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The UK’s biggest banks are gearing up for yet another fight with regulators over how they’ll compensate consumers who were missold car loans — even after they set aside an additional £1.5 billion to resolve the saga in recent weeks.

Barclays Plc on Wednesday said it had roughly quadrupled the amount of cash it has set aside to compensate customers who were impacted by the scandal. One day later, Lloyds Banking Group Plc saw its pre-tax profit in the third quarter slump 36% because of an additional £800 million charge tied to the matter.

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Introduction

As an important cognitive factor, attention plays an important role in pain processing.^1–5 Distraction through synchronized activities may reduce pain sensitivity. According to the theory of limited cognitive resources in psychology,⁶ the two-way interaction between pain and cognition can be explained from “bottom-up” and “top-down” mechanisms.⁷ Acute pain triggers bottom-up automated attentional capture through thalamo-insular pathways that prioritize cognitive resources to initiate protective responses. Experimental evidence has shown that nociceptive input can divert attention from the current task to the nociceptive stimuli. Irrelevant nociceptive stimuli interfere with cognitive performance by competing for limited attentional resources.⁸ Simultaneously, top-down attention control can modulate pain perception through distraction. When attention is allocated to the tasks without pain, the processing of nociceptive signals is suppressed due to resource competition.^9–11

Notably, working memory (WM) constitutes an essential component of executive function, optimizing attention by maintaining memory traces of attention sets and shielding goal-directed processes from interference during task execution.¹¹ It is essential in modulating the attention–pain interaction, primarily by balancing cognitive resources between nociceptive distraction and goal-directed attention.^12–16 The prefrontal cortex (PFC), involved in both executive functioning and pain processing, may experience competition for limited neural resources in pain distraction.¹⁷ Effective cognitive control of pain requires diverting attention from nociceptive stimuli and maintaining task focus through WM engagement. Different WM load may directly modulate efficiency in attention regulation of pain through resource allocation mechanism.¹⁸ Recent studies have shown that high cognitive load increases demand on attention, reducing available resources for processing extraneous stimuli and preserving task performance.^19,20 Although Deldar Z et al²⁰ have demonstrated that performing high load WM tasks may increase the allocation of attention resources and reduce pain perception, they also have reported that high load tasks may diminish the contribution of WM to distraction analgesia due to factors such as cognitive effort and ceiling effect, ultimately reducing the distraction analgesic effect. The competitive occupation of attention resources by cognitive fatigue can lead to increased distraction and a reduced ability to alleviate pain.²¹ When WM capacity reaches the limit under high-load conditions, it results in a ceiling effect on WM-based pain inhibition.²⁰ These factors collectively imply a nonlinear relationship between WM load and distraction analgesia. The effect of WM load on distraction analgesia and its underlying mechanism remains further investigation.

Neurophysiological studies have shown that distraction analgesia involves decreased neural activity in pain-processing regions such as the primary somatosensory cortex (S1) and insular,^22,23 and increased activations in PFC and periaqueductal gray matter.⁹ However, no studies have yet investigated functional networks of pain-related brain regions in distraction analgesia. Functional near-infrared spectroscopy (fNIRS) is a non-invasive brain imaging technique primarily used to monitor real-time changes in cortical blood oxygen metabolism to reflect neural activity. Compared to fMRI, fNIRS better accommodates comfortable posture and resists motion artifacts, enabling real-time monitoring of pain responses in this study.²⁴ Time-series analysis of blood-oxygen-dependent signals can capture neural activity of the brain during the process of distraction analgesia modulation. Additionally, by combining neural activity and functional connectivity between brain regions, the cortical regulatory mechanism of cognitive load dependence in distraction analgesia can be deeply understood.²⁵

This study aims to verify the effectiveness of distraction analgesia at both the neural and behavioral levels, investigate the effect of n-back tasks during different WM load on pain perception and to reveal the pattern and cortical mechanism, potentially providing a neuroscientific basis for clinical cognitive-based analgesia interventions.

Material and Methods

Participants

Forty healthy participants (23 females and 17 males; 21–26 years old) were enrolled by social media platforms. Participants had a normal corrected or unaided vision and were right-handed. The exclusion criteria included (1) cardiovascular/respiratory disorders, chronic/acute pain conditions, auditory impairments, or neuropsychiatric diagnoses; (2) pregnancy status, regular drugs use, or chronic medication regimens (excluding contraceptive pills); (3) acute sleep deprivation (<6 hours before the experiment) or recent analgesic/anti-inflammatory drugs administration (<12 hours before the experiment); (4) cognitive and sensory disorders; (5) caffeine intake (<2 hours before the experiment) or intense physical activity on the day of the experiment.

Experimental Design

This experiment employed a mixed design. In the first part of the session, all participants completed a pain calibration followed by a pain-rating task (pain task). In the second part of the session, a 2 × 2 within-subject design was used to assess the distraction effect on pain perception. Participants performed an n-back WM task during two cognitive load conditions: high load (2-back) and low load (0-back), while receiving pain stimuli (with or without laser stimuli) to their right hand. All participants completed tasks in five experimental conditions: pain (laser stimuli without n-back), 0-back (without laser stimuli), 2-back (without laser stimuli), 0-back with pain, and 2-back with pain. Pain intensity ratings and cognitive performance were recorded throughout the experiment.

Experimental Procedures

At the beginning of the study, all participants completed a brief demographic questionnaire. Then, participants underwent a pain calibration procedure. Laser stimuli were delivered to the dorsum of the right hand. After each laser stimulus, participants were instructed to verbally rate the perceived pain intensity using a Numerical Ratings Scale (NRS) ranging from 0 to 10, where 0 represented no pain and 10 represented the most unbearable pain.^26–28 Two different levels of stimuli intensity were determined for each participant, eliciting low (NRS = 4) and high (NRS = 6) pain. After pain calibration, participants were instructed to complete the pain ratings task with fNIRS recording. In the second part of the experiment, participants were required to complete both the n-back task and the n-back with pain task. They were instructed to perform the WM paradigms with fNIRS recording. The n-back task, with different WM load levels (0-back and 2-back), was used to engage WM. Before the task, participants were informed to practice the task and receive real-time feedback of their accuracy to ensure full understanding of the task. During the n-back task with pain (0-back and 2-back tasks with laser stimuli), they completed the n-back task, while pain stimuli were delivered to their dorsum of their right hand. Participants performed the n-back task and the n-back with pain task in a random order. The total duration of the experiment was approximately 50 minutes. The experimental design and procedure are shown in Figure 1.

Figure 1 Experimental design and procedure. Notes: The numbers inside the squares represent the presentation of random number stimuli. The numbers above the squares indicate the order in which the number stimuli appear (eg 1,3,4). The laser is presented once for every three number stimuli shown.

