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Wednesday, June 7, 2023

Neurocomputational mechanisms underlying fear-biased adaptation studying in altering environments


People are in a position to adapt to the fast-changing world by estimating statistical regularities of the setting. Though concern can profoundly impression adaptive behaviors, the computational and neural mechanisms underlying this phenomenon stay elusive. Right here, we performed a behavioral experiment (n = 21) and a useful magnetic resonance imaging experiment (n = 37) with a novel cue-biased adaptation studying job, throughout which we concurrently manipulated emotional valence (fearful/impartial expressions of the cue) and environmental volatility (frequent/rare reversals of reward possibilities). Throughout 2 experiments, computational modeling persistently revealed the next studying price for the setting with frequent versus rare reversals following impartial cues. In distinction, this versatile adjustment was absent within the setting with fearful cues, suggesting a suppressive function of concern in adaptation to environmental volatility. This suppressive impact was underpinned by exercise of the ventral striatum, hippocampus, and dorsal anterior cingulate cortex (dACC) in addition to elevated useful connectivity between the dACC and temporal-parietal junction (TPJ) for concern with environmental volatility. Dynamic causal modeling recognized that the driving impact was situated within the TPJ and was related to dACC activation, suggesting that the suppression of concern on adaptive behaviors happens on the early stage of bottom-up processing. These findings present a neuro-computational account of how concern interferes with adaptation to volatility throughout dynamic environments.


People and animals are in a position to adapt to the fast-changing world. Concern, essentially the most studied emotion regardless of variations concerning its definition and measurement [14], profoundly influences adaptive habits [5,6]. Though adaptation to dynamic environments is a key type of behavioral flexibility, how concern impacts adaptation to dynamic environments stays unclear. Evolutionarily, concern acts as an alerting sign for self-protection [7]. A wealthy literature illustrates useful results of concern on flexibility [2,6,8,9]. Thus, elicitation of fearful indicators could facilitate adaptation to dynamic environments. In distinction, when dominating consciousness, concern can disrupt techniques supporting versatile habits [5,10], and lesions of concern circuits could even facilitate versatile efficiency [11]. Consequently, the acutely aware expertise of concern could suppress adaptation to altering environments.

Adaptation to dynamic environments depends upon the interior illustration of uncertainties [12], which may be categorized into 2 varieties: anticipated uncertainty and sudden uncertainty [1214]. The previous refers to noise within the motion–consequence affiliation, for instance, when selecting the proper possibility often leads to an undesirable consequence. The latter is characterised as volatility or the frequency at which motion–consequence contingencies change. For instance, after the swap of the motion–consequence affiliation, an motion that was primarily related to a given consequence turns into predominantly related to one other. Optimum adaptive habits depends upon correct identification of the supply of uncertainty [1417]. Extra particularly, if sudden outcomes are attributable to noise, the present motion is optimally guided by averaging earlier observations. As a substitute, if sudden outcomes consequence from environmental volatility, solely latest outcomes are crucial to find out the current motion. In line with reinforcement studying principle [18], human learners can adapt to altering environments, exhibiting the next studying price within the risky relative to steady setting [14,16]. The Bayesian learner has additionally been demonstrated to dynamically observe environmental volatility with optimum efficiency [14,17]. Important steps ahead in uncertainty-related research level towards the relevance of affective representations [1921]. Emotional responses contribute to adaptation to volatility [14], and failure to adapt to environmental volatility has been recognized as a serious contributor to affective problems [17,2224]. Animal research demonstrated a facilitating function of serotonin neurons in versatile adaptation to volatility [19] and noticed that ranges of the neurotransmitter serotonin modulated concern processing [25], suggesting an vital hyperlink between concern and adaptation to volatility.

Earlier neuroimaging research on adaptation to volatility have implicated a distributed mind system involving the dorsal anterior cingulate cortex (dACC) that represents subjective estimation of environmental volatility [14,26], the amygdala and the hippocampus (HI) that encode valance-dependent conditioning and storage [13,27,28], and the ventral striatum (VS) and the orbitofrontal cortex (OFC) course of uncertain-related worth data [27,29]. For instance, Behrens and colleagues (2007) has noticed that the dACC is concerned within the estimation of reward construction for selections to be made successfully. Apparently, these mind areas are additionally of significance for emotional processing, specifically fearful expertise. Alerts from the dACC have been linked to the processing of emotion–cognition integration (facilitation and inhibition) [30]. As a concern circuitry hub, the amygdala has been proven to affect interactions between fearful reactions and government capabilities [30]. The HI has lengthy been thought-about essential for concern reminiscence [6,31]. The VS and OFC have been answerable for affective computing from studying indicators [32,33]. Due to this fact, these candidate mind areas could affect the interaction between concern and adaptation to volatility.

This research aimed to look at the impact of concern on adaptation to volatility and its underlying neuro-computational mechanisms. From an evolutionary perspective, it may be hypothesized that concern would facilitate adaptation to volatility studying. Alternatively, concern may stop adaptation to volatility studying as a result of cognitive price of concern processing and a flight response induced by fearful stimuli with which the person seeks to flee the risky setting. The area of pursuits (ROIs) within the fear-biased adaption to volatility included the dACC, amygdala, HI, VS, and OFC. To check these hypotheses, we used computational modeling and useful magnetic resonance imaging (fMRI) in a novel cue-biased adaptation studying job. Based mostly on the framework of the probabilistic reward reversal studying job [14,24], the present job concurrently manipulated emotional valence of the cue (fearful/impartial facial expressions) and environmental volatility (frequent/rare reversals of reward likelihood) [16]. We additionally took under consideration particular person variations in propensity for emotion processing and regulation. Extra particularly, this regards alexithymia, which refers to a lowered capacity to determine, describe, and regulate one’s emotions [34]. Given cognitive and emotional deficits in alexithymia [35], we explored associations between alexithymia ranges and fear-biased adaptation to volatility studying.

We noticed the next studying price for the setting with frequent in comparison with rare reversals with the cue of impartial face, which was in keeping with earlier research [14,17,24]. Nonetheless, this sample was absent within the fearful cues, suggesting a suppressive function of concern in adaptation to volatility and thus supporting the second speculation. Apparently, this bias was stronger with greater ranges of alexithymia. We additionally revealed the distributed neural substrates underlying computations of fear-biased adaptation to volatility studying, together with integration techniques, studying techniques, and reminiscence techniques. Particularly, the TPJ-dACC pathway was discovered to affect the interaction between concern and adaptation to volatility studying. It implies that the suppression of concern impacts adaptive behaviors at a comparatively early stage of processing bottom-up inputs.


