The increasing incidence (e.g., Xiong et al., 2022) and high relapse rates (Essau et al., 2018; Yonkers et al., 2003) of anxiety-related disorders call for a better understanding of anxiety- and stress-related processes which might contribute to improving existing treatments or developing more effective interventions. In the laboratory, these processes can be studied using fear conditioning paradigms (Dunsmoor et al., 2022; Fullana et al., 2020; Milad and Quirk, 2012).

In differential fear conditioning protocols (see Lonsdorf et al., 2017a) one stimulus is repetitively paired with an aversive unconditioned stimulus (US; e.g., electrotactile stimulation), and as a consequence becomes a conditioned stimulus (CS+) while another stimulus, the CS−, is never paired with the US. After this acquisition training phase, CSs are presented without the US (extinction training) leading to a gradual waning of the conditioned response. Critically, the fear memory (CS+/US association) is not erased, but a competing inhibitory extinction memory (CS+/no US association) is assumed to be formed during extinction training (Milad and Quirk, 2012; Myers and Davis, 2007). Subsequently, return of fear (RoF) can be induced by procedural manipulations such as a time delay (spontaneous recovery), a contextual change (renewal, Vervliet et al., 2013a), or a (re-)presentation of an aversive event (reinstatement, Haaker et al., 2014). Conditioned responding can be subsequently probed in an RoF test phase during which either the absence (i.e., extinction retention) or the return of conditioned responding (i.e., RoF) can be observed (Bouton, 2004; Lonsdorf et al., 2017a).

Findings from studies employing fear conditioning paradigms hold strong potential for translating neuroscientific findings into clinical applications (Anderson and Insel, 2006; Cooper et al., 2022a; Fullana et al., 2020; Milad and Quirk, 2012). More precisely, extinction learning is assumed to be the active component of exposure-based treatment (Graham and Milad, 2011; Milad and Quirk, 2012; Rachman, 1989; Vervliet et al., 2013b) and experimental RoF manipulations have been suggested to serve as a model of clinical relapse (Scharfenort et al., 2016; Vervliet et al., 2013a). Important findings in the fear conditioning field include the deficient learning of the safety signal (CS−) during acquisition training, impaired extinction learning (Duits et al., 2015) and the tendency of fear generalization to innocuous stimuli (Cooper et al., 2022a) in patients suffering from anxiety-related disorders as compared to healthy controls.

To date, both clinical and experimental research using the fear conditioning paradigm have primarily focused on group-level, basic, general mechanisms such as the effect of experimental manipulations – which is important to investigate (Lonsdorf and Merz, 2017b). Successful clinical translation (e.g., ‘Why do some individuals develop pathological anxiety while others do not?’) and particularly treatment outcome prediction (e.g., ‘Why do some patients benefit from treatment while others relapse?’), however, requires that both the experimental paradigm and the measures employed allow for individual-level predictions over and above prediction of group averages (Fröhner et al., 2019; Hedge et al., 2018; Lonsdorf and Merz, 2017b). A prerequisite for this is that the measures show stability within and reliable differences between individuals over time. Hence, tackling clinical questions regarding individual-level predictions of symptom development or treatment outcome requires a shift toward and a validation of research methods tailored to individual differences – such as a focus on measurement reliability (Zuo et al., 2019). This is a necessary prerequisite for the long-term goal of developing individualized intervention and prevention programs. This further relates to the pronounced heterogeneity in symptom manifestation among individuals diagnosed with the same disorders (e.g., post-traumatic stress disorder, PTSD, Galatzer-Levy and Bryant, 2013b) which cannot be captured in binary clinical diagnoses as two patients with for example a PTSD diagnosis may not share a single symptom (Galatzer-Levy and Bryant, 2013b).

Measurement reliability has only recently gained momentum in experimental cognitive research (Fröhner et al., 2019; Hedge et al., 2018; Zuo et al., 2019) and can be assessed through test–retest and longitudinal reliability (i.e., test–retest reliability over longer time intervals, typically assessed through e.g., intraclass correlation coefficients, ICCs, see Table 1). Importantly, longitudinal reliability (for definitions and terminology, see Table 1) also has implications for the precision with which associations of one variable (e.g., conditioned responding) with another (individual difference) variable can be measured because the correlation between those two variables cannot exceed the correlations within, that is the reliability, of these two variables (Spearman, 1910).

