Adaptive Asymmetric Adjustment in Affective Evaluation and Reciprocity for Altruistic Behaviors from Exogenous Uncertainty to Certainty

Adaptive Asymmetric Adjustment in Affective Evaluation and Reciprocity for Altruistic Behaviors from Exogenous Uncertainty to Certainty | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Adaptive Asymmetric Adjustment in Affective Evaluation and Reciprocity for Altruistic Behaviors from Exogenous Uncertainty to Certainty View ORCID Profile Xuqi Liu , Rui Liao , Yu Nan , Yuankun Fang , View ORCID Profile Yang Hu , View ORCID Profile Xiaolin Zhou , View ORCID Profile Xiaoxue Gao doi: https://doi.org/10.1101/2025.05.03.651994 Xuqi Liu 1 Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, School of Psychology and Cognitive Science, East China Normal University , Shanghai 200062, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Xuqi Liu Rui Liao 1 Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, School of Psychology and Cognitive Science, East China Normal University , Shanghai 200062, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yu Nan 1 Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, School of Psychology and Cognitive Science, East China Normal University , Shanghai 200062, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yuankun Fang 1 Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, School of Psychology and Cognitive Science, East China Normal University , Shanghai 200062, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yang Hu 1 Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, School of Psychology and Cognitive Science, East China Normal University , Shanghai 200062, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Yang Hu Xiaolin Zhou 1 Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, School of Psychology and Cognitive Science, East China Normal University , Shanghai 200062, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Xiaolin Zhou Xiaoxue Gao 1 Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, School of Psychology and Cognitive Science, East China Normal University , Shanghai 200062, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Xiaoxue Gao For correspondence: gxx114455{at}gmail.com xxgao{at}psy.ecnu.edu.cn Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Direct reciprocity requires the beneficiary’s real-time evaluation of others’ altruistic behaviors under exogenous uncertainty, i.e., environmentally imposed uncertainty that can be resolved upon the disclosure of outcomes (e.g., uncertainty in others’ cost to rescue oneself in a natural disaster). However, the neurocognitive mechanisms underpinning this dynamic adjustment to exogenous uncertainty fluctuations remain unexplored. Combining interpersonal tasks simulating exogenous uncertainty-to-certainty transitions with one fMRI experiment applying multivariate pattern analyses and three behavioral experiments, we uncover an adaptive asymmetric adjustment in the beneficiary’s affective evaluation and reciprocity in response to altruistic behaviors: the beneficiary’s gratitude and ensuing reciprocity intensify when the final benefactor-cost (or self-benefit) exceeds the expectation under exogenous uncertainty; however, a parallel reduction in benefactor-cost (or self-benefit) does not elicit equivalent decreases. This asymmetric adjustment, perceived as morally superior by third parties, challenges classical theories of gratitude, decision-making, and social learning involving uncertainty. We resolve this paradox by proposing a mechanism of prosocial information integration: guided by the adaptive goal (e.g., gathering social acceptance), the beneficiary tends to asymmetrically weigh prior information (e.g., the cost the benefactor willing to undertake under uncertainty) against posterior information (e.g., final benefactor’s cost) when evaluating benefactor’s intention, contributing to the observed adaptive asymmetry in gratitude and reciprocity dynamics. This process is supported by neural representations within the theory-of-mind system, particularly the dorsomedial prefrontal cortex. By reframing direct reciprocity as a dynamic process shaped by prosocial information integration, this work extends the theoretical framework of cooperation under uncertainty, offering new insights into human social adaptation. Significance Statement Despite extensive research on endogenous uncertainty in beliefs about others in direct reciprocity, how beneficiaries dynamically evaluate and respond to altruistic behaviors under environmentally imposed exogenous uncertainty fluctuations remains unknown. We uncover an adaptive asymmetric adjustment in this dynamic process, which challenges classical theories of gratitude, decision-making, and social learning regarding endogenous uncertainty. This asymmetric adjustment arises from prosocial information integration, wherein beneficiaries asymmetrically integrate prior and posterior information regarding exogenous uncertainty fluctuations to sustain social acceptance. This process is underpinned by theory-of-mind related neural representations, particularly the dmPFC. By redefining direct reciprocity as a context-sensitive process driven by adaptive goals, this work provides new perspectives for understanding cooperation in uncertain environments and informs strategies to foster prosociality. Introduction Direct reciprocity, whereby beneficiaries evaluate and reciprocate benefactors’ altruistic acts, constitutes a cornerstone for human cooperation and adaptation ( 1 – 5 ). Real-world direct reciprocal interactions are often fraught with uncertainty that dynamically fluctuates with the interaction process. On the one hand, the beneficiary may face endogenous uncertainty regarding their beliefs about the benefactor’s intentions and actions (e.g., whether and how much the benefactor expects for reciprocity), which influences their own future responses and actions ( 6 – 8 ). Individuals may proactively resolve such uncertainty through automatic inference, controlled inference, or social learning along with the dynamic interaction process, resulting in the fluctuations of endogenous uncertainty ( 6 ). On the other hand, the beneficiary may face exogenous uncertainty that exists in the environment outside the individual’s general sphere of influence ( 7 , 9 – 14 ) (e.g., the uncertainty in the benefactor’s cost to rescue the beneficiary in a natural disaster). Unlike endogenous uncertainty, this exogenous uncertainty is commonly out of control by the two parties in the interaction, but can generally transition to certainty upon the disclosure of outcomes (e.g., the post-disaster actual benefactor’s cost, which may be higher or lower than the beneficiary previously expected), leading to the fluctuations of exogenous uncertainty ( 7 , 10 , 12 , 15 ). In everyday reciprocal interactions that are closely intertwined with the fluctuations of these two types of uncertainty, the ability to dynamically adjust evaluation (e.g., the social emotion of gratitude) and behavioral responses (e.g., reciprocity) to the benefactor’s altruistic behaviors is essential for the beneficiary to preserve interpersonal relationships and ensure effective social adaptation ( 6 , 16 – 19 ). Understanding these processes is crucial for comprehending and predicting human reciprocity and cooperation, which is essential for guiding individuals’ daily social decision-making ( 6 , 15 , 17 , 20 ), as well as for informing social and economic policy aimed at fostering cooperation ( 21 – 25 ). However, previous studies have either focused only on the beneficiary’s responses in non-dynamic and certain contexts, or on how the beneficiary deals with endogenous uncertainty fluctuations, the reciprocal interactions involving exogenous uncertainty fluctuations have been largely overlooked. How does the beneficiary’s gratitude and reciprocity dynamically adjust from being faced with exogenous uncertain outcomes to ultimately facing better or worse actual certain outcomes? What are the psychological and neural processes involved? These questions remain unsolved. Firstly, the majority of studies have focused on non-dynamical events in certain contexts, identifying key cognitive appraisals that contribute to the beneficiary’s social emotions (e.g., gratitude, indebtedness) and subsequent reciprocal behaviors: the perceived benefactor’s intention, the benefactor’s cost, and the beneficiary’s benefit ( 1 , 26 – 32 ), and the social expectation formed from other benefactors’ offers ( 33 – 35 ). Recent work has suggested intention evaluation as a crucial factor that mediates the effects of other appraisals (e.g., benefactor’s cost and the beneficiary’s benefit) on the beneficiary’s social emotions and subsequent reciprocity ( 1 , 26 ). Neural evidence further supports this view by showing that the involvements of brain regions crucial for social intention representation, i.e., the ventromedial prefrontal cortex (vmPFC) ( 1 , 31 , 32 , 36 – 40 ) and the dorsal medial prefrontal cortex ( 1 , 36 , 38 , 39 ). However, these studies have overlooked the presence of both types of uncertainty and the corresponding dynamic processes. Secondly, a series of studies have provided evidence supporting that the beneficiary responds to endogenous uncertainty fluctuations in beliefs about the benefactor’s intentions and actions (i.e., the aforementioned key appraisals) via automatic inference, controlled inference, and social learning ( 6 ). For one thing, the beneficiary may obtain extra information from automatic and controlled inferences. For instances, the beneficiary’s automatic inferences derived from the impression formation for the benefactor affect their beliefs or evaluation of the benefactor’s intentions and altruistic behaviors ( 16 , 18 , 41 – 43 ). By engaging in controlled inference, individuals could infer the benefactor’s intentions based on information regarding the actual altruistic decision-making, such as whether the benefactors seek information or engage long-time cost-benefit calculation ( 24 , 44 – 47 ). For another, the beneficiary can resolve endogenous uncertainty by dynamic social learning, in which their expectations about the benefactor’s intentions and actions can be updated through actual outcomes and the expectation-outcome deviations (i.e., expectation violation or prediction error) ( 48 – 52 ). One recent study has revealed that the cognitive computations in these social learning (e.g., expectations, and expectation violations) contribute to the beneficiary’s dynamic adjustment in gratitude during multiple interactions ( 49 ). Nevertheless, unlike endogenous uncertainty, individuals are difficult to resolve exogenous uncertainty, arising from the uncontrollable and even stochastic nature of environmental factors ( 7 , 10 , 12 , 15 ), through mechanisms like inferences or social learning proactively. Consequently, the dynamic adjustment mechanisms established for endogenous uncertainty contexts may not be effectively applied to contexts dominated by exogenous uncertainty. Thirdly, our previous study (Xiong et al., 2020) ( 39 ) has revealed how exogenous uncertainty in the benefactor’s cost modulates the beneficiary’s gratitude and the resulting reciprocity. The findings showed that, given that individuals are commonly aversive to uncertainty ( 15 , 25 , 53 – 56 ), as the level of uncertainty in the benefactor’s cost increased from certain, risky to ambiguous ( 12 , 53 , 57 – 59 ), as determined by the external environment (i.e., the computer system), participants anticipated a lower likelihood of being helped, which increased their perceived kind intention, and thus enhanced gratitude and reciprocity when they actually received help. This suggested that intention evaluation remains important in the occurrence of gratitude and reciprocity in exogenous uncertain contexts. At the neural level, both the gratitude processing under exogenous risk and ambiguity are related to the processing of fear and anxiety, involving orbital frontal cortex (OFC) and anterior insula; ambiguity processing additionally engages the processing of theory-of-mind (ToM) and conflict monitoring, involving dorsal medial prefrontal cortex (dmPFC) and dorsal anterior cingulate (dACC). However, in this study, the final outcomes in the exogenous uncertain situations were never revealed, leaving the dynamic processes from exogenous uncertainty to certainty unexplored. In the current work, we aimed to answer how the beneficiaries dynamically adjust gratitude and reciprocity from facing exogenous uncertain outcomes to ultimately facing better or worse actual certain outcome at both the cognitive and neural levels. Combining an interpersonal task adapted from Xiong et al. (2020) ( 39 ) simulating exogenous uncertainty-to-certainty transitions in benefactors’ costs with fMRI multivariate pattern analyses (Experiment 1), we elucidated the differences in gratitude, reciprocity and neural representations before and after the display of final actual outcomes in the Uncertain-to-Certain situation, and revealed the distinctions between the Uncertain-to-Certain and the Constantly-Certain situations. We conducted two additional behavioral experiments (Experiment 2 and Experiment 3) that manipulated not only the exogenous uncertainty in benefactors’ costs (Experiment 3) but also that in participants’ benefits (Experiment 2 and Experiment 3) to further validate our findings (see SI Methods ). To test whether participants’ responses to the benefactor’s altruistic behaviors in the Uncertain-to-Certain situation serve social adaptive functions, an additional sample of participants (Experiment 4) were recruited to make third-party social evaluation on the beneficiaries, who had completed the interpersonal task and exhibited different patterns of gratitude-induced reciprocity. Results The beneficiary’s gratitude and