Individuals accomplished the cue-biased adaptation studying job (Fig 1) in Experiment 1 (exp1; n = 21, behavioral research) and Experiment 2 (exp2; n = 40, fMRI research; see Desk 1 for demographic data). Utilizing the framework of the probabilistic reward reversal studying job [14,24], we developed a 2 [emotional valence of cue: fearful/neutral expressions (fear/neut)] by 2 [environmental volatility: frequent/infrequent reversals (freq/infreq); Fig 1] within-subject design to check how concern influences adaptation to volatility.


Fig 1. Experimental design.

(A) Trial design of the fear-biased volatility studying job and (B) an instance of contingency between cue and environmental volatility. These facial expressions we used as examples are freed from copyright from the TFEID [74]. TFEID, Taiwanese Facial Expression Picture Database.

Manipulation checks

We first examined the validity of concern induction (i.e., presentation of fearful/impartial facial expressions earlier than every trial). We collected score information for fearful and impartial facial expressions of “how fearful do you’re feeling when seeing this face” utilizing a Likert scale from 0 (no expertise of concern in any respect) to eight (robust expertise of concern). Paired-sample t take a look at confirmed a major stronger fearful expertise for fearful versus impartial expressions (p = 0.001; the imply score for fearful expressions was 3.52; see S5 Textual content and S3 Fig), suggesting that individuals certainly skilled slight-to-moderate concern throughout the experiment. To look at whether or not individuals have been in a position to study the reward construction of our job, according to Piray and colleagues (2019) [36], we plotted individuals’ efficiency after reversal. Outcomes confirmed that individuals discovered the reward schedule efficiently (Fig 2A and 2B). As well as, a small variety of lacking trials (lower than 14; <5.8%) and a low ratio of brief response time (<200 ms; <1.67%; S2 Desk) steered enough engagement throughout the job.


Fig 2. Behavioral outcomes.

(A, B) Efficiency after reversals in every situation of two experiments. Stable traces symbolize actual information (imply ± customary error). Shaded panels symbolize 95% HDI from the profitable mannequin. (C, D) Studying price from the profitable mannequin. The road inside the violin plot represents the median worth. Observe: *p < 0.05; ~p < 0.1. The information underlying this determine may be discovered at HDI, excessive density interval.

Computational mechanisms underlying fear-biased adaptation to volatility studying

To formally quantify cognitive mechanisms underlying fear-biased adaptation to volatility studying, we constructed 12 fashions, with issues of studying by trial and error (Rescorla–Wagner (RW) mannequin), attentional lapses, forgetting, and studying by consideration (Pearce-Corridor mannequin; see Supplies and strategies). Utilizing indices of leave-one-out data criterion (LOOIC) and extensively relevant data criterion (WAIC), mannequin comparability confirmed that the profitable mannequin was Mannequin 1 (M1) in each exp1 and exp2 [except slightly better for model 5 (M5) at the index of LOOIC in exp2, ΔLOOIC = −0.4; Table 2]. Particularly, M1 assumed that every situation was discovered in a different way with the trial-and-error technique (1 studying price per situation). The pseudoR2 and balanced accuracy for M1 have been 0.247% and 64.9% for exp1 and 0.189% and 61.1% for exp2, which have been first rate [37]. The balanced accuracy in each exp1 and exp2 was considerably higher than the possibility stage (i.e., 50%; ps < 0.001). We additional validated this profitable mannequin (M1) in 4 methods for each exp1 and exp2. First, mannequin simulation from M1 confirmed excessive correlation coefficients between actual accuracy and simulated accuracy for each situation (exp1: rs > 0.82, ps < 0.001; exp2: rs > 0.85, ps < 0.001; S4 Fig). Second, mannequin simulation from M1 confirmed excessive correlations between actual and simulated win-stay and loss-switch (WSLS) habits for every situation (exp1: rs > 0.80, ps < 0.001; exp2: rs > 0.78, ps < 0.001; S5 and S7 Figs). Third, parameter restoration for M1 confirmed excessive correlations between actual parameters and simulated parameters (see S2 Textual content and S6 Fig). Lastly, mannequin restoration for M1 confirmed that the profitable mannequin amongst 12 candidate fashions was identifiable (see S2 Textual content and S3 Desk). As well as, simulated information intently resembled the actual efficiency (Fig 2A and 2B), confirming the validity of parameter estimation.

To look at how studying charges from the profitable mannequin M1 have been modulated by emotional cue and environmental volatility, we carried out linear mixed-effect fashions (LMMs) with topic as a random issue and with cue (concern/neut) and volatility (freq/infreq) as within-subject elements for exp1 and exp2, respectively. We noticed a major interplay impact between cue and environmental volatility in every experiment (exp1: F = 19.095, p < 0.001, partial η2 = 0.24; exp2: F = 22.509, p < 0.001, partial η2 = 0.16; Fig 2 and 2D). Easy impact evaluation persistently confirmed the next studying price for environments with freq versus infreq in impartial cues (exp1: F = 13.126, p = 0.004, partial η2 = 0.18; exp2: F = 22.667, p < 0.001, partial η2 = 0.16), replicating earlier outcomes concerning adaptation to volatility studying [14,17,24]. Nonetheless, this sample disappeared or reversed within the face of fearful cues (exp1: F = 6.538, p = 0.079, partial η2 = 0.10, freq < infreq; exp2: F = 3.799, p = 0.322, partial η2 = 0.03). These findings recommend that concern interferes with adaptation to volatility studying. To determine whether or not the manipulated variables (volatility and cue) affect studying charges, moderately than inverse temperature, we additionally added inverse temperature because the covariate within the regression mannequin. Outcomes confirmed the identical sample (see S1 Textual content for particulars), suggesting that the manipulated variables particularly impacted studying charges. As well as, M5, including a element of attentional lapse, carried out barely higher than M1 on the index of LOOIC in exp2 (ΔLOOIC = −0.4; Desk 2). The implementation of LMM for M5 confirmed the identical sample as M1 (S9 Fig), suggesting that the present outcomes have been extremely sturdy. We additionally discovered the next proportion to pick out the optimum possibility within the third trial after reversal for environments with frequent versus rare reversals in face of impartial cues (F = 9.622, p = 0.003, partial η2 = 0.14), whereas a reversal sample was noticed when cued by fearful facial expressions (F = 4.291, p = 0.043, partial η2 = 0.07), displaying the identical sample with conditional variations in studying charges. It means that atypical psychological computations for adaptation studying below concern lead to aberrant studying efficiency, particularly after reversal (see S2 Textual content and S8 Fig). Particulars of statistical outcomes have been reported in S1 Textual content and S2 Textual content.

