Module 2 — Figure candidates

Panel A is the four-phase cognitive cycle itself, drawn as a loop: Mind-wandering to Awareness of MW to Shifting Attention to Sustained Focus, and back around. It is the exact model the module passage describes in prose, and the loop shape makes the key reassurance visible at a glance -- the cycle is designed to repeat, not to be escaped. Panel B shows how the button press anchors the four modeled intervals in the scanner, which covers the second half of the claim about how the study was run. This is the single highest-value figure for this section.

A helpful starting point comes from a 2012 study (Hasenkamp et al., 2012) that aimed to understand mind wandering during meditation in much finer detail... they developed a model with four phases. First, the mind wanders. Second, you become aware that the mind has wandered. Third, you shift your attention back to your object of focus. And fourth, you sustain that focus. To test this model, the researchers then asked experienced meditators to focus on their breath while lying in an fMRI scanner. Each time a meditator noticed that their mind had wandered, they pressed a button... They found that the brain data closely aligned with the meditation manuals, with different brain regions associated with each of the four phases.

This is the direct visual evidence for the claim's final sentence -- that different brain regions were associated with each of the four phases. The four stacked, colour-coded panels map one-to-one onto the four phases of the cycle in Figure 1 (AWARE green, SHIFT orange, FOCUS yellow, MW blue), so a reader who has just seen Figure 1 can read this in seconds without knowing any neuroanatomy. Pairing it with Figure 1 makes the point that the phenomenological model and the brain data line up. The colour scheme deliberately matches Figure 1, which is why the two work best presented together.

A helpful starting point comes from a 2012 study (Hasenkamp et al., 2012) that aimed to understand mind wandering during meditation in much finer detail... they developed a model with four phases. First, the mind wanders. Second, you become aware that the mind has wandered. Third, you shift your attention back to your object of focus. And fourth, you sustain that focus. To test this model, the researchers then asked experienced meditators to focus on their breath while lying in an fMRI scanner. Each time a meditator noticed that their mind had wandered, they pressed a button... They found that the brain data closely aligned with the meditation manuals, with different brain regions associated with each of the four phases.

This is the source table for the three-column taxonomy the module reproduces: attentional, constructive, and deconstructive families, each broken into named sub-orientations with concrete example practices. Loving-kindness and compassion appear explicitly as the first entry under 'Relation orientation' in the constructive family, which anchors the module's placement of LKM. A clinician can scan it in under 20 seconds and immediately see where familiar practices (MBSR, MBCT, ACT, vipassana, centering prayer) sit within the framework. Note the footnote's own caveat that most practices contain elements of all three families, which is exactly the module's point about LKM engaging all three at once.

meditative fitness consists of three broad families of skills. The attentional family cultivates monitoring and focusing; the constructive family builds warmth and compassion; and the deconstructive family trains letting go (Dahl et al., 2015). While many other types of meditation might be more specialized, LKM engages all three at once.

A four-box loop diagram: engagement in meditation produces positive affect during meditation, which builds nonconscious motives for meditation, which feeds back into more engagement. This is a direct visual statement of the module's causal claim that finding a practice rewarding is what sustains it, rather than willpower or discipline. It is the clearest single image in the batch for explaining why intrinsic reward was the selection criterion for LKM.

we chose LKM because many people find it intrinsically rewarding, which helps generate motivation to continue practicing (Fredrickson et al., 2008; Van Cappellen et al., 2020).

A simple four-bar chart showing that a first exposure to loving-kindness meditation produced markedly higher post-meditation positive emotions than the mindfulness control condition, with non-overlapping standard error bars. This is the specific evidence behind 'we chose LKM' rather than another practice: LKM was the more enjoyable of the two on first contact. The second factor (control vs Pripos+ passage) is the paper's micro-intervention manipulation and is incidental to the module's claim, so the caption may need a one-line gloss for readers.

we chose LKM because many people find it intrinsically rewarding, which helps generate motivation to continue practicing (Fredrickson et al., 2008; Van Cappellen et al., 2020).

Two stacked bar charts showing that the more positive emotion people felt right after meditating, the more often (1.5 to 2.5 sessions per week) and the longer (31 to 49 minutes per week) they actually meditated over the next three weeks. This is the 'continued practice' half of the module's claim shown as behaviour, not self-report intention, and it completes the loop asserted in Figure 1. If only one Van Cappellen figure is used, Figure 1 is the better conceptual anchor; this one is the better empirical payoff.

we chose LKM because many people find it intrinsically rewarding, which helps generate motivation to continue practicing (Fredrickson et al., 2008; Van Cappellen et al., 2020).