reciprocity exhibited asymmetric dynamic adjustment in the Uncertain-to-Certain situation In each round of the interpersonal task of Experiment 1 (fMRI experiment; see details in Procedures ), participants were paired with a different anonymous co-player, who decided whether to endure an amount of pain (i.e., benefactor’s cost) to reduce the participant’s pain by half (from 10 to 5 times). In the Uncertain-to-Certain situation, the co-player made altruistic decision under an exogenous uncertain cost (with a 50% chance of being either 2 or 8 times, determines randomly by the computer system, expected to be 5 times of pain stimulation). After presenting the benefactor’s decision to help under exogenous uncertainty during the Outcome_Unknown phase (Uncertain_OutcomeUnknown condition), we additionally included an Outcome_Display phase, where participants learned the co-player’s actual cost, which was either 8 (higher than the expected 5; Uncertain_Outcome8 condition) or 2 (lower than the expected 5; Uncertain_Outcome2 condition). In contrast, in the Constantly-Certain situation, the benefactor’s cost was constantly certain, with 2, 5 or 8 times (Certain_Outcome2, Certain_Outcome5, or Certain_Outcome8 conditions). We collected participants’ gratitude ratings, monetary allocations, and perceived kind intention ratings for each of the above six conditions. In Experiment 1, first, to reveal whether and how a beneficiary dynamically adjusts the feeling of gratitude and subsequent reciprocity from facing an exogenous uncertain outcome (i.e., benefactor’s cost with a 50% chance of being either 2 or 8, expected to be 5 times of pain stimulation) to facing a final actual outcome of higher (i.e., 8) or lower (i.e., 2) benefactor’s cost than expected, we conducted a one-way (Outcome in the Uncertain-to-Certain situation: Uncertain_Outcome2 vs. Uncertain_OutcomeUnknown vs. Uncertain_Outcome8) repeated-measure analysis of variance (ANOVA), and observed significant main effects for both the gratitude ratings ( F (1.57, 70.59) = 112.33, p < 0.001, η 2 = 0.71, Fig. 2A , Table S1) and monetary allocations ( F (1.51, 67.78) = 61.82, p < 0.001, η 2 = 0.58, Fig. 2D , Table S1). Download figure Open in new tab Fig. 1. Procedures for Experiment 1 (fMRI Experiment). The participant was to receive 10 times of pain stimulation of Level 8 in each trial. At the beginning of each trial, the participant was paired with an anonymous same-gender co-player, who was distinct from those in any other trials and would interact with the participant only once. The participant would be presented with the information regarding which situation (Uncertain-to-Certain or Constantly-Certain) the co-player’s cost belonged to in the current trial (Situation information phase). For the Uncertain-to-Certain situation, the Situation information phase would present an orange pie, with the numbers 2 and 8 on the top and the bottom respectively (locations counterbalanced across trials), indicating the co-player’s uncertain cost of either 2 times or 8 times of pain stimulation, each with 50% probability. Then the co-player’s decision on whether to help the participant under uncertainty (Outcome_Unknown phase of Uncertain-to-Certain situation) would display under the orange pie. In trials of Uncertain_OutcomeUnknown condition (A), the participant should immediately allocate points to the co-player paired on a scale of 0 to 20 (Allocation phase; 20 Yuan ∼ $2.76 U.S.) after the Outcome_Unknown phase without knowing the co-player’s actual cost. Then the participant would see the actual outcome of co-player’s cost (Outcome_Display phase). In trials of the Uncertain_Outcome2 and Uncertain_Outcome8 conditions (B), the participant would see the actual outcome of co-player’s cost first (Outcome_Display phase), and then allocate points to the co-player (Allocation phase). For the Constantly-Certain situation (C), this phase would present an orange pie, with the number of co-player’s certain cost (2, 5 or 8 times) in the center. Then after the co-player’s decision on whether to help the participant or not would display on the orange pie (Outcome-Display screen), the participant should allocate points to the co-player (Allocation phase). The participant was informed that all co-players were unaware of such allocation (Allocation phase). The ultimate money rewards and pain stimulation that co-player and participant would receive at the end of the game were based on the outcomes of randomly selected 20 trials. Download figure Open in new tab Fig. 2. Behavioral results of Experiment 1 (fMRI Experiment). (A, D, G) The participants’ gratitude ratings (A), monetary allocations (D), and perceived kind intention ratings (G) adjusted asymmetrically when transitioning from facing an uncertain outcome to facing a final actual outcome of higher or lower benefactor’s cost than expectation. (B, E, H) When the final actual benefactor’s cost was ascertained to be 8 times, which was higher than expected (5 times), the extent of adjustments in gratitude ratings (B), monetary allocations (E), and perceived kind intention ratings (H) were significantly larger than those when benefactor’s cost was ascertained to be 2 times, which was lower than expected. (C, F, I) The participants’ gratitude ratings (C), monetary allocation (F), and perceived kind intention ratings (I) in response to the actual outcomes of the benefactor’s cost (2 vs. 8) differed between the Constantly-Certain situation and the Uncertain-to-Certain situation. (J) Perceived kind intention ratings mediated the effect of Outcome (Uncertain_Outcome2 vs. Uncertain_OutcomeUnknown vs. Uncertain_Outcome8) on the participants’ gratitude ratings in the Uncertain-to-Certain situation. (K) Perceived kind intention ratings mediated the effect of Situation (Uncertain-to-Certain vs. Constantly-Certain) and Actual Outcome (2 vs. 8) on participants’ gratitude ratings. * p < 0.05, ** p < 0.01, and *** p < 0.001. Specifically, consistent with previous evidence on the effect of benefactor’s cost on gratitude ( 32 ), the participants’ gratitude ratings and monetary allocations were significantly higher when the actual benefactor’s cost was 8 times than when it was 2 times (Uncertain_Outcome8 vs. Uncertain_Outcome2, gratitude: t (45) = 12.10, p < 0.001, Cohen’s d = 2.18; allocation: t (45) = 2.12, p < 0.001, Cohen’s d = 1.59). Moreover, from the perspective of Uncertain-to-Certain transitions, compared with the Uncertain_OutcomeUnknown condition, the participants’ gratitude ratings and monetary allocations significantly increased in the Uncertain_Outcome8 condition (gratitude: t (45) = 10.07, p < 0.001, Cohen’s d = 1.43; allocation: t (45) = 9.07, p < 0.001, Cohen’s d = 1.14), and significantly decreased in the Uncertain_Outcome2 condition (gratitude: t (45) = −6.61, p < 0.001, Cohen’s d = - 0.75; allocation: t (45) = −3.63, p = 0.002, Cohen’s d = −0.45). To be noted, before the experiments, participants reported that they anticipated the averaged actual benefactor’s cost would be approximately 5 times in Uncertain_OutcomeUnknown condition (mean = 5.09 ± 1.35 (SD), one-sample t -test with 5 as baseline: t (45) = 0.44, p = 0.664, Cohen’s d = 0.06). This indicated that, the transitions from Uncertain_OutcomeUnknown condition to Uncertain_Outcome8 condition and to Uncertain_Outcome2 condition were associated with the equal absolute value changes in the benefactor’s cost (i.e., |8 - 5| = |2 - 5| =3 times). Did these two directions of Uncertain-to-Certain transitions exhibit similar or different magnitudes of influences on the dynamic adjustments in the beneficiary’s gratitude? To answer this question, we subtracted the gratitude ratings and monetary allocations in the Uncertain_Outcome2 and Uncertain_Outcome8 conditions from those in Uncertain_OutcomeUnknown condition respectively, and took the absolute values as the indicators of the extent of dynamic adjustments in gratitude and gratitude-induced reciprocity. Paired-samples t -test showed that, when the final actual benefactor’s cost was ascertained to be 8 times, which was higher than expected, both the extent of adjustments in gratitude ratings and monetary allocations were significantly larger than those when benefactor’s cost was ascertained to be 2 times, which was lower than expected (gratitude: t (45) = 3.70, p < 0.001, Cohen’s d = 0.55, Fig. 2B , Table S4; allocation: t (45) = 3.68, p < 0.001, Cohen’s d = 0.54, Fig. 2E , Table S4). These findings demonstrated a “asymmetric dynamic adjustment” in participant’s gratitude-related responses: the beneficiary’s gratitude and subsequent reciprocity intensified when the benefactor’s final actual cost exceeded expectations; however, a parallel reduction in the benefactor’s cost did not elicit equivalent decreases. The beneficiary’s gratitude and reciprocity to the final certain outcomes differed between the Constantly-Certain situation and the Uncertain-to-Certain situation Second, we aimed to examine whether and how the influence of actual outcomes of the benefactor’s cost (2 vs. 8) on the beneficiary’s gratitude differ across two distinct scenarios: one in which the outcome of help is ascertained from the outset (i.e., Constantly-Certain situation), and another wherein the outcome evolves from an initial state of exogenous uncertainty to eventual certainty (i.e., Uncertain-to-Certain situation), despite the final outcomes being equivalent in both situations. To this end, we combined the data of the Certain_Outcome2, Certain_Outcome8, Uncertain_Outcome2 and Uncertain_Outcome8 conditions for gratitude ratings and monetary allocations, and conducted 2 (Situation: Uncertain-to-Certain vs. Constantly-Certain) ξ 2 (Actual Outcome: 2 vs. 8) ANOVA, respectively. Results revealed significant main effects of Actual Outcome (gratitude: F (1, 45) = 323.71, p < 0.001, η partial 2 = 0.88, Fig. 2C ; allocation: F (1, 45) = 173.97, p < 0.001, η partial 2 = 0.79, Fig. 2F ). The main effects of Situation did not reach significance (gratitude: F (1, 45) = 3.73, p = 0.060, η partial 2 = 0.08, Fig. 2C ; allocation: F (1, 45) = 0.03, p = 0.859, η partial 2 = 0.00, Fig. 2F ). Importantly, we observed significant interaction effects between Situation and Actual Outcome (gratitude: F (1, 45) = 140.72, p < 0.001, η partial 2 = 0.76, Fig. 2C ; monetary allocation: F (1, 45) = 86.43, p < 0.001, η partial 2 = 0.66, Fig. 2F ). Specifically, participants reported significantly higher levels of gratitude and allocated significantly more money to the co-player when the benefactor’s cost was high (i.e., 8) compared to when it was low (i.e., 2) in the Constantly-Certain situation (gratitude: t (45) = 19.04, p < 0.001, Cohen’s d = 3.32; allocation: t (45) = 13.09, p < 0.001, Cohen’s d = 2.72; Table S8); these effects were significantly reduced in the Uncertain-to-Certain situation (gratitude: t (45) = 12.10, p < 0.001, Cohen’s d = 1.60; allocation: t (45) = 8.60, p < 0.001, Cohen’s d = 1.01; Table S8). These results indicated that the contributions of the benefactor’s cost to gratitude and subsequent reciprocity were reduced in the Uncertain-to-Certain situation compared to the Constantly-Certain situation, suggesting that there may be different cognitive processes involved in the two situations. The asymmetric dynamic adjustment in intention evaluation contributed to that in gratitude and reciprocity in the Uncertain-to-Certain situation Based on previous literatures highlighting the role of kind intention evaluation in the occurrence of gratitude ( 1 , 26 , 31 , 39 ), we then tested whether there also existed “asymmetric dynamic adjustment” in participant’s perceived kind intention ratings in the Uncertain-to-Certain situation by conducting the same analyses as we did on gratitude ratings and money allocations. Similar as gratitude ratings and money allocations, from the perspective of dynamic adjustments, in the Uncertain-to-Certain situation, a significant main effect of Outcome was observed for perceived kind intention ratings ( F (1.66, 74.75) = 44.39, p < 0.001, η 2 = 0.50, Fig. 2G , Table S1). Participants perceived significantly stronger kind intention when the actual benefactor’s cost was 8 times than when it was 2 times (Uncertain_Outcome8 vs. Uncertain_Outcome2, t (45) = 7.89, p < 0.001, Cohen’s d = 1.35). Moreover, from the perspective of Uncertain-to-Certain transitions, compared to the Uncertain_OutcomeUnknown condition, the participants’ perceived kind intention significantly increased in Uncertain_Outcome8 condition ( t (45) = 6.28, p < 0.001, Cohen’s d = 0.96), and significantly decreased in Uncertain_Outcome2 condition ( t (45) = 3.46, p = 0.004, Cohen’s d = 0.39). More importantly, when the actual benefactor’s cost was ascertained to be 8 times (higher than expected), the extent of adjustment in perceived kind intention was significantly larger than that when benefactor’s cost was ascertained to be 2 times (lower than expected) ( t (45) = 3.50, p = 0.001, Cohen’s d = 0.52, Fig. 2H , Table S4). From the perspective of situational differences, 2 (Situation: Uncertain-to-Certain vs. Constantly-Certain) ξ 2 (Actual Outcome: 2 vs. 8) ANOVA revealed a significant main effect of Actual Outcome ( F (1, 45) = 227.08, p < 0.001, η partial 2 = 0.84, Fig. 2I ). The main effect of Situation was not significant ( F (1, 45) = 0.21, p = 0.650, η partial 2 = 0.01, Fig. 2I ). Importantly, we observed a significant interaction effect between Situation and Actual Outcome ( F (1, 45) = 73.94, p < 0.001, η partial 2 = 0.62, Fig. 2I ). Similar as gratitude ratings and money allocations, participants reported significantly stronger perceived kind intention when the actual benefactor’s cost were high (i.e., 8) compared to when they were low (i.e., 2) in the Constantly-Certain situation ( t (45) = 14.36, p < 0.001, Cohen’s d = 2.43, Table S8); this effect was significantly reduced in the Uncertain-to-Certain