Moreover, to check the influences of concern on punishment studying, we performed one other experiment with the identical design [2 (fear/neut) by 2 (freq/infreq)] below the punishment context (expS1 in S4 Textual content). We noticed the identical sample with our findings within the reward context, suggesting that fear-biased adaptation to volatility is reward/punishment-independent (for particulars, see S4 Textual content and S10 Fig). We additionally performed one other experiment (expS2 in S4 Textual content) with the design of two (joyful versus impartial facial expressions) by 2 (freq/infreq) within the reward context to regulate for the potential impression of attention-grabbing (with out experiencing concern) on adaptation to volatility. Comfortable expressions have been used right here as a result of fearful and joyful expressions have related properties of attention-grabbing however distinct affective expertise [38]. We didn’t discover a important interplay impact between cue and volatility (S11 Fig), suggesting that concern expertise, however not the attentional-distracting property, disrupts versatile adaptation to volatility (see S4 Textual content for particulars).

Neural correlates of fear-biased adaptation to volatility studying: Studying charges

To look at how fear-biased adaptation to volatility studying was represented within the human mind from the attitude of studying charges, we carried out the primary generalized linear mannequin (GLM1). The behavioral bias was outlined as [(fear & freq–fear & infreq)–(neut & freq–neut & infreq)] in studying charges, whereas the neural bias was outlined as [(fear & freq–fear & infreq)–(neut & freq–neut & infreq)] in BOLD indicators on the consequence stage. This research targeted on the dACC, VS, amygdala, OFC, and HI which were recognized to hyperlink emotion to adaptation to environmental volatility (see Introduction for particulars). The dACC was chosen from Behrens and colleagues [14] (MNI: x = −6, y = 26, z = 34 mm, a sphere with 10 mm radius), a classical research on adaptation to volatility with fMRI. To maintain the selective process goal, different ROIs have been obtained from AAL atlas. Small quantity correction (SVC) was used for ROIs. All imaging outcomes have been corrected with the edge of p < 0.001 on the voxel stage and with the edge of p < 0.05 on the cluster stage utilizing AlphaSim process (carried out in DPABI toolbox: model 3.1 [39]). These mind areas have been thus hypothesized to contribute to fear-biased adaptation studying. Observe that the coordinates for activated mind areas we reported symbolize peak coordinates, which have been from second-level contrasts of the group. ROI evaluation confirmed constructive correlations between the behavioral bias and the neural bias within the VS (ROI evaluation, peak at [4 8 –4], r = 0.578, p < 0.001, ok = 7; Fig 3B) and HI (ROI evaluation; peak at [28 –16 –22]; r = 0.606, p < 0.001, ok = 10; Fig 3C). No important activation within the amygdala, dACC, and OFC was discovered. We additionally performed exploratory whole-brain evaluation, revealing a constructive correlation between the behavioral bias and the neural bias within the posterior parietal cortex (PPC; whole-brain evaluation; peak at [–50 –60 50] in MNI coordinates; r = 0.586, p < 0.001, ok = 103; Fig 3A). These findings remained important when controlling for potential variations in consequence (see S3 Textual content for particulars). These outcomes recommend that the PPC, VS, and HI encode fear-biased adaptation to volatility studying.


Fig 3. Exercise and connectivity outcomes.

Neural correlates of fear-biased adaptation to volatility studying when it comes to studying price within the (A) PPC (whole-brain evaluation, peak at [–50 –60 50], ok = 103), (B) VS (ROI evaluation, peak at [–4 8 –4], ok = 7), and (C) HI (ROI evaluation, peak at [28 –16 –22], ok = 10). Activation outcomes from fear-biased adaptation to volatility studying when it comes to subjective volatility within the (D) dACC (ROI evaluation, peak at [–2 32 32], ok = 8) and (E) VS (ROI evaluation, peak at [10 16 –8], ok = 9). (F) Practical connectivity between the dACC (seed area) and TPJ (goal area, whole-brain evaluation, peak at [–64 –52 36], ok = 263) for every situation. Important voxels are computed throughout the two circumstances. Circles in pink symbolize ROI evaluation. Activation maps with out the pink circle symbolize whole-brain evaluation. Each the scatter and violin plot present the imply activation inside the corresponding cluster. The road inside the violin plot represents the median worth. Observe: PPC, posterior parietal cortex; VS, ventral striatum; HI, hippocampus; dACC, dorsal anterior cingulate cortex; n.s., not important; *p < 0.05; ~p < 0.1. The supply information may be discovered at

Neural substrates of fear-biased adaptation to volatility studying: Subjective volatility

To look at representations of the human mind for fear-biased adaptation to volatility studying, we carried out model-based fMRI evaluation (GLM2) with parametric modulation of subjective volatility on the stage of consequence. Once more, the neural bias was outlined as [(fear & freq–fear & infreq)–(neut & freq–neut & infreq)] within the parametric activation of subjective volatility. Observe that trial-by-trial subjective volatility was derived from Bayesian Learner mannequin (see Supplies and strategies for detailed equations; see S12 Fig for an instance participant). We noticed important activations within the dACC (ROI evaluation; peak at [–2 32 32]; ok = 8, Fig 3D) and VS (ROI evaluation; peak at [10 16 –8]; ok = 9; Fig 3E). We additionally validated important activation within the dACC with completely different sphere radiuses (e.g., 8 mm and 12 mm; see S3 Textual content for particulars). No important activation within the amygdala, HI, or OFC was discovered. Particularly, for each the dACC and VS, we first noticed important interplay results between cue and environmental volatility (dACC: F = 14.609, p < 0.001, partial η [2] = 0.29; VS: F = 15.274, p < 0.001, partial η [2] = 0.30). Easy impact evaluation confirmed important elevated activation in infreq versus freq with impartial cues (dACC: F = 13.272, p = 0.001, partial η2 = 0.27; Fig 3D; VS: F = 8.191, p = 0.007, partial η2 = 0.19; Fig 3E). Nonetheless, such patterns disappeared or reversed for fearful cues (dACC: F = 1.833, p = 0.184, partial η2 = 0.05; Fig 3D; VS: F = 3.966, p = 0.054, partial η2 = 0.10; Fig 3E). These findings remained important when controlling for potential variations in consequence (see S3 Textual content for particulars). These outcomes point out that the dACC and VS are engaged to encode subjective volatility in fear-biased adaptation.