Pain Task

Noxious laser stimuli (radiant heat) were generated using an infrared Nd:YAP laser (Electronical Engineering, Italy) with a wavelength of 1.34 μm and a pulse duration of 4 milliseconds. The He-Ne laser pulse was transmitted through optical fibers and focused by a lens to a spot approximately 7 mm in diameter,²⁹ synchronously activating nociceptive nerve endings in the superficial skin layers. The noxious stimuli were delivered at 2 individualized energy levels (NRS = 4 and 6) to a circular region (3 cm in diameter) on the dorsum of the right hand. A total of 30 laser stimuli (15 for each intensity) were delivered in a pseudo-randomized sequence, with an interval ranging from 20 to 21 seconds. Participants were uncertain of the exact number of pain stimuli they would experience, and the pain perception induced by laser stimuli was variable. The experiment procedure is shown in Figure 1. Each trial started with a 7–8 second fixation, followed by a short noxious stimulus delivered to the dorsum of the hand. After a 10-second interval, participants were asked to rate the perceived pain intensity using a standardized 0–10 NRS scale. To minimize the risk of nociceptor fatigue or sensitization, the laser target site was manually shifted at least 1 cm in a random direction after each stimulus.

N-Back Task

The WM task used a modified n-back paradigm, with two levels of WM load manipulation (0-back and 2-back). As a validated measure of the central executive system of WM, n-back paradigm reflects core WM functions, including attention control and updating.^30,31 It is widely utilized across psychiatric, neurological, and cognitive research domains.^32,33 The n-back paradigm was designed using E-Prime 3.0 (Psychology Software Tools, Pittsburgh, USA). Random numbers ranging from 0 to 9 were presented on the screen. In the 0-back task, participants were instructed to identify whether the current number was “0” by pressing “Q” for “yes” and “W” for “no”. In the 2-back task, they were required to determine whether the current number matched the one presented before two trials, again pressing “Q” for “yes” and “W” for “no”. The 0-back and 2-back task were repeated in a 0-2-0-2-0-2 sequence in three blocks, with each block containing 30 number stimuli. Thirty percent of the stimuli were target stimuli. The stimuli were presented for 1 second, and participants were given 2 seconds to respond. A 15-second rest interval was implemented between task blocks.

N-Back with Pain Task

The distraction paradigm comprised four conditions in total, each presented in two blocks: 0-back with low laser stimuli, 0-back with high laser stimuli, 2-back with low laser stimuli, and 2-back with high laser stimuli. The order was randomized. The duration of each block was fixed at 60 seconds. Before each block, a cue indicating the upcoming task (0-back or 2-back) was presented for 2 seconds. Each number in the task sequence appeared on the screen for 1000 milliseconds (ms), followed by a 1000 ms blank interval. A total of 10 pain stimuli were delivered during each block. At the end of each block, a 3-second instruction was shown, instructing participants to provide a verbal rating of the perceived pain intensity using a 0–10 NRS.

fNIRS Data Acquisition

In this experiment, a fNIRS system (NirScan, Danyang Huichuang Medical Equipment Co., Ltd., Jiangsu, China) was utilized to assess cortical response from 53 channels. Three different wavelengths (730 nm, 808 nm, and 850 nm) were employed, with a sampling rate of 11 Hz to capture the near-infrared spectroscopy signals. Concentration changes in oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HbR) were obtained based on the modified Beer-Lambert law. The optical system consisted of 18 sources and 18 detectors, with adjacent sources and detectors spaced 3 cm apart. The connection between sources and detectors was defined as a channel. The coordinates of each probe were determined based on the International 10–20 system and the coordinate localization feature of SPM software, referencing both the coordinates and the optical electrode positions on the Montreal Neurological Institute (MNI) brain template. Figure 2A shows the complete arrangement of the fNIRS probes and channels, while Figure 2B shows the location of the 41 selected channels out of 53, covering 9 brain regions of interest: right and left secondary somatosensory cortex (RS2; LS2), right and left premotor cortex (RPMC; LPMC), right and left primary somatosensory cortex (RSM1; LSM1), right and left dorsolateral prefrontal cortex (RDLPFC; LDLPFC), and anterior prefrontal cortex (aPFC).

Figure 2 fNIRS channel settings. Abbreviations: aPFC, anterior prefrontal cortex; DLPFC, dorsolateral prefrontal cortex; PMC, premotor cortex; SM1, primary somatosensory cortex; S2, secondary somatosensory cortex; L, left; R, right; S, source; D, detector. Notes: fNIRS 36-probe and 53-channel montage placement (A) and the distribution of fNIRS 53-channel in S2, PMC, SM1, DLPFC and aPFC (B). The numbers in (A) represent 18 light sources and detectors. The numbers in (B) represent 53 channels.

fNIRS Preprocessing and Analysis

In this study, fNIRS signals were preprocessed and analyzed using NirSpark software (HuiChuang, China) and matlab (Mathworks, MA, USA). First, we conducted a preliminary inspection of the raw data, identifying and removing channels with poor signal quality. Signal quality was assessed based on the coefficient of variation (cv), where values ≤5 were considered good, 5–20 were considered acceptable, and >20 were considered poor. Next, the raw light intensity data series were converted into optical density (OD) changes. We applied spline interpolation to correct motion artifacts in the channels. Motion artifacts typically manifest as pulsatile or abrupt jumps caused by relative movement between the scalp and the probe.³⁴ We applied a bandpass filter (0.01–0.2 Hz) to remove physiological noise, such as respiration, cardiac activity, and low-frequency signal drift. Subsequently, based on the modified Beer-Lambert law, we calculated changes in oxygenated hemoglobin (HbO), deoxygenated hemoglobin (HbR), and total hemoglobin (HbT) concentrations.

During the analysis phase, we focused on data from 41 channels that covered nine ROIs, which are significantly associated with pain based on previous studies. Specifically, multiple studies have demonstrated that brain regions such as S1, S2, M1, aPFC, DLPFC and PMC are critical for pain processing.³⁵ Previous studies on distraction analgesia have confirmed that during the process of distraction, activation in brain regions such as the insula and S1 decreases, while neural responses in regions including the DLPFC and parietal lobe show increased activation and reduced functional connectivity.³⁶ Meanwhile, brain function studies related to analgesia have also reported that when pain perception decreases, the activation level of SM1 and its functional connectivity with the DLPFC/S1 are significantly reduced.³⁷ Different time windows were set to extract stable hemoglobin time series: for the pain task, the time window was 13 seconds after each stimulus; for the n-back task, the time window was 30 seconds; and for the n-back with pain task, the time window was 60 seconds. The 2-second pre-task period was used as a baseline for correction. By averaging across all blocks for each task, we generated the mean hemodynamic reaction time series curves related to the task. Previous studies have shown that HbO is more sensitive to changes in brain region blood flow signals than HbR,^38,39 therefore, we primarily focused on the changes in the mean HbO values under different task conditions as indicators of brain activation.

To further explore the load-dependent neural mechanism of distraction analgesia, we conducted functional connectivity analysis to observe the inter-regional connectivity of the brain during different tasks. The relative changes in HbO concentration within each block were extracted for functional connectivity analysis using the brain network module in the NirSpark software package. Pearson correlation coefficients between the HbO concentrations of different brain regions in the time series were calculated, followed by Fisher Z transformation. The transformed values were then defined as the functional connectivity strength.

