups showed opposite tendencies of attributional style - as religiosity increased, self-serving bias decreased in Buddhists but increased in Christians.

Multistability of the Brain Network for Self-other Processing.

Early fMRI studies suggested that brain areas processing self-related and other-related information were highly overlapping. Hypothesising functional localisation of the cortex, researchers have tried to locate "self-specific" and "other-specific" regions within these overlapping areas by subtracting suspected confounding signals in task-based fMRI experiments. Inspired by recent advances in whole-brain dynamic modelling, we instead explored an alternative hypothesis that similar spatial activation patterns could be associated with different processing modes in the form of different synchronisation patterns.

Developing the Own-Race Advantage in 4-, 6-, and 9-Month-Old Taiwanese Infants: A Perceptual Learning Perspective.

Previous infant studies on the other-race effect have favored the view, or declined sensitivities to rarely exposed other-race faces. Here we wish to provide an alternative possibility, , manifested by improved sensitivity for frequently exposed own-race faces in the first year of life. Using the familiarization/visual-paired comparison paradigm, we presented 4-, 6-, and 9-month-old Taiwanese infants with oval-cropped Taiwanese, Caucasian, Filipino faces, and each with three different manipulations of increasing task difficulty (i.

Enhanced spontaneous oscillations in the supplementary motor area are associated with sleep-dependent offline learning of finger-tapping motor-sequence task.

Sleep is beneficial for various types of learning and memory, including a finger-tapping motor-sequence task. However, methodological issues hinder clarification of the crucial cortical regions for sleep-dependent consolidation in motor-sequence learning. Here, to investigate the core cortical region for sleep-dependent consolidation of finger-tapping motor-sequence learning, while human subjects were asleep, we measured spontaneous cortical oscillations by magnetoencephalography together with polysomnography, and source-localized the origins of oscillations using individual anatomical brain information from MRI.

Strategic cognitive sequencing: a computational cognitive neuroscience approach.

We address strategic cognitive sequencing, the "outer loop" of human cognition: how the brain decides what cognitive process to apply at a given moment to solve complex, multistep cognitive tasks. We argue that this topic has been neglected relative to its importance for systematic reasons but that recent work on how individual brain systems accomplish their computations has set the stage for productively addressing how brain regions coordinate over time to accomplish our most impressive thinking. We present four preliminary neural network models.

Assembling old tricks for new tasks: a neural model of instructional learning and control.

We can learn from the wisdom of others to maximize success. However, it is unclear how humans take advice to flexibly adapt behavior. On the basis of data from neuroanatomy, neurophysiology, and neuroimaging, a biologically plausible model is developed to illustrate the neural mechanisms of learning from instructions.

Task attention facilitates learning of task-irrelevant stimuli.

Attention plays a fundamental role in visual learning and memory. One highly established principle of visual attention is that the harder a central task is, the more attentional resources are used to perform the task and the smaller amount of attention is allocated to peripheral processing because of limited attention capacity. Here we show that this principle holds true in a dual-task setting but not in a paradigm of task-irrelevant perceptual learning.

Cortical dynamics of contextually cued attentive visual learning and search: spatial and object evidence accumulation.

How do humans use target-predictive contextual information to facilitate visual search? How are consistently paired scenic objects and positions learned and used to more efficiently guide search in familiar scenes? For example, humans can learn that a certain combination of objects may define a context for a kitchen and trigger a more efficient search for a typical object, such as a sink, in that context. The ARTSCENE Search model is developed to illustrate the neural mechanisms of such memory-based context learning and guidance and to explain challenging behavioral data on positive-negative, spatial-object, and local-distant cueing effects during visual search, as well as related neuroanatomical, neurophysiological, and neuroimaging data. The model proposes how global scene layout at a first glance rapidly forms a hypothesis about the target location.

ARTSCENE: A neural system for natural scene classification.

How do humans rapidly recognize a scene? How can neural models capture this biological competence to achieve state-of-the-art scene classification? The ARTSCENE neural system classifies natural scene photographs by using multiple spatial scales to efficiently accumulate evidence for gist and texture. ARTSCENE embodies a coarse-to-fine Texture Size Ranking Principle whereby spatial attention processes multiple scales of scenic information, from global gist to local textures, to learn and recognize scenic properties. The model can incrementally learn and rapidly predict scene identity by gist information alone, and then accumulate learned evidence from scenic textures to refine this hypothesis.