Yet, in fear conditioning research, surprisingly little is known about longitudinal reliability at the individual level with time intervals ranging from 9 days to 8 months in prior work (Supplementary file 1; Cooper et al., 2022b; Fredrikson et al., 1993; Ridderbusch et al., 2021; Torrents-Rodas et al., 2014; Zeidan et al., 2012). Generally (details in Supplementary file 1), individual-level longitudinal reliability of risk ratings, skin conductance responses (SCRs), and fear potentiated startle (FPS) was within the same range (Cooper et al., 2022b; Torrents-Rodas et al., 2014) whereas it was numerically somewhat lower for the BOLD response as compared to different rating types (Ridderbusch et al., 2021). Longitudinal reliability at the individual level appeared higher for acquisition training than for extinction training (SCRs: Fredrikson et al., 1993; Zeidan et al., 2012), but comparable to generalization (Cooper et al., 2022b; Torrents-Rodas et al., 2014). Moreover, it appeared higher for extinction training than for reinstatement-test (for BOLD fMRI but not ratings: Ridderbusch et al., 2021) and higher for CS+ than CS− responses (SCRs: Fredrikson et al., 1993) and CS discrimination (ratings and BOLD fMRI: Ridderbusch et al., 2021; SCRs: Zeidan et al., 2012).

However, it is difficult to extract a comprehensive picture from these five studies as they differ substantially in sample size (N = 18–100), paradigm specifications, experimental phases reported, outcome measures, time intervals, and employed reliability measures (see Supplementary file 1).

Given that the predominance of research on group-level generic mechanisms in fear conditioning research, it is even more surprising that, to our knowledge, no study to date has investigated longitudinal reliability at the group level and only few studies have (Fredrikson et al., 1993) targeted internal consistency (i.e., the degree to which all test items capture the same construct, see Table 1). More precisely, longitudinal reliability at the group level indicates the extent to which responses averaged across the group as a whole are stable over time, which is important to establish when investigating basic, generic principles such as the impact of experimental manipulations. Even though it has to be acknowledged that the group average is not necessarily representative of any individual in the group and the same group average may arise from different and even opposite individual responses at both time points in the same group, group-level reliability is important to establish in addition to individual-level reliability. Group-level reliability is relevant not only to work focusing on the understanding of general, generic processes but also for questions about differences between two groups of individuals such as patients vs. controls (e.g., see meta-analyses of Cooper et al., 2022a; Duits et al., 2015). Of note, many fear conditioning paradigms were initially developed to study general group-level processes and to elicit robust group effects (Lonsdorf and Merz, 2017b). Hence, it is important to investigate both group- and individual-level reliability given the challenges of attempts to employ cognitive tasks that were originally designed to produce robust group effects in individual difference research (Elliott et al., 2020; Hedge et al., 2018; Parsons, 2020; Parsons et al., 2019).

As pointed out above, individual-level reliability is a prerequisite for individual-level predictions such as treatment outcomes. Since the different experimental phases of fear conditioning paradigms serve as experimental models for the development, treatment, and relapse of anxiety- and stress-related disorders, it is also an important question whether responding across phases can be reliably predicted at the individual level. Interestingly, it is often implicitly assumed that responding in one experimental phase reliably predicts responding in a subsequent phase (e.g., see Milad et al., 2009; critically discussed in Lonsdorf et al., 2019a) even though empirical evidence is lacking. As a result it has been suggested to routinely ‘correct for responding’ during fear acquisition training when studying performance in later experimental phases such as extinction training or retention/RoF test (critically discussed in Lonsdorf et al., 2019a). However, empirical evidence on this cross-phases predictability (for definition and terminology, see Table 1) is scarce to date.

Evidence from experimental work on cross-phase predictability in rodents and humans is mixed. In rodents, freezing during acquisition training and 24-hrs-delayed extinction training were uncorrelated (Plendl and Wotjak, 2010) and responding during extinction training did not predict extinction retention (i.e., lever-pressing suppression: Bouton et al., 2006; or freezing behavior: Shumake et al., 2014). Similarly, in humans, extinction performance (FPS, SCRs, and US expectancy ratings) did not predict performance at 24-hrs-retention test (Prenoveau et al., 2013). Yet, a computational modeling approach suggests that the mechanism of extinction learning (i.e., the formation of a new extinction memory trace in comparison to an update of the original fear memory trace) predicts the extent of spontaneous recovery in SCRs (Gershman and Hartley, 2015).