We subsequent sought to discover the underlying mind networks modulating fear-biased adaptation to volatility studying. Based mostly on model-based fMRI outcomes, generalized psychophysiological interplay (gPPI) [40] evaluation was carried out with the dACC and VS as seed areas, individually. We discovered important connectivity of the dACC with the temporal parietal junction (TPJ; whole-brain evaluation; peak at [–64 –52 36]; ok = 263; Fig 3F). The interplay impact (F = 26.551, p < 0.001, partial η [2] = 0.42) additional confirmed that useful connectivity between the dACC and TPJ elevated in infreq as in comparison with freq following impartial cues (F = 11.306, p = 0.002, partial η2 = 0.24), however decreased following fearful cues (F = 4.748, p = 0.036, partial η2 = 0.12). The gPPI outcomes recommend that the dACC-TPJ circuit encodes subjective volatility calculations in fear-biased adaptation.

To additional take a look at instructions of data stream between the dACC and TPJ underlying fear-biased adaptation to volatility, we performed dynamic causal modeling (DCM) evaluation [41]. We constructed 6 fashions with completely different assumptions of modulatory results and driving results whereas we mounted the total intrinsic connectivity (Fig 4A; see Supplies and strategies for particulars). The random-effect (RFX) Bayesian mannequin choice (BMS) utilizing indices of exceedance likelihood and anticipated posterior likelihood advisable mannequin 3 because the profitable mannequin (Fig 4B and 4C). The mannequin 3 assumed a driving impact from the TPJ, a modulatory impact from the TPJ to dACC, and one other modulatory impact from the dACC to TPJ. We discovered a major interplay impact between cue and environmental volatility within the driving impact on the TPJ (F = 8.321, p = 0.007, partial η2 = 0.19; Fig 4D). Easy impact evaluation confirmed a major enhance in infreq than freq in impartial cues (F = 9.827, p = 0.003, partial η2 = 0.21; Fig 4D), however not fearful cues (F = 0.064, partial η2 = 0.00; Fig 4D), suggesting the modulation of the driving impact from the TPJ on fear-biased adaptation to subjective volatility. On condition that the profitable mannequin (DCM mannequin 3) was near mannequin 2, as proven by exceedance likelihood for mannequin 3 (about 0.6) and mannequin 2 (about 0.3; Fig 4C), we additionally checked variations within the driving impact on the TPJ. Outcomes confirmed the identical sample with the profitable mannequin (S1 Textual content), indicating the robustness of this discovering. Moreover, the driving bias from the TPJ was positively correlated to parametric bias for BOLD indicators in subjective volatility of the dACC (r = 0.371, p = 0.024; Fig 4E). Taken collectively, in keeping with our behavioral modeling outcomes, these neural outcomes regarding subjective volatility help the notion that concern interferes with adaptation to volatility studying and additional uncover the pushed computation from the TPJ within the dACC-TPJ pathway underlying fear-biased adaptation to volatility studying.


Fig 4. DCM outcomes.

(A) Mannequin house for DCM evaluation. Black arrows symbolize intrinsic connectivity. Blue arrows symbolize the driving impact. Orange arrows symbolize modulatory results. The profitable mannequin was highlighted in pink. Mannequin comparability utilizing indices of (B) anticipated posterior likelihood and (C) exceedance likelihood. (D) Driving impact on TPJ for every situation. The road inside the violin plot represents the median worth. (E) Correlation between driving impact on the TPJ and parametric activation of subjectivity volatility within the dACC when it comes to fear-biased adaptation to volatility studying. The scatter plot reveals the imply activation inside the corresponding cluster. Observe: TPJ, temporal parietal junction; dACC, dorsal anterior cingulate cortex; RFX, random-effect; n.s., not important; *p < 0.05. The supply information may be discovered at

Alexithymia was associated to fear-biased adaptation to volatility studying

We discovered a major correlation between the whole rating of the Bermond–Vorst Alexithymia Questionnaire (BVAQ) [42] and the behavioral bias (in studying charges; r = 0.351, p = 0.006; Fig 5A). Please be aware that greater scores on the BVAQ represented decrease ranges of alexithymia. The upper rating for behavioral bias [(fear & freq–fear & infreq)–(neut & freq–neut & infreq)] mirrored weaker affect of concern on adaptation to volatility studying. Due to this fact, the constructive correlation means that the interference of concern with adaptation to volatility studying was stronger the extra alexithymic people have been. To look at contributions of cognitive (BVAQ-C) and affective dimensions (BVAQ-A) of alexithymia for studying price bias, we carried out correlations of studying price bias with BVAQ-C and BVAQ-A, respectively. We noticed a major correlation of studying rates-bias with BVAQ-C (r = 0.291, p = 0.023; Benjamini–Hochberg FDR corrected; Fig 5B), whereas the correlation with BVAQ-aff was not important (r = 0.227, p = 0.078; Fig 5C), indicating that the cognitive dimension of alexithymia defined fear-biased adaptation to volatility studying. As well as, these outcomes maintain when controlling for anxiousness and melancholy (see S2 Textual content).


Fig 5.

Correlations of studying price when it comes to fear-biased adaptation to volatility studying with BVAQ (A), BVAQ-C (B), and BVAQ-A (C). Observe: BVAQ, the Bermond–Vorst Alexithymia Questionnaire; BVAQ-C, cognitive dimension of BVAQ; BVAQ-A, affective dimension of BVAQ. The supply information may be discovered at


This research examined neuro-computational mechanisms of how concern influenced adaptation to volatility. In 2 impartial experiments, we persistently discovered that the setting with frequent reversals elicited the next studying price than the setting with rare reversals in impartial cues, replicating earlier research [14,17,24]. Nonetheless, this distinction was absent within the face of fearful cues, supporting the speculation that concern prevented adaptation to volatility. This suppressive impact was underpinned by exercise of the PPC, VS, HI, and dACC, in addition to useful connectivity between the dACC and temporal-parietal junction (TPJ), suggesting distributed mind techniques for computations of fear-biased adaptation. Efficient connectivity outcomes additional confirmed that this bias primarily resulted from the driving impact from the TPJ, indicating that computations underlying fear-biased adaptation to volatility could happen at a comparatively early stage of processing bottom-up inputs. Lastly, this bias was stronger in people with greater ranges of alexithymia.