A four-box left-to-right chain: loving-kindness meditation, increases in positive emotions, increase in resource, increase in life satisfaction. This is precisely the two-stage structure the module sentence asserts, immediate emotional lift first and durable resources second, rendered in a form a busy clinician grasps instantly. It also makes explicit that the resource-building is mediated by positive emotion rather than being a direct effect of the practice, which is the paper's actual finding.

Even brief practices can produce immediate increases in social connection and positive emotion, while sustained training may build more durable internal resources, including meaning, resilience, physiological regulation, and well-being (Fredrickson et al., 2008).

Titled in-figure as 'Primary Meta-analysis for Life Satisfaction,' this forest plot pools 15 trials of loving-kindness and compassion meditation to Hedges' g = 0.312 (95% CI 0.191-0.434, p < .001), and is the specific evidence for the life-satisfaction half of the claim, complementing the Zheng anxiety plot rather than duplicating it. Two production notes: the figure is printed sideways in the PDF, so the shipped crop has been rotated 90 degrees to upright, and the axis is labelled only 'Favours A / Favours B', which should be relabelled to control/LKM for a lay reader. Honest caveat: the RCT-only subset (Fig. 4, p. 12, six trials) was non-significant at g = 0.106, p = .520, so the life-satisfaction effect rests largely on non-randomised trials.

systematic reviews and meta-analyses linking LKM to reduced depression and anxiety and greater life satisfaction.

This is the dose-response figure the module's second sentence is actually about: each dot is one CCT participant, the x-axis is weekly minutes of formal meditation practice and the y-axis is percent improvement, with a rising fit line annotated r = 0.24, p < .05. It is the most persuasive single image in this batch for the module's core pedagogical point that warmth is trainable and that more practice yields more change, and a clinician reads it instantly. One honesty note for the authors: the practice-amount correlation held only for compassion for others (p > .09, > .43, > .10 for the other three scales), so the relationship is real but narrow and the scatter is wide.

A randomized controlled trial found that compassion cultivation training, a practice similar to loving-kindness meditation, increased mindfulness and happiness while reducing worry. Participants who practiced more also tended to experience greater improvements, further suggesting that these capacities strengthen through repeated practice (Jazaieri et al., 2014).

This paper publishes no forest plot, so its Table 2 is the only figure that carries the pooled result, and it maps one-to-one onto the module sentence: against passive control, mindfulness g = 0.22 (p = .022), compassion g = 0.36 (p = .020), positive affect g = 0.44 (p < .001), negative affect g = 0.44 (p = .004), and psychological symptoms g = 0.36 (p = .006). A reader can scan the passive-control rows and see all five claimed outcomes improve with small-to-moderate effects. It is the densest item in this batch and the heterogeneity columns are specialist material, so consider reproducing only the Outcome / Comparison / n / g / 95% CI / p columns. Honest caveat visible in the same table: against active controls and alternative treatments most effects shrink to non-significance, which the module text does not currently mention.

A 2024 systematic review and meta-analysis found that LKM interventions increased positive emotions, mindfulness, compassion, and reduced adverse psychological symptoms (Petrovic et al., 2024).

A clean forest plot of nine randomised trials of LKM versus waitlist on daily positive emotion, with an overall Hedges' g of 0.395 (95% CI 0.258-0.533) sitting clearly on the 'Favours LKM' side of zero. The explicit 'Favours Waitlist / Favours LKM' axis labels make it readable in seconds by a clinician who has never seen a forest plot, and the loving-kindness vs. compassion subgroups show the effect holds for both framings of the practice. This is the single best visual anchor for the 'increases positive emotion' half of the module sentence, and it can carry the Galante co-citation too since that paper's own forest plots are missing. Note Figures 3-5 and 7-8 in this same paper are near-duplicate sensitivity/non-RCT reruns of the same data and would be redundant with this one; Figure 6 pools only two studies.

Systematic reviews and clinical trials indicate that loving-kindness and compassion meditation can increase positive emotion, mindfulness, and life satisfaction while also reducing symptoms of depression and anxiety (Galante et al., 2014; Zeng et al., 2015).

The RCT forest plot from 'Effects of Loving-Kindness and Compassion Meditation on Anxiety,' pooling 19 randomised trials to a standardised mean difference of -0.28 (95% CI -0.42 to -0.14), i.e. a small but reliable reduction in anxiety. The red weighted squares and clear zero line make the direction obvious at a glance, and it is the direct evidentiary backing for the 'reduced anxiety' half of this claim. The visible caveats are worth keeping if reproduced: I-squared = 96% and a prediction interval of [-0.86, 0.30] that crosses zero. I deliberately skipped this paper's Fig. 4 (non-randomised trials, p. 9) as redundant with this one and less legible, since a single outlier at -2.36 stretches its axis to plus or minus 3 and squashes every other study.

systematic reviews and meta-analyses linking LKM to reduced depression and anxiety and greater life satisfaction.