Also evidence from work in patient samples is mixed (for a review, see Craske et al., 2008). The extent of fear reduction within therapeutic sessions was unrelated to overall treatment outcome in some studies (Kozak et al., 1988; Pitman et al., 1996; Riley et al., 1995), while others observed an association (Foa et al., 1983). Similarly, significant correlations of fear reduction between therapeutic sessions with treatment outcome were observed for reported distress (Rauch et al., 2004) and heart rate, but not for SCR (Kozak et al., 1988; Lang et al., 1970) and for self-reported fear post treatment, but not at follow-up (Foa et al., 1983). In addition, evidence that responding in different phases is related comes from pharmacological manipulations with the cognitive enhancer D-cycloserine which facilitates learning and/or consolidation. D-cycloserine promoted long-term extinction retention (Rothbaum et al., 2014; Smits et al., 2013a; Smits et al., 2013b) only if within-session learning was achieved.

With this pre-registered study, we follow the call for a stronger appreciation and more systematic investigations of measurement reliability (Zuo et al., 2019). We address longitudinal reliability and internal consistency as well as predictability of cross-phase responding in SCRs, fear ratings, and the BOLD response. For this purpose, we reanalyzed data from 120 participants that underwent a differential fear conditioning paradigm twice (at time points T0 and T1, 6 months apart) – with habituation and acquisition training on day 1 and extinction, reinstatement and reinstatement-test on day 2 to allow for fear memory consolidation prior to extinction. Part of the data have been used previously in method focused work (Kuhn et al., 2022; Lonsdorf et al., 2022; Lonsdorf et al., 2019a; Sjouwerman et al., 2022) and work investigating the association of conditioned responding with brain morphological measures (Ehlers et al., 2020).

Specifically, we (1) estimated internal consistency of SCRs at both time points and (2) systematically assessed longitudinal reliability of SCRs, fear ratings and BOLD fMRI at the individual level by calculating ICCs. This was complemented by investigations of response similarity (SCR and BOLD fMRI) and the degree of overlap of activated voxels at both time points (BOLD fMRI) as additional measurements of longitudinal reliability at the individual level that allow for a more detailed picture than the coarser ICCs (see Table 1 for terminology and definitions). We also (3) assessed whether SCR and BOLD fMRI show longitudinal reliability at the group level. Finally, we (4) investigated if individual level responding during an experimental phase is predictive of individual-level responding during subsequent experimental phases. All hypotheses are tested across different pre-registered data specifications to account for procedural heterogeneity in the literature (see Supplementary file 1): More precisely, we follow a pre-registered multiverse-inspired approach and include (1) responses to the CS+, CS−, US, and CS discrimination, (2) different phase operationalizations, (3) different data transformations none, log-transformed, log-transformed and range-corrected, and (4) ordinally ranked vs. non-ranked data (for justification of these choices, see Table 1). We acknowledge that the specifications used here are not intended to cover all potentially meaningful combinations as in a full multiverse study (Lonsdorf et al., 2022; Sjouwerman et al., 2022; Steegen et al., 2016) but can be viewed as a manyverse (Kuhn et al., 2022) in which we a priori pre-registered a number of meaningful combinations.

For a comprehensive overview of the different reliability measures used here and of the analyses conducted, see Table 1.

Satisfactory internal consistency

To assess internal consistency of SCRs, trials were split into odd and even trials (i.e., odd–even approach), averaged for each individual subject and then correlated (Pearson’s correlation coefficient). This was done separately for each time point and experimental phase. Internal consistency at T0 (see Figure 1A) and T1 (see Figure 1B) of raw SCRs to the CS+ and CS− ranged from 0.54 to 0.85 and for raw SCRs to the US from 0.91 to 0.94 for all phases. In comparison, internal consistency was lower for CS discrimination with values ranging from −0.01 to 0.60. Log-transformation did not impact internal consistency but log-transformation in combination with range correction largely resulted in reduced reliability (see Figure 1—figure supplement 1).

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Higher within- than between-subject similarity in BOLD fMRI but not SCRs

While ICCs provide information on the absolute quantity of longitudinal reliability at the individual level, comparison of within- and between-subject similarity as a complementary measure of longitudinal reliability at the individual level (see Table 1) reflects the extent to which responses in SCR and BOLD activation of one individual at T0 were more similar to themselves at T1 than to other individuals at T1 (see Figures 2 and 3).