Utilizing computational modeling, this research revealed that concern inhibits adaptation to volatility. In our management circumstances of impartial cues, individuals confirmed the next studying price to the setting with frequent (as in comparison with rare) reversals, in keeping with earlier findings of a better studying price for risky than steady circumstances [14,17,24]. This discovering helps the notion that people are in a position to alter studying charges in adaptation to volatility [14,16,17,24]. Critically, this adjustment was disrupted by fearful indicators. It has been proven that concern disrupts techniques supporting versatile habits by way of dominating consciousness [6,10,11]. Due to this fact, the acutely aware expertise of concern probably inhibits adaptive operate, specifically adaption to volatility. This helps the notion of mutual inhibition between emotion and cognition and the twin competitors mannequin of emotion–cognition integration [30,43,44]. As well as, we noticed that concern prevented versatile changes to volatility in each reward and punishment contexts (see S4 Textual content), suggesting that our findings of fear-biased adaptation to volatility have been reward/punishment-independent. Evidently our manipulation was much like these from Browning and colleagues (2015) and noticed inconsistent outcomes. Though electrical shocks utilized in Browning and colleagues (2015) additionally evoked robust concern, our research systematically examined the function of concern (as in comparison with the impartial baseline) on adaptation to volatility. Importantly, their manipulation of concern was contingent on the result (shock or not shock), whereas fearful facial expressions in our research have been related to environmental volatility, which is the next stage within the Bayesian Learner mannequin [14]. Earlier research have proven completely different representations between consequence encoding and volatility processing [45,46]. It has additionally been proven rigid adaptation to volatility in anxious people [17]. Given the excessive similarity in ideas and buildings between anxiousness and concern [47], we complemented Browning and colleagues (2015)’s findings from particular person distinction in anxiousness to the within-subject results of fearful cues. Collectively, these findings collectively recommend that fear-biased adaption to volatility characterizes fear-related affective problems.

Neuroimaging outcomes confirmed that the dACC encoded fear-biased adaptation to volatility studying. In our management circumstances, decreased activation within the dACC was noticed in environments with frequent versus rare reversals. This discovering was opposite to earlier findings of a constructive correlation of dACC indicators with subjective volatility [14,26]. Nonetheless, it has been proposed that job problem modulates the connection between activation within the dACC and cognitive demand in an inverted U-shape sample [48,49]. For instance, stronger dACC activation has been discovered for processing fearful versus impartial faces below low cognitive demand (e.g., 0-back job), whereas decrease responses of the dACC to fearful versus impartial expressions have been noticed below excessive cognitive demand (e.g., 2-back job) [30]. One rationalization for the discrepancy in activation of the dACC may be that the present job was harder than these of the earlier probabilistic reward studying job attributable to our design of interleaved trials from 4 circumstances. Importantly, the sign of volatility within the dACC was absent in fearful circumstances, suggesting a suppressive function of concern within the adaptive operate of the dACC. The dACC is taken into account to be a higher-order integrative hub for a number of data [30,50,51]. For instance, integration between fearful indicators and government capabilities has been discovered within the dACC [30]. Due to this fact, these outcomes point out that concern modulates the dACC’s indicators to trace environmental volatility and help the integrative processing of emotion and cognition within the dACC.

The TPJ was additionally discovered to work in live performance with the dACC to affect fear-biased adaptation to volatility in our research. A nexus mannequin of the TPJ has contended a fundamental integrative hub for the TPJ operate, together with attentional processing, reminiscence storage, and social processing [52]. For instance, the TPJ was recognized to be concerned in world Gestalt integration on the perceptive stage [53]. Moderation of dACC-TPJ useful connectivity on fear-biased adaptation to volatility in our research suggests a circuit of data interchange between low- and high-level integrative processing of concern and adaptation studying. Whereas bidirectional data stream between the dACC and TPJ throughout fear-biased adaptation studying was noticed, fear-biased adaptation to volatility was influenced by the driving impact from the TPJ. This may occasionally recommend that concern primarily impedes bottom-up data enter and low-level integrative processing between concern and adaptation to volatility. In sum, together with the numerous correlation between the activated bias within the dACC and the driving bias within the TPJ (Fig 5E), these outcomes reveal a vital function of the TPJ-dACC neural pathway in volatility studying of fear-biased adaptation.

We additionally noticed activation within the VS, however not the amygdala, in fear-biased adaptation to volatility, which suggests differential roles of the VS and amygdala. Each the VS and amygdala have been demonstrated to symbolize uncertainties throughout dynamic environments and emotion processing [27,5456]. Inside studying contexts, the amygdala is linked to Pavlovian conditioning, whereas the VS has been implicated in instrumental studying [6]. The present outcomes could thus mirror the interference of instrumental adaptation by concern within the VS. One other rationalization regards a doable function of Pavlovian-instrumental switch. This switch impact underlying the amygdala and VS has been demonstrated in animal fashions [6,57]. On this research, Pavlovian tendencies have been progressively dominated by instrumental studying. This switch impact thus explains activation within the VS, however not the amygdala, in fear-biased adaptation studying. Nonetheless, interpretations concerning the detrimental results of activation within the amygdala ought to be cautious. As well as, the HI and PPC have been discovered to reasonable fear-biased adaptation to volatility in our research. The HI has been thought to encode and retailer reminiscence [58,59], whereas the PPC was implicated in reminiscence retrieval [60]. These 2 mind areas have been additionally concerned in studying and worth illustration within the unsure setting [13,28,61,62]. Due to this fact, these outcomes point out that concern interferes with reminiscence indicators within the HI and PPC for adaptation to dynamic environments. Our research didn’t discover any important OFC activation. That is shocking given its essential involvements in emotion-related valuation and versatile studying [29,63,64]. One potential rationalization is low signal-noise ratio for the OFC BOLD indicators [65]. Briefly, distributed mind areas concerned in reminiscence and studying techniques appear to contribute to fear-biased adaptation to volatility studying.