Statistical Analysis

Data was analyzed using IBM SPSS Statistics for Windows, Version 25.0 (Armonk, NY: IBM Corp). The measured data were expressed as means and standard deviations. Normality of the data was assessed using the Shapiro–Wilk test. Independent samples t-tests and paired t-tests were applied for normally distributed continuous variables, while non-parametric tests were used for variables that were not normally distributed. To compare the effect of different WM loads on pain ratings, a one-way repeated measures analysis of variance (ANOVA) was conducted, with false discovery rate (FDR) correction applied. At the neural level, we first verified whether pain elicited activation in the corresponding brain regions by conducting paired t-tests on the HbO mean values for the n-back task and n-back with pain task, with FDR correction applied. Secondly, we analyzed the impact of WM load on pain-related neural activity by conducting paired t-tests on the HbO mean values for 0-back with pain task and 2-back with pain task, with FDR correction applied. To examine whether WM load influenced pain-related behavior through alterations in pain-related neural activity, a mediation analysis was conducted by using the SPSS 25 (test of joint significance approach) and Mplus8.11 (path-analytic method).⁴⁰ In this model, the independent variable (X) represented the WM load (0-back = 1; 2-back = −1), the dependent variable (Y) was the pain-related behavioral measures (defined as the change in pain ratings on the NRS), and the mediator (M) was the pain-related neural activity. Pain-related neural activity and behavioral measures were measured twice in the same subject at both WM loads. The pain-related behavioral or neural measures were quantified as the contrast between the n-back with pain task and n-back task. They were measured twice in the same subject at both WM loads. The indirect effect was considered statistically significant when the 95% confidence interval (CI) did not include zero, with a significance threshold set at p < 0.05. Additionally, a two-way repeated measures ANOVA was used to compare the differences in functional connectivity between brain regions across the n-back and n-back with pain tasks, and simple effects analysis was conducted when the interaction effect was significant. To assess the interference of pain on cognitive task performance, a two-way repeated measures ANOVA was conducted to compare WM reaction time (RT) and accuracy (ACC) for each task (0-back and 2-back) when performed with or without pain. Simple effects analysis was conducted when the interaction effect was significant, with FDR applied. The partial eta squared (η²p) effect sizes of significant effects in the ANOVA were reported, where 0.01 represented a small effect, 0.06 represented a medium effect, and 0.14 represented a large effect.⁴¹ Statistical significance was set at p < 0.05 for all tests, and all p – values were two-tailed.

Power Analysis

The primary outcome of this study was the effect of WM load on pain ratings, which was assessed using a one-way repeated measures ANOVA. An a priori power analysis conducted with G*Power 3.1, assuming a medium effect size (f = 0.25), a significance level of α = 0.05, and a statistical power of (1 – β) = 0.80, indicated a required sample size (n = 28), ensuring adequate power at the behavioral level. To ensure sufficient power for neural and connectivity analyses, additional power calculations were performed for paired-sample t-tests and two-way repeated measures ANOVA. The sample size are n = 27 and n = 24. The largest sample size among these analyses was adopted as the reference (n = 28). Considering a 10–15% attrition rate, a minimum of 32 participants should be enrolled in the study. Thus, the study was sufficiently powered across behavioral, neural, and connectivity measures.

Results

Descriptive Statistics

Five participants were excluded from the initial sample of N = 40. Data from 2 participants were incomplete due to missing marking records caused by equipment failure. In addition, data from 3 participants were excluded due to excessive motion during the task phase of fNIRS measurements (n = 2) and poor signal quality in some channels caused by inadequate fitting of the measurement cap to the head shape (n = 1). Thus, we analyzed data from 35 participants. Table 1 presents the baseline demographic and pain-related clinical characteristics of the participants. The gender ratio of participants included in this study was not balanced. Results from baseline-related questionnaires have excluded the confounding effect of gender, confirming the homogeneity and stability of the study sample.

Table 1 Demographic and Pain-Related Clinical Characteristics of the Participants

Modulation of Pain Ratings by WM Load

As shown in Figure 3, Repeated measures ANOVA was performed on the perceived pain intensity NRS ratings during different conditions (pain, 0-back with pain, and 2-back with pain). The results showed significant differences in pain intensity ratings across conditions (F = 17.666; p < 0.001***; η²p = 0.342). Compared to the pain task, NRS were significantly lower in both 0-back with pain (p = 0.001**) and 2-back with pain (p < 0.001***). Additionally, NRS was significantly lower in the 2-back with pain compared to 0-back with pain (p = 0.002**).

Figure 3 Pain ratings in pain task and n-back with pain task. Notes: Violin plots show the data distributions, mean (dashed line), and quartiles (black line). Each colored dot represents an individual participant. ***p < 0.001.

Modulation of Pain-Related Brain Activity by WM Load

In the whole-brain analysis, noxious laser stimuli activated a broad range of brain areas associated with pain, including RS2 (t = 2.473, p = 0.034*), RPMC (t = 3.108, p = 0.012*), RSM1 (t = 2.210, p = 0.044*), LSM1 (t = 2.262, p = 0.044*), RDLPFC (t = 3.932, p < 0.001***), LDLPFC (t = 2.716, p = 0.023*), and aPFC (t = 3.655, p = 0.005**), as shown in Table 2 and Figure 4.

Table 2 Effect of N-Back with Pain Task on Brain Responses to Pain Stimuli

Figure 4 Brain activation during the n-back and n-back with pain task. Abbreviations: aPFC, anterior prefrontal cortex; DLPFC, dorsolateral prefrontal cortex; PMC, premotor cortex; SM1, primary somatosensory cortex; S2, secondary somatosensory cortex; L, left; R, right. Notes: Brain activation plots for n-back (A), n-back with pain (B) and the difference (C). The redder the color in the brain map, the greater the activation.

Further comparison of brain activation during 0-back with pain and 2-back with pain task revealed significant decreases in activation in the left S2 (t = 2.757, p = 0.041*) and left SM1 (t = 2.834, p = 0.041*) regions during 2-back with pain, as shown in Table 3 and Figure 5. Mediation analysis was used to determine the contribution of neural activity in the effect of cognitive load on pain perception. As shown in Figure 6, WM load indirectly affected pain intensity by modulating brain activity in the LSM1 (a*b = −0.014, SE = 0.039, CI = [−0.102, 0.059]) and in the LS2 (a*b = 0.004, SE = 0.050, CI = [−0.112, 0.096]), but the effects were not significant. The point estimates of the indirect effects were not close to zero. The result is more likely attributed to insufficient statistical power (limited sample size) rather than a genuine absence of the mediating effect. Thus, the results could not confirm the mediating role of LSM1/LS2 neural activity, but they also do not rule out this potential pathway. A two-way repeated measures ANOVA was used to compare the functional connectivity between brain regions during different tasks. Significant interaction effects were found in the functional connectivity between RS2-aPFC (F = 6.475, p = 0.016*, η²p = 0.160), RSM1-RDLPFC (F = 6.225, p = 0.018*, η²p = 0.155), RSM1-aPFC (F = 7.439, p = 0.010**, η²p = 0.180), and LSM1-aPFC (F = 6.523, p = 0.015*, η²p = 0.161), as shown in Table 4. Simple effects analysis was conducted on the significant interaction effects in the functional connectivity between brain regions.