Figure 2

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Figure 3

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SCR

For SCRs, within-subject similarity (i.e., within-subject correlation of trial-by-trial SCR across time points) and between-subject similarity (i.e., correlation of trial-by-trial SCR between one individual at T0 and all other individuals at T1; see Figure 2) did not differ significantly for most data specifications. This was true for CS discrimination (t(64) = 1.78, p = 0.079, d = 0.22) as well as for SCRs to the CS+ (t(61) = 0.84, p = 0.407, d = 0.11) and CS− (t(55) = 1.50, p = 0.138, d = 0.20) during acquisition training and for CS discrimination (t(44) = −0.23, p = 0.823, d = −0.03) and SCRs to the CS+ (t(39) = 0.25, p = 0.801, d = 0.04) during extinction training. This indicates that SCRs of one particular individual at T0 were mostly not more similar to their own SCRs than to those of other individuals at T1. The only exceptions where within-subject similarities were significantly higher than between-subject similarity were SCRs to the US during acquisition training (t(70) = 2.54, p = 0.013, d = 0.30) and to the CS− during extinction training (t(31) = 2.05, p = 0.049, d = 0.36). Note, however, that within-subject similarity had a very wide spread pointing to substantial individual differences (while this variance is removed in calculations of between-subject similarity).

fMRI data

In contrast to what was observed for SCRs, within-subject similarity was significantly higher than between-subject similarity in the whole brain (p < 0.001) and most of the ROIs for fear acquisition training (see Figure 3A and Supplementary file 6). This suggests that while absolute values for similarity might be low, individual brain activation patterns during fear acquisition training at T0 were – at large – still more similar to the same subject’s activation pattern at T1 than to any others at T1. For extinction training, however, no significant differences between within- and between-subject similarity were found for any ROI or the whole brain (all p’s > 0.306; see Figure 3B and Supplementary file 6).

Robust longitudinal reliability at the group level

While longitudinal reliability at the individual level relies on (mean) individual subject responding at both time points, longitudinal reliability at the group level relies on the percentage of explained variance of group averaged trials at T1 by group averaged trials at T0 (i.e., R squared for SCR) or the degree of group level overlap of significant voxels expressed as Dice and Jaccard indices (i.e., BOLD fMRI).

SCR

For acquisition training (see Figure 4A), 40.66% ($F\left(\mathrm{1,11}\right)=7.54$, ${\displaystyle \mathrm{p}=0.019}$), 63.59% ($F\left(\mathrm{1,11}\right)=19.21$, ${\displaystyle \mathrm{p}=0.001}$) and 75.67% ($F\left(\mathrm{1,11}\right)=34.20$, ${\displaystyle \mathrm{p}<0.001}$) of the variance of SCRs at T1 could be explained by SCRs at T0 for CS discrimination, CS+ and CS−, respectively, indicating robust longitudinal reliability of SCRs at the group level for CS responding during acquisition. Interestingly, only 19.53% ($F\left(\mathrm{1,12}\right)=2.91$, ${\displaystyle \mathrm{p}=0.114}$) of the variance of SCRs to the US could be explained. For extinction training, in contrast, only 19.58% ($F\left(\mathrm{1,11}\right)=2.68$, ${\displaystyle \mathrm{p}=0.130}$) and 21.70% ($F\left(\mathrm{1,11}\right)=3.05$, ${\displaystyle \mathrm{p}=0.109}$) of the SCR variance at T1 could be explained by SCRs at T0 for CS discrimination and CS+, respectively, indicating only limited longitudinal reliability at the group level. However, with 67.35% ($F\left(\mathrm{1,11}\right)=22.69$, ${\displaystyle \mathrm{p}=0.001}$) explained variance at T1, longitudinal reliability of SCRs to the CS− appeared to be more robust as compared to CS discrimination and responses to the CS+ (see Figure 4B).

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BOLD fMRI

In stark contrast to the low overlap of individual-level activation (see Table 2A), the overlap at the group level was rather high with 62.00% for the whole brain and up to 89.80% for individual ROIs (i.e., dACC and dlPFC; Jaccard) for CS discrimination during acquisition training (see Table 2B). Similar to what was observed for overlap at the individual level, a much lower overlap for extinction training as compared to acquisition training was observed for the whole brain (5.70% overlap) and all ROIs (all close to zero).

Cross-phases predictability of conditioned responding

Finally, we investigated if responding in any given experimental phase predicted responding in subsequent experimental phases. To this end, simple linear regressions with robust standard errors were computed for both SCRs and fear ratings and all data specifications (see Figure 5 and Supplementary file 7, Supplementary file 8). To approximate these analyses, correlations of patterns of BOLD brain activation between experimental phases were calculated (see Figure 6).