A hierarchical regression that adds predictors in the order a reader would want them: baseline self-compassion and prior meditation experience first, then frequency of formal practice, then compassion practice quality (CPQS). Frequency adds 7% of explained variance and is not flagged significant; practice quality then adds a further 18% (delta R-squared = .18*), driven by somatic perception during the LAST meditation (beta = 0.42, p = .011). The delta R-squared column lets a healthcare professional see the quality-over-frequency point without parsing any coefficient. This is the most directly on-claim table of the three LKM/compassion papers.

The same pattern appears in research on LKM and compassion focused meditation. The ability to evoke and sustain warmth and compassion predicts greater well-being, more positive attitudes toward others, and increased self-compassion. As with research in mindfulness, the benefits of LKM hinged more on measures of quality than on frequency (Zeng et al., 2023; Andreu et al., 2022; Navarrete et al., 2021).

The clearest single piece of evidence in this section. Six models line up 'Minutes med' (practice time) against 'PQ-M slope' (improvement in practice quality) as predictors of psychological functioning. At posttreatment both matter about equally (betas .29 and .28). At 5-month follow-up practice time collapses to a nonsignificant beta = 0.12 [-0.15, 0.39], p = .399, while practice quality holds at beta = 0.43 [0.15, 0.72], p = .003. A clinician can read the two follow-up rows and immediately see that what endures is how you practised, not how long. Effect sizes come with 95% CIs, which reads well for a science-literate audience.

In these studies, higher-quality practice is defined as repeatedly returning to present-focused attention with an accepting, nonresistant attitude. This quality of engagement predicts better psychological outcomes more strongly than the amount of time spent meditating (Del Re et al., 2013; Goldberg et al., 2014).

Three diverging arrows on a performance-vs-experience plot: 'Expert Performance' keeps climbing, while 'Arrested Development' and 'Everyday Skills' flatten out once the activity becomes automatic. This is the single clearest visual statement of the module's core claim that accumulating experience is not the same as improving, and that skill keeps developing only when the practitioner deliberately stays in the effortful 'cognitive/associative' mode rather than coasting. A nurse or physician will recognise the arrested-development arrow immediately as the career pattern the module is warning against, and it reads in well under 20 seconds.

learning requires breaking the practice into concrete skills and refining them over time (Ericsson, 2006; Ericsson & Pool, 2016).

A staircase of discrete performance states (1), (2), (3) ... (n), with a magnified inset showing deliberate practice moving the learner from Mechanism (i-1) to Mechanism (i) to Mechanism (i+1). This is the closest thing in the literature to a picture of the module's actual sentence: expertise is built by breaking practice into concrete sub-skills and refining them one at a time. It gives the reader a mental model for why a 'recipe' of specific steps beats an undifferentiated review of the whole practice.

learning requires breaking the practice into concrete skills and refining them over time (Ericsson, 2006; Ericsson & Pool, 2016).

The famous accumulated-practice-hours curves for violinists, cleanly separated by attainment level, with amateur pianists flat at the bottom. It is the empirical backbone of the whole deliberate-practice literature and the origin of the '10,000 hours' idea the module's audience will already have heard of. Its value here is that the groups differ in accumulated deliberate practice, not in total years of exposure — which is the distinction the section is drawing. Note it is about musicians, so it supports the general principle rather than anything about meditation specifically.

learning requires breaking the practice into concrete skills and refining them over time (Ericsson, 2006; Ericsson & Pool, 2016).

A three-circle Venn whose axes are Intrinsic Motivation, Playful Intention, and Game Context; only the centre — where all three overlap — counts as 'Pure Play', while activities missing intrinsic motivation fall out as 'Extrinsic Play', 'Serious Gaming' or 'Distraction'. It draws exactly the boundary the module sentence draws, that play must be voluntary and intrinsically motivated to be play at all, and it pairs naturally with Gray's definition in the same passage. Instantly legible with no specialist vocabulary.

research suggests that voluntary, intrinsically motivated play can foster increased creativity, resilience, and adaptability (Whitton, 2022; Diaz-Varela & Wright, 2019). Consistent with this view, the psychologist Peter Gray (2017) defines play as an activity that is self-chosen, imaginative, and focused more on process than outcome.