Table 3 Brain Responses to N-Back with Pain Task with Different WM Load

Table 4 Effect of WM Load and Pain Distraction on Brain Functional Connectivity

Figure 5 Brain activation differences during the n-back with pain task during different WM load in LS2 and LSM1. Abbreviations: LSM1, left primary somatosensory cortex; LS2, left secondary somatosensory cortex. Notes: Violin plots and brain activation plots for LS2 (A) and the LSM1 (B). Violin plots show the data distributions, mean (dashed line), and quartiles (black line). Each colored dot represents an individual participant. Positive values represent positive activation (increase), negative values represent negative activation (decrease). The redder the color in the brain map, the greater the activation. *p < 0.05.

Figure 6 Mediating role of neural responses on the effect that WM had on pain perception. Abbreviations: LSM1, left primary somatosensory cortex; LS2, left secondary somatosensory cortex. SE, standard error. Notes: Mediating role of LSM1 neural responses (A) and LS2 neural responses (B) on the effect that WM had on pain perception. Violin plots show the data distributions, mean (dashed line), and quartiles (black line). Each colored dot represents an individual participant. The redder the color in the brain map, the greater the activation. Dotted paths indicate significance, while solid paths indicate non-significance. *p < 0.05; **p < 0.01; ***p < 0.001.

The results revealed that during high load task, the additional pain stimuli in the n-back with pain task reduced the functional connectivity between brain regions compared to the n-back task in RS2-aPFC (p = 0.003**), RSM1-RDLPFC (p < 0.001***), RSM1-aPFC (p = 0.004**), and LSM1-aPFC (p = 0.034*), as shown in Figure 7A. Functional connectivity in the n-back task increased in RS2-aPFC (p = 0.002**), RSM1-RDLPFC (p = 0.002**), and RSM1-aPFC (p = 0.003**) with increasing load, as shown in Figure 7B.

Figure 7 Results of functional connectivity differences within the high load WM and the n-back. Abbreviations: aPFC, anterior prefrontal cortex; DLPFC, dorsolateral prefrontal cortex; SM1, primary somatosensory cortex; S2, secondary somatosensory cortex; L, left; R, right. Notes: Violin plots and FC plots for the high load WM (A) and the n-back (B). Violin plots show the data distributions, mean (dashed line), and quartiles (black line). Each colored dot represents an individual participant. The redder the color in the line of the brain map, the greater the differences. *p < 0.05; **p < 0.01; ***p < 0.001.

Interference of Pain with Cognitive Performance

A two-way repeated measures ANOVA was conducted to compare WM RT and ACC for each task (0-back and 2-back) when performed with or without pain. No significant interaction effects were found between WM load and pain intensity on ACC (F = 0.628, p = 0.434) and RT (F = 0.0005, p = 0.983). Focusing on the main effects, significant main effects of WM load were found on both ACC (F = 28.799, p < 0.001***, η²p = 0.459) and RT (F = 61.253, p < 0.001***, η²p = 0.643). Additionally, a significant main effect of pain was found in ACC (F = 5.346, p = 0.027*, η²p = 0.136), as shown in Figure 8.

Figure 8 Reaction time and accuracy during different WM load. Abbreviations: ACC, accuracy; RT, reaction time. Notes: Reaction time during different WM load (A) and accuracy during different WM load (B). Violin plots show the data distributions, mean (dashed line), and quartiles (black line). Each colored dot represents an individual participant. *p < 0.05; ***p < 0.001.

Discussion

This study used fNIRS to explore cognitive load-dependent perception modulation and cortical mechanism in distraction analgesia in healthy individuals. The results showed that high-load WM significantly reduced the perceived intensity⁴² and pain-related neural activation in the S2 and SM1. Under high load, the functional connectivity between brain regions (RS2-aPFC, RSM1-RDLPFC, RSM1-aPFC, and LSM1-aPFC) was significantly lower during the n-back with pain task compared to the n-back task. It indicated that as WM load increased, the coupling between the network involved in pain processing was significantly attenuated.

Based on the neurocognitive model of pain, WM regulates the allocation of attention resources through central executive system, and its load levels directly affect the effectiveness of pain perception suppression.⁴³ This study systematically revealed the gradient effect of WM load on pain perception inhibition by manipulating load levels of WM. We found that WM significantly reduced pain ratings, with the analgesic effect of the 2-back task being greater than that of the 0-back task. Our findings were consistent with prior research,²⁰ which involves shifting cognitive resources away from pain and prioritizing task-related stimuli through “top-down” control. This reduces the occupation of limited cognitive resources by pain, thereby decreasing pain perception.⁷ Furthermore, considering that pain expectation and continuous stimulation may lead to a decline in perceptual levels,⁴⁴ we set two different pain levels and randomized both the spatiotemporal delivery (arrival and location) and intensity levels of noxious stimuli. This setting effectively avoided interference from expectation effects and sensory habituation during pain assessment,⁴⁵ reinforcing the central role of the resource competition theory. Although this study found a positive correlation between WM load and pain suppression, previous research suggests that this modulation pattern may be influenced by factors such as cognitive fatigue and ceiling effects.²⁰ A pain study by Zoha Deldar et al²⁰ found that both 0-back and n-back tasks reduced pain, with n-back being more effective than 0-back, but no significant differences in pain suppression were observed between 2-back and 3-back tasks. Based on the research by Vogel et al²¹, we proposed potential explanations for the absent analgesic effect in n-back tasks. WM affects the allocation of attention resources in competition, when load exceeds individuals’ execution capacity threshold, indirectly reversing the analgesic advantage and reducing the contribution of WM to distraction analgesia. Therefore, the ceiling effect caused by limited WM capacity is one of the important factors affecting distraction analgesia. Future research can establish a multi-gradient model of WM capacity and assess individual difference in execution function to explore the optimal load range for maximizing analgesic effects.

At the neural level, analgesia effects induced by WM distraction might involve a hierarchical gate control mechanism driven by PFC. We compared brain activation between the n-back task and the n-back with pain task, and found that SM1, S2, PMC, aPFC, and DLPFC were activated during pain stimuli in the n-back with pain task, forming a dynamic regulatory network for pain processing.³⁵ SM1 and S2 are involved in nociceptive pain and sensory processing,^46,47 and more significant brain activation during 0-back suggests less inhibitory effect of low WM load on pain processing. As WM load increased, activation in the S2 and SM1 decreased during the n-back with pain task. We supposed that high WM load might enhance thalamocortical inhibitory gating, resulting in suppression of nociceptive signal transmission from S2 and SM1. An fMRI study by Valet et al⁴⁸ showed that attention diversion induced by single cognitive load Stroop tasks reduced brain activities encoding pain, such as the thalamus and somatosensory cortex, supporting the effect of distraction analgesia at a single-load level. Moreover, an fMRI study by Legrain V et al^49,50 found that when focusing on a primary visual task, the responses in S1/M1 and the insula to noxious stimuli are reduced. Neural responses in S1/S2 are affected by attention loads, with the spatial pattern of distraction analgesia being highly consistent with the findings of this study. Based on fNIRS data, this study further suggests that WM distraction modulates pain processing through “top-down” attention regulation, reducing somatosensory cortical neural activity and thus affecting the processing of pain stimuli.