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Figure 6

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SCR

Stronger CS discrimination in SCRs during (delayed) fear recall (i.e., first trial of extinction training) was significantly predicted by both average and end-point performance (i.e., last two trials) during acquisition training for most data specifications (Figure 5A, columns 1 and 2). In contrast, average CS discrimination during extinction training was significantly predicted by acquisition training performance only if data were ordinally ranked (columns 3 and 4). Strikingly, all predictions of extinction end-point performance (columns 5 and 6) as well as performance at reinstatement-test (columns 7–11) were non-significant – irrespective of phase operationalizations and data transformation.

The majority of predictions of SCRs to the CS+ and CS− were significant with few exceptions (see white cells in Figure 5A) – irrespective of experimental phases, their operationalization and data transformation. Most non-significant regressions included log-transformed and range corrected data. Strikingly, extinction end-point performance never predicted performance at reinstatement-test – irrespective of data transformation (column 11).

Fear ratings

Higher ratings for the CS+ as well as higher CS discrimination during acquisition training predicted higher CS+ ratings and CS discrimination at fear recall (Figure 5B, columns 1 and 2), extinction training (columns 3 and 4), and at reinstatement-test (columns 7 and 8). Higher responding to the CS+ and higher CS discrimination at fear recall predicted higher responding at reinstatement-test (column 9) – irrespective of data transformations. In contrast, predictions of CS discrimination and CS+ ratings after extinction training were mostly non-significant (columns 5 and 6). Higher CS+ ratings during extinction training significantly predicted higher ratings at reinstatement-test which was not true for CS discrimination (columns 10 and 11).

Higher CS− ratings after acquisition training predicted higher CS− ratings at fear recall as well as after extinction training and CS− ratings after extinction training predicted the performance at reinstatement-test – irrespective of ranking of the data (columns 2, 6, and 11). Furthermore, when based on ordinally ranked data, the difference between ratings prior to and after acquisition predicted CS− ratings at fear recall and CS− ratings after acquisition training predicted the difference between CS− ratings prior to and after extinction training (columns 1 and 4). All other predictions were non-significant.

In sum, all significant predictions observed were positive with weak to moderate associations and indicate that higher responding in preceding phases predicted higher responding in subsequent phases for both SCRs and fear ratings.

BOLD fMRI

In short, all but one association (CS discrimination in the NAcc) was positive, showing that higher BOLD response during acquisition was associated with higher BOLD responding during extinction training (see Figure 6). However, the standardized beta coefficients are mostly below or around 0.3 except for CS+ associations in the dACC, indicating non-substantial associations for all ROIs and CS specifications that were near absent for CS discrimination. Analysis of CS+ and CS− data was included here as the analysis is based on beta maps and not T-maps (as in previous analyses) where a contrast against baseline is not optimal.

In fear conditioning research, little is known about longitudinal reliability (in the literature often referred to as test–retest reliability) for common outcome measures and almost nothing is known about their internal consistency and to what extent predictability across experimental phases is possible.

Here, we aimed to fill this gap and complement traditionally used approaches focusing on ICCs (summarized in Supplementary file 1) with (1) analyses of response similarity, (2) the degree of overlap of individual-level brain activation patterns as well as (3) by exploring longitudinal reliability at the group level in addition to (4) internal consistency across outcome measures.

Moreover, we also directly investigated predictability of responding from one experimental phase to subsequent experimental phases. For all analyses, we followed a multiverse-inspired approach (Parsons, 2020) by taking into account different data specifications.

Overall, longitudinal group-level reliability was robust for SCRs (see Figure 4) and the BOLD response (see Table 2B) while longitudinal individual-level reliability as assessed by ICCs (see Figure 1C–F), and individual-level BOLD activation overlap (see Table 2A) was more limited across outcome measures and data specifications – particularly during extinction training. This is in line with previous work in fear conditioning (Cooper et al., 2022b; Fredrikson et al., 1993; Ridderbusch et al., 2021; Torrents-Rodas et al., 2014; Zeidan et al., 2012) reporting figures for longitudinal individual-level reliability comparable to ours across outcome measures (SCRs, fear ratings, BOLD fMRI) and experimental phases. Importantly, however, it remains a challenge to interpret the results as benchmarks for ICCs are derived from psychometric work on trait self-report measures and it is plausible that what is interpreted as ‘low’ and ‘high’ reliability in experimental work should be substantially lower (Parsons et al., 2019).

Our complementary analyses beyond traditional ICCs indicate that SCRs of one individual at T0 were not more similar to responses of the same individual at T1 than compared to others at T1 (see Figure 2). For BOLD fMRI, however, acquisition-related individual BOLD activation patterns at T0 were more similar to their own activation patterns at T1 than to other individuals’ activation patterns (see Figure 3). This was, however, not the case for extinction. Hence, this may suggest that BOLD fMRI might be more sensitive to detect similarity at individual-level responses within participants than SCRs in our data – maybe due to the dependence on spatial (i.e., voxel-by-voxel) rather than temporal (i.e., trial-by-trial) patterns.

