Figure 1 includes violin plots of additive versus subtractive advice given across studies. Full statistical reporting of all effects is available in the online supplement.
Note. Number of additive and subtractive pieces of advice given in response to hypothetical vignettes (Study 1, A; Study 2, B), on Reddit (Study 3, C) and in response to vignettes that emphasised the engagement in activities that were harmful for mental health by their presence (‘positive’, e.g. gambling) or through their absence (‘negative’, e.g. not exercising)(Study 6.5, D; Study 7, E; Study 8, F).
Advice in response to hypothetical mental health difficulties
In Study 1, participants were shown accounts of people experiencing mental health difficulties with information on what activities in their life were affecting their mental health. Participants gave open text advice to each account to help them improve their mental health and this advice was coded for the amount of additive versus subtractive advice given. Participants offered more additive (EMMean = 2.801; SE = 0.060) than subtractive advice (EMMean = 0.931; SE = 0.060), F(1, 1652) = 63.061, p < 0.001, partR2 = 0.005, 95% CI[0.013, 0.001].
Study 2 used the same design to examine if Study 1 findings replicated. Participants suggested more additive (EMMean = 1.516; SE = 0.029) than subtractive advice (EMMean = 0.447; SE = 0.029) in their open text responses to accounts of mental health difficulties, F(1, 1419.12) = 1291.66, p < 0.001, partR2 = 0.073, 95% CI [0.088, 0.051]. Again, although there was an interaction between the amount of additive versus subtractive advice offered and participant gender, F(1, 1419.12) = 22.592, p < 0.001, partR2 = 0.006, 95% CI[0.016, 0.001], both men, b = 0.927, SE = 0.043, t(1420) = 21.846, p < 0.001, 95% CI[0.844, 1.010], and women, b = 1.210, SE = 0.042, t(1421) = 29.051, p < 0.001, 95% CI[1.128, 1.290], suggested more additive than subtractive solutions. There was also an interaction between the amount of advice offered of each type and participant age, F(1, 1418.90) = 4.7202, p = 0.030, partR2 = 0.003, 95% CI[0.011, 0.000], such that participants became more additive the older they were, b = 0.006, SE = 0.003, t(365.58) = 2.304, p = 0.022, 95% CI[0.001, 0.012], but there was no statistically significant association between age and subtractive advice, b = 0.000, SE = 0.003, t(365.22) = 0.107, p = 0.915, 95% CI[−0.005, 0.006].
Naturalistic advice-giving on Reddit
In Study 3, we examine whether the preference for additive advice giving occurs in naturalistic settings. Advice-seeking posts and their associated comments from r/Depression and r/Anxiety on Reddit were scraped. Comments were coded for the amount of additive and subtractive advice they gave. Users offered more additive (EMMean = 1.860; SE = 0.113) than subtractive advice (EMMean = 0.174; SE = 0.113), F(1, 119) = 80.837, p < .001, partR2 = 0.229, 95% CI[0.320, 0.146]. There was no statistically significant main effect of the number of activities a person reported themselves as already engaged in, F(1, 119) = 0.000, p = 0.993, partR2 = 0.000, 95% CI[0.022, 0.000], or interaction of this with Solution, F(1, 119) = 0.103, p = 0.749, partR2 = 0.000, 95% CI[0.022, 0.000].
Effectiveness and feasibility
Study 4 examined whether additive advice is given more frequently than subtractive advice because it is viewed as more effective or feasible. Participants were shown the cases of mental health difficulties, and were then shown three different advice options (entirely additive, entirely subtractive or mixed) and asked to rate the effectiveness and feasibility of each. Participants rated entirely additive advice as being more effective than entirely subtractive advice, b = 0.732, SE = 0.038, t(2840) = 19.497, p < 0.001, 95% CI[0.644, 0.821], and advice that was a mixture of additive and subtractive, b = 0.338, SE = 0.038, t(2840) = 9.003, p < 0.001, 95% CI[0.250, 0.426]. The additive advice was also rated as more acceptable or feasible than subtractive, b = 0.594, SE = 0.038, t(2840) = 15.451, p < 0.001, 95% CI[0.504, 0.684], and mixed advice, b = 0.259, SE = 0.038, t(2840) = 6.727, p < 0.001, 95% CI[0.168, 0.349]. Participants also said that if they experienced the same difficulty as the person depicted in the scenario that they would be more likely to adopt the additive advice than the subtractive, b = 0.800, SE = 0.049, t(2840) = 16.233, p < 0.001, 95% CI[0.684, 0.915] or mixed advice, b = 0.376, SE = 0.049, t(2840) = 7.642, p < 0.001, 95% CI[0.261, 0.492](See Fig. 2).