We conducted mediation analysis on the neural responses of the SM1 and S2 regions to test whether WM loads indirectly affected pain ratings through brain activation. The results showed no significant mediation effect, indicating that the effect of loads on pain ratings was not completely dependent on the neural response changes in individual brain regions. The effect of distraction analgesia may involve a complex functional network. Therefore, to observe the changes in brain functional networks induced by distraction under different loads, we calculated the functional connectivity between target brain regions.

The results of the functional connectivity between target brain regions indicated that with increasing load, functional connectivity in the WM task increased between RS2-aPFC, RSM1-aPFC, and RDLPFC-RSM1, reflecting the recruitment of “top-down” modulatory resources at the cortical level due to WM load. However, as the load increased, the additional pain stimuli in the n-back with pain task reduced the functional connectivity between the RS2-aPFC, bilateral SM1-aPFC, and RDLPFC-RSM1 brain regions compared to the n-back task. The results indicated that high load distraction inhibited the control of the sensory cortex by the PFC and reduced the coupling between pain-related brain networks, which might be related to the saturation effect in prefrontal control pathways. The DLPFC supports executive control over attention, WM, and pain inhibition via top-down modulation.^51,52 aPFC, an essential component of the PFC, contributes to self-referential processing, attention regulation, WM, decision-making, and salience detection. These findings indicated that aPFC was important in appropriate attention shift and the reallocation of pain awareness and response.³⁵ The functional connectivity between the PFC and SM1 might indicate the contribution of prefrontal cognitive processing to pain processing. The PFC might be a key node in the network related to nociceptive processing and pain regulation, transmitting core pain processing through its connection with SM1.⁵² A study by Peng Weiwei et al³⁷ found that α-tACS on SM1 may inhibit pain perception and neural responses by decoupling SM1 from key sensory-motor, emotional, and cognitive processing networks involved in pain. Similar findings were also reported by Wagner et al in their study on placebo analgesia.⁵³ They found that placebo manipulation may exert analgesic effects by decoupling the somatosensory network responsible for pain processing from the descending modulatory network. Furthermore, Deng xue et al^54,55 reported that VR-induced analgesia, as shown in fNIRS studies, is characterized by reduced S1 connectivity and diminished pain-related processing. The weakened coupling between cortical pain-related regions and other brain areas may serve as a critical mechanism disrupting normal pain signaling, consistent with our findings. Therefore, we speculated that WM distraction analgesia may similarly reduce pain through decoupling the core networks involved in pain perception and cognitive processing.

We also examined the potential impact of pain on WM performance. A significant main effect of pain intensity on ACC was observed, indicating that the presence of pain significantly impaired WM performance. This damage might arise through two potential pathways. Pain signals are transmitted through the spinal-thalamic-cortical pathway to cortical regions such as S1 and S2.⁵⁶ Through inter-regional neural connectivity or reorganization, the PFC evaluates pain and allocates attention, thereby competing for cognitive resources and reducing the neural encoding precision of WM representations.⁵⁷ Additionally, the insula and DLPFC, as key regions for cognitive control, may be involved in analgesia processing. This mechanism might relate to diminished “top-down” cognitive control from the DLPFC to the insula, ACC, and thalamus.^58–61 Pain-induced negative emotions may decrease the coupling of the PFC and ACC during cognitive reappraisal.²² Moreover, there was no significant interaction between WM load and pain in terms of accuracy, suggesting that the impairing effect of pain might be similar under both 0-back and 2-back conditions. In high-load condition, the increase in reaction time alongside a decrease in accuracy supports Baddeley’s limited capacity theory of the central executive system.⁶² When cognitive demand exceeds the individual’s resource threshold, the cost of conflict monitoring in the ventral attention network (VAN) increases, and more time is needed for information matching and conflict resolution.⁶³ Notably, the observed “speed-accuracy decoupling” under pain conditions suggests the adoption of a behavioral compensation strategy, whereby participants maintain response speed at the expense of accuracy.⁶⁴ Our findings support the hierarchical hypothesis of the attention competition model, demonstrating that pain disrupts cognition through two mechanisms: (1) direct competition for prefrontal resources, and (2) emotion-mediated impairment of cognitive control networks.²² This provides a new interpretation for cognitive deficits observed in chronic pain. Prolonged pain may reconstruct neural networks, reduce the availability of cognitive resources and reinforce negative affective processing in the limbic system,^65,66 leading to a vicious “pain-cognition” cycle and driving the brain’s functional reorganization toward a “pain salience-prioritized” mode. Future research may focus on interventions such as neurofeedback training, transcranial electrical stimulation, or virtual reality-based distraction tasks to reconstruct neural connectivity, and improve cognitive impairments in chronic pain patients.

The results of the current study should be considered within the study design and its potential limitations. First, as the participants were all young and healthy university students, the findings may not be generalizable to broader or more diverse populations. Second, the load of the WM paradigm was simple. Future research could use more refined gradients and incorporate broader attention tests. Third, the indirect effects of WM load on pain ratings via LSM1/LS2 did not reach significance, which may be attributed to insufficient statistical power. Future research could expand the sample size and improve measurement precision.

Conclusion

Our study demonstrates that WM distraction reduces both pain perception and neural responses to experimental laser pain stimuli in healthy individuals and that a significant reduction in functional coupling between regional networks involved in pain processing was observed. Therefore, our findings suggest that WM load may reduce pain perception by decreasing neural responses in pain-related regions and promoting the decoupling of related brain networks, providing neuroscientific evidence for cognitive strategy-based analgesia interventions.

Data Sharing Statement

For additional details, please reach out to the corresponding author. The datasets analyzed in this study can be obtained from the corresponding author upon reasonable request.

Ethics Approval and Informed Consent

All participants gave written informed consent. The study procedures were approved by the Ethics Committee of Zhujiang Hospital, Southern Medical University (Ethical approval number: 2024-KY-427-02). The clinical registration number is ChiCTR2500100508. This study complies with the Declaration of Helsinki.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

This study was supported by National Natural Science Foundation of China (NNSFC), China; Contract grant number: 82172526, 82372553; Guangdong Basic and Applied Basic Research Foundation, China; Contract grant number: 2023A1515010200.

Disclosure

The author(s) report no conflicts of interest in this work.