Furthermore, we observed a few differences in longitudinal reliability at the individual level depending on data processing specifications (see also Parsons, 2020). For most data specifications, reliability was slightly higher for log-transformed and range-corrected SCRs (as opposed to raw and only log-transformed data) while – in contrast to what has been shown for other paradigms and outcome measures (Baker et al., 2021; see also https://shiny.york.ac.uk/powercontours/) – an increasing number of trials included in the calculation of ICCs did not generally improve reliability (see Figure 1—figure supplements 3–8). Together, this suggests that longitudinal reliability at the individual level is relatively stable across different data transformations and paradigm specifications (e.g., number of trials within the range used here, i.e., 1 to maximum 14) which is important information facilitating the integration of previous work using different time intervals, reliability indices, and paradigms (see Supplementary file 1; Cooper et al., 2022b; Fredrikson et al., 1993; Ridderbusch et al., 2021; Torrents-Rodas et al., 2014; Zeidan et al., 2012).

In contrast, we observed quite robust longitudinal reliability at the group level for both SCRs (see Figure 4) and BOLD fMRI (see Table 2B) between both time points with substantial (i.e., up to 90%) overlap in group-level BOLD fMRI activation patterns (whole brain and ROI based) as well as substantial (i.e., up to 76%) explained variance at T1 by variance at T0 for SCRs. However, this was generally only true for acquisition but not extinction training. This pattern of higher reliability during acquisition compared to extinction training has been described in the literature (SCRs: Fredrikson et al., 1993; Zeidan et al., 2012) and was also evident in the similarity analyses of BOLD fMRI and the group-level reliability of SCRs. While this pattern did not emerge across all analyses, it appears to be particularly present when examining reliability of CS discrimination as it was the case for BOLD fMRI and as it also emerged in individual-level reliability analyses of CS discrimination in SCRs (internal consistency and ICCs) and fear ratings (ICCs). Since CS discrimination is typically lower during extinction as compared to acquisition training, this restriction of variance potentially resulted in a floor effect which might have lowered the internal consistency and longitudinal reliability of CS discrimination during extinction training.

Reports regarding this discrepancy between group- and individual-level longitudinal reliability were recently highlighted for a number of (classic) experimental paradigms (Fröhner et al., 2019; Hedge et al., 2018; Herting et al., 2018; Plichta et al., 2012; Schümann et al., 2020). Our results add fear conditioning and extinction as assessed by SCRs and BOLD fMRI to this list and have important implications for translational questions aiming for individual-level predictions – particularly since findings obtained at the group level are not necessarily representative for any individual within the group (Fisher et al., 2018).

In addition to these methods-focused insights, we observed significant associations between responding in different experimental phases for SCR (see Figure 5A), fear ratings (see Figure 5B) and BOLD fMRI (see Figure 6) revealing that higher responses in previous phases were generally modestly associated with higher responses in subsequent phases in all outcome measures. However, a remarkable amount of predictions were non-significant – which was particularly true for CS discrimination in SCRs and BOLD fMRI. This may be explained by difference scores (i.e., CS+ minus CS−) being generally less reliable (Infantolino et al., 2018; Lynam et al., 2006) due to a subtraction of meaningful variance (Moriarity and Alloy, 2021) particularly in highly correlated predictors (Thomas and Zumbo, 2012). Especially at the end of the extinction, CS discrimination is low and hence, variance limited. Therefore, floor effects may contribute to the non-significant effects for extinction end-point performance.

Mixed findings in the literature support both the independence of conditioned responding in different experimental phases (Bouton et al., 2006; Plendl and Wotjak, 2010; Prenoveau et al., 2013; Shumake et al., 2014) but also their dependence – particularly in clinical samples (Foa et al., 1983; Rauch et al., 2004; Rothbaum et al., 2014; Smits et al., 2013a; Smits et al., 2013b). These diverging findings in experimental and clinical studies might point toward a translational gap. However, our work may suggest that the strengths of associations between responding in different phases depended on the specific outcome measure and its specifications (e.g., responses specified as CS discrimination, CS+, or CS−). Yet another explanation – in particular for predictions spanning a 24 hrs delay in experimental phases − might be that individual differences in consolidation efficacy (e.g., how efficiently the fear and extinction memories are consolidated after performing acquisition and extinction training, respectively) may underlie differences in predictability. For example, the performance during a retention or RoF test phase is considered to be determined by the strength of the fear and extinction memory, respectively. Memory strength, however, is not only determined by the strength of the initially acquired memory but also by its consolidation (discussed in Lonsdorf et al., 2019b). Thus, as acquisition training preceded the extinction training and reinstatement-test by 24 hrs, it is highly likely that individual differences in consolidation efficacy also impact on performance at test. This has also implications for the common practice of correcting responses during one experimental phase for responding during preceding experimental phases (discussed in Lonsdorf et al., 2019b).