situation ( t (45) = 7.89, p < 0.001, Cohen’s d = 0.86, Table S8). To examine whether perceived kind intention mediated the effects of experimental manipulations on gratitude, two lines of multivariate mediation analyses were conducted. For one thing, from the perspective of dynamic adjustment in Uncertain-to-Certain situation, we found that the perceived kind intention significantly mediated the effect of Outcome in Uncertain-to-Certain situation (Uncertain_Outcome2 vs. Uncertain_OutcomeUnknown vs. Uncertain_Outcome8) on gratitude (normalized coefficient of the mediating effect = 0.146, p < 0.001, c = 0.488, p < 0.001, c’ = 0.342, p < 0.001, partial mediation) (see Fig. 2J for the path coefficients). For another, from the perspective of situational differences, we found that the perceived kind intention significantly mediated the effects of Situation (Uncertain-to-Certain vs. Constantly-Certain) and Actual Outcome (2 vs. 8) on gratitude (normalized coefficient of overall mediating effect = 0.099, p < 0.001, c = 0.623, p < 0.001, c’ = 0.524, p < 0.001, partial mediation) (see Fig. 2K for the path coefficients). Consistent with previous studies ( 1 , 26 , 31 , 39 ), these findings suggested the intention evaluation behind the help as a crucial mechanism for the feeling of gratitude, and the asymmetric dynamic adjustment in kind intention may contribute to that in gratitude in the Uncertain-to-Certain situation. Prosocial information integration contributed to the asymmetric dynamic adjustment in intention evaluation, gratitude and reciprocity in the Uncertain-to-Certain situation The above findings from the perspective of transition from uncertainty to certainty and from the perspective of the differences between the Uncertain-to-Certain and the Constantly-Certain situations consistently suggest that, compared to the Constantly-Certain situation, there might exist potential unique cognitive mechanisms underlying the kind intention evaluation and the resulting gratitude and reciprocity responses in the Uncertain-to-Certain situation. We hypothesize that this uniqueness may arise from the process of information integration. Specifically, in the Uncertain-to-Certain situation, 1) the co-player’s decision to help in the Outcome_Unknown phase reflected the cost the benefactor was willing to undertake (expected to be 5 based on probability statistics); 2) the Outcome-Display phase determined the co-player’s final actual cost (2 or 8); 3) the participant’s benefit was constant and certain ( 5 ). Therefore, unlike the Constantly-Certain situation, in the Uncertain-to-Certain situation, participants may need to consider not only the posterior information of actual benefactor’s cost in the Outcome_Display phase, but also the prior information about how much cost the benefactor was willing to undertake in the Outcome_Unknown phase (the expectation of 5 in the current study), and integrate these two lines of information to generate the evaluation of benefactor’s kind intention. According to previous theories and evidence, four alternative hypotheses can be proposed regarding this information integration process: Hypothesis 1: Outcome-oriented evaluation. It is possible that beneficiaries may be outcome-oriented ( 27 , 30 , 60 – 62 ), with their gratitude-related responses depending mainly on posterior information of the actual help outcome in the Outcome_Display phase. Consequently, compared to the Outcome_Unknown phase, participants’ perceived kind intention and gratitude-related responses in the Outcome_Display phase should rise with the increases in benefactor’s cost and fall with the decreases, exhibiting symmetric dynamic adjustments for both directions of transitions. Hypothesis 2: Anchoring evaluation. Inspiring by the anchoring effect ( 63 – 67 ), which shows that individuals tend to rely excessively (or “anchor”) on the prior information, it is plausible that that beneficiaries’ gratitude-related responses may depend mainly on prior information of the cost the benefactor was willing to undertake reflected in the Outcome_Unknown phase. Consequently, compared to the Outcome_Unknown phase, participants’ perceived kind intention and gratitude in the Outcome_Display phase might not change with the final benefactor’s cost increasing or decreasing, showing no dynamic adjustments for both directions of transitions. Hypothesis 3: Prosocial information integration. Previous theories have suggested that the adaptive goal of moral emotions, such as gratitude, is to promote moral behaviors, enhance individual social reputation, and thus maintain social relationships ( 68 – 74 ). To achieve this social goal, beneficiaries should evaluate and respond to the help in a direction that favors reputation and social relationship maintenance, which we term adaptive asymmetric dynamic adjustment : when the actual benefactor’s cost is higher than expected, beneficiaries should focus more on posterior information in the Outcome_Display phase and increase the perceived kind intention and gratitude-related responses, and when that is lower than expected, they should focus on prior information in the Outcome_Unknown phase and not decrease the perceived kind intention and gratitude-related responses; since taking the minimum value of evaluation in any case could be considered as “moral opportunism” by other societal members ( 19 ). Hypothesis 4: Selfish information integration. Although the Uncertain-to-Certain situation provides individuals with the opportunity to demonstrate their willingness for social cooperation through gratitude-related responses, it also brings out the chance for “moral opportunism” ( 19 ). Individuals with the goal of maximizing self-interest can minimize the damage to their own interests caused by gratitude and reciprocity by taking the minimum value of prior and posterior information, resulting in selfish asymmetric dynamic adjustment : when the actual benefactor’s cost is higher than expected, they consider more on the information in the Outcome_Unknown phase, and vice versa. Given that the above results of intention evaluation, gratitude and reciprocity were consistent with the adaptive asymmetric dynamic adjustment predicted by the cognitive hypothesis of Prosocial information integration (H3), we applied representational similarity analysis (RSA) to formally test this hypothesis. Specifically, we constructed a representational dissimilarity matrix (RDM) for this cognitive hypothesis (RDM_H3) based on the Euclidean distance of [the hypothesized integrated intention evaluation, and the actual benefactor’s cost] between Uncertain_Outcome2, Uncertain_OutcomeUnknown, and Uncertain_Outcome8 conditions (Fig. S1). We also included three RDMs for other three alternative hypotheses: one assuming that participants only used the posterior information (RDM_H1), one assuming that participants only used the prior information (RDM_H2), and another assuming that participants used the prior information more when the actual cost was higher than expected and vice versa (RDM_H4). Then, for each participant, we respectively constructed four behavioral RDMs using perceived kind intention ratings, gratitude ratings, and monetary allocations from the three conditions in the Uncertain-to-Certain situation, and examined the similarity between the behavioral RDMs and each cognitive RDM. Results demonstrated that, at the group level, the similarities between RDM_H3 and the three behavioral RDMs (perceived kind intention, gratitude, and allocation) were all significantly higher than the RDMs for the other three alternative hypotheses, which supported the cognitive mechanism of prosocial information integration proposed in our Hypothesis 3 (Fig. S2; Table S9 for more details). Behavioral validations of asymmetric dynamic adjustments in gratitude and reciprocity by simultaneously manipulating exogenous uncertainty in benefactor’s cost and beneficiary’s benefit We conducted two additional behavioral experiments (Experiment 2 and Experiment 3) to validate the robustness of the asymmetric dynamic adjustments in gratitude-related responses observed in the fMRI experiment (Experiment 1). The procedures of these two behavioral experiments were similar as the fMRI experiment, except that ( 1 ) in Experiment 2, we manipulated the exogenous uncertainties in the participants’ own benefits rather than that in the benefactor’s cost, while in Experiment 3, we included two blocks, manipulating the exogenous uncertainties in the benefactor’s cost and in the participants’ own benefits, respectively; ( 2 ) in both Experiments 2 and 3, participants were asked to rate their feelings of gratitude to the benefactor’s help during the interpersonal task, instead of the monetary allocation (see SI Methods ). The results of Experiment 2 and Experiment 3 revealed the same pattern of asymmetric dynamic adjustments in gratitude-related responses as observed in Experiment 1, not only for the benefactor’s cost, but also for the beneficiary’s benefit, with no significant difference between these two types of manipulations (see SI Results ; Fig. S3 and Fig. S4; Table S7). In the present study, participants interacted with different co-players in each round, which precluded the possibility of participants learning about the co-player’s behaviors. Meanwhile, in the exogenous Uncertain-to-Certain condition, the outcomes of co-player’s cost were independently and randomly determined by the computer, making it difficult for participants to learn from the actual outcomes. However, there remains a possibility that the discrepancy between the final outcome and the expected outcomes under uncertainty, i.e., expectation violation, might influence participants’ responses. Specifically, previous research has shown that individuals learn more from negative expectation violations (e.g., the co-player’s cost increased from an expected 5 to 8) than from positive expectation violations (e.g., the co-player’s cost decreases from an expected 5 to 2) ( 18 , 75 – 77 ). This expectation violation based learning could produce a similar pattern of behavioral responses as the mechanism of prosocial information integration in Experiment 1. However, the results of Experiment 2 and Experiment 3 helped us rule out this possibility, which showed that the participants’ gratitude and reciprocity increased when the actual self-benefit was higher than expected (positive expectation violation) under exogenous uncertainty, but did not plummet when that was equally lower than expected (negative expectation violation). This finding suggests that the expectation violation based social learning could not fully account for the asymmetric dynamic adjustment we observed, thereby further supporting the mechanism of prosocial information integration. The goal of social adaptation may guide the prosocial information integration underlying the asymmetric dynamic adjustment in gratitude and reciprocity Why beneficiaries exhibited prosocial information integration during dynamic adjustments in intention evaluation and gratitude-related responses in the exogenous Uncertain-to-Certain situation? Considering the social adaptive role of gratitude ( 70 , 72 , 73 ), it is plausible to suggest that the goal of social adaptation, such as helping the beneficiaries garner a higher level of social acceptance and reputation, may guide the cognitive process of prosocial information integration. If this is the case, then the prosocial information integration and the resulting asymmetric dynamic adjustment in gratitude-related responses should be more accepted and appreciated by other social members. To test this hypothesis, an additional sample of participants were recruited to complete a third-party social evaluation questionnaire (Experiment 4). In the questionnaire, each participant made social evaluation as a third-party on beneficiaries, who completed an interpersonal game that was similar as our fMRI experiment and exhibited different patterns of gratitude-induced reciprocity ( Fig. 3A ; see Materials and Methods ). Here, Symmetric Adjustment & More Amount (SAM_H1) and Symmetric Adjustment & Less Amount (SAL_H1) corresponded to the Outcome-oriented evaluation (Hypothesis 1), No Adjustment (NA_H2) corresponded to Anchoring evaluation (Hypothesis 2), Complete Adaptive Asymmetric Adjustment (CAAA_H3) and Incomplete Adaptive Asymmetric Adjustment (IAAA_H3) corresponded to Prosocial information integration (Hypothesis 3), and Selfish Asymmetric Adjustment (SAA_H4) corresponded to Selfish information integration (Hypothesis 4). In line with our hypothesis, results of one-way ANOVA (Table S10) showed that individuals displaying “Complete Adaptive Asymmetric Adjustment” or “Incomplete Adaptive Asymmetric Adjustment” patterns were perceived as more moral ( F (3.29, 158.03) = 33.34, p < 0.001, Fig. 3B ), more well-meaning ( F (3.51, 168.26) = 39.24, p < 0.001, Fig. S5A), more likable ( F (3.04, 145.72) = 35.19, p < 0.001, Fig. S5B), less stingy ( F (3.68, 176.51) = 34.22, p < 0.001, Fig. S5C) and less utilitarian ( F (3.78, 181.39) = 20.46, p < 0.001, Fig. S5D) than others, and the third-party participants were more inclined to become friends with them ( F (3.46, 166.30) = 28.32, p < 0.001, Fig. 3C ). Download figure Open in new tab Fig. 3. Questionnaire design and results in Experiment 4 (A) Six different reciprocity patterns in the questionnaire. Participants were asked to make social evaluation as a third-party on beneficiaries, who completed an interpersonal game that was similar to our fMRI experiment and exhibited different patterns of gratitude-induced reciprocity. Here, Symmetric Adjustment & More Amount (SAM_H1) and Symmetric Adjustment & Less Amount (SAL_H1) corresponded to the Outcome-oriented evaluation (Hypothesis 1), No Adjustment (NA_H2) corresponded to Anchoring evaluation (Hypothesis 2), Complete Adaptive Asymmetric Adjustment (CAAA_H3) and Incomplete Adaptive Asymmetric Adjustment (IAAA_H3) corresponded to Prosocial information integration (Hypothesis 3), and Selfish Asymmetric Adjustment (SAA_H4) corresponded to Selfish information integration (Hypothesis 4). (B-C) Individuals displaying “Complete Adaptive Asymmetric Adjustment” or “Incomplete Adaptive Asymmetric Adjustment” patterns were perceived as more moral than others (B), and the third-party participants were more inclined to become friends with them (C). Different letters above the boxes indicate significant differences between groups (corrected p < 0.05). Taken together, the above behavioral evidence from interpersonal tasks and third-party evaluation experiments suggested the social adaptive prosocial information integration as the key mechanism underpinning the adaptive asymmetric dynamic adjustment in gratitude-related responses. Neural representations of different cognitive components of gratitude-related processing To identify the neural bases that support the prosocial information integration and adaptive asymmetric dynamic adjustment in gratitude-related responses, first, we focused on the neural data of the Experiment 1 (fMRI experiment) and applyed representational similarity analysis (RSA) ( 78 , 79 ) ( Fig. 4A , see Methods for details) to identify brain regions associated with each of the different cognitive components of gratitude-related processing, including the experience of gratitude indicated by gratitude ratings, the gratitude-induced reciprocity indicated by monetary allocation, the evaluation of kind intention indicated by perceived kind intention ratings, and the processing of benefactor’s cost implemented in the experimental design. These regions were treated as regions of interest (ROIs) for subsequent ROI-based multivariate pattern analysis (MVPA). Download figure Open in new tab Fig. 4. Within-subject RSA of fMRI data. (A) At the neural level, each contrast image for each condition and each participant were divided into 200 parcels using a priori 200-parcel whole-brain parcellation ( 19 , 81 , 82 ). For each parcel of each participant, we created a RDM of neural activities using the pairwise correlation dissimilarity between each pair of conditions. At behavioral level, we constructed the behavioral representational dissimilarity matrixes (RDM) for each of the behavioral indicators of these four cognitive components (gratitude rating, monetary allocation, kind intention rating, and benefactor’s cost) respectively by estimating the Euclidean distance between the corresponding values of each pair of conditions. Then, for each parcel and each participant, we estimated the correlation between the neural RDM and each behavioral RDM using the Spearman rank-ordered correlation. For each parcel, we extracted the correlation coefficients ( ρ values) from all participants, conducted a Fisher z -transformation, and then conducted a one-sample sign permutation test to evaluate the association between the parcel dissimilarity matrix and each behavioral dissimilarity matrix at group-level. Multi-tests were corrected using Bonferroni correction (i.e., p < 0.00025, two-tailed). (B-E) The brain regions associated with the experience of gratitude (B), the gratitude-induced reciprocity (C), the evaluation of kind intention (D), the processing of benefactor’s cost (E), respectively. Error bars represent the SEs; significance: * p < 0.05; ** p < 0.01; *** p < 0.001. Results showed that, for self-reported gratitude, we only identified one brain parcel located in the thalamus after whole-brain correction ( Fig. 4B ). Given that previous studies have identified that crucial role of vmPFC in gratitude processing ( 1 , 31 , 32 , 36 , 37 , 37 – 40 , 80 ), we further conducted ROI-based analysis using the parcel corresponding to the peak coordinate of vmPFC (MNI coordinate: 3, 44, 4) identified in Xiong et al. (2020) ( 39 ). As expected, we discovered that the multivoxel activity pattern of this brain parcel was significantly correlated with the gratitude RDM ( r = 0.15, p = 0.007; Fig. 4B ). For monetary allocation, we identified two significant brain parcels, located in dorsolateral prefrontal cortex (dlPFC) and supplementary motor area (SMA) ( Fig. 4C ). For perceived kind intention ratings, we found forty significant brain parcels, including dmPFC, dlPFC, right insula, and bilateral temporo-parietal junction (TPJ; Fig. 4D ). For benefactor’s cost, we observed twenty-six significant brain parcels, which were mainly located in the subcortical nucleus, including bilateral insula, bilateral striatum, hippocampus, midcingulate cortex (MCC), and thalamus ( Fig. 4E ). Whole-brain and ROI-based multivariate classifications unveil the unique neural representations of gratitude-related responses in the Uncertain-to-Certain situation versus the Constantly-Certain situation Next, we identified the neural bases that support the prosocial information integration and adaptive asymmetric dynamic adjustment by revealing the unique neural representations of gratitude responses in the Uncertain-to-Certain situation in comparison to the Constantly-Certain situation using MVPA (see Methods ). At the whole-brain level, on one hand, from the perspective of the Uncertain-to-Certain transition, the observed “adaptive asymmetric dynamic adjustment” indicated that there might exist different neural representations between the Uncertain_Outcome8 and the Uncertain_Outcome2 conditions. This is indeed what we observed, using the contrast images of these two conditions from all participants and linear Support Vector Machine (SVM) ( 83 , 84 ), we were able to train a whole-brain within-participant multivariate pattern classifier discriminating these two conditions (force-choice classification accuracy = 58.7 ± 8.7%, p = 0.301, but non-force-choice classification accuracy of 63.0 ± 1.6%, p = 0.016). On the other hand, from the perspective of situational difference, we observed the differences in the influence of actual outcome on gratitude between the Constantly-Certain situation and the Uncertain-to-Certain situation, indicating the potential unique neural representations in the Uncertain-to-Certain situation. We tested this hypothesis by comparing the actual outcome processing in the Uncertain-to-Certain situation (Uncertain_Outcome8 vs. Uncertain_Outcome2 contrast maps) with that in the Constantly-Certain situation (Certain_Outcome8 vs. Certain_Outcome2 contrast maps). Specifically, similar to Uncertain_Outcome8 vs. Uncertain_Outcome2 conditions, we used linear SVM to train a whole-brain within-participant multivariate pattern classifier discriminating Certain_Outcome8 and Certain_Outcome2 conditions (force-choice classification accuracy of 71.7 ± 10.6%, p = 0.005). In order to compare the differences in neural representations between the Uncertain-to-Certain and the Constantly-Certain situations, we performed cross-situation classification, i.e., examining whether the Uncertain-to-Certain classifier can distinguish the contrast maps of the two conditions in Constantly-Certain situation and vice versa. Results of cross-situation classification revealed that the Uncertain-to-Certain classifier was able to distinguish between Certain_Outcome8 and Certain_Outcome2 conditions (force-choice classification accuracy = 69.6% ± 10.3%, p = 0.011; Fig. 5A ), but the Constantly-Certain classifier was not able to distinguish between Uncertain_Outcome8 and Uncertain_Outcome2 conditions (force-choice classification accuracy = 63.0% ± 9.3%, p = 0.104; Fig. 5B ). These evidence at whole-brain level indicated that, compared with the Constantly-Certain situation, there might existed unique neural representations in the Uncertain-to-Certain situation. Download figure Open in new tab Fig. 5. Differential neural representations for gratitude-related responses in the Uncertain-to-Certain and in the Constantly-Certain situations. (A-B) Pattern expression values of the Uncertain-to-Certain classifier (A) and the Constantly-Certain classifier (B) in the four conditions respectively. (C) Classification accuracies for Uncertain_Outcome8 vs. Uncertain_Outcome2 conditions (orange bar) and Certain_Outcome8 vs. Certain_Outcome2 conditions (blue bar) in regions that showed differential sensitivities to the Uncertain-to-Certain situation and the Constantly-Certain situation. Results were thresholded at p < 0.05, Bonferroni corrected, two-tailed. (D) Whole-brain multivariate pattern classifier discriminating Uncertain_Outcome8 vs. Uncertain_Outcome2 conditions (the Uncertain-to-Certain classifier) and Certain_Outcome8 vs. Certain_Outcome2 conditions (the Constantly-Certain classifier), respectively. (E) Meta-analytical decoding results. The orange line represents the similarities between the meta-analytical maps of terms of psychological components generated from Neurosynth database and the Uncertain-to-Certain classifier, and the blue line corresponds to these for the Constantly-Certain classifier. The results showed that the processing in the Uncertain-to-Certain situation was more strongly linked to “Theory of Mind” and “Social” terms, whereas that in the Constantly-Certain situation was more closely associated with “Outcome” and “Reward” terms. To search for specific brain regions that involved in the unique neural representations in the Uncertain-to-Certain situation, we conducted ROI-based MVPA within each of the sixty-six gratitude-related brain regions identified in RSA, and applied linear SVM ( 83 , 84 ) to train local classifiers discriminating Uncertain_Outcome8 vs. Uncertain_Outcome2 conditions. This analysis revealed only one brain parcel in the dmPFC showing significant classification ability for Uncertain_Outcome2 and Uncertain_Outcome8 conditions (force-choice classification accuracy = 78.3% ± 11.5%, p < 0.001, p Bonferroni = 0.010, Fig. 5C ). Meanwhile, the neural representations in these parcels could not discriminate Certain_Outcome8 vs. Certain_Outcome2 conditions (force-choice classification accuracy = 47.8% ± 7.1%, p = 0.883, p Bonferroni = 1.000). These results demonstrated that the dmPFC, a region that plays an important role in the processing of ToM as suggested by previous studies ( 32 , 85 – 88 ), was specifically involved in the gratitude-related processing in the Uncertain-to-Certain situation. To search for specific brain regions involved in the Constantly-Certain situation, we conducted similar analyses as the Uncertain-to-Certain situation. This analysis revealed three brain parcels that could discriminate Certain_Outcome2 and Certain_Outcome8 conditions, locating in bilateral dlPFC (force-choice classification accuracy = 78.3% ± 11.5%, p < 0.001, p Bonferroni = 0.010), SMA (force-choice classification accuracy = 78.3% ± 11.5%, p < 0.001, p Bonferroni = 0.010), and lingual gyrus (force-choice classification accuracy = 80.4% ± 11.9%, p < 0.001, p Bonferroni = 0.003), respectively ( Fig. 5C ). Moreover, the neural representations in these parcels could not discriminate Uncertain_Outcome8 and Uncertain_Outcome2 conditions (dlPFC, force-choice classification accuracy = 54.3% ± 8.0%, p = 0.659, p Bonferroni = 1.000; SMA, force-choice classification accuracy = 45.7% ± 6.7%, p = 0.659, p Bonferroni = 1.000; lingual gyrus, force-choice classification accuracy = 23.9% ± 3.5%, p < 0.001, p Bonferroni = 0.035), indicating that these regions were specifically involved in the gratitude-related processing in the Constantly-Certain situation. Given that both evidence from the whole-brain and ROI-based analyses suggested that the gratitude-related processing in the Uncertain-to-Certain and the Constantly-Certain situations may involve differential cognitive components, we formally test this notion by performing meta-analytic decoding for the whole-brain Uncertain-to-Certain classifier and the whole-brain Constantly-Certain classifier separately using the Neurosynth database ( 89 ). The results showed that the processing in the Uncertain-to-Certain situation was more strongly linked to “Theory of Mind” and “Social” terms, whereas that in the Constantly-Certain situation was more closely associated with “Outcome” and “Reward” terms ( Fig. 5E ). These findings demonstrated the differences in cognitive processing between the two situations, and suggested the ToM system as the crucial neural bases that supported the prosocial information integration and the resulting adaptive asymmetric dynamic adjustment in gratitude-responses. Discussion Fluctuations in exogenous uncertainty occur frequently in everyday social interactions. The ability to dynamically adjust social emotional and behavioral responses to others’ altruistic behaviors in the presence of such exogenous uncertainty fluctuations is pivotal for human social adaptation ( 7 , 10 , 12 , 15 ). Prior research on direct reciprocity under uncertainty has predominantly focused either on how individuals respond to others’ altruistic behaviors with gratitude and reciprocity from a static perspective ( 18 , 39 , 41 – 43 , 90 , 91 ), or on how they resolve endogenous uncertainty dynamically in multiple interactions ( 48 – 52 ), overlooking the dynamic adjustments in response to exogenous uncertainty fluctuations. Our study addresses this gap by employing an interpersonal task to simulate the transition from exogenous uncertainty to certainty and comparing it with the constantly certain situation. Using four experiments, we identified a novel response pattern, characterized as the adaptive asymmetric dynamic adjustment in gratitude and direct reciprocity in the exogenous Uncertain-to-Certain situation: participants’ ratings of gratitude and amounts of reciprocity increased when the actual benefactor-cost (or self-benefit) was higher than expected but did not plummet when that was equally lower than expected. This pattern of responses aligned with our cognitive hypothesis of prosocial