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Story Continues

People are missing out on vital protections by using buy now, pay later instead of credit cards to pay for holidays, experts warn.

Buy now, pay later (BNPL) has grown hugely in recent years, and holiday firms and hotel chains have been adding it to the options for payment when booking online, saying it can make trips more attainable.

“Stay now, pay later” is the new slogan from budget hotel chain Travelodge, which recently announced that guests can now pay via Klarna, Clearpay or PayPal – the three companies that dominate the UK BNPL market.

Similarly, a number of travel agents and flight booking sites offer BNPL under the banner of “Fly now pay later”. Customers do not have to pay the full cost of their flights upfront – they can spread the cost over instalments.

And Airbnb announced in late 2023 that it was teaming up with Klarna in the UK so guests could spread the cost of stays over weeks or months. The service is available for reservations priced between £35 and £4,000.

Data issued this week showed that searches on Google for phrases such as “buy now pay later flights” and “buy now pay later hotels” are up sharply on earlier this year, suggesting people are looking for ways to book more flexibly.

BNPL is a form of credit where the cost of what you are buying is typically split into three or four instalments. If you keep to your repayment plan, you will not usually pay interest or charges.

However, there is concern that some people could end up taking out loans they cannot afford to pay back on time, thereby incurring charges, tipping them into debt and damaging their credit score.

Experts warn that using BNPL to pay for holidays or trips also offers fewer consumer protections than more traditional credit.

“While it can be really convenient, it’s worth remembering that it doesn’t come with the same protections as a credit card,” says Matthew Sheeran from Money Wellness, a debt solutions and budgeting website.

If you pay with a credit card, section 75 of the Consumer Credit Act means that if a purchase between £100 and £30,000 goes wrong, the credit card provider is jointly liable with the retailer.

Sheeran says that with BNPL, if there is a problem, “you’ll usually have to chase the retailer or travel provider yourself, which can be stressful and time-consuming. It’s worth checking whether the BNPL provider offers any dispute process, but these aren’t as robust or guaranteed as section 75”.

He adds that while this form of payment is fine for smaller low-risk purchases, for bigger spends, a credit card still offers a safety net.

“BNPL is starting to edge into travel because it offers a way for people to ‘buy now, budget later’,” says Maisie Blewitt at Transfer Travel, an online marketplace where people can buy and sell unused trips.

She says that if you pay using BNPL and the airline or hotel goes bust, for example, your money could be at risk of being lost.

“Refunds can be messy, too, because if a trip is cancelled, instalments can keep coming out of your account until the refund clears, which could take weeks,” she says.

She adds that as this is a developing area of regulation, terms and protections can differ from provider to provider.

“Before using buy now, pay later for a holiday, make sure you carefully read and fully understand the small print,” says Blewitt.

People who use BNPL in this way typically do not have to pay for the trip before they travel, so charges may still be coming out of their account months after they have been away.

There is no universal maximum spending limit, so how much you can borrow depends on which provider you use, your creditworthiness, and how much risk it is willing to take.

“It feels risk-free, and that’s the problem,” says Sebrina McCullough from Money Wellness. “Interest-free offers make it feel like a payment method, not borrowing. But it’s still credit, and if you use it to fund what you can’t afford, the risks grow.”

The UK’s financial regulator, the Financial Conduct Authority, is to start regulating BNPL from July 2026.

This means BNPL loans will become regulated credit agreements and, crucially, people using this form of credit will be covered by section 75. They will also be able to access the Financial Ombudsman Service if they need to make a complaint.

Ripple’s recent wave of high-profile acquisitions signals growing strength and ambition in bridging traditional finance with crypto.

Yet concerns persist that Ripple’s reliance on XRP-linked financing exposes weaknesses in the company’s long-term financial sustainability and its ecosystem’s real utility.

Ripple’s recent acquisitions, including Hidden Road and GTreasury, underline its accelerated push into traditional finance and its effort to expand financial infrastructure into corporate markets.

However, Ripple’s growing footprint in traditional finance has reignited long-standing concerns about XRP’s utility and relevance. These newly acquired services primarily target institutional clients that rely on conventional financial instruments, leaving XRP with little to no role in their core operations.

This disconnect has become a focal point of growing scrutiny among analysts and investors, who question whether Ripple’s business expansion truly supports the long-term value of its token.

Despite recent acquisitions, Ripple’s financial reality still heavily depends on XRP sales and tokenomics. The company continues to hold and release large volumes of XRP.

These periodic sales, managed through an escrow system, have long served as a key source of liquidity and operational funding for the firm.

Yet this reliance on selling XRP contrasts with the company’s long-promoted vision of the token as a functional bridge currency rather than a financial asset.

For years, the narrative has been that XRP would become the bridge currency, settlement fuel, and utility token within XRPL and Ripple’s infrastructure. But new data introduces a structural disconnect.

An effective example is Ripple’s RLUSD stablecoin.

As of the beginning of October, RLUSD has reached a market cap of nearly $789 million. Yet, BeInCrypto reported earlier that around 88% of RLUSD’s supply is on Ethereum, not XRPL.

Many XRP holders expected RLUSD adoption to increase demand for the token. Transactions on the XRP Ledger require small XRP fees that are burned. However, most RLUSD activity happens outside the Ledger altogether, limiting its impact on the token’s overall utility.

This situation has created a strategic tension for Ripple, which is expanding beyond XRP’s original purpose. Once expected to benefit from this growth, the token plays only a limited role in new operations.

So far, this shift has not led to greater XRP usage or burns, raising doubts about its real-world utility.

The debate over XRP’s relevance has now expanded to include how Ripple manages and influences the circulation of its token.

Ripple’s intervention in XRP’s market has added another layer to the debate over the token’s utility.

The company recently revealed plans to raise $1 billion worth of XRP to establish a digital asset treasury, one of the largest fundraising efforts centered on a single cryptocurrency.

Supporters view the plan as a sign of confidence in XRP’s long-term prospects and an attempt to bring market stability.

However, critics argue that a company raising capital to buy its own token risks blurring the line between financial strategy and price support.

Some analysts warn that such large-scale interventions could reinforce the perception that Ripple’s success still depends on XRP speculation, rather than genuine on-chain or institutional utility.

Ultimately, the initiative highlights the same structural challenge facing Ripple’s ecosystem. While the company swiftly expands into traditional finance, XRP’s practical role within that growth remains limited.

Read original story Ripple Grows Beyond Crypto—But Can XRP Keep Up? by Camila Grigera Naón at beincrypto.com

SHANGHAI, Oct. 25, 2025 (GLOBE NEWSWIRE) — Track Test Drive: From October 15 to 16, the EXEED International User Summit launched its track test drive in Shanghai. A total of 124 guests from 24 countries attended. The event featured on-track experiences and static displays, allowing participants to witness EXEED’s industry-leading “firsts” and “onlys” in person.

BEV Highlights: As a premium global NEV brand, EXLANTIX showcased its BEV lineup with outstanding performance. The models can accelerate from 0 to 100 km/h in around 3 seconds, delivering supercar-level power. They also feature a turning radius of just 5.65 meters — the smallest in their class.