A three-tier pyramid setting out what playful learning consists of: surface (game) structures, deep (play) structures, and implicit (playful) structures. The bottom tier lists 'intrinsic motivation' and 'acceptance of risk-taking and failure', and the middle tier lists 'imagining possibilities' and 'novelty and surprise' — i.e. the mechanisms by which the module claims play produces creativity, resilience, and adaptability. It gives a course reader a compact vocabulary for what a playful attitude toward one's own meditation practice would actually involve. Note the framework is written for higher-education teaching, not clinical practice, so it transfers by analogy.

research suggests that voluntary, intrinsically motivated play can foster increased creativity, resilience, and adaptability (Whitton, 2022; Diaz-Varela & Wright, 2019). Consistent with this view, the psychologist Peter Gray (2017) defines play as an activity that is self-chosen, imaginative, and focused more on process than outcome.

Ranks the eleven motivational factors reported across 24 studies of medical students worldwide, grouped as scientific, humanitarian, and societal. 'Work for people/help underprivileged' is the single most frequently cited factor in the whole review at 75% of studies, ahead of interest in medicine (66.7%) and far ahead of social status (33.3%) and financial security (25%). For an audience of practising clinicians this is the most direct and most personally resonant evidence in the batch for the module's claim — it puts a number on the wish to help others being the modal reason people enter the profession. Compact and legible at a glance.

many are drawn to medicine by the wish to relieve suffering and help others (Goel et al., 2018; Rother et al., 2024).

A bar chart of five explicit motives — achievement, affiliation, altruism, freedom, power — rated by 525 medical students, split by gender, with standard errors. Altruism is the highest-rated motive for both women and men, and power is the lowest, which is precisely the module's claim shown as data rather than asserted. This is the single most engaging figure in the batch for an audience of practising nurses and physicians: it invites them to locate their own reason for entering the profession on the chart, which is exactly what the 'staying connected to aspiration' section is asking them to do. Reads in a few seconds and needs no statistical background.

many are drawn to medicine by the wish to relieve suffering and help others (Goel et al., 2018; Rother et al., 2024).

The paper's central result, laid out as three tiers of motivation — External (duty, disposition, suggestion from others, self-regulation), Internal (developing higher capacities, spiritual experience, self-realization, self-improvement), and Service (meditating for something or someone other than oneself) — each with characteristics and verbatim participant quotes. It maps almost line-for-line onto the module sentence: the External tier includes coping and 'to relax in order to avoid pain', the Internal tier is self-understanding and balance, and the Service tier is service. The participant quotes make the progression feel human rather than schematic, which is the effect this section wants. It is a full-page table, so it needs to be reproduced at a readable size.

while many people begin meditating primarily for stress reduction or health, their motivations frequently expand to include self-understanding, balance, and service (Sparby & Ott, 2018).

This is the single most useful figure in the batch: a compact 2x2 that crosses type of experience (cognitive reflection vs. experiential absorption) with focus of attention (external world vs. internal self), yielding the four primary savoring processes -- thanksgiving/gratitude, basking/pride, marveling/awe, and luxuriating/physical pleasure. It converts the abstract word 'savoring' into four named, recognizable states in about ten seconds of reading. For a module telling clinicians that savoring is a distinct, trainable skill, this is the definitional anchor, and 'luxuriating' (experiential absorption in physical pleasure) maps almost exactly onto the module's own topic of savoring pleasant sensations during meditation.

In the past twenty years, scientists have begun investigating savoring as a distinct and trainable skill (Bryant & Veroff, 2007).

The expanded companion to Table 5.1: it walks the four savoring processes across eight criteria including conceptual definition, focus of attention, dominant feeling, real-world examples, timing, and outward bodily expression. It is denser than a 20-second figure, but it reads as a reference table rather than a statistical one, and the 'outward expression' and 'real-world examples' rows are unusually concrete for clinicians (e.g. eyes closed with a satisfied 'mmm' for luxuriating). Best used as an optional expandable companion beneath Table 5.1 rather than as the primary graphic.

In the past twenty years, scientists have begun investigating savoring as a distinct and trainable skill (Bryant & Veroff, 2007).

The forest plot pooling 20 randomized trials of savoring interventions, with a clearly labelled overall estimate of g = 0.51 [0.26, 0.77]. This is the strongest evidentiary figure in the batch for the module's summary claim that training savoring works: it shows a medium, statistically significant pooled benefit while being honest that individual trials scatter widely and several cross zero. Well-formatted and readable at a glance, and it includes Klibert 2022 as one of its rows, which visually ties this citation to the trial described earlier in the same section.

Intervention studies similarly suggest that training this skill can strengthen well-being and resilience while reducing distress and depressive symptoms (Smith & Hanni, 2019; Smith & Hollinger-Smith, 2015; Chen et al., 2026).

This is precisely the condition-comparison graph the module sentence describes. Grouped bars show mean positive emotion for all five arms -- true control, guided imagery control, savoring through reminiscence, savoring through anticipation, and savoring the moment -- at three time points. The groups are indistinguishable at Time 1 and Time 2 and then fan apart at Time 3, with Savoring the Moment highest and true control lowest, which is exactly the claim that present-moment savoring outperformed no intervention, neutral imagery, and savoring a memory. A reader gets the whole result at a glance without needing the ANOVA.

