Large-area field application confirms the effectiveness of toxicant-infused bait for managing Helicoverpa armigera (Hübner) in maize fields.

BACKGROUND: Maize is one of the world's most important crops, so its stable production and supply is crucial for food security and socioeconomic development. The cotton bollworm, Helicoverpa armigera (Hübner), is one of the major pests in maize. We evaluated the control effect of a bio-bait, an adult attractant, combined with insecticide, a 'toxicant-infused bait', on H. armigera populations in maize fields, as well as the impact on crop yield and quality through large-scale field applications in Hebei Province, China over a period spanning 2019 to 2021. RESULT: The number of male and female H. armigera adults killed by strip application ranged from 1 to 37 and 4 to 36 per strip, respectively, of which female moths were 53%. Following the application of toxicant-infused bait, we observed a significant reduction in the populations of eggs and larvae, with the average adjusted decrease range from 58% to 63% for eggs and from 34% to 62% for larvae. The application of toxicant-infused bait also resulted in a notable reduction in the proportion of damaged maize plants, with an adjusted decline rate ranging from 59% to 69%. Concurrently, we observed an increase in yield by 4% to 8%. The concentration of aflatoxin in harvested maize grains was significantly reduced from an initial level of 1.24 to 0.1 ug/kg. CONCLUSION: By applying toxicant-infused bait, there was a significant reduction in the population of H. armigera adults and their offspring, resulting in an improved yield and quality of maize. Toxicant-infused bait has great application potential in the integrated pest management of H. armigera. © 2023 Society of Chemical Industry.

Explaining reversal learning deficits in anxiety with electrophysiological evidence.

Reversal learning is a crucial aspect of behavioral flexibility that plays a significant role in environmental adaptation and development. While previous studies have established a link between anxiety and impaired reversal learning ability, the underlying mechanisms behind this association remain unclear. This study employed a probabilistic reversal learning task with electroencephalographic recording to investigate these mechanisms. Participants were divided into two groups based on their scores on Spielberger's State-Trait Anxiety Inventory: high trait-anxiety (HTA) and low trait-anxiety (LTA), consisting of 50 individuals in each group. The results showed that the HTA group had poorer reversal learning performance than the LTA group, including a lower tendency to shift to the new optimal option after rule reversals (reversal-shift). The study also examined event-related potentials elicited by reversals and found that although the N1 (related to attention allocation), feedback-related negativity (FRN: related to belief updating), and P3 (related to response inhibition) were all sensitive to the grouping factor, only the FRN elicited by reversal-shift mediated the relationship between anxiety and the number/reaction time of reversal-shift. From these findings, we suggest that abnormalities in belief updating may contribute to the impaired reversal learning performance observed in anxious individuals. In our opinion, this study sheds light on potential targets for interventions aimed at improving behavioral flexibility in anxious individuals.

Event-related potentials in response to early terminated and completed sequential decision-making.

The process of outcome evaluation effectively navigates subsequent choices in humans. However, it is largely unclear how people evaluate decision outcomes in a sequential scenario, as well as the neural mechanisms underlying this process. To address this research gap, the study employed a sequential decision task in which participants were required to make a series of choices in each trial, with the option to terminate their choices. Based on participants' decisions, two outcome patterns were classified: the "reached" condition and the "unreached" condition, and the event-related potentials (ERPs) were recorded. Further, in the unreached condition, we investigated how the distance (i.e., the position interval between the actual outcome and potential outcome) modulated outcome evaluation. Behavioral data showed a higher emotion rating when people got a reward rather than a loss (i.e., the reached condition), while the opposite was true in the unreached condition. ERP results showed a larger feedback-related negativity (FRN), a smaller P3, and a larger late positive potential (LPP) when people got a loss compared to a reward. Importantly, a hierarchical processing pattern was found in the unreached condition: people processed separately the potential outcome and the distance at the early stage, manifested in the FRN amplitude; subsequently, the brain focused on the distance-a lower distance elicited an enhanced P3 amplitude. Finally, the potential outcome and distance were processed interactively in the LPP amplitude. Overall, these findings shed light on the neural underpinnings of outcome evaluation in sequential decision-making.

Prefrontal control of social influence in risk decision making.