information integration, which was perceived as more moral by third-party evaluators compared to other response patterns, such as outcome-oriented evaluation, anchoring evaluation, and selfish information integration. Utilizing fMRI scanning, we uncovered the neural bases of this process, showing that the gratitude processing in the Outcome_Display phase of the Uncertain-to-Certain situation diverged from that of the Constantly-Certain situation, with an increased involvement of ToM-related processing, especially the neural representations of dmPFC. It is worth noting that the adaptive asymmetric dynamic adjustment in gratitude observed in this study cannot be fully accounted for by existing evidence on the generation and fluctuations of gratitude ( 27 – 32 , 34 , 35 , 39 , 49 ), social learning theory ( 6 ), or economic decision-making ( 12 , 15 , 24 , 25 , 53 – 55 , 59 , 67 , 92 ). By redefining gratitude based direct reciprocity as a context-sensitive process driven by adaptive goals, the underlying mechanism of prosocial information integration revealed in the current study not only enriches the theoretical framework and methodological foundations within the fields of gratitude based direct reciprocity, but also provides a new perspective for understanding cooperation in uncertain environments and informs strategies to foster prosociality. First, within the realm of gratitude research, the majority of theoretical and empirical studies have focused on single, non-dynamical events in certain contexts, which are insufficient to explain the dynamic processes ( 27 – 32 , 34 , 35 ). A recent mood-cue-predictive (MCP) model, grounded in predictive coding and social learning theory, provided evidence for understanding the mechanisms underlying gratitude dynamics in multi-round social interactions involving the endogenous uncertainty in the beneficiary’s beliefs about the benefactor’s altruistic actions ( 49 ). However, it is insufficient to explain the dynamic adjustments in gratitude in the transition from exogenous uncertainty to certainty here. According to the MCP model, which did not distinguish between positive or negative expectation violations, when the benefactor’s cost becomes equally higher or lower than expected, the same degree of expectation violations should lead to symmetric adjustments, but not the adaptive asymmetric dynamic adjustment in gratitude observed here. Second, recent research emphasizes the role of social learning in how individuals dynamically deal with endogenous uncertainty ( 6 ). Although the MCP model of gratitude ( 49 ) did not distinguish the valence of expectation violations, studies on other social learning processes have suggested that individuals are more sensitive to negative than positive expectation violations ( 18 , 75 – 77 ); therefore, when the benefactor’s cost was higher than expected, individuals might exhibit heightened responses. However, this theory could not explain why we observe the same patterns of adaptive asymmetric dynamic adjustment in gratitude-related responses when manipulating the exogenous uncertainty fluctuations in the benefactor’s cost and the beneficiary’s benefit (see details in Results ). Third, in the fields of economic decision-making, extensive research has investigated how individuals make decisions under uncertainty and respond to uncertain outcomes ( 67 , 93 – 95 ). On one hand, it has been established that individuals generally exhibit aversion to uncertainty (risk or ambiguity) ( 15 , 24 , 25 , 53 – 57 , 67 , 92 , 96 – 98 ). This aversion to uncertainty has been leveraged to explain why individuals perceived a reduced likelihood of being helped in exogenous uncertain situation, which then led to greater perceived kind intention, and increased gratitude when others help in this situation ( 39 ). However, this could not explain the asymmetric adjustments in gratitude that we observed following the resolution of exogenous uncertain outcomes in this study. On the other hand, from the perspective of valuation, for one thing, the anchoring effect ( 63 – 65 , 67 ) indicates that individuals tend to rely excessively (or “anchor”) on the information they initially obtain. Although this theory implies that individuals, in the context of our study, would integrate the prior information of the cost the benefactor was willing to undertake, as reflected by the help decision during the Outcome_Unknown phase, into their final gratitude related processing during the Outcome-Display phase, it does not delineate the precise directionality of this integration process. For another, the prospect theory ( 67 , 99 , 100 ) highlights that individuals are more sensitive to losses than gains during valuation. If participants treat the mathematical reductions (from expected 5 to 2) as loss, they would be more sensitive to the reduction than to the increase in both the benefactor’s cost and the self-benefit; if participants treat the increase of the benefactor’s cost and the decrease of the self-benefit as losses, they would show greater adjustments in these two conditions than in other conditions. However, these patterns of responses are not in line with our findings. More importantly, our results of neural decoding suggested that the gratitude-related processing in the Constantly-Certain situation involved more reward- and outcome-related processes, while that in the Uncertain-to-Certain situation involved more ToM- and social-related processes, indicating that the adaptive asymmetric dynamic adjustment was not merely due to asymmetric valuation. Based on our findings and the above theoretical analyses, we posit that the adaptive asymmetric dynamic adjustment in intention evaluation, gratitude and reciprocity, as identified in this study, is a manifestation of prosocial information integration. In the context of receiving help, gratitude is considered to play an important find-remind-bind role ( 39 , 49 , 68 , 70 ), reminding individuals of the existence of high-quality cooperators and promoting adaptive behavioral responses that help bind relationships with this cooperator. Within the purview of this social goal, it is imperative for the beneficiary to exhibit gratitude-related responses that incentivize the benefactor to engage in relationship-building, which is facilitated by the ToM system based prosocial information integration. Our third-party evaluation results supported this notion; this pattern of gratitude-related responses is perceived more moral and makes people more willing to establish cooperative relationships with the beneficiary. At the neural level, we found that the adaptive asymmetric dynamic adjustment in gratitude was related to ToM-related process, primarily involving the dmPFC ( 32 , 85 – 88 ). This result further supported the mechanism of prosocial information integration and brings new insights. The ToM-related process may 1) play a pivotal role in integrating prior and posterior information, in alignment with social goals (i.e., information integration), 2) be involved in the intention evaluation based on the integration of information from multiple stages of evidence (i.e., intention inference), which echoes the controlled inferences about endogenous uncertainty resolution ( 6 ), or 3) be instrumental in anticipating third-party social evaluation of one’s gratitude-related responses (i.e., the processing of social goals). Indeed, these represent the three potential cognitive components that constitute the processing of prosocial information integration; whether these three processes unfold sequentially or concurrently is an interesting and significant question that merits further investigations. This study provides important implications for future research and daily life decision-making. First, our study pioneers an investigation into how beneficiaries dynamically adjust their evaluation and responses to others’ altruistic behaviors under exogenous uncertainty fluctuations. We identify a novel phenomenon of adaptive asymmetric dynamics in gratitude and reciprocity, supported by prosocial information integration. This mechanism diverges from expectation-violation-based social learning frameworks previously proposed for endogenous uncertainty processing ( 6 ), suggesting potential differential neurocognitive bases for processing exogenous versus endogenous uncertainty. While our design did not directly compare these uncertainty types, this distinction represents a critical hypothesis for future research. Moreover, our focus on one-shot exogenous uncertainty-to-transitions raises further questions about how beneficiaries manage repeated exogenous uncertainty transitions. Specifically, whether such processes engage recursive social learning mechanisms and how they contrast with the learning architectures adapted to endogenous uncertainty remain open questions warranting systematic investigation. These unresolved issues highlight promising avenues for advancing our understanding of exogenous uncertainty processing in prosocial decision-making. Second, previous research on economic reciprocity using the hidden-multiplier trust game (HTMG) has identified substantial variability in individual responses ( 19 ). Different individuals may apply different strategies when reciprocating, such as inequity aversion, guilt aversion, moral opportunism, etc. While the group-level analyses of our study substantiated prosocial information integration, this did not preclude the possibility of individual variability. For instance, a minority of participants might exhibit outcome-oriented evaluation, anchoring evaluation, or selfish information integration. Future research with more refined manipulations and large sample size are needed to identify the potential individual differences and the underlying neurocognitive bases. Our findings further imply that the scarcity of gratitude-related responses adhering to alternative cognitive hypotheses within this experimental setting is likely attributable to their diminished social adaptability, as such patterns are potentially perceived as more egoistic and less moral. This insight lays the theoretical bases for understanding and guiding individual daily reciprocal decisions and informs the development of social and economic policies aimed at fostering prosocial behavior. In summary, the present study uncovers an adaptive asymmetry in gratitude and direct reciprocity dynamics during exogenous uncertain-to-certain transitions. Evidence from both behavioral and neural levels suggests that this adaptive asymmetric adjustment is driven by a prosocial information integration, wherein beneficiaries asymmetrically integrate prior and posterior information regarding uncertainty fluctuations to adjust gratitude and sustain social acceptance. This process is underpinned by the neural representations in the theory-of-mind system, particularly the dmPFC. By redefining gratitude-based direct reciprocity as a context-sensitive process driven by adaptive goals, this work provides new perspective and methodological foundations for understanding cooperation in uncertain environments. Methods Participants For Experiment 1 (fMRI experiment), 49 undergraduate and graduate Chinese Han students were recruited from Shanghai, China, to participate in the fMRI experiment at East China Normal University. All participants were right-handed with normal or corrected-to-normal vision. Three participants were excluded from data analysis due to excessive head movements (>2mm of locomotion or >2° of rotation), leaving 46 participants (23 females; 21.7 ± 2.1 years) for data analysis. For Experiment 2, 34 undergraduate and graduate Chinese Han students were recruited from Shanghai, China, to complete a behavioral experiment at East China Normal University. Three participants were excluded due to failing the comprehension test, leaving 31 participants (22 females; 21.4 ± 1.6 years) for data analysis. For Experiment 3, 39 undergraduate and graduate Chinese Han students were recruited from Shanghai, China, to complete a behavioral experiment via a Chinese online experiment platform (NAODAO: www.naodao.com ) ( 101 ). Six participants were excluded due to failing the comprehension test, leaving 33 participants (16 females; 21.4 ± 2.0 years) for data analysis. For Experiment 4 (online questionnaire), 49 undergraduate and graduate Chinese Han students (21 females; 21.9 ± 2.0 years) were recruited from a Chinese online experiment platform (NAODAO: www.naodao.com ) to complete an online questionnaire. For all experiments, none of the participants reported any history of psychiatric, neurological, or cognitive disorders. All experiments were carried out in accordance with the Declaration of Helsinki and were approved by the Ethics Committee of East China Normal University. Informed written consent was obtained from each participant prior to participating. Procedures Experiment 1—fMRI experiment manipulating uncertainty in benefactor’s cost Overview Experiment 1 consisted of two sessions. In the first session (pain titration), we measured each participant’s pain threshold and determined the intensity of pain was what the participant considered to be a moderate punishment. In the second session (main task), the participants (the beneficiaries) performed an interpersonal task, in each round of which they would receive a pain stimulation and were randomly paired with an anonymous co-player (the benefactor), who could decide whether to help them reduce the number of pains by undertaking a number of pains. Pain titration Pain titration was conducted following the procedure outlined in previous studies on gratitude ( 31 , 32 , 39 ). During the titration process, an intra-epidermal needle electrode was attached to the back of each participant’s left hand for cutaneous electrical stimulation ( 102 ). The initial pain stimulation consisted of eight repeated pulses, each