In a randomized controlled trial, participants first completed a stressful social task and then practiced different forms of savoring. Focusing on positive experience in the present moment helped participants experience positive emotion. It was more effective than no intervention, neutral guided imagery, or savoring a memory (Klibert et al., 2022).

Mean happiness at baseline, post-intervention, 1 month, and 3 months, split by whether participants actually completed the savoring practice (high vs. low fidelity). The two groups start close together and then diverge, with the high-fidelity group highest at the 3-month follow-up -- directly supporting the module's clause that gains were still evident three months later. The dose-response framing (people who practiced more benefited more) also reinforces the section's core premise that savoring is a trainable skill rather than a trait.

In one study, a week of savoring practice increased happiness, life satisfaction, and resilience, with many of these gains still evident three months later (Smith & Hanni, 2019).

The resilience counterpart to Figure 3, on the same four-point timeline through the 3-month follow-up. The separation is cleaner here than for happiness: the two groups are identical at baseline and the high-fidelity group rises and stays elevated at 1 and 3 months while the low-fidelity group does not. Resilience is the outcome most likely to matter to a nurse or physician audience, so if only one Smith and Hanni figure is used, this is the stronger choice.

In one study, a week of savoring practice increased happiness, life satisfaction, and resilience, with many of these gains still evident three months later (Smith & Hanni, 2019).

A simple four-bar chart: naming the feeling lowered rated distress when the triggering image was highly intense, but slightly raised distress when the image was only mildly intense. This is exactly the 'sometimes helps' nuance the module sentence is making, and it gives a reader a concrete rule of thumb (labeling pays off when the emotion is strong, not when it is mild). Readable in well under 20 seconds with no specialist knowledge.

beginners sometimes benefit from using brief mental labels, like 'planning,' 'worrying,' or 'daydreaming.' This can sometimes help clarify the moment of recognition, and there's also evidence that it may reduce distress in some cases (Levy-Gigi et al., 2022).

Two side-by-side panels showing that when people tried to regulate an emotion, adding a verbal label left them feeling worse than regulating alone — and that this held for both reinterpretation (panel a) and acceptance (panel b). The acceptance panel matters here because acceptance is the strategy closest to what meditators are doing, so this is the strongest available image for the module's caveat that labeling can make emotions feel more stuck. Bars are close together, so the caption's significance markers carry the message; still readable at a glance.

However, labeling can also sometimes make emotions feel more fixed or stuck (Nook et al., 2021), so we encourage you to experiment.

This is the module's claim made visible: the left panel is the error-related negativity, and the solid black 'Meditation Error' trace dips further than the grey 'Control Error' trace in the ~0-100 ms window after a mistake. It literally shows the brain catching an error within a tenth of a second, which is the neural analogue of the Recognize step. Readers will need one sentence of scaffolding (that downward = larger response, and that the axis is inverted with negative up), but the group difference is legible without EEG training.

researchers used EEG to measure brain responses while participants rapidly identified visual targets among multiple competing distractions (Lin et al., 2019). Compared to a control group, participants who completed a 20-minute open-monitoring meditation displayed a stronger signal associated with noticing their mistakes. A single session wasn't enough to improve actual performance on the task, but it did strengthen the neurological signal associated with catching mistakes.

A textbook-clean forest plot: thirteen trials, 2,371 participants, every point estimate on the favorable side of zero, and a pooled SMD of −0.32 (95% CI −0.41 to −0.24) with I² = 0%. It supports the 'reductions in psychological distress' part of the claim with the most recent and methodologically strongest design in the batch (individual participant data), and the near-zero heterogeneity makes it an unusually easy read for a non-specialist. It is partly redundant with Goyal Figure 1A — both are 'the evidence is favorable but modest' figures — so if the module wants only one, Goyal covers more outcome domains while Galante is the more current and more visually approachable. Showing both is defensible since Goyal is a breadth-of-domains summary and Galante is a depth-on-one-outcome forest plot.

systematic reviews and meta-analyses consistently report stress-related benefits from mindfulness-based programs. These include reductions in psychological distress, anxiety, and stress, along with favorable changes in cortisol, blood pressure, and heart rate (Goyal et al., 2014; Galante et al., 2023; Pascoe et al., 2017).