Importantly, together with our observation of robust internal consistency (see Figure 1 and also Fredrikson et al., 1993), this pattern of findings suggests that individual-level predictions at short intervals are plausible but might be more problematic for longer time periods as suggested by the limited stability over time in our data.

Yet, we would like to point out that the values we report may in fact point toward good and not limited longitudinal individual-level reliability as our interpretation is guided by benchmarks that were not developed for experimental data but from psychometric work on trait self-report measures. We acknowledge that the upper bound of maximally observable reliability may differ between both cases of application as empirical neuroscientific research inherently comes with more noise. The problem remains that predictions in fear conditioning paradigms appear to not be meaningful for longer periods of time (~6 months). Thus, a key contribution of our work is that it highlights the need to pay more attention to measurement properties in translational research in general and fear conditioning research specifically (e.g., implement reliability calculations routinely in future studies). To date, it remains an open question what ‘good reliability’ in experimental neuroscientific work actually means (Parsons et al., 2019).

Yet, before discussing implications of our results in detail, some reflections on potential (methodological) reasons for (1) limited individual-level but robust group-level reliability and (2) on the role of time interval lengths deserve attention:

First, the limited longitudinal individual-level reliability might indicate that the fear conditioning paradigm employed here – which is a rather strong paradigm with 100% reinforcement rate – may be better suited for investigations of group effects and to a lesser extent for individual difference questions – potentially due to limited variance between individuals (Hedge et al., 2018; Parsons, 2020; Parsons et al., 2019). However, high reliability appears to be possible in principle, as we can conclude from the robust internal consistency of SCRs that we observed. This speaks against a limited between-subject variance and a general impracticability of the paradigm for individual difference research. Hence, we call for caution and warn against concluding from our report that fear conditioning and our outcome measures (SCRs, BOLD fMRI) are unreliable at the individual level.

Second, limited individual-level but robust group-level longitudinal reliability might be (in part) due to different averaging procedures which impacts error variance (Kennedy et al., 2021). More precisely, compared to individual-level data, group-level data are based on highly aggregated data resulting in generally reduced error variance which increases group-level reliability.

Third, different operationalizations of the same measurement might have different reliabilities (Kragel et al., 2021). For instance, amygdala habituation has been shown to be a more reliable measure than average amygdala activation (Plichta et al., 2014) and more advanced analytical approaches such as intraindividual neural response variability (Månsson et al., 2021) and multivariate imaging techniques Kragel et al., 2021; Marek et al., 2020; Noble et al., 2021; Visser et al., 2021 have been suggested to have better (longitudinal) reliability than more traditional analyses approaches. Similarly, methodological advances (e.g., techniques to adjust the functional organization of the brain across participants, Kong et al., 2021; or hyperalignment, Feilong et al., 2021) in measurement quality and tools may ultimately result in better reliability estimates (DeYoung et al., 2022).

Fourth, as discussed above, caution is warranted as traditional benchmarks for ‘good’ reliability were not developed for experimental work but mainly from psychometric work on trait self-report measures (see above).

Finally, longitudinal reliability refers to measurements obtained under the same conditions and hence it is both plausible and well established that higher reliability is observed at short test–retest intervals (see also Noble et al., 2021; Werner et al., 2022). Longer intervals are more susceptible to true changes of the measurand – for instance due to environmental influences such as seasonality, temperature, hormonal status, or life events (see Specht et al., 2011; Vaidya et al., 2002). Indeed most longitudinal reliability studies in the fMRI field used shorter intervals (<6 months, see Elliott et al., 2020; Noble et al., 2021) than our 6-month interval and hence our results should be conceptualized as longitudinal stability rather than a genuine test–retest reliability. The satisfactory internal consistency speaks against excessive noisiness inherent to our measures as a strong noisiness would also be evident in measurements within one time point and not only emerge across our retest interval. Thus, we rather suggest a true change of the measurand during our retest interval and hence a potentially stronger state than trait dependency.