This research used the standard reinforcement studying mannequin and the Bayesian Learner mannequin, that are complementary to one another concerning cognitive and neural mechanisms and behaviors [66]. We discovered shared (e.g., activation within the VS) and distinct neural mechanisms between a majority of these fashions. Unexpectedly, no direct correlation between studying price and subjective volatility was noticed on this research. The Bayesian Learner mannequin has been proposed in a hierarchical construction [14] with (i) subjective estimation of environmental volatility on the first stage; (ii) subjective estimation of the profitable likelihood on the second stage; and (iii) the noticed consequence on the third stage. Based mostly on the reinforcement studying mannequin with out hierarchical processing, studying charges have been match from the subjective likelihood throughout trials on the second stage. Earlier research certainly confirmed a constructive correlation between volatility indicators and studying price [14,17]. Nonetheless, a latest simulation research demonstrated that studying charges have been collectively estimated from each stochasticity and volatility [16]. Though separate correlations of activation within the HI have been noticed with the volatility index and the training price, the absence of a direct correlation between volatility and studying price could also be as a result of ignorance of stochasticity in our mannequin house, which was according to earlier research [14,17,24]. Future research could profit from taking stochasticity under consideration when setting up computational fashions to look at the connection between studying price and volatility.

We noticed that maladaptation to volatility following concern cues correlated with alexithymia, suggesting a stronger affect of concern on adaptation to volatility studying in people with greater ranges of alexithymia. Alexithymia refers to difficulties in emotion processing [34]. Alexithymia has been thought-about as a transdiagnostic threat issue for numerous affective problems, resembling anxiousness and melancholy [67,68]. Whereas a wealthy literature confirmed each cognitive and emotional deficits in alexithymia [35,49,69,70], the present outcomes additional recommend an integrative problem between concern and adaptation to volatility in people with excessive alexithymia ranges. On condition that abnormalities in regulating fearful indicators are on the core of affective problems [6] and failure to appropriately alter studying to dynamic environments contributes to affective problems [17,2224], our findings present further insights into pathological mechanisms of alexithymia-related affective problems.

A number of limitations of the current research ought to be talked about. First, along with fear-biased adaptation to volatility when it comes to studying charges, we additionally noticed that fearful relative to impartial faces induced hypo-learning velocity throughout rare reversal environments and hyper-learning price for environments with frequent reversals, suggesting suboptimal studying below concern. Future research can be profit from investigating whether or not concern influences adaption to volatility (variations between frequent and rare reversal environments) or concern induces suboptimal studying in environments each with frequent and rare reversals, ensuing within the noticed maladaptation. Second, there are completely different variants for Bayesian Learner mannequin, e.g., hierarchical Gaussian filter [21]. We adopted essentially the most classical analysis on adaptation to volatility and solely used Bayesian Learner mannequin [14]. Future research would profit from analyzing shared and distinct neural responses amongst these variants. Final, individuals certainly reported concern in response to fearful expressions. Though subjective report has been thought-about to be significance within the measurement of concern [3,4], future research would profit from real-time goal measures utilizing physiological recordings (e.g., electrocardiography and eye-tracking) to look at the interplay between emotion swing and studying throughout uncertainty.

To conclude, this research supplies a neuro-cognitive account of how concern interferes with adaptation to volatility throughout dynamic environments on the computational stage. We additionally present that this bias is said to particular person variations in propensity for emotion processing and regulation. Our findings reveal distributed mind substrates underlying fear-biased adaptation to volatility, together with integration techniques, studying techniques, and reminiscence techniques. The TPJ-dACC pathway, specifically, influences the interaction between concern and adaptation to volatility studying, suggesting that the suppression of concern on adaptive behaviors could happen at a comparatively early stage of processing bottom-up inputs. Our work thus sheds gentle on the neuro-computational mechanisms underlying fear-biased adaptation to volatility studying.

Supplies and strategies


Sixty-four right-handed wholesome faculty college students participated in 2 experiments. Experiment 1 (exp1) was a behavioral research, together with 21 individuals (11 females, age = 20.81 ± 1.94). Experiment 2 (exp2) was an fMRI research, together with 43 individuals. As a consequence of impatience (1 participant), and lots of lacking trials (2 individuals failed to reply in additional than 10% of the trials; 62 and 46 within the 240 trials, respectively), and extreme head movement (3 individuals with greater than 10% scans with frame-wise displacement (FD) > 0.5) [71], the ultimate pattern for exp2 included 40 individuals (18 females, age = 21.75 ± 2.13) for behavioral evaluation and 37 individuals (16 females, age = 21.57 ± 2.06) for fMRI evaluation. See Desk 1 for demographic data. All of the individuals had no historical past of neurological and psychiatric problems or head harm. The research was authorized by the Heart for Mind Problems and Cognitive Sciences Institutional Evaluation Board (IRB quantity: CBDCS202107080020) at Shenzhen College and carried out in full compliance with the newest Declaration of Helsinki. Written knowledgeable consent was obtained from all individuals.

Normal process

After signing the knowledgeable consent, individuals have been requested to finish a three-stage coaching job to grasp the likelihood and reversal elements underlying the duty [72]. The cue-biased adaptation studying job was then carried out (whereas individuals in exp2 concurrently skilled fMRI scanning). After the duty, individuals have been requested to finish the Chinese language model of the BVAQ [42] and the Temper and Nervousness Signs Questionnaire (MASQ) [73]. Lastly, individuals have been paid primarily based on their studying efficiency, which was instructed earlier than the experiment.