The widely reproduced summary figure from the JAMA Internal Medicine review: a combined table and forest plot giving, for each outcome domain (anxiety, depression, stress/distress, negative affect, quality of life, pain and others), the number of trials, total N, direction and magnitude of effect, and a GRADE strength-of-evidence rating, all against a 'favors meditation / favors control' axis. It is the best single figure in the batch for the module's 'reviews consistently report' sentence because it shows breadth and calibrated confidence at the same time — moderate evidence for anxiety, depression and pain; low or insufficient elsewhere. Two caveats for the authors: the caption text sits at the foot of the page rather than beneath the panel, so it is supplied here verbatim rather than inside the crop, and panel 1B (comparisons against specific active controls, almost all rated 'insufficient') was deliberately excluded; include it only if the module wants to make the point that meditation has not been shown superior to other active treatments.

systematic reviews and meta-analyses consistently report stress-related benefits from mindfulness-based programs. These include reductions in psychological distress, anxiety, and stress, along with favorable changes in cortisol, blood pressure, and heart rate (Goyal et al., 2014; Galante et al., 2023; Pascoe et al., 2017).

Supports the first half of the claim. Sagittal brain slices with the subgenual ACC cluster highlighted in red, each paired with a scatterplot of Perceived Stress Scale score against amygdala-sgACC connectivity in the n = 130 discovery sample. A clinician sees immediately both where the effect is in the brain and that the relationship is a positive but modest linear trend. The brain-plus-scatter pairing is exactly the kind of anchor the module needs, and the visible scatter keeps the reader honest about effect size (R ≈ 0.10-0.15).

higher perceived stress has been associated with stronger resting connectivity between the amygdala and other stress-related brain regions. In a randomized trial, a three-day intensive mindfulness program reduced this functional coupling (Taren et al., 2015).

This is the randomized-trial figure that carries the second half of the claim. The right panel is the group x time interaction in its simplest possible form: four bars in which only Post-HEM (the 3-day mindfulness retreat arm) drops below zero while the matched relaxation-training arm stays positive. That contrast is readable in well under twenty seconds and makes the point that the effect is specific to the mindfulness component rather than to resting at a retreat. The left panel gives the anatomical location. This is the single best figure in the batch for this citation.

higher perceived stress has been associated with stronger resting connectivity between the amygdala and other stress-related brain regions. In a randomized trial, a three-day intensive mindfulness program reduced this functional coupling (Taren et al., 2015).

This is the paper's core result table: five HRV metrics (RMSSD, HF normalized, parasympathetic nervous system index, SD2/SD1 ratio, and stress index) measured at baseline and during each of three brief practices (grounding, deep breathing, body scan), with p values against baseline. It is the single clearest evidence for the module's claim, showing that short relaxation exercises shifted clinicians toward parasympathetic predominance and lowered the composite stress index. It is well designed, self-explanatory to a healthcare reader, and honest about the small sample (n = 13) in its own footnote. Note that deep breathing actually lowered HF, so the table is not uniformly positive, which is a useful nuance if the module wants it.

Evidence for this shift can be seen in a small study of pediatric nurses, nurse practitioners, and physicians. Brief relaxation practices were associated with lower self-reported stress and increased heart-rate variability, which is a physiological measure of improved parasympathetic function (Wolfe et al., 2024).

Duchenne's original 1862 photograph of a spontaneous smile of enjoyment: the corners of the mouth are up, but the cheeks are also raised and the skin is visibly gathered in around the eyes. This is the 'genuine' half of the exact contrast the module describes in words. Its caption explicitly instructs the reader to compare cheeks and eye-skin with Figure 2, so the two photographs are designed to be shown as a pair. Note the image is a 19th-century clinical photograph of an elderly patient and reads as somewhat startling out of context, so it needs a sentence of framing.

Fifth, there's **false smiling.** Researchers who study facial expression have long distinguished between genuine smiles and artificial ones performed without actual enjoyment. A false smile lifts the corners of the mouth, but it does not spread naturally through the rest of the face. The cheeks do not rise naturally, and the eyes do not soften or gently narrow.

The same man with his zygomaticus major electrically stimulated by Duchenne's electrodes, producing a mouth-only smile. The mouth corners are pulled up but the eyes stay wide and unsoftened and the cheeks are not lifted — literally a smile with the enjoyment removed. Shown beside Figure 1 this is the single most direct visual for the module's sentence that a false smile 'lifts the corners of the mouth, but it does not spread naturally through the rest of the face.' The visible wires make the manipulation self-evident without any statistics.

Fifth, there's **false smiling.** Researchers who study facial expression have long distinguished between genuine smiles and artificial ones performed without actual enjoyment. A false smile lifts the corners of the mouth, but it does not spread naturally through the rest of the face. The cheeks do not rise naturally, and the eyes do not soften or gently narrow.