What do our findings imply? Fear conditioning research has been highlighted as a particularly promising paradigm for the translation of neuroscientific findings into the clinics (Anderson and Insel, 2006; Cooper et al., 2022a; Fullana et al., 2020; Milad and Quirk, 2012) and some of the most pressing translational questions are based on individual-level predictions such as predicting treatment success. Our results, however, suggest that measurement reliability may allow for individual-level predictions for (very) short but potentially less so for longer time intervals (such as our 6 months retest interval). Importantly, however, robust group-level reliability appears to allow for group-level predictions over longer time intervals. This applies to SCRs and BOLD fMRI in our data but note that the latter was not investigated for fear ratings. A potential solution and promising future avenue to make use of both good group-level reliability and individual-level predictions might be the use of homogenous (latent) subgroups characterized by similar response profiles (e.g., rapid, slow or no extinction, Galatzer-Levy et al., 2013a) – to exploit the fact that reliability appears to be higher for more homogenous samples (Gulliksen, 1950).

While general recommendations and helpful discussions on the link between reliability and number of trials (Baker et al., 2021), statistical power (Parsons, 2020), maximally observable correlations (Parsons, 2020), sample and effect size (Hedge et al., 2018; Parsons, 2020) considerations exist, our results highlight the need for field and subdiscipline specific considerations. Our work allows for some initial recommendations and insights. First, we highlight the value of using multiple, more nuanced measures of reliability beyond traditional ICCs (i.e,. similarity, overlap, Fröhner et al., 2019) and second, the relation between number of trials and reliability in an experiment with a learning component (i.e., no increase in reliability with an increasing number of trials). Importantly, our work can also be understood as an empirically based call for action, since more work is needed to allow for clear-cut recommendations, and as a starting point to develop and refine comprehensive guidelines in the future. We also echo the cautionary note of Parsons that ‘estimates of reliability refer to the measurement obtained – in a specific sample and under particular circumstances, including the task parameters’ (cf. Parsons, 2020). Hence, it is important to remember that reliability is a property of a measure that is not fixed and may vary depending on task specifications and samples. In other words, reliability is not a fixed property of the task itself, here fear conditioning.

We argue that we may need to take a (number of) step(s) back and develop paradigms and data processing pipelines explicitly tailored to individual difference research (i.e., correlation) or experimental (i.e., group level) research questions (e.g., Parsons, 2020) and focus more strongly on measurement reliability in experimental work – which has major consequences on effect sizes and statistical power (Elliott et al., 2020). More precisely, multiverse-type investigations (Parsons, 2020; Steegen et al., 2016) that systematically scrutinize the impact of several alternative and equally justifiable processing and analytical decisions in a single dataset (Kuhn et al., 2022; Lonsdorf et al., 2022; Sjouwerman et al., 2022) – as also done here for transformations and number of trials – may be helpful to ultimately achieve this overarching aim. This could be complemented by systematically varying design specifications (Harder, 2020) which are extensively heterogeneous in fear conditioning research (Lonsdorf et al., 2017a). Calibration approaches, as recently suggested Bach et al., 2020 follow a similar aim.

Such work on measurement questions should be included in cognitive-experimental work as a standard practice (Parsons, 2020) and can (often) be explored in a cost and resource effective way in existing data which in the best case are openly available – which, however, requires cross-lab data sharing and data management homogenization plans. Devoting resources and funds to measurement optimization is a valuable investment into the prospect of this field contributing to improved mental health (Moriarity and Alloy, 2021) and to resume the path to successful translation from neuroscience discoveries into clinical applications.

The data that support the findings of this study and the R Markdown files that generate this manuscript are openly available in Zenodo at https://doi.org/10.5281/zenodo.7323547.

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Author details

1. Maren Klingelhöfer-Jens

   Institute for Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Visualization, Methodology, Writing - original draft, Pre-registration of the study

   For correspondence

   m.klingelhoefer-jens@uke.de

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5393-7871

2. Mana R Ehlers

   Institute for Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany

   Contribution

   Conceptualization, Formal analysis, Visualization, Methodology, Writing - original draft

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1316-3787

3. Manuel Kuhn

   1. Institute for Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
   2. Department of Psychiatry, Harvard Medical School, and Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, United States

   Contribution

   Data curation, Software, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2210-9130

4. Vincent Keyaniyan

   Institute for Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany

   Contribution

   Formal analysis, Visualization, Methodology, Writing – review and editing, Pre-registration of the study

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5674-5197

5. Tina B Lonsdorf

   Institute for Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Methodology, Writing - original draft, Pre-registration of the study

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1501-4846

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