Hybrid Highlights: In hybrid technology, EXEED combines the industry’s first REEV Golden Extended-Range system with the world’s exclusive Quad-Motor AWD PHEV. During the event, four upcoming global hybrid models also made appearances.

From October 15 to 16, the EXEED International User Summit launched its track test drive in Shanghai. A total of 124 guests from 24 countries attended. Through immersive on-track experiences and static displays, EXEED presented its latest achievements in electrification, hybrid technology, intelligence, and design. The event allowed guests to experience multiple industry “firsts” and “onlys” in person.

After test-driving several EXEED models including the ES, ET, E01 PHEV, and VX PHEV, top Qatari auto influencer Mohammad Nehad Ahmad Alomari-Zrdifd from Horsepower commented: All of these cars deliver a great driving experience, each with its own strengths. For instance, the ES has a futuristic design, the ET handles turns very well. But what they all share is impressive acceleration and excellent handling!

BEV Highlights: ES BEV and ET BEV, High-Performance Electric Models

As one of the key models on the track, the ES BEV demonstrates exceptional aerodynamics with a drag coefficient of just 0.205Cd and delivers an impressive 8,000 N·m of wheel-end torque. It sprints from 0 to 100 km/h in only 3.7 seconds. Equipped with the IAS intelligent air suspension and CDC adaptive damping system, the ES BEV continuously adjusts ride height and damping stiffness according to road conditions and driving modes. This intelligent coordination effectively minimizes body roll during high-speed cornering, enhancing handling precision and overall stability.

The ET BEV also delivered impressive performance, with an electric motor producing a peak output of 353 kW. It accelerates from 0 to 100 km/h in just 4.8 seconds, unleashing strong power from the very first moment on the track. Its chassis features class-leading IAS intelligent air suspension and CDC adaptive damping, capable of completing dynamic adjustments within 1 second. Paired with an advanced double-wishbone structure, the ET BEV maintains a stable body posture even under aggressive track driving. Notably, its 5.65-meter turning radius—the smallest in its class—ensures agile handling during slalom tests and adds practical convenience for everyday driving.

Norwegian top 3 auto KOL Falch Krister Riis also shared his feedback after testing the ES and ET: Both cars offer great handling. The ES accelerates quickly, while the ET is very comfortable to drive — and its charging speed is amazing!

Hybrid Highlights: ET REEV and RX PHEV Lead the Way, Balancing Performance and Efficiency Across the Hybrid Range

In the hybrid lineup, the ET REEV and RX PHEV served as the key models for the track test. The VX PHEV and E01 PHEV also made appearances, showcasing the breadth of EXEED’s hybrid offerings. The ET REEV features the industry-first Golden Extended-Range technology, equipped with a high-efficiency engine achieving 44.5% thermal efficiency. One liter of fuel can generate 3.7 kWh of electricity, the highest in its class. It also comes with front and rear electric motors, delivering a total system output of 345 kW. The AWD system provides robust power, enabling 0–100 km/h acceleration in just 4.9 seconds. Even in a discharged state, the vehicle can reach 100 km/h in 5.7 seconds. On the track, the ET REEV’s electric-motor-driven system ensures rapid throttle response during straight-line acceleration, delivering a driving feel on par with that of pure electric vehicles.

The RX PHEV is equipped with the world’s exclusive Quad-Motor AWD PHEV system. It delivers a combined output of 395 kW and 650 N·m of torque, accelerating from 0–100 km/h in just 4.9 seconds. This demonstrates the performance advantages of its four-motor hybrid system. On the track, the vehicle’s hybrid “Sport Mode” coordinates power delivery, providing strong acceleration during straight-line runs. Additionally, with a combined range exceeding 1,300 km, the RX PHEV eliminates range anxiety for everyday driving.

Intelligent Technology: ET and ES Feature VPD + RPA for an Enhanced Parking Experience

Beyond performance, EXEED also showcased its advancements in intelligent technology during the track event. Both the ET and ES models are equipped with VPD (Valet Parking Drive) and RPA (Remote Parking Assistance), offering a new level of convenience and safety for parking.

As the only model in its segment equipped with VPD, the system features two modules: Remote Valet Parking and Remote Smart Summon. Once the vehicle enters a designated area, the driver can activate Remote Valet Parking and leave. The system autonomously searches for a parking space, parks the car, and then automatically locks, closes the windows, and powers down. For the next use, drivers can activate Remote Smart Summon via the mobile app. The system will then bring the vehicle out of the parking spot and drive it to the designated pick-up point.

The RPA addresses the common problem of parking in tight spaces where exiting the vehicle is difficult. The driver selects a suitable parking space, engages the P gear, and applies the handbrake. Using Bluetooth or the key fob, the system is activated with a single command, and the vehicle parks itself automatically. For tight spaces where getting back in is difficult, the vehicle can also be remotely controlled to exit the spot with ease.

Premium Highlights: E02 and MX Presented, Showcasing New Technology Premium

In the static display area, the all-new E02 and MX drew significant attention. Their striking design language captivated media and KOLs, who stopped to admire and photograph the vehicles. The E02 features a distinctive “Cloud Waterfall” grille, conveying strength and presence. Its side profile highlights the “Wind-Flow Horizon” beltline. This line stretches from the headlights to the rear and runs parallel to the roofline, creating an elegant and cohesive silhouette. At the rear, the “Sunset Glow” through-tail lamps feature a clean, sculpted design. When illuminated at night, they showcase the fusion of Eastern aesthetics and modern technology.

The MX features a “Starwing” light strip that runs parallel from the headlights to the taillights. Its minimalist yet profound design creates a dreamlike effect, as if the entire vehicle is surrounded by starlight. The light strip houses 472 LEDs, stretching a total length of 3,499.3 mm, to deliver a dazzling starry visual experience. The MX is paired with five-spoke star-pattern wheels, crafted with a hollow multi-layer structure. The interplay of light and shadow creates a three-dimensional starburst effect, enhancing the vehicle’s refined, tech-premium appeal.

The track test drive in Shanghai offered more than 120 guests an immersive experience. It showcased EXEED’s achievements and capabilities across performance, intelligent technology, and design in the new energy sector. Looking ahead, EXEED plans to introduce its new energy vehicles to high-regulation markets such as Norway and Denmark, providing local customers with more premium mobility solutions.

Contact: Ting Li, lixueting@mychery.com

The Co-op has quietly told staff to boost promotion of vapes in an effort to win back customers and sales after a devastating cyber-attack.

The ethical retailer is making vapes more prominent in stores via new​ displays and additional advertising, according to an internal document seen by the Guardian. It is also stocking a bigger range of vapes and nicotine pouches.

The action plan is to tackle a big sales drop after the April hack that resulted in gaps on its shelves.

Called Powering Up: Focus Sprint: Cigs, Tobacco and Vape, the document says: “Sales haven’t recovered compared to pre-cyber.” In a section headed “Why we need to focus on this category?”, it says there are “£1m missing sales per week” and 100,000 fewer transactions.