Four photographs of pencil-held facial configurations; panels (c) and (d) are the modern, non-clinical version of the Duchenne contrast — (c) is lip-corner pulling only, (d) adds cheek raising, and the difference around the eyes is plainly visible between the two. This is a gentler and more relatable alternative to the 19th-century Duchenne plates if the authors want only one photographic figure. Panels (a) and (b) are experimental controls that the module does not discuss, so the authors may prefer to crop to (c) and (d) alone.

Fifth, there's **false smiling.** Researchers who study facial expression have long distinguished between genuine smiles and artificial ones performed without actual enjoyment. A false smile lifts the corners of the mouth, but it does not spread naturally through the rest of the face. The cheeks do not rise naturally, and the eyes do not soften or gently narrow.

The study's main result, and it doubles as the figure showing the three facial-posing techniques the brief asked for — facial mimicry, voluntary facial action and pen-in-mouth are the three panel columns. Happy poses beat neutral poses in the two techniques that involve deliberately forming an expression, both with and without positive images present, supporting 'initiates and amplifies'. Important honesty note for the authors: the pen-in-mouth column shows essentially no effect, so the module's word 'reliably' is stronger than this figure supports and the caption should acknowledge that the effect depends on the posing method. There is no forest plot by site in this paper; Fig. 1 is only a world map of country sample sizes and is not worth reproducing.

A large multi-laboratory study of nearly 4,000 participants across 19 countries found that voluntary and genuine smiling reliably both initiates and amplifies feelings of happiness (Coles et al., 2022).

Two rows that make the claim almost self-reading: Duchenne smiles correlate .703 with amusement, .594 with happiness, .387 with excitement and .401 with interest — all significant — while other smiles correlate .135, .180, .080 and .189, none significant. A reader scanning two rows sees immediately that only the genuine smile tracks reported enjoyment. This is the cleanest quantitative support for the 'more closely associated with enjoyment' wording anywhere in this batch.

In classic studies, genuine smiles were more closely associated with enjoyment and positive emotion than false, mouth-only smiles (Ekman et al., 1990, Soussignan, 2002).

A four-bar chart of seconds-to-cardiovascular-recovery after a fear film, by the emotion of the film shown next: contentment ~17s and amusement ~19s versus neutral ~40s and sadness ~61s. This is the canonical 'undoing effect' graph and it makes the module's claim legible in a glance — contentment, the state the practice cultivates, is the fastest condition, and the ordering is monotone in the direction the text asserts. Requires no statistical training to read.

cultivating states such as contentment and warmth can help the cardiovascular system return to baseline more quickly after stress exposure. Positive emotion appears to reverse some of the residual arousal that stress leaves behind (Fredrickson & Levenson, 1998; Fredrickson et al., 2000).

The 1998 result replicated in a larger sample: mean duration of cardiovascular reactivity is ~22-23s for the contentment and amusement groups versus ~35s neutral and ~42s sadness. Because it is the same bar-chart form as the 1998 figure, the two can be shown side by side to make the replication visible, or the authors can pick one. Note the effect is smaller here than in 1998, which is worth not over-claiming in the caption.

cultivating states such as contentment and warmth can help the cardiovascular system return to baseline more quickly after stress exposure. Positive emotion appears to reverse some of the residual arousal that stress leaves behind (Fredrickson & Levenson, 1998; Fredrickson et al., 2000).

The chopsticks manipulation: three photographs of the same woman holding chopsticks in a neutral position, a standard (mouth-only) smile, and a Duchenne smile with the eyes engaged. It makes an otherwise odd-sounding method instantly concrete, and it independently reinforces the near-enemies distinction using a clean modern photo instead of the 19th-century plates. Pair it with Fig. 2 so the reader sees the manipulation and then its physiological consequence.

During laboratory stressors, participants who held a smiling expression showed faster heart-rate recovery afterward than those who held a neutral expression (Kraft & Pressman, 2012).

Panel (a) is exactly the module's claim: heart rate across the 6-minute recovery window after the star-tracer stressor, with the neutral group settling near 70 bpm while standard smilers settle near 67 and Duchenne smilers near 64, and the three curves clearly separated with error bars. Panel (b) breaks the smilers into aware and nonaware subgroups, a nuance the module does not discuss — if space is tight the authors can crop to panel (a) alone. Fig. 3 in the same paper repeats this for the cold-pressor stressor and is redundant here.

During laboratory stressors, participants who held a smiling expression showed faster heart-rate recovery afterward than those who held a neutral expression (Kraft & Pressman, 2012).

A four-bar chart of self-reported affective reaction by facial configuration. The Duchenne smile group reports roughly twice the positive reaction of the non-Duchenne (mouth-only) group, which in turn barely differs from the two non-smiling controls. That is precisely the module's 'genuine versus false, mouth-only' comparison rendered as one readable graph, and it pairs naturally with Soussignan Figure 1, whose panels (c) and (d) are the very configurations labelled n-DSG and DSG here.

