Connected in Bad Times and in Good Times: Empathy Induces Stable Social Closeness.

Humans need social closeness to prosper. There is evidence that empathy can induce social closeness. However, it remains unclear how empathy-related social closeness is formed and how stable it is as time passes. We applied an acquisition-extinction paradigm combined with computational modeling and fMRI, to investigate the formation and stability of empathy-related social closeness. Female participants observed painful stimulation of another person with high probability (acquisition) and low probability (extinction) and rated their closeness to that person. The results of two independent studies showed increased social closeness in the acquisition block that resisted extinction in the extinction block. Providing insights into underlying mechanisms, reinforcement learning modeling revealed that the formation of social closeness is based on a learning signal (prediction error) generated from observing another's pain, whereas maintaining social closeness is based on a learning signal generated from observing another's pain relief. The results of a reciprocity control study indicate that this feedback recalibration is specific to learning of empathy-related social closeness. On the neural level, the recalibration of the feedback signal was associated with neural responses in anterior insula and adjacent inferior frontal gyrus and the bilateral superior temporal sulcus/temporoparietal junction. Together, these findings show that empathy-related social closeness generated in bad times, that is, empathy with the misfortune of another person, transfers to good times and thus may form one important basis for stable social relationships.

Neural and computational underpinnings of biased confidence in human reinforcement learning.

While navigating a fundamentally uncertain world, humans and animals constantly evaluate the probability of their decisions, actions or statements being correct. When explicitly elicited, these confidence estimates typically correlates positively with neural activity in a ventromedial-prefrontal (VMPFC) network and negatively in a dorsolateral and dorsomedial prefrontal network. Here, combining fMRI with a reinforcement-learning paradigm, we leverage the fact that humans are more confident in their choices when seeking gains than avoiding losses to reveal a functional dissociation: whereas the dorsal prefrontal network correlates negatively with a condition-specific confidence signal, the VMPFC network positively encodes task-wide confidence signal incorporating the valence-induced bias. Challenging dominant neuro-computational models, we found that decision-related VMPFC activity better correlates with confidence than with option-values inferred from reinforcement-learning models. Altogether, these results identify the VMPFC as a key node in the neuro-computational architecture that builds global feeling-of-confidence signals from latent decision variables and contextual biases during reinforcement-learning.

The hidden arrow in the FedEx logo: Do we really unconsciously "see" it?

The FedEx logo makes clever use of figure-ground ambiguity to create an "invisible" arrow in the background space between "E" and "x". Most designers believe the hidden arrow can convey an unconscious impression of speed and precision about the FedEx brand, which may influence subsequent behavior. To test this assumption, we designed similar images with hidden arrows to serve as endogenous (but camouflaged) directional cues in a Posner's orienting task, where a cueing effect would suggest subliminal processing of the hidden arrow. Overall, we observed no cue congruency effect, unless the arrow is explicitly highlighted (Experiment 4). However, there was a general effect of prior knowledge: when people were under pressure to suppress background information, those who knew about the arrow could do so faster in all congruence conditions (i.e., neutral, congruent, incongruent), although they fail to report seeing the arrow during the experiment. This was true in participants from North America who had heard of the FedEx arrow before (Experiment 1 & 3), and also in our Taiwanese sample who were just informed of such design (Experiment 2). These results can be well explained by the Biased Competition Model in figure-ground research, and together suggest: (1) people do not unconsciously perceive the FedEx arrow, at least not enough to exhibit a cueing effect in attention, but (2) knowing about the arrow can fundamentally change the way we visually process these negative-space logos in the future, making people react faster to images with negative space regardless of the hidden content.

The elusive effects of incidental anxiety on reinforcement-learning.

Anxiety is a common affective state, characterized by the subjectively unpleasant feelings of dread over an anticipated event. Anxiety is suspected to have important negative consequences on cognition, decision-making, and learning. Yet, despite a recent surge in studies investigating the specific effects of anxiety on reinforcement-learning, no coherent picture has emerged. Here, we investigated the effects of incidental anxiety on instrumental reinforcement-learning, while addressing several issues and defaults identified in a focused literature review. We used a rich experimental design, featuring both a learning and a transfer phase, and a manipulation of outcomes valence (gains vs losses). In two variants (N = 2 × 50) of this experimental paradigm, incidental anxiety was induced with an established threat-of-shock paradigm. Model-free results show that incidental anxiety effects seem limited to a small, but specific increase in postlearning performance measured by a transfer task. A comprehensive modeling effort revealed that, irrespective of the effects of anxiety, individuals give more weight to positive than negative outcomes, and tend to experience the omission of a loss as a gain (and vice versa). However, in line with results from our targeted literature survey, isolating specific computational effects of anxiety on learning per se proved to be challenging. Overall, our results suggest that learning mechanisms are more complex than traditionally presumed, and raise important concerns about the robustness of the effects of anxiety previously identified in simple reinforcement-learning studies. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Robust valence-induced biases on motor response and confidence in human reinforcement learning.

