Distinct basal ganglia contributions to learning from implicit and explicit value signals in perceptual decision-making

Metacognitive confidence and affect - two sides of the same coin?

Decision confidence is a prototypical metacognitive representation that is thought to approximate the probability that a decision is correct. The perception of being correct has also been associated with affective valence such that being correct feels more positive and being mistaken more negative. This suggests that, similarly to confidence, affective valence reflects the probability that a decision is correct. However, both fields of research have seen very little interaction. Here, we test if affect, similarly to confidence reflects probability that a decision is correct in two perceptual decision-making experiments where we compare the relationships of theoretically relevant variables (e.g. evidence, accuracy, and expectancy) with both confidence and affect ratings. The findings indicate that confidence and affect ratings are similarly sensitive to changes in accuracy, evidence, and expectancy, indicating that both track the subjective probability that a decision is correct. We identify various mechanisms that can explain these results. We also envision future research for clarifying the role of cognitive and affective aspects of metacognition relying on deeper integration of the respective research fields.

Wearable Haptics for Orthotropic Actuation Based on Perpendicularly Nested Auxetic SMA Knotting

Smart wearable tactile systems, designed to deliver different types of touch feedback on human skin, can significantly improve engagement through diverse actuation patterns in virtual or augmented reality environments. Here, a perpendicularly nested auxetic wearable haptic interface is reported for orthotropically decoupled multimodal actuation (WHOA), capable of producing diverse tactile feedback modes with 3D sensory perception. WHOA incorporates shape memory alloy wires that are intricately knotted into an auxetic structure oriented along orthotropic dual axes. Its perpendicularly nested auxetic structure enables orthotropic actuation, allowing independent expansion and contraction along both x and y-axes, as confirmed by force-strain and displacement-time performance tests. Additionally, the perylene coating provides orthogonal electrical isolation to WHOA, allowing for stripe-specific localized actuation and enabling multiple tactile feedback modes. As an orthotropic wearable haptic interface, WHOA distinguishes between x-axis and y-axis directions and ultimately delivers multi-dimensional information regarding movements in 3D space through tactile feedback. As a result, when worn on the foot or arm, WHOA naturally delivers spatiotemporal tactile information to the user, facilitating navigation and teleoperation with 3D sensory perception.

Noise exposure-induced the cerebral alterations: From emerging evidence to antioxidant-mediated prevention and treatment

It's well acknowledged that noise exposure has become a major environmental risk factor of public health. The previous standpoint holds that the main harm of noise exposure is to cause hearing loss of human. However, in the past two decades a large number of studies have linked the noise exposure to various cerebral changes. In this review, we summarized that noise exposure led to cerebral changes through breaking the redox balance, inducing neuroinflammation and neuronal apoptosis and altering the neurotransmission in numerous brain areas, including cortex, thalamus, hippocampus, amygdala, striatum and cerebellum. Those cerebral changes finally result in a variety of disorders, such as tinnitus, anxiety, depression, cognitive impairment and motor dysfunction. Furthermore, we reviewed several antioxidants, such as resveratrol, vitamin C, curcumin, N-acetylcysteine and α-asarone, and highlighted their protective mechanisms against noise exposure, aiming to provide a promising strategy to prevent and treat noise exposure-induced diseases. Taken together, noise exposure induces various cerebral changes and further leads to disorders in the central nervous system, which can be ameliorated by the treatment with antioxidants.

Confidence control for efficient behaviour in dynamic environments

Signatures of confidence emerge during decision-making, implying confidence may be of functional importance to decision processes themselves. We formulate an extension of sequential sampling models of decision-making in which confidence is used online to actively moderate the quality and quantity of evidence accumulated for decisions. The benefit of this model is that it can respond to dynamic changes in sensory evidence quality. We highlight this feature by designing a dynamic sensory environment where evidence quality can be smoothly adapted within the timeframe of a single decision. Our model with confidence control offers a superior description of human behaviour in this environment, compared to sequential sampling models without confidence control. Using multivariate decoding of electroencephalography (EEG), we uncover EEG correlates of the model's latent processes, and show stronger EEG-derived confidence control is associated with faster, more accurate decisions. These results support a neurobiologically plausible framework featuring confidence as an active control mechanism for improving behavioural efficiency.

A low-dimensional approximation of optimal confidence

Human decision making is accompanied by a sense of confidence. According to Bayesian decision theory, confidence reflects the learned probability of making a correct response, given available data (e.g., accumulated stimulus evidence and response time). Although optimal, independently learning these probabilities for all possible data combinations is computationally intractable. Here, we describe a novel model of confidence implementing a low-dimensional approximation of this optimal yet intractable solution. This model allows efficient estimation of confidence, while at the same time accounting for idiosyncrasies, different kinds of biases and deviation from the optimal probability correct. Our model dissociates confidence biases resulting from the estimate of the reliability of evidence by individuals (captured by parameter α), from confidence biases resulting from general stimulus independent under and overconfidence (captured by parameter β). We provide empirical evidence that this model accurately fits both choice data (accuracy, response time) and trial-by-trial confidence ratings simultaneously. Finally, we test and empirically validate two novel predictions of the model, namely that 1) changes in confidence can be independent of performance and 2) selectively manipulating each parameter of our model leads to distinct patterns of confidence judgments. As a tractable and flexible account of the computation of confidence, our model offers a clear framework to interpret and further resolve different forms of confidence biases.