In classic studies, genuine smiles were more closely associated with enjoyment and positive emotion than false, mouth-only smiles (Ekman et al., 1990, Soussignan, 2002).

A clean isometric schematic of the rapid serial visual presentation stream: a rising staircase of letter frames with the two digit targets T1 and T2 arrowed and labelled, the 50 + 34 ms frame duration marked, and the two end-of-trial probe questions shown. The module explains the attentional blink paradigm in prose, and a reader who has never seen an RSVP task will grasp it far faster from this one image than from the sentence. This is the diagram that makes the rest of the attentional-blink discussion (including the Wang et al. figure) interpretable.

The attentional blink is a brief period in which a second event is less likely to be noticed when it appears soon after the first... Meditation training appears to decrease this attentional blink. In one study, participants completed three months of intensive meditation training before performing an attentional blink task. Compared to controls, they became significantly better at detecting rapidly presented visual targets. EEG recordings also suggested that their attentional resources were being allocated more efficiently (Slagter et al., 2007).

The pre/post T2-accuracy result for the 3-month retreat. Panel B is the 20-second takeaway and carries the module's claim on its own: at the short T1–T2 interval (inside the blink window) practitioners jump from ~61% to ~81% T2 accuracy between Time 1 and Time 2, while novices move only ~61% to ~70%; at the long interval both groups barely move, showing the gain is specific to the blink window rather than general practice. Panel A is a per-participant bar chart that is denser and needs the red/blue legend to read, so if the authors want maximum clarity they may prefer to reproduce panel B alone and label it 'Figure 2B'. I cropped the complete figure with its full caption so the choice stays with them.

Meditation training appears to decrease this attentional blink. In one study, participants completed three months of intensive meditation training before performing an attentional blink task. Compared to controls, they became significantly better at detecting rapidly presented visual targets (Slagter et al., 2007).

Four accuracy-by-lag panels with pre/post curves for the mindfulness and waitlist-control groups. Panel b is the money panel: the characteristic attentional-blink dip at lag2–lag3 is unmistakable, and the solid red post-mindfulness curve sits clearly above the dashed red pre-mindfulness curve across the recovery lags while the two blue control curves stay close together. It replicates the Slagter result in an ordinary 8-week program rather than a 3-month retreat, which is the point the module is making, and the shared lag axis makes it visually comparable to the Slagter figure. Caveat: the figure shows the accuracy improvement only; the correlation with FFMQ non-reactivity that the module's second clause cites is reported in the paper's Table 7, not here.

A similar pattern was observed in a randomized waitlist-controlled study of an eight-week mindfulness program. Participants showed improved ability to detect rapidly presented targets in sequence, and these improvements were associated with increases in non-reactivity to thoughts and emotions (Wang et al., 2022).

A single forest plot ranking all 16 cognitive outcomes by Hedge's g against active controls, with CIs and p-values printed alongside. It directly displays the three constructs the module names: Global Cognition (g = 0.21), Executive Attention (g = 0.19), Shifting Accuracy (g = 0.20), and Sustained Attention Accuracy (g = 0.21), all p < .01. The 'MBIs harmful / MBIs protective' axis labels make the direction legible at a glance to a non-specialist, and the visible spread between significant and non-significant domains conveys honestly that mindfulness helps attention specifically rather than cognition uniformly. Note the plot compares MBIs to ACTIVE controls, so these are conservative estimates; it does not itself show the stress/anxiety/depression moderator effect, which the module's second sentence claims and which lives in the paper's supplementary moderator tables.

a meta-analysis of over one hundred randomized controlled trials found that mindfulness practice significantly improves global cognitive function, particularly executive attention abilities such as shifting attention between objects and sustaining attention over time. These improvements were especially large among individuals initially experiencing stress, anxiety, or depression (Zainal & Newman, 2024).

The paper's entire outcome result in three rows: burnout fell 23.34 to 21.72 (g = -0.26, p = .011) and secondary traumatic stress fell 23.63 to 21.28 (g = -0.48, p = .005), while compassion satisfaction was unchanged (p = .96). It is a well-designed, colour-blocked table that a nurse or physician reads instantly, and it maps one-to-one onto the module's two named outcomes. The unchanged compassion-satisfaction row is worth keeping rather than trimming, since it shows the effect was specific to the distress measures. I rendered every page of this 8-page paper; page 5 holds the only two tables and there are no figures, and Table 1 is participant demographics, which the brief deprioritises.

during demanding clinical shifts, engaging in a three-minute mindfulness breathing micropractice helped reduce compassion fatigue and emotional exhaustion among nurses (Owens et al., 2020).