with an intensity of 0.2 mA and a duration of 0.5-ms, with a 10-ms interval between each pulse. We progressively increased the intensity of each single pulse until the participant rated the pain as 8 on a 10-level pain scale (1 = not painful, 10 = intolerable). All participants were informed that the pain stimulation that each individual would receive in the interpersonal task would be the one that they rated as 8 in the pain titration session. Interpersonal task (main task) We used a multi-round interpersonal task ( Fig. 1 ) to induce dynamic changes from uncertainty to certainty in help-receiving situation, which was adapted from the previous study on how the uncertainty in benefactor’s cost influenced beneficiary’s gratitude ( 39 ). The participants completed the interpersonal task in an MRI scanner. Following the pain titration session, each participant was instructed on the general rules of the interpersonal task. In each trial, the participant was paired with an anonymous same-gender co-player, who was distinct from the ones in any other trials and would only interact with the participant once. In each round, the participant was to receive 10 times of pain stimulation of Level 8. The participant was instructed that each co-player had come to the lab before the participant and had already decided whether or not to help the participant reduce half the number of pain stimulation by enduring a number of pain stimulation (benefactor’s cost). Once being helped, the number of pain stimulation that the participant had to endure would be reduced to 5 times, regardless of the co-player’s cost; otherwise, the pain stimulation would continue to be 10 times. The participant was informed that each co-player decided whether to help the participant under one of the two situations regarding the cost of help, the Uncertain-to-Certain situation and the Constantly-Certain situation, which was randomly chosen by the computer program. In each trial of the Uncertain-to-Certain situation, the co-player decided whether to help the participant under exogenous uncertain (risky) cost of receiving the pain stimulation of either 2 times or 8 times, each with 50% probability, i.e., the mathematic expectation of final outcome equaled 5, determined by the computer system ( 39 ). After the decision had been made by the co-player, it cannot be changed no matter what the actual cost was. Then the actual cost that the co-player undertook, either 2 times (lower than expectation) or 8 times (higher than expectation), was determined randomly by the computer and shown to both the participant and the co-player. In some of the trials of Uncertain-to-Certain situation, the participants should allocate 20 points between themselves and the co-player in the corresponding trial (Allocation phase; 1 point = 1 Yuan, 20 Yuan ∼ 2.76 USD; the participant could adjust the amount of allocation in increments of 1 point), at the time point before the co-player’s actual cost was presented, i.e., under uncertainty (Uncertain_OutcomeUnknown condition). In the other trials, they should allocate points at the time point after knowing the co-player’s actual cost of 2 times (Uncertain_Outcome2 condition) or 8 times (Uncertain_Outcome8 condition). These three conditions in the Uncertain-to-Certain situation formed a one-way three-level within-subject design. The comparisons in the amount of monetary allocation between Uncertain_OutcomeUnknown and Uncertain_Outcome2 conditions, as well as between Uncertain_OutcomeUnknown and Uncertain_Outcome8 conditions, would reveal how the beneficiary dynamically adjusts their feeling of gratitude when transitioning from facing an uncertain outcome to facing an actual and certain outcome of higher or lower benefactor’s cost. In each trial of the Constantly-Certain situation, the co-player decided whether to help the participant under certain cost, which could be 2, 5 or 8 times, determined randomly by the computer and displayed to the co-player prior to their decisions. Being presented with the co-player decision on whether to help or not under certain cost of 2 times (Certain_Outcome2 condition), 5 times (Certain_Outcome5 condition) or 8 times (Certain_Outcome8 condition), the participants should allocate points between themselves and the co-player in the corresponding trial. The Certain_Outcome2 and Certain_Outcome8 conditions in the Constantly-Certain situation were included as baselines, along with the Uncertain_Outcome2 and Uncertain_Outcome8 conditions, forming a 2 Situation (Uncertain-to-Certain vs. Constantly-Certain) × 2 Outcome (2 vs. 8) within-subject design. This design enabled us to elucidate distinctions in the processing of gratitude across two distinct scenarios: one in which the outcome of help is ascertained from the outset, and another wherein the outcome evolves from an initial state of uncertainty to eventual certainty, despite the ultimate outcomes of benefactor’s cost being equivalent in both conditions. The participant was informed that all co-players were unaware of the procedure of monetary allocation, eliminating the possibility that the co-players’ decisions to help were due to monetary concerns. The participant was also informed that, after the whole experiment, 20 trials would be randomly selected from all the trials and be realized to determine the corresponding co-player’s final amount of pain stimulation and monetary bonus. The participant would receive the average amount of points that each participant assigned to themself and the average number of pain stimulation throughout the randomly selected trials. During the scanning, in each trial ( Fig. 1 ), after being paired with an anonymous co-player (Regrouping phase, 2 to 4 s), the participant would see information regarding which situation (Uncertain-to-Certain or Constantly-Certain) the co-player’s cost belonged to in the current trial (Situation information phase, 2 to 4 s). For the Uncertain-to-Certain situation, the Situation information phase would present an orange pie, with the numbers 2 and 8 on the top and the bottom, respectively (locations counterbalanced across trials), indicating the co-player’s uncertain cost of either 2 times or 8 times of pain stimulation, each with 50% probability. Then the co-player’s decision on whether to help the participant under uncertainty would display under the orange pie (Outcome_Unknown phase for Uncertain-to-Certain situation, 3s). In trials of the Uncertain_OutcomeUnknown condition ( Fig. 1A ), the participant should immediately allocate points to the co-player paired on a scale of 0 to 20 (Allocation phase, < 12 s; 20 Yuan ∼ $2.76 U.S.) after the Outcome_Unknown phase without knowing the co-player’s actual cost. Then the participant would see the actual outcome of co-player’s cost (Outcome_Display phase, 3s). In trials of the Uncertain_Outcome2 and Uncertain_Outcome8 conditions ( Fig. 1B ), the participant would see the actual outcome of co-player’s cost first (Outcome-Display phase, 3 s), and then allocate points to the co-player (Allocation phase, < 12 s). For the Constantly-Certain situation ( Fig. 1C ), this phase would present an orange pie, with the number of co-player’s certain cost (2, 5 or 8) in the center. Then after the co-player’s decision on whether or not to help the participant would display under the orange pie (Outcome_Display phase, 3s), the participant should allocate points to the co-player (Allocation phase, < 12 s). For the purpose of fMRI signal deconvolution, before and after the Allocation phase and the Outcome_Display phase, a fixation was presented for 1 - 5 s. The experiment consisted of 126 trials. There were 12 Help trials and 6 NoHelp trials each for the conditions of Uncertain_Outcome2, Uncertain_Outcome8, Certain_Outcome2, Certain_Outcome5, and Certain_Outcome8, as well as 24 Help trials and 12 NoHelp trials for the Uncertain_OutcomeUnknown condition, ensuring that the Allocation phase occurred equally before and after the outcome of Uncertain situation was revealed. The task was divided into 3 runs with equal number of trials for each condition in each run. Each run consisted of 42 trials in total and lasted for about 15 mins. Trials within each run were pseudo-randomly mixed to ensure that no more than two consecutive trials were from the same condition. To avoid the influence of trial sequence, six sequences with pseudo-random order of trials were pre-determined and counterbalanced across participants by using Latin square design. Unbeknown to the participants, all the co-players’ decisions were predetermined by a computer program. No participant disputed the authenticity of the co-players when reporting their comments and feelings after the experiment. Subjective ratings regarding the interpersonal task After the interpersonal task and before the payment for participation, each participant rated their feelings of gratitude to the co-player on a scale of 0 (‘not at all’) to 100 (‘very strong’), under each of the six Help conditions (Uncertain_OutcomeUnknown, Uncertain_Outcome2, Uncertain_Outcome8, Certain_Outcome2, Certain_Outcome5, and Certain_Outcome8) respectively. They were also asked to rate their perceived kind intention from help on a scale of −50 (‘very unkind’) to 50 (‘very kind’), under each of the six Help conditions and the six NoHelp conditions, respectively. For instance, the questions for the Uncertain_Outcome2 condition were “When the co-player decided to help you under Uncertain cost and then the final cost was determined to 2 times, how grateful do you feel for the co-player’s help?” and “When the co-player decided to help you under Uncertain cost and then the final cost was determined to 2 times, how kind do you think the co-player’s intention was?” Since the co-players were anonymous during the social interaction task, participants made each rating regarding all the co-players in the same condition as a whole. The condition-specific gratitude and intention ratings were mapped onto each corresponding trial and would be used in within-subject representational similarity analyses of fMRI. Experiment 2—Behavioral experiment manipulating uncertainty in beneficiary’s benefit Experiment 2 was conducted to exclude the possibility that the asymmetric dynamic adjustments in gratitude-related responses observed in the fMRI experiment (Experiment 1) were due to the sensitivity to negative prediction violation ( 18 , 75 – 77 ). The procedure of this behavioral experiment was the same as the fMRI experiment, except that ( 1 ) Experiment 2 manipulated the exogenous uncertainty in the participants’ own benefits, instead of the benefactor’s cost, and ( 2 ) participants were asked to rate their feelings of gratitude to the benefactor’s help during the interpersonal task, instead of the monetary allocation (see SI Methods ). Experiment 3—Behavioral replications manipulating uncertainties in benefactor’s cost and beneficiary’s benefit simultaneously Experiment 3 was conducted to validate the robustness of the asymmetric dynamic adjustments in gratitude-related responses observed in Experiments 1 and 2, and to examine whether there existed difference between these effects for the exogenous uncertainties in the benefactor’s cost and the beneficiary’s benefit. The procedure of this behavioral experiment was the same as Experiment 2, except that the experiment included two blocks, manipulating the exogenous uncertainties in the benefactor’s cost and in the participants’ own benefits respectively (see SI Methods ). Experiment 4—Online questionnaire To test the hypothesis that the prosocial information integration and the resulting adaptive asymmetric dynamic adjustment in gratitude-related responses are guided by the social adaptive role and thus should be more accepted and appreciated by other social members, an additional sample of participants were recruited to complete a third-party social evaluation questionnaire. In the questionnaire, each participant made social evaluation as a third-party on beneficiaries, who completed an interpersonal game that was similar as our fMRI experiment and exhibited different patterns of gratitude-induced reciprocity: Complete Adaptive Asymmetric Adjustment (CAAA_H3): reciprocating significantly more in Uncertain_Outcome8 (i.e., 16 points) than in Uncertain_OutcomeUnknown (i.e., 10 points), and the same as Uncertain_OutcomeUnknown in Uncertain_Outcome2 (i.e., 10 points). This pattern of reciprocity was corresponded to Prosocial information integration (Hypothesis 3). Incomplete Adaptive Asymmetric Adjustment (IAAA_H3): reciprocating significantly more in Uncertain_Outcome8 (i.e., 18 points) than in Uncertain_OutcomeUnknown (i.e., 10 points), and slightly less in Uncertain_Outcome2 (i.e., 8 points) than in Uncertain_OutcomeUnknown (i.e., 10 points). This pattern of reciprocity was corresponded to Prosocial information integration (Hypothesis 3), and was similar to the pattern of participants’ average amounts of allocation in Experiment 1. Symmetric Adjustment & More Amount (SAM_H1): compared with Uncertain_OutcomeUnknown (i.e., 12 points), reciprocating more in Uncertain_Outcome8 (i.e., 18 points) and equivalently less in Uncertain_Outcome2 (i.e., 6 points), with more amount of allocation in Uncertain_OutcomeUnknown but the same total amount of allocation compared with Complete Adaptive Asymmetric Adjustment and Incomplete Adaptive Asymmetric Adjustment. This pattern of reciprocity was corresponded to the Outcome-oriented evaluation (Hypothesis 1). Symmetric Adjustment & Less Amount (SAL_H1): compared with Uncertain_OutcomeUnknown (i.e., 10 points), reciprocating more in Uncertain_Outcome8 (i.e., 16 points) and equivalently less in Uncertain_Outcome2 (i.e., 4 points), with the same amount of allocation in Uncertain_OutcomeUnknown but less total amount of allocation compared with Complete Adaptive Asymmetric Adjustment and Incomplete Adaptive