It states: “We know at least 40% of this is customers forming a new habit, shopping elsewhere as they wouldn’t go without their cigarettes, tobacco or vapes. This means we’ve also lost sales from what would’ve been in their basket.”

The Co-op’s approach to selling vape products in its more than 2,000 grocery stores complies with UK legislation and government guidelines but staff have raised concerns about whether it is contrary to its standing as an “ethical” retailer.

On its website, the Co-op spells out that it puts “principles before profit”. It says: “As well as having clear financial and operational objectives and employing 54,000 people, we’re a recognised leader for our social goals and community-led programmes.”

The activity comes at a time of mounting concern about youth vaping after evidence showing that the numbers of under-18s trying or using vapes has soared in recent years. The brightly coloured packaging and flavours such as bubblegum or candy floss are a significant part of their appeal.

England’s chief medical officer, Prof Chris Whitty, has raised concerns about the marketing of vapes, saying: “If you smoke, vaping is much safer; if you don’t smoke, don’t vape.”

A source told the Guardian that staff had not been told explicitly to sell more vapes but whereas before their presence in store was low-key, there were now ads strategically placed in high-traffic areas and eye-catching display units.

“Before [the hack] even if I didn’t always enjoy work I respected the Co-op,” the source said. “They present the lovely idea of ethical shopping – you might pay a bit more but they are doing things right. This strategy goes against everything we’ve done until now.”

They said the Co-op was known for its ethical business model and that set it apart from other companies. “This recent decision to exploit a known health problem and make a profit goes against the values the Co-op was built on and stands for.”

The government’s tobacco and vapes bill, which is making its way through parliament, will outlaw vape advertising and sponsorship. It will also restrict the flavours, packaging and display of vapes and other nicotine products.

A Co-op spokesperson said: “As a member-owned organisation, our longstanding commitment to ethical values and responsible retailing remains steadfast and at the heart of how we do business.

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“The sophisticated cyber-attack we experienced means we are now even more focused on powering up all aspects of our stores to serve the needs of shoppers.”

They added: “It is important to be clear that the sale of vape products in our stores is fully compliant with all UK legislation and government guidelines, in their recognised role as a successful route to smoking cessation.”

Co-op managers are trying to repair its finances after the cyber incident, which forced it to shut down parts of its IT systems. In a recent business update, the retailer said the fallout pushed it into the red in the first six months of its financial year.

The cyber-attack led to gaps on shelves in its grocery stores, while its more than 800 funeral parlours were forced to return to operating some services via paper-based systems because of having no access to digital services.

The upheaval wiped more than £200m off sales, and the group anticipates the final bill will result in a £120m hit to full-year profits.

The document seen by the Guardian relates to what is a store-wide “Power Up” programme covering all product categories.

Major investors, spooked by AI exuberance yet wary of betting against it, are shifting from hyped-up stocks into potential next-in-line winners, reviving a strategy from the 1990s dotcom era that helped some sidestep the crash.

As U.S. stocks have hit successive records and AI chipmaker Nvidia’s valuation has surged beyond $4 trillion, professional investors have been trying to find ways to make money from the bull market while avoiding excessive risk.

Some are looking back to the 1990s internet boom, which spread from startups to telecoms and tech, and where hedge funds rode the wave by flipping out of highly-valued stocks before they peaked and picking others that had room to rise.

“What we are doing is what worked from 1998 to 2000,” said Francesco Sandrini, multi-asset head and Italy CIO at Europe’s largest asset manager Amundi.

He highlighted signs of irrational exuberance on Wall Street, such as frenzied trading in risky options pegged to the share prices of big AI stocks. But he said he expected the new tech enthusiasm to continue and hoped to bank gains via bets on reasonably valued assets that might rally next.

Sandrini said this involved trying to find “the highest growth opportunities that so far the market had failed to spot”, with moves into software groups, robotics and Asian tech.

Other investors also expected to edge out of Wall Street’s Magnificent Seven stocks after shares in Nvidia more than tripled in two years, but want to keep their diversification within the AI sphere.

ASSET MANAGERS NEED TO BE NIMBLE TO RIDE THE WAVE

“The odds of this (AI boom) being a bust are very high because you’ve got companies spending trillions and all fighting for the same market that does not yet exist,” said Goshawk Asset Management CIO Simon Edelsten, who worked on telecom IPOs at stockbroker Dresdner Kleinwort Benson in London in 1999.

He expected the next phase of AI fever to spread from Nvidia and others like Microsoft and Alphabet into
related sectors.

Timing the phases of a bubble has historically been a way to play it without the risk of trying to call the peak too early.

A study by economists Markus Brunnermeir and Stefan Nagel showed that hedge funds mostly did not bet against the dotcom bubble, but rode it skillfully enough to beat the market by about 4.5% per quarter from 1998-2000 and avoid the worst of the downturn.

They shed high-priced internet stocks in time to recycle profits into others before they caught the attention of less sophisticated investors.

“There were good profits to be made for the fleet of foot even during 2000 when the top came,” Edelsten at Goshawk said, adding the current market environment was similar to 1999.

He favoured IT consultants and Japanese robotics groups that can potentially pick up revenues from AI heavyweights, in what he said was the typical chronology of a market gold rush.

“When someone strikes gold, (you) buy the local hardware store where the prospectors will buy all their shovels.”

INVESTORS TRY TO STAY IN AI WITHOUT EXCESSIVE RISK

Investors are also attempting to benefit from the trillion of dollars so-called hyperscalers such as Amazon, Microsoft and Alphabet are committing to AI data centres and advanced chips without taking on more direct exposure to these companies.

Fidelity International multi-asset manager Becky Qin said uranium was her favoured new AI trade because power-hungry AI data centres could gobble up nuclear energy.

Kevin Thozet, investment committee member at asset manager Carmignac, was taking profits on Magnificent Seven stocks and building up a position in Taiwan’s Gudeng Precision, which makes delivery boxes for AI chipmakers including TSMC.

Asset managers are also concerned that the rush to build data centres could result in overcapacity, as in the fiber-optic cable boom in the telecoms industry.

“In any new technological paradigm we don’t get from A to B without excesses along the way,” said Pictet Asset Management senior multi-asset strategist Arun Sai.

Even though top AI stocks like Microsoft, Amazon, and Alphabet are being powered by strong earnings, he still sees “the building blocks of a bubble” and favours Chinese stocks as a hedge if rapid AI advancements in China sap Wall Street’s AI enthusiasm.

Some investors, though, do not favour this relative value approach to AI investing as a way to mitigate future losses.

Oliver Blackbourn, portfolio manager at Janus Henderson, said he was hedging his U.S. tech positions with European and healthcare assets lest an AI stock crash takes the U.S. economy down with it.

He said it was impossible to forecast how long the AI craze would roll on because calling the peak was usually only possible with hindsight.

“We’re in 1999 until the bubble pops.”

Published – October 25, 2025 09:55 am IST