Note. Self-reported ratings of effectiveness (A) and acceptability (B) of, and willingness to use (C), solutions that are purely additive, subtractive or a mixture of the two (Study 4).
Emphasising subtractive advice
Studies 5 and 6 presented hypothetical accounts (as in studies 1 and 2) and gave participants information about additive and subtractive advice, gave examples of each and gave them practice giving each. It also guided participants towards giving five of each advice type. Participants also ranked the advice they gave for their potential benefit. In Study 5, participants still gave more additive (EMMean = 3.290; SE = 0.085) than subtractive advice (EMMean = 2.690; SE = 0.085), F(1, 472.69) = 174.722, p < 0.001, partR2 = 0.009, 95% CI[0.029, 0.000], even though they were explicitly taught about additive and subtractive responses, practiced both types, and were prompted to give (up to five) subtractive advice first. When asked to rank each piece of advice in terms of their potential benefit, participants also ranked more additive (EMMean = 2.44; SE = 0.042) than subtractive (EMMean = 1.93; SE = 0.042) advice in their top five most beneficial pieces of advice, F(1, 447.42) = 75.414, p < 0.001, partR2 = 0.014, 95% CI[0.039, 0.002]
Study 6 replicated these effects with participants again offering more additive (EMMean = 3.57; SE = .086) than subtractive advice (EMMean = 2.91; SE = 0.086), F(1, 517.07) = 235.018, p < 0.001, partR2 = 0.051, 95% CI[0.087, 0.024], and ranking more additive (EMMean = 2.45; SE = 0.042) than subtractive (EMMean = 2.17; SE = 0.042) advice in their top five most beneficial, F(1, 667) = 22.652, p < 0.001, partR2 = 0.103, 95% CI[0.149, 0.065].
Pre-existing activities and their harm
Study 6.5 recoded the scenarios from studies 5 and 6 to create a new variable capturing whether the advisee was described as engaging in mostly positive harms (i.e. activities that were harmful by virtue of their presence, e.g. smoking)
or negative harms (i.e. activities that were harmful by virtue of the absence of benefit, e.g. not exercising).
Even with this new factor included with analyses, there was still a main effect of advice type, F(1, 998.73) = 395.17, p < 0.001, partR2 = 0.058, 95% CI[0.084, 0.036]. There was also an interaction between advice type and harm type, F(1, 998.73) = 86.156, p < 0.001, partR2 = 0.022, 95% CI [0.022, 0.002], but participants suggested more additive than subtractive solutions irrespective of whether people were engaged in predominately in negative, b = 1.063, SE = 0.052, t(998) = 20.605, p < 0.001, 95% CI[0.962, 1.164], or positive harms, b = 0.386, SE = 0.052, t(998) = 7.498, p < 0.001, 95% CI[0.285, 0.487](See Fig. 1). The same was also true in the analysis of benefit rankings where the main effect of advice type was present, F(1, 1278) = 116.655, p < 0.001, partR2 = 0.105, 95% CI[0.137, 0.076], and the interaction with harm type, F(1, 1278) = 46.205, p < 0.001, partR2 = 0.020, 95% CI[0.038, 0.008]. Additive advice was ranked as more beneficial than subtractive advice irrespective of whether for participants were advising people predominantly engaged in positive harms, b = 0.186, SE = 0.066, t(958) = 2.831, p = 0.005, 95% CI[0.057, 0.315], or negative harms, b = 0.817, SE = 0.066, t(958) = 12.444, p < 0.001, 95% CI[0.688, 0.946].