Asymmetric Adjustment. This pattern of reciprocity was corresponded to the Outcome-oriented evaluation (Hypothesis 1). No Adjustment (NA_H2): reciprocating the same as Uncertain_OutcomeUnknown in both Uncertain_Outcome8 and in Uncertain_Outcome2 (i.e., 10 points in all conditions). This pattern of reciprocity was corresponded to Anchoring evaluation (Hypothesis 2). Selfish Asymmetric Adjustment (SAA_H4): reciprocating significantly less in Uncertain_Outcome2 (i.e., 4 points) than in Uncertain_OutcomeUnknown (i.e., 10 points), and the same as Uncertain_OutcomeUnknown in Uncertain_Outcome8 (i.e., 10 points). This pattern of reciprocity was corresponded to Selfish information integration (Hypothesis 4). To be noted, with the exception of Symmetric Adjustment & More Amount, the amounts of the beneficiary’s allocation were fixed at 10 points in the Uncertain_OutcomeUnknown condition for all other patterns of gratitude-induced reciprocity. Then for each pattern, the amounts of the beneficiary’s allocation in the Uncertain_Outcome2 condition and the Uncertain_Outcome8 condition were determined according to the type of the corresponding pattern and the amount of points in the Uncertain_OutcomeUnknown condition. For Symmetric Adjustment & More Amount, the amount of the beneficiary’s allocation was set to 12 points in Uncertain_OutcomeUnknown condition, so that the total amount of allocation in Symmetric Adjustment & More Amount was comparable to Complete Adaptive Asymmetric Adjustment and Incomplete Adaptive Asymmetric Adjustment, which excluded the potential confounding caused by the different total amounts of allocation. Specifically, in the questionnaire, each participant read the following background information: “In each round of an interpersonal game, the participant No. X was to receive 10 times of pain stimulation as a punishment for failing the task, and was randomly paired with a different anonymous co-player. The co-player (benefactor) decided whether to help No. X reduce the number of pain stimulation by 5 times, under uncertain cost of receiving a pain stimulation of either 2 times or 8 times, each with 50% probability. If the co-player decided to help, then the actual outcome of the cost that the co-player undertook, either 2 times or 8 times, was determined randomly by the computer and shown to both No. X and the co-player. The No. X had an opportunity to allocate some monetary points from own endowment (20 points) to the co-player as reciprocity. This decision might be made at one of the following three timepoints: ( 1 ) before the final outcome was ascertained (i.e., Uncertain_OutcomeUnknown condition); ( 2 ) after the final outcome was ascertained and the co-player actually undertook 2 times of pain stimulation (i.e., Uncertain_Outcome2 condition); ( 3 ) after the final outcome was ascertained and the co-player actually undertook 8 times of pain stimulation (i.e., Uncertain_Outcome8 condition).” Then each participant would be presented with six scenarios describing the No. X’s amounts of allocations in the three conditions, corresponding to the above six allocation patterns, with the order of scenarios counterbalanced between participants. After reading each scenario, participants should rate the perceived morality (from −50, ‘very immoral,’ to 50, ‘very moral’), intention (from −50, ‘very unkind,’ to 50, ‘very kind’), stinginess (from 0, ‘very unstingy,’ to 100, ‘very stingy’), and utilitarianism (from 0, ‘very non-utilitarian,’ to 100, ‘very utilitarian’) of the person described. Additionally, they should rate their liking for this person (from −50, ‘very much disliked,’ to 50, ‘very liking’), and their willingness to be friends with this person (from 0, ‘no willingness,’ to 100, ‘very willing’). For example, for “Complete Adaptive Asymmetric Adjustment”, participants made ratings after seeing the following instructions: “One co-player decided to help No. X. If the allocation was made before the final outcome was ascertained, No. X decided to give the co-player 10 points. If the allocation was made after the final outcome was ascertained and the co-player actually undertook 2 times of pain stimulation, No. X decided to give the co-player 10 points. If the allocation was made after the final outcome was ascertained and the co-player actually undertook 8 times of pain stimulation, No. X decided to give the co-player 16 points. How would you make ratings about No. X?” Behavioral data analyses Behavioral data analyses were all carried out in R 4.2.3 ( https://www.r-project.org/ ). Bonferroni corrections were used for multiple comparison correction, and Greenhouse-Geisser corrections were used when the assumption of sphericity was violated. Experiment 1 In Experiment 1, first, to reveal whether and how a beneficiary dynamically adjusts gratitude-related responses from facing an uncertain outcome (i.e., benefactor’s cost with a 50% chance of being either 2 or 8, expected to be 5 times of pain stimulation) to facing a final actual outcome of higher (i.e., 8) or lower (i.e., 2) benefactor’s cost than expected, we conducted one-way (Outcome in the Uncertain-to-Certain situation: Uncertain_Outcome2 vs. Uncertain_OutcomeUnknown vs. Uncertain_Outcome8) repeated-measure analyses of variance (ANOVA) for gratitude ratings, monetary allocations and intention ratings, respectively. To further reveal how the direction of outcome change (from an uncertain cost with the expectation of 5 to final cost of 2 or to final cost of 8) influence the magnitude of dynamic adjustment in gratitude-related responses, we subtracted the gratitude ratings, monetary allocations and intention ratings in the Uncertain_Outcome2 and Uncertain_Outcome8 conditions from those in the Uncertain_OutcomeUnknown condition respectively, and took the absolute values as the indicators of the extent of the dynamic adjustment, and conducted Paired-samples t -tests. Second, we aimed to examine whether and how the influence of actual outcomes of the benefactor’s cost (2 vs. 8) on the beneficiary’s gratitude-related responses differ between the Constantly-Certain situation and the Uncertain-to-Certain situation, despite the final outcomes of benefactor’s cost being equivalent in both situations, we combined the data of gratitude ratings, monetary allocations and intention ratings in the Certain_Outcome2, Certain_Outcome8, Uncertain_Outcome2 and Uncertain_Outcome8 conditions, and performed 2 (Situation: Uncertain-to-Certain vs. Constantly-Certain) ξ 2 (Actual Outcome: 2 vs. 8) ANOVA for each variable respectively. Third, the findings from the perspective of transition from uncertainty to certainty and from the perspective of the differences between the Uncertain-to-Certain and the Constantly-Certain situations consistently suggest that, there might exist potential unique cognitive mechanisms underlying the kind intention evaluation and the resulting gratitude responses in the Uncertain-to-Certain situation, compared to the Constantly-Certain situation. We hypothesize that this uniqueness may arise from the prosocial information integration. That is, in the Uncertain-to-Certain situation, participants may need to consider not only the posterior information of actual benefactor’s cost in the Outcome_Display phase, but also the priori information about how much cost the benefactor is willing to undertake in the Outcome_Unknown phase (the expectation of 5 in the current study), and generate the evaluation of benefactor’s kind intention. Moreover, participants may consider more prior information from the Outcome_Unknown phase when the actual cost is lower than expected, and less prior information when the actual cost is higher than expected during cognitive integration, contributing to the observed “adaptive asymmetric dynamic adjustment” of gratitude (Hypothesis 3). To test this hypothesis, we constructed a matrix of [hypothesized integrated intention evaluation, actual benefactor’s cost] for each of the three conditions in the Uncertain-to-Certain situation, i.e., the Uncertain_Outcome2 [5, 2], Uncertain_OutcomeUnknown [5, 5], and Uncertain_Outcome8 [8, 8], and constructed a representational dissimilarity matrix (RDM) for this cognitive hypothesis (RDM_H3 based on the Euclidean distance between conditions. We also included three RDMs for other three alternative cognitive hypotheses: one assuming that participants only used the posterior information (RDM_H1), one assuming that participants only used the prior information (RDM_H2), and another assuming that participants used the prior information more when the actual cost was higher than expected and vice versa (RDM_H4). Meanwhile, we respectively constructed four behavioral RDMs using perceived kind intention ratings, gratitude ratings, and monetary allocations from the three conditions in the Uncertain-to-Certain situation. Then, for each participant, we applied Pearson correlations on the lower triangle of the matrices to calculate the correlation between each cognitive RDM and each behavioral RDM. It should be noted that here, we focused on the difference of values of behavioral indicators between different conditions, rather than ranking according to the values of behavioral indicators. Therefore, we chose Pearson correlation instead of Spearman correlation in the representational similarity analysis. Finally, for each behavioral indicator, we extracted the correlation coefficients ( r values) from all participants, conducted a Fisher z -transformation, and then performed one-way ANOVA across four cognitive hypotheses to identify which of hypotheses better fitted the actual behavioral patterns. Experiment 2 and Experiment 3 The analyses of Experiment 2 and Experiment 3 were the same as Experiment 1, except that in Experiment 3, we further compared the extent of asymmetric dynamic adjustments in gratitude-related responses between the context involving exogenous uncertainty in the benefactor’s cost and the context involving exogenous uncertainty in the beneficiary’s (participant’s) benefit by including context as an independent variable (see SI Results ). Experiment 4 To test whether the prosocial information integration and the resulting adaptive asymmetric dynamic adjustment in gratitude-related responses exhibited social adaptive function of increasing social acceptance and reputation, for each type of third-party ratings (perceived morality, intention, stinginess and utilitarianism, the liking for this person, and the willingness to be friends with this person), we performed one-way ANOVA on the rating values across the six scenarios of allocations patterns (Complete Adaptive Asymmetric adjustment, Incomplete Adaptive Asymmetric adjustment, Symmetric Adjustment & More Amount, Symmetric Adjustment & Less Amount, No Adjustment, and Selfish Asymmetric adjustment). FMRI data acquisition and preprocessing of Experiment 1 Images were acquired through a 3T MRI scanner (Siemens Prisma; Siemens, Erlangen, Germany) with a 64-channel head coil. T2-weighted functional images were acquired using a single-shot T2*-weighted gradient-echo echo-planar imaging pulse sequence (TR = 1000 ms, TE = 32 ms, flip angle [FA] = 55°, each volume comprising 72 axial slices, matrix = 96 × 96, field of view [FoV] read = 192 mm, voxel size = 2 × 2 × 2 mm 3 ). The fMRI data preprocessing and univariate analyses were conducted using the Statistical Parametric Mapping software SPM12 (Wellcome Trust Department of Cognitive Neurology, London, UK). Images were slice-time corrected (with the middle slice as the reference, i.e., the 36th slice), motion corrected, resampled to 2 mm × 2 mm × 2 mm isotropic voxels, and normalized to MNI space using the EPInorm approach in which functional images are aligned to an EPI template, which is then nonlinearly warped to stereotactic space ( 103 ). Images were then spatially smoothed with a 5 mm FWHM Gaussian filter, and temporally filtered using a high-pass filter with a cutoff frequency of 1/128 Hz. T1-based normalization was not applied as the field maps necessary to adjust geometric distortion of EPI relative to the T1 images were not obtained ( 103 ). General linear model (GLM) analysis A GLM analysis was conducted at the individual level (i.e., first-level analysis) in SPM12 to identify participants’ brain responses to the co-player’s decision of help or not help in each of the Uncertain_OutcomeUnknown, Uncertain_Outcome2, Uncertain_Outcome8, Certain_Outcome2, Certain_Outcome5, and Certain_Outcome8 conditions. In the GLM, we built a design matrix with separable run-specific partitions and with twelve key regressors in each run. All these 12 key regressors spanned from the presentation of the corresponding phase to the end of this event (3 s): – R1: Help_Uncertain_OutcomeUnknown, onset of the Outcome_Unknown phase of Help trials in the Uncertain_OutcomeUnknown condition, which captured the brain responses to the co-player’s help under uncertainty in the Uncertain-to-Certain situation; – R2: Help_Uncertain_Outcome2, onset of the Outcome_Display phase of Help trials in the Uncertain_Outcome2 condition, which captured the brain responses to the co-player’s help after knowing the benefactor’s actual cost was lower than expected in the Uncertain-to-Certain situation; – R3: Help_Uncertain_Outcome8, onset of the Outcome_Display phase of Help trials in the Uncertain_Outcome8 condition, which captured the brain responses to the co-player’s help after knowing the benefactor’s actual cost was higher than expected in the Uncertain-to-Certain situation; – R4: Help_Certain_Outcome2, onset of the Outcome_Display phase of Help trials in the Certain_Outcome2 condition, which captured the brain responses to the co-player’s help under certain cost of 2 times in the Constantly-Certain situation; – R5: Help_Certain_Outcome5, onset of the Outcome_Display phase of Help trials in the Certain_Outcome5 condition, which captured the