Cue-biased adaptation studying job

Impressed by Behrens and colleagues (2007) [14] and Piray and colleagues (2019) [36], we developed a cue-biased adaptation studying job (Fig 1). Utilizing the framework of the probabilistic reward reversal studying job [14,24], we manipulated the kind of cue (fearful/impartial expressions) and environmental volatility (frequent/rare reversals) [16]. Particularly, frequent reversals (freq) have been outlined as a scenario by which the contingency reversed each 9 to 11 trials randomly, whereas rare reversals (infreq) referred to a scenario by which the contingency reversed each 18 to 22 trials randomly. Briefly, the present research with a two by two within-subject design consisted of 240 trials, with 60 trials per situation. Please be aware that trials for every situation have been intermixed inside a block. Individuals have been requested to study by trial and error to maximise their payoff. Though individuals have been explicitly knowledgeable that reward construction would change all through the duty, they wanted to deduce the second on which reversal occurred and the velocity at which reversal modified [45]. Initially of every trial, a cue (a fearful or impartial face) was first proven on the display screen heart with period of 1 s (Fig 1A). To remove potential gender results, facial expressions with the identical gender with individuals have been used as cues. For instance, the feminine face with the fearful or impartial expression was proven on the cue for the feminine individuals. We chosen 4 feminine faces and 4 male faces from the Taiwanese Facial Expression Picture Database (TFEID) [74], with 2 fearful and impartial expressions for every gender. The normative score (class and depth) from the TFEID may be seen in S1 Desk. Please be aware that the cue kind and the environmental volatility have been randomly matched throughout individuals. After a fixation cross with a random period (0.2 to 1.5 s for exp1 and 1 to three s for exp2), 2 choices (horizon and vertical Gabor patches) have been introduced randomly on both sides. Individuals have been required to decide inside 2 s. Upon response, the chosen possibility can be highlighted for 0.2 s, adopted by a query mark on the display screen heart with a jitter interval at 1 to three s. Then, the result was proven for 1 s, with “+1” in inexperienced indicating reward and “+0” in pink reflecting no reward. End result was delivered with a likelihood of reward at both 85% or 15%, primarily based on the result schedule (Fig 1B). If individuals didn’t reply inside 2 s, which was outlined as a lacking trial, the choice wouldn’t be highlighted and consequence was “+0.” On the finish of a trial, a fixation cross was introduced for 0.3 to five.6 s to make sure that every trial lasted for 9 s for exp1 and 0.8 to six.8 s to make sure that every trial lasted for 11 s for exp2. Exp1 and exp2 lasted for 36 and 44 min, respectively. All experimental procedures have been introduced utilizing E-prime 2.0 (Psychology Software program Instruments, Pittsburgh, Pennsylvania, United States of America).

Self-report questionnaires

To evaluate alexithymia, we used the Chinese language model of the BVAQ [42]. This questionnaire consists of 35 objects, every reply being scored on a five-point Likert scale of 1 (this by no means applies to me) to five (this positively applies to me). BVAQ contains each cognitive and affective elements with acceptable reliability and validity. Greater scores for BVAQ represented decrease ranges of alexithymia. To manage for potential confounding results of hysteria and melancholy, individuals additionally accomplished the Chinese language model of the MASQ [73]. The MASQ consists of 62 objects which might be assessed with a four-point Likert scale of 1 (by no means) to 4 (extraordinarily). It measures signs of hysteria and melancholy primarily based on a tripartite mannequin, together with common misery, which may be additional divided into common misery: anxiousness (GDA) and common misery: melancholy (GDD), anxious arousal (AA), and anhedonia melancholy (AD) subscales.

Computational modeling of job efficiency

To greatest describe individuals’ studying efficiency in our fear-biased volatility studying job, we performed a stage-wise mannequin development process [24,75]. That’s, we added every element to the mannequin or modified an current element progressively, primarily based on one of the best mannequin from the earlier stage. Mannequin comparability used the LOOIC and the WAIC to keep away from overfitting. Decrease scores of LOOIC or WAIC indicated higher out-of-sample prediction accuracy of the candidate mannequin. Parameter estimation was carried out utilizing hierarchical Bayesian evaluation. Posterior inference was carried out with Markov chain Monte Carlo (MCMC) sampling with 4,000 iterations throughout 4 chains from the posterior distribution. Your complete modeling-related procedures have been carried out utilizing the hBayesDM package deal [76]. In complete, we examined 12 candidate fashions utilizing the stage-by-stage mannequin development process in exp1. For exp2, we in contrast these fashions straight.

We used the straightforward RW mannequin (Eqs 13) because the baseline mannequin [18], which was extensively utilized in learning-related research [14,17,24]. The easy RW mannequin assumed that individuals discovered reward construction by trial and error (Eqs 13) [18].


Right here, the worth V of the chosen possibility is up to date trial-by-trial, that are decided by each the prediction error and studying price α (0 < α < 1). The prediction error is derived from the distinction between acquired consequence (O) and anticipated worth (V) from the earlier trial. For simplicity, the worth of the unchosen possibility is considered the other worth for the chosen possibility [14,17]. Lastly, we used a softmax operate with a choice parameter β (or inverse temperature parameter; 0 < β < 10) to calculate the chosen likelihood for every possibility.

In Stage 1, we in contrast a mannequin (M1) that assumed every of 4 circumstances was discovered in a different way with a mannequin (M2) that assumed that individuals discovered in a different way between volatility [14], however not the kind of cue. That’s, M2 assumed that there was no cue (emotional) impact for all parameters. Thus, M1 included a studying parameter (studying price) and a choice parameter (inverse temperature) for every situation, whereas M2 consisted of two studying parameters and choice parameters for circumstances of frequent and rare reversals. On condition that it was properly established that the setting with frequent reversals elicited greater studying price than that with rare reversals [14,17,24], we thus didn’t embody the mannequin assuming that there was no impact of volatility. M1 with 8 parameters confirmed higher efficiency than M2 with 4 parameters (Desk 2).

In Stage 2, we eliminated/added some elements to the M1 to validate the profitable mannequin from Stage 1. M3 assumed that individuals discovered in a different way for every situation however shared a choice parameter throughout circumstances (5 parameters). M4 assumed that individuals regarded our job because the two-arm bandit and solely up to date the worth of the chosen possibility (8 parameters), moderately than the one-arm bandit (Eq 2). M5 added a parameter of the lapse in consideration (ε; 0 < ε < 1; Eq 4), assuming that individuals often made random selections attributable to a lapse in consideration (9 parameters).


Mannequin comparisons amongst M1, M3, M4, and M5 indicated that M1 carried out higher (see Desk 2).

Individuals wanted to study 4 varieties of cue-option-outcome contingencies in our job (as in comparison with 1 kind within the earlier probabilistic reward reversal studying job), together with the choice of forgetting [77]. Due to this fact, in Stage 3, we added forgetting parameters ϕ to the M1 (0 < ϕ < 1; Eq 5). This parameter pulled the estimated worth towards the random stage (0.5).


M6 assumed a sharing forgetting parameter throughout circumstances (9 parameters). M7 assumed ϕ have been modulated by volatility, with 2 forgetting parameters for environments with frequent and rare reversals (10 parameters). Reasonably, we assumed ϕ have been modulated by emotional cues in M8 (10 parameters). Mannequin comparisons amongst M1, M6, M7, and M8 confirmed that M1 carried out greatest (Desk 2).

