Author response: Distinct neural contributions to metacognition for detecting, but not discriminating visual stimuli

Article Figures and data Abstract Introduction Results Discussion Materials and methods Appendix 1 Appendix 2 Appendix 3 Appendix 4 Appendix 5 Appendix 6 Appendix 7 Appendix 8 Appendix 9 Appendix 10 Appendix 11 Data availability References Decision letter Author response Article and author information Metrics Abstract Being confident in whether a stimulus is present or absent (a detection judgment) is qualitatively distinct from being confident in the identity of that stimulus (a discrimination judgment). In particular, in detection, evidence can only be available for the presence, not the absence, of a target object. This asymmetry suggests that higher-order cognitive and neural processes may be required for confidence in detection, and more specifically, in judgments about absence. In a within-subject, pre-registered and performance-matched fMRI design, we observed quadratic confidence effects in frontopolar cortex for detection but not discrimination. Furthermore, in the right temporoparietal junction, confidence effects were enhanced for judgments of target absence compared to judgments of target presence. We interpret these findings as reflecting qualitative differences between a neural basis for metacognitive evaluation of detection and discrimination, potentially in line with counterfactual or higher-order models of confidence formation in detection. Introduction When foraging for berries, one first needs to decide whether a certain bush bears fruit or not. Only if berries are detected, can one proceed to examine and classify them into a category - are these raspberries or blackberries? The first is a detection task: a decision about whether something is there or not, and the second is a discrimination task: a decision about which item is there. For these types of decisions, it is important not only to understand the decision process that leads to deciding present or absent, or raspberries or blackberries, but also our ability to reflect on and estimate the quality of the decision, known as metacognition. For instance, two foragers working together may want to share their confidence in deciding which bush to tackle next (Bahrami et al., 2010; Frith, 2012). There is an increasing understanding of the neural basis of confidence in simple decisions, with a network of prefrontal and parietal regions being identified as important for tracking metacognitive beliefs about the accuracy of both perceptual and value-based decisions (see Domenech and Koechlin, 2015; Meyniel et al., 2015, for reviews). Accordingly, neuropsychological data in humans suggest that damage or impairment of prefrontal function can lead to metacognitive impairments such as noisy or inappropriate confidence judgments (see Rouault et al., 2018, for a review). However, in a majority of these cases, the study of confidence has been restricted to discrimination, or deciding whether a stimulus is from category A or B. Despite their ubiquity and importance in decision-making, much less is known about how confidence is formed in detection settings, in which subjects are asked to make a judgment about whether a target stimulus is present or not. Computational considerations and behavioral findings suggest that computing confidence in detection judgments may differ from computing confidence in the more commonly studied discrimination tasks. In particular, detection is unique in the landscape of perceptual tasks in that evidence can only be available to support the presence, not the absence, of a target object. This makes confidence ratings in judgments about absence a unique case, where confidence is decoupled from the amount of supporting perceptual evidence. Accordingly, behavioral evidence indicates that metacognitive sensitivity, or the alignment between subjective confidence and objective performance, for judgments about absence is typically impaired compared to metacognitive sensitivity for judgments about presence (Meuwese et al., 2014; Kanai et al., 2010). Under one family of models (first-order models), confidence in detection judgments is formed in the same way as confidence in discrimination judgments. For example, in evidence-accumulation models, confidence can be evaluated as the distance of the losing accumulator from the threshold at the time of decision (Vickers, 1979; Merkle and Van Zandt, 2006). Similarly, in models of discrimination confidence based on Signal Detection Theory (SDT), decision confidence is assumed to be proportional to the strength of the available evidence supporting the decision, which is modeled as the distance of the perceptual sample from the decision criterion on a strength-of-evidence axis (Wickens, 2002, section 5.2). While first-order models are traditionally symmetric, they can be adapted to account for the asymmetry between judgments about presence and absence. For example, unequal-variance (uv-SDT) and multi-dimensional SDT models account for the inherent difference between presence and absence by making the signal distribution wider than the noise distribution (Wickens, 2002, section 3.4), or by assuming a high-dimensional stimulus space, in which the absence of a signal is represented as a distribution centered around the origin (King and Dehaene, 2014; Wickens, 2002, section 7.2). Importantly, first-order models treat the process of metacogntive evaluation of detection and discrimination as qualitatively similar, with any differences between detection and discrimination emerging from differences in the underlying distributions (uv-SDT), or the mapping between stimulus features and responses (two-dimensional SDT). In contrast with first-order models of detection confidence, higher-order models treat confidence in judgments about target absence as emerging from a distinct, higher-order cognitive process. For instance, in one version of the higher-order approach, confidence in judgments about absence is assumed to be based on counterfactual estimation of the likelihood of a hypothetical stimulus to be detected, if presented. In other words, subjects may be more confident in the absence of a target object when they believe they would not have missed it, based on their global estimation of task difficulty, or on their current level of attention. A similar type of modeling has been successfully employed in studies of memory, to explain how participants form judgments that an item was not presented during the preceding learning phase, based on their counterfactual expectations about remembering an item (Glanzer and Adams, 1990). When applied to the comparison of detection and discrimination, this approach predicts that qualitatively distinct cognitive and neural resources will be recruited when judging confidence in detection responses, due to the additional demand on counterfactual and self-monitoring processes, and that this recruitment will be most pronounced for confidence about absence. In particular, the counterfactual account predicts that responses in the frontopolar cortex, a region which has been shown to track counterfactual world states (Boorman et al., 2009), will show specificity for confidence judgements when inferring the absence of a target. To test for such qualitative differences, here we set out to directly compare the neural basis of metacognitive evaluation of detection and discrimination responses within two similar low-level perceptual tasks, while controlling for differences in task performance. In a pre-registered design, we asked whether parametric relationships between subjective confidence ratings and the blood-oxygenation-level-dependent (BOLD) signal in a set of predefined prefrontal and parietal regions of interests (ROIs) would show systematic interaction with task (detection/discrimination) and, within detection, type of response (present/absent). To anticipate our results, we observed a quadratic effect of confidence on regional responses in frontopolar cortex for detection, but not for discrimination judgments. In further whole-brain exploratory analyses, we found stronger confidence-related effects for judgments of absence compared to presence in right temporoparietal junction. Results A total of 35 participants performed two