Study 7 further examined the effects of prior engagement with types of harmful activities by showing participants scenarios of people either exclusively engaged in positive harms or negative harms. Again, participants were asked to provide advice and then to rank this advice for anticipated benefit. In addition to replicating the main effect of advice type present in other studies, F(1, 938.02) = 42.810, p < 0.001, partR2 = 0.000, 95% CI[0.006, 0.000], there was also a significant interaction between advice and harm type, F(1, 1298.16) = 166.268, p < 0.001, partR2 = 0.007, 95% CI[0.019, 0.001]. Participants suggested more additive than subtractive advice to people who were engaged in negative harms, b = 1.152, SE = 0.058, t(1298) = 19.880, p < 0.001, 95% CI[1.039, 1.266], but there was no statistically significant difference in the number of additive and subtractive solutions suggested to people who were engaged in positive harms, b = 0.098, SE = 0.058, t(1298) = 1.690, p = 0.091, 95% CI[−0.016, 0.211]. The same was true for benefit rankings with additive advice being ranked as significantly more beneficial than subtractive advice for people engaged exclusively in negative harms, b = 1.030, SE = 0.086, t(1269) = 12.035, p < 0.001, 95% CI[0.862, 1.198], but there was no significant difference for people engaged exclusively in positive harms, b = −0.126, SE = 0.085, t(1269) = −1.478, p = 0.140, 95% CI[−0.293, 0.041].
The importance of who is being advised
In Study 7, in addition to advising strangers (as in previous studies) participants were also asked to think about themselves and a close friend and to advise each on how to improve their mental health. The amount of additive versus subtractive advice participants gave differed based on whether it was given to a stranger, ourselves or a friend, F(2, 938.02) = 17.344, p < 0.001, partR2 = 0.006, 95% CI[0.018, 0.000]. There was no statistically significant difference between the amount of additive and subtractive advice recommended to close friends, b = 0.049, SE = 0.073, t(938) = 0.667, p = 0.505, 95% CI[−0.094, 0.192], but people were more additive than subtractive when advising strangers, b = 0.620, SE = 0.073, t(938) = 8.500, p < 0.001, 95% CI[0.477, 0.764], and themselves, b = 0.157, SE = 0.073, t(938) = 2.155, p = 0.031, 95% CI[0.014, 0.300]. People were significantly more subtractive when advising their friends, b = 0.691, SE = 0.073, t(938) = 9.474, p < 0.001, 95% CI[0.520, 0.862], and themselves, b = 0.704, SE = 0.073, t(938) = 9.655, p < 0.001, 95% CI[0.875, 0.533], than they were when advising strangers. Subtractive solutions were also ranked as more beneficial than additive solutions when advising friends, b = −0.368, SE = 0.113, t(903) = −3.262, p = 0.001, 95% CI[−0.590, −0.147], whereas when advising strangers, additive solutions were rated as more effective than subtractive solutions, b = 0.438, SE = 0.113, t(903) = 3.876, p < 0.001, 95% CI[0.216, 0.660]. There was no statistically significant difference between solution types when advising oneself, b = −0.104, SE = 0.113, t(903) = −0.924, p = 0.356, 95% CI[−0.325, 0.117].
Advice-giving by ChatGPT
In Study 8, vignettes from previous studies were given to GPT 4o along with the same instructions as previous studies to provide advice to improve mental health. GPT responded similarly to participants in other studies in that it was more additive than subtractive, F(1, 796.01) = 13139.938, p < 0.001, partR2 = 0.703, 95% CI [0.722,0.683]. There was evidence of differences in these effects based on vignette gender, F(1, 796.01) = 4.696, p = 0.031, partR2 = 0.004, 95% CI [0.013, 0.000], and the harm type that the person in the vignette was said to be engaged in, F(1, 796.01) = 215.912, p < 0.001, partR2 = 0.050, 95% CI [0.072, 0.031](Fig. 1). However, irrespective of the levels of these potential moderating variables, significantly more additive solutions were offered than subtractive solutions (all p’s < 0.001 within paired contrasts).
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