brain responses to the co-player’s help under certain cost of 5 times in the Constantly-Certain situation; – R6: Help_Certain_Outcome8, onset of the Outcome_Display phase of Help trials in the Certain_Outcome8 condition, which captured the brain responses to the co-player’s help under certain cost of 8 times in the Constantly-Certain situation. The settings for R7 - R12, i.e., NoHelp_Uncertain_OutcomeUnknown, NoHelp_Uncertain_Outcome2, NoHelp_Uncertain_Outcome8, NoHelp_Certain_Outcome2, NoHelp_Certain_Outcome5, NoHelp_Certain_Outcome8, were the same as R1 - R6 respectively, but focused on the NoHelp trials. Regressors of no interest included Regrouping (onset of the Regrouping phase, 2-4 s), Situation information (onset of the Situation information phase, 2-4 s), Outcome_AfterAllocation (onset of the Outcome_Display phase of the trials in Uncertain_OutcomeUnknown condition, these brain responses were excluded from our main analysis due to the concern that the Allocation procedure might influence the subsequent processing of actual outcomes), Allocation (onset of the Allocation phase, spanning to the time that the participant finished allocation), and Miss_Allocation (onset of the Allocation phase of the trials in which participants failed to respond, 12 s). Six movement parameters were included as regressors of no interest. All regressors were convolved with a double gamma hemodynamic response function (HRF), and high-pass temporal filtering was applied with a default cutoff value of 128 s to eliminate low-frequency drifts. We defined 12 contrasts corresponding to the simple effects of the 12 key regressors. Representational similarity analysis (RSA) of fMRI data The within-subject RSA was carried out in Python 3.8.10 using the NLTools package version 0.4.7 ( https://nltools.org/ ) to search for the specific brain regions that were involved in different cognitive components of gratitude-related processing, including the experience of gratitude indicated by gratitude ratings, the gratitude-induced reciprocity indicated by monetary allocation, the evaluation of kind intention indicated by perceived kind intention ratings, and the processing of benefactor’s cost implemented in the experimental design, respectively. Each contrast image for each condition and each participant was divided into 200 parcels using a priori 200-parcel whole-brain parcellation based on meta-analytically functional co-activation of the Neurosynth database ( 19 , 81 , 82 ) ( https://www.neurosynth.org/ ). The use of parcellation is less computationally demanding and exhibit higher homogeneity with functional neuroanatomy than the more conventional searchlight approach ( 19 , 81 , 104 ), and has been proven efficient in previous studies ( 19 , 81 ). To search for brain regions involved in benefactor’s cost related processing, for each parcel of each participant, we created a representational dissimilarity matrix (RDM) of neural activities using the pairwise correlation dissimilarity between each pair of the 12 key conditions (6 Help and 6 NoHelp conditions). Meanwhile, we constructed a behavioral RDM based on the values of benefactor’s cost as implemented in the experimental design ( Fig. 4A ) by estimating the Euclidean distance between the corresponding cost values of each pair of the 12 key conditions (6 Help and 6 NoHelp conditions). Then, for each parcel and each participant, we applied Spearman’s rank-order correlations on the lower triangle of the matrices to calculate the correlation between the parcel dissimilarity matrix and each behavioral dissimilarity matrix. For each parcel, we extracted the correlation coefficients ( ρ values) from all participants, conducted a Fisher z -transformation, and then conducted a one-sample sign permutation test to evaluate the association between the parcel dissimilarity matrix and each behavioral dissimilarity matrix at group-level. Multi-tests were corrected using Bonferroni correction (i.e., p < 0.00025, two-tailed). A similar procedure was adopted for searching for brain regions involved in intention-related and allocation-related processing, with the self-reported perceived kind intention and amount of monetary allocation, respectively, in the 12 key conditions (6 Help and 6 NoHelp conditions) as the inputs for the behavioral RDM ( Fig. 4A ). However, since participants made ratings on self-reported gratitude only regarding the six Help conditions, when searching for brain regions involved in gratitude-related processing, we constructed both the behavioral and neural RDMs using the data from these six conditions for each participant. Moreover, previous studies ( 31 , 32 , 36 , 39 ) have consistently identified the association between gratitude and neural activities in ventral medial prefrontal cortex (vmPFC). Therefore, in addition to whole-brain parcellation based RSA, we conducted regions of interest (ROI) based analysis using the parcel ID 148 in the priori 200-parcel whole-brain parcellation of the Neurosynth database, which corresponding to the peak coordinate of vmPFC (MNI coordinate: 3, 44, 4) identified in Xiong et al. (2020) ( 39 ). Multivariate pattern analysis (MVPA) MVPA was carried out in Python 3.8.10 using the NLTools package version 0.4.7 ( https://nltools.org/ ). For each binary classification, we used the contrast images of the corresponding binary conditions from participants and linear Support Vector Machine (SVM) ( 83 , 84 ) to train a whole-brain multivariate pattern classifier discriminating these two conditions (e.g., Uncertain_Outcome8 condition, coded as 1 vs. Uncertain_Outcome2 condition, coded as 0). SVM was conducted using linear kernel (regularization parameter C = 1), which has been suggested as a reasonable setting in multivariate pattern analysis ( 105 ). With a leave-one-subject-out cross-validation (LOSO) method, we calculated the accuracy and significance of the SVM classifier using the forced-choice discrimination test ( 84 , 106 , 107 ). Similar classification procedure was applied to all the following classification analyses. To be noted, first, during cross-validation, we ensured the independence of training and test data by holding out all images from the same participant together. Second, in the forced-choice discrimination test, we compared the pattern expression values, calculated as the dot product of the vectorized activation image with the classifier weights, between two conditions within the same individual that was not part of the training sample. The condition with the higher pattern expression value was labeled as the 1 (e.g., Uncertain_Outcome8 condition), while the one with the lower value was labeled as 0 (e.g., Uncertain_Outcome2 condition) ( 84 , 106 , 107 ). Comparing the differences in neural representations between the Outcome_Display phases in the Constantly-Certain situation and in the Uncertain-to-Certain situation On the one hand, from the perspective of Uncertain-to-Certain transition, we observed an “adaptive asymmetric dynamic adjustment” in gratitude-related responses at the behavioral level, indicating that there might exist different neural representations between the Uncertain_Outcome8 and the Uncertain_Outcome2 conditions. Therefore, we reveal these neural differences by conducting binary classifications between these two conditions (Help_Uncertain_Outcome8 vs. Help_Uncertain_Outcome2 contrast maps). On the other hand, at the behavioral level, we conducted a 2 (Situation: Uncertain-to-Certain vs. Constantly-Certain) x 2 (Outcome: 2 vs. 8) analysis and revealed the differences in the influence of benefactor’s cost on self-reported gratitude between the Outcome_Display phases in the Constantly-Certain situation and in the Uncertain-to-Certain situation. From this perspective, we explored the unique neural representations of gratitude in the Uncertain-to-Certain situation by comparing the neural representation of the benefactor’s cost in the Outcome_Display phases in the Uncertain-to-Certain situation (Help_Uncertain_Outcome8 vs. Help_Uncertain_Outcome2 contrast maps) and in the Constantly-Certain situation (Help_Certain_Outcome8 vs. Help_Certain_Outcome2 contrast maps). Whole-brain multivariate classifications We applied SVM to train a whole-brain multivariate pattern classifier discriminating Help_Uncertain_Outcome8 vs. Help_Uncertain_Outcome2 contrast maps (the Uncertain-to-Certain classifier), as well as a whole-brain multivariate pattern classifier discriminating Help_Certain_Outcome8 vs. Help_Certain_Outcome2 contrast maps (the Constantly-Certain classifier). Next, we conducted cross-situation classifications to examine whether there existed similar or different neural representations of the benefactor’s cost in the Outcome_Display phases in these two situations at whole-brain level. To test whether the pattern for the Uncertain-to-Certain classifier could predict the two conditions in the Constantly-Certain situation, for each participant, we computing the dot-products (i.e., pattern expression values) of the contrast maps for Certain_Outcome8 and Certain_Outcome2 conditions based on the Uncertain-to-Certain classifier. Significantly higher pattern expression values in Certain_Outcome8 condition than in Certain_Outcome2 condition would indicate similar neural representations across situations, while insignificant difference would indicate differential neural representations. Similar analysis was conducted to test whether the Constantly-Certain classifier could predict the two conditions in the Uncertain-to-Certain situation. Functional parcellation-based MVPA We then aimed to search for specific brain regions that were specifically involved in the Constantly-Certain situation and in the Uncertain-to-Certain situation, respectively, within the 66 regions of interest (ROI) identified in the RSA. For each ROI, we applied SVM ( 83 , 84 ) to train a multivariate pattern classifier discriminating Help_Uncertain_Outcome8 vs. Help_Uncertain_Outcome2 contrast maps, as well as a multivariate pattern classifier discriminating Help_Certain_Outcome8 vs. Help_Certain_Outcome2 contrast maps. Multi-tests were corrected using Bonferroni correction (i.e., p < 0.00076, two-tailed). Neurosynth meta-analytical decoding To investigate whether the processing of gratitude in the Constantly-Certain situation and the Uncertain-to-Certain situation were associated with differential psychological components, we conducted meta-analytical decoding on the multivariate pattern maps for these two situations using the Neurosynth Image Decoder ( neurosynth.org ) ( 89 ). This analysis allowed us to quantitatively evaluate the level of similarity between these two multivariate pattern maps ( Fig. 5D ) and each selected meta-analytical image generated by the Neurosynth database, indicated by the effect of spatial correlation ( r value) between the two maps. If a term of psychological component was involved more in the Uncertain-to-Certain situation than in the Constantly-Certain situation, the similarity between this term and the multivariate pattern map for the Uncertain-to-Certain situation would be larger than that between this term and the multivariate pattern map for the Constantly-Certain situation, and vice versa. Referring to previous studies on social emotions in help-receiving situation ( 1 , 39 ), Psychological terms were selected according to previous reviews on basic cognition (i.e., Imagine, Switching, Salience, Conflict, Memory, Attention, Cognitive control, Inhibition, Emotion, Anxiety, Fear, and Default mode) ( 108 ), social cognition (Empathy, Theory of mind, Social, and Imitation) ( 109 ) and decision-making (Reward, Punishment, Learning, Prediction error, Choice, and Outcome) ( 20 ). Author Contributions X.L., R.L., Y.H., X.Z., and X.G. designed the experiments; X.L. and Y.N. implemented the study designs and collected the data; X.L., R.L., Y.N., Y.F., and X.G. carried out the analyses; X.L., R.L., Y.N., Y.F., Y.H., X.Z., and X.G. wrote the paper; and X.Z. and X.G. supervised the work. All authors provided critical revisions and approved the final paper for submission. Competing interests The authors declare no competing interests. Data and Code Availability All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials . Original materials will be made available in a trusted open-access repository (e.g., Github) after being accepted for publication. Acknowledgements This work was supported by the STI 2030 - Major Projects 2021ZD0200500 (X.Z. and X.G.), the National Natural Science Foundation of China (32371094, 31900798, X.G.), the Young Elite Scientists Sponsorship Program by China Association for Science and Technology (YESS20210176, 2021QNRC001, X.G.), the Research Project of Shanghai Science and Technology Commission (20dz2260300, X.G. and X.Z.), and the Fundamental Research Funds for the Central Universities (X.G. and X.Z.). Funder Information Declared STI 2030 - Major Projects , 2021ZD0200500 National Natural Science Foundation of China (NSFC) , 32371094 Young Elite Scientists Sponsorship Program by China Association for Science and Technology , 31900798 Young Elite Scientists Sponsorship Program by China Association for Science and Technology , YESS20210176 Young Elite Scientists Sponsorship Program by China Association for Science and Technology , 2021QNRC001 Research Project of Shanghai Science and Technology Commission , 20dz2260300 Fundamental Research Fund for the Central Universities Footnotes Classification: Social Sciences/Psychological and Cognitive Sciences Biological Sciences/Psychological and Cognitive Sciences References 1. ↵ X. Gao , et al. , The psychological, computational, and neural foundations of indebtedness . Nat Commun 15 , 68 ( 2024 ). 2. ↵ C. Hilbe , K. Chatterjee , M. A. Nowak , Partners and rivals in direct reciprocity . Nat Hum Behav 2 , 469 – 477 ( 2018 ). 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