To optimize our decisions, we may change our mind by utilizing social information. Here, we examined how changes of mind were modulated by Social Misalignment Sensitivity (SMS), egocentric tendency, and decision preferences in a decision-making paradigm including both risk and social information. Combining functional magnetic resonance imaging with computational modeling, we showed that both SMS and egocentric tendency modulated changes of mind under the influence of social information. While SMS was represented in the dorsal anterior cingulate cortex (dACC) and superior parietal gyrus (SPG) in the socially aligned situation, a distributed brain network was activated in the misaligned condition, including not only the dACC and SPG but also superior frontal gyrus and precuneus. These results suggest that SMS is related to a monitoring brain system, the scope of which varies according to the level of misalignment with social majority. The dorsolateral prefrontal cortex selectively interacted with SMS among the participants with a low switching threshold, indicating that its regulation on SMS may be sensitive to inter-individual variation. Our findings highlight the predominant roles of SMS and the prefrontal control system towards changes of mind under social influence.

The hierarchical sensitivity to social misalignment during decision-making under uncertainty.

Social misalignment occurs when a person's attitudes and opinions deviate from those of others. We investigated how individuals react to social misalignment in risky (outcome probabilities are known) or ambiguous (outcome probabilities are unknown) decision contexts. During each trial, participants played a forced-choice gamble, and they observed the decisions of four other players after they made a tentative decision, followed by an opportunity to keep or change their initial decision. Behavioral and event-related potential data were collected. Behaviorally, the stronger the participants' initial preference, the less likely they were to switch their decisions, whereas the more their decisions were misaligned with the majority, the more likely they were to switch. Electrophysiological results showed a hierarchical processing pattern of social misalignment. Misalignment was first detected binarily (i.e. match/mismatch) at an early stage, as indexed by the N1 component. During the second stage, participants became sensitive to low levels of misalignment, which were indexed by the feedback-related negativity. The degree of social misalignment was processed in greater detail, as indexed by the P3 component. Moreover, such hierarchical neural sensitivity is generalizable across different decision contexts (i.e. risky and ambiguous). These findings demonstrate a fine-grained neural sensitivity to social misalignment during decision-making under uncertainty.

Electrophysiological indexes of option characteristic processing.

Decision making is vital to human behavior and can be divided into multiple stages including option assessment, behavioral output, and feedback evaluation. Studying how people evaluate option characteristics in the option assessment stage would provide important knowledge on human decision making. Using the event-related potential (ERP) method, the present study investigated the neural mechanism of evaluating two types of option characteristics (i.e., reward magnitude and degree of uncertainty) in the temporal dimension. Thirty-five volunteers participated in a monetary gambling task, where they either accepted or rejected gambles. The ERP results showed a double dissociation pattern, with the early P1 component being sensitive to magnitude but insensitive to degree of uncertainty, while both the N2 and P3 components showed the opposite pattern. The results suggest that these two fundamental option features are assessed rapidly and separately in the human brain. Specifically, small magnitude elicited a larger P1 than did large magnitude, indicating that the perceptual and attentional processing of options is modulated by magnitude. Both the N2 and P3 amplitudes evoked by the risky context were larger than those evoked by the ambiguous one, reflecting that more cognitive conflicts and resources are involved in the former condition. Furthermore, the P1, but not the N2 or P3, amplitude was sensitive to decisions, suggesting that early attentional processes may contribute to human decision making. These findings may provide insight into the temporal mechanisms of option characteristic processing.

Wavelet-based method for removing global physiological noise in functional near-infrared spectroscopy.

Functional near-infrared spectroscopy (fNIRS) is a fast-developing non-invasive functional brain imaging technology widely used in cognitive neuroscience, clinical research and neural engineering. However, it is a challenge to effectively remove the global physiological noise in the fNIRS signal. The global physiological noise in fNIRS arises from multiple physiological origins in both superficial tissues and the brain. It has complex temporal, spatial and frequency characteristics, casting significant influence on the results. In the present study, we developed a novel wavelet-based method for fNIRS global physiological noise removal. The method is data-driven and does not rely on any additional hardware or subjective noise component selection procedure. It consists of two steps. Firstly, we use wavelet transform coherence to automatically detect the time-frequency points contaminated by the global physiological noise. Secondly, we decompose the fNIRS signal by using the wavelet transform, and then suppress the wavelet energy of the contaminated time-frequency points. Finally, we transform the signal back to a time series. We validated the method by using simulation and real data at both task- and resting-state. The results showed that our method can effectively remove the global physiological noise from the fNIRS signal and improve the spatial specificity of the task activation and the resting-state functional connectivity pattern.