In Stage 4, we thought-about hybrid fashions of the Pearce-Corridor mannequin with the RW mannequin provided that such a hybrid mannequin carried out greatest among the many candidate fashions throughout the emotion- and volatility-related job [36]. M9 assumed a shared weighting parameter ω throughout circumstances (0 < ω < 1; 7 parameters). M10 assumed distinct weighting parameters ω for fearful and impartial cues (8 parameters). M11 assumed completely different scale parameters κ of studying price for fearful and impartial circumstances (0 < κ < 1; 8 parameters). M9–11 have been the identical as M2, M4, and M5 in Piray and colleagues (2019), respectively. Once more, M1 received amongst these candidate fashions (see Desk 2).

We additionally added a mannequin (M12) assuming linear relationships amongst 4 studying charges. Particularly, every parameter was linearly represented by the training price for the impartial and rare situation. M1 outperformed than M12 (Desk 2), suggesting that it’s higher to independently symbolize every parameter within the present experiment, although there are important correlations between every parameter.


We additional carried out mannequin validation for the profitable mannequin (M1) in exp1 utilizing the generated information from MCMC sampling (4,000 iterations). First, we computed correlations between actual accuracy and simulated accuracy for every situation. Please be aware that imply simulated accuracy throughout 4,000 iterations per situation and participant was used. Second, we analyzed the generated information utilizing the index of efficiency after reversal and computed 95% excessive density posterior interval (HDI) to check the distinction between simulated information and actual information.

Subsequent, we in contrast these 12 candidate fashions straight in exp2. The identical procedures of mannequin validation have been carried out for exp2 behavioral information.

Bayesian learner mannequin

Bayesian Learner mannequin has been proven to dynamically observe environmental volatility [14]. Particularly, the learner estimates the likelihood to achieve reward on the following trial (Vt+1) given the obtained consequence and the estimated likelihood on the present trial (Vt). Please be aware that Vt will also be represented as likelihood provided that the magnitude in our research was mounted at 1. Benefiting from the Markovian assumption, the brand new estimated likelihood solely depends upon data from the final trial, however not the total historical past of earlier trials. Due to this fact, the estimation of likelihood on trial t+1 may be represented utilizing a beta distribution with the estimated likelihood on trial t because the imply and a width parameter sv, the place sv equals to exp (SV), representing the estimated subjective volatility of the setting.


One other parameter is a mistrust parameter ok, representing uncertainty within the present estimation of environmental volatility. Subjective volatility of the following trial (SVt+1) may be represented as a traditional distribution, with SVt because the imply and Ok because the width parameter, the place Ok = exp(ok).


The joint likelihood of Vt+1, SVt+1, and ok is estimated primarily based on the result (y) of every trial:

Trial-by-trial estimates of the person parameters (Vt+1, SVt+1, ok) are obtained by the marginalization of the joint likelihood operate. For extra particulars, see Behrens and colleagues (2007).

Picture acquisition and preprocessing

MRI information have been acquired with a Siemens Trio 3T scanner. Each the fMRI and high-resolution 3D structural mind information have been obtained utilizing a 64-channel phased-array head. The fMRI information have been acquired via a gradient-echo echo-planar imaging sequence containing the next parameters: repetition time (TR) = 1,000 ms, echo time (TE) = 30 ms, 78 multiband slices, voxel measurement = 2 × 2 × 2 mm [3], flip angle = 35°, area of view (FOV) = 192 mm × 192 mm, information matrix = 96 × 96, and 240 volumes scanned in 2,640 seconds. Moreover, the 3D structural mind pictures (1 mm [3] isotropic) have been acquired for every participant utilizing a T1-weighted 3D magnetization-prepared fast gradient echo sequence with the next parameters: TR/TE = 2,300 ms/2.26 ms, flip angle = 8°, information matrix = 232 × 256, FOV = 232 mm × 256 mm, BandWidth = 200 Hz/pixel, 192 picture slices alongside the sagittal orientation, obtained in about 9 min.

Practical MRI information have been preprocessed with DPABI [39] (, a software program package deal primarily based on SPM12 (model no.7219; program/spm12/). It comprised the next steps: (i) realignment; (ii) co-registering the T1-weighted picture to the corresponding imply useful picture; (iii) segmenting into grey matter, white matter, and cerebrospinal fluid by DARTEL; (iv) normalizing to the usual Montreal Neurological Institute house (MNI template, resampling voxel measurement 2 × 2 × 2 mm [3]); (v) smoothing with a Gaussian kernel of 6 mm full width at half most (FWHM).

Generalized linear fashions (GLM)

SPM12 (model no.7219; program/spm12/) was used for common linear mannequin (GLM) evaluation. The present research targeted on the interplay impact between cue (concern/neut) and volatility (infreq/freq), or fear-biased adaptation to volatility studying, which was outlined as [(fear & freq–fear & infreq)–(neut & freq–neut & infreq)].

Generalized psychophysiological interplay (gPPI)

To evaluate how the useful connectivity between BOLD indicators within the seed area and BOLD indicators within the goal area was modulated by fear-biased adaptation to volatility studying, we carried out gPPI utilizing the gPPI toolbox [40]. The seed area(s) have been decided by activation outcomes from GLM2. Particularly, we extracted the deconvolved (with HRF) time course from the primary eigenvariate of the seed area (the physiological time period) for every participant. The subjective volatility at consequence onset for every situation was used because the psychological time period. The interplay time period was then generated by multiplying the physiological time period with the psychological time period. The convolved interplay time period was entered into the GLM evaluation.

Dynamic causal modeling (DCM)

To evaluate efficient connectivity between the mind areas underlying the modulation of fear-biased adaptation, we carried out DCM utilizing the DCM12 toolbox [41]. We used the identical parametric indicators (subjective volatility) from every situation to check fear-biased adaptation results. Six fashions have been constructed with completely different assumptions of modulatory results (B matrix) and driving results (C matrix) whereas we mounted the total intrinsic connectivity (A matrix; Fig 4A). For the profitable mannequin, ANOVAs have been carried out with cue (concern/neut) and volatility (freq/infreq) as within-subject elements in A, B, and C matrices, respectively. Bonferroni correction was used to appropriate for a number of comparisons.


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