In simple instrumental-learning tasks, humans learn to seek gains and to avoid losses equally well. Yet, two effects of valence are observed. First, decisions in loss-contexts are slower. Second, loss contexts decrease individuals' confidence in their choices. Whether these two effects are two manifestations of a single mechanism or whether they can be partially dissociated is unknown. Across six experiments, we attempted to disrupt the valence-induced motor bias effects by manipulating the mapping between decisions and actions and imposing constraints on response times (RTs). Our goal was to assess the presence of the valence-induced confidence bias in the absence of the RT bias. We observed both motor and confidence biases despite our disruption attempts, establishing that the effects of valence on motor and metacognitive responses are very robust and replicable. Nonetheless, within- and between-individual inferences reveal that the confidence bias resists the disruption of the RT bias. Therefore, although concomitant in most cases, valence-induced motor and confidence biases seem to be partly dissociable. These results highlight new important mechanistic constraints that should be incorporated in learning models to jointly explain choice, reaction times and confidence.

Dynamic combination of sensory and reward information under time pressure.

When making choices, collecting more information is beneficial but comes at the cost of sacrificing time that could be allocated to making other potentially rewarding decisions. To investigate how the brain balances these costs and benefits, we conducted a series of novel experiments in humans and simulated various computational models. Under six levels of time pressure, subjects made decisions either by integrating sensory information over time or by dynamically combining sensory and reward information over time. We found that during sensory integration, time pressure reduced performance as the deadline approached, and choice was more strongly influenced by the most recent sensory evidence. By fitting performance and reaction time with various models we found that our experimental results are more compatible with leaky integration of sensory information with an urgency signal or a decision process based on stochastic transitions between discrete states modulated by an urgency signal. When combining sensory and reward information, subjects spent less time on integration than optimally prescribed when reward decreased slowly over time, and the most recent evidence did not have the maximal influence on choice. The suboptimal pattern of reaction time was partially mitigated in an equivalent control experiment in which sensory integration over time was not required, indicating that the suboptimal response time was influenced by the perception of imperfect sensory integration. Meanwhile, during combination of sensory and reward information, performance did not drop as the deadline approached, and response time was not different between correct and incorrect trials. These results indicate a decision process different from what is involved in the integration of sensory information over time. Together, our results not only reveal limitations in sensory integration over time but also illustrate how these limitations influence dynamic combination of sensory and reward information.

Contrast Enhancement Algorithm Based on Gap Adjustment for Histogram Equalization.

Image enhancement methods have been widely used to improve the visual effects of images. Owing to its simplicity and effectiveness histogram equalization (HE) is one of the methods used for enhancing image contrast. However, HE may result in over-enhancement and feature loss problems that lead to unnatural look and loss of details in the processed images. Researchers have proposed various HE-based methods to solve the over-enhancement problem; however, they have largely ignored the feature loss problem. Therefore, a contrast enhancement algorithm based on gap adjustment for histogram equalization (CegaHE) is proposed. It refers to a visual contrast enhancement algorithm based on histogram equalization (VCEA), which generates visually pleasing enhanced images, and improves the enhancement effects of VCEA. CegaHE adjusts the gaps between two gray values based on the adjustment equation, which takes the properties of human visual perception into consideration, to solve the over-enhancement problem. Besides, it also alleviates the feature loss problem and further enhances the textures in the dark regions of the images to improve the quality of the processed images for human visual perception. Experimental results demonstrate that CegaHE is a reliable method for contrast enhancement and that it significantly outperforms VCEA and other methods.

Visual Contrast Enhancement Algorithm Based on Histogram Equalization.

Image enhancement techniques primarily improve the contrast of an image to lend it a better appearance. One of the popular enhancement methods is histogram equalization (HE) because of its simplicity and effectiveness. However, it is rarely applied to consumer electronics products because it can cause excessive contrast enhancement and feature loss problems. These problems make the images processed by HE look unnatural and introduce unwanted artifacts in them. In this study, a visual contrast enhancement algorithm (VCEA) based on HE is proposed. VCEA considers the requirements of the human visual perception in order to address the drawbacks of HE. It effectively solves the excessive contrast enhancement problem by adjusting the spaces between two adjacent gray values of the HE histogram. In addition, VCEA reduces the effects of the feature loss problem by using the obtained spaces. Furthermore, VCEA enhances the detailed textures of an image to generate an enhanced image with better visual quality. Experimental results show that images obtained by applying VCEA have higher contrast and are more suited to human visual perception than those processed by HE and other HE-based methods.

Neural mechanisms for integrating prior knowledge and likelihood in value-based probabilistic inference.

In Bayesian decision theory, knowledge about the probabilities of possible outcomes is captured by a prior distribution and a likelihood function. The prior reflects past knowledge and the likelihood summarizes current sensory information. The two combined (integrated) form a posterior distribution that allows estimation of the probability of different possible outcomes. In this study, we investigated the neural mechanisms underlying Bayesian integration using a novel lottery decision task in which both prior knowledge and likelihood information about reward probability were systematically manipulated on a trial-by-trial basis. Consistent with Bayesian integration, as sample size increased, subjects tended to weigh likelihood information more compared with prior information. Using fMRI in humans, we found that the medial prefrontal cortex (mPFC) correlated with the mean of the posterior distribution, a statistic that reflects the integration of prior knowledge and likelihood of reward probability. Subsequent analysis revealed that both prior and likelihood information were represented in mPFC and that the neural representations of prior and likelihood in mPFC reflected changes in the behaviorally estimated weights assigned to these different sources of information in response to changes in the environment. Together, these results establish the role of mPFC in prior-likelihood integration and highlight its involvement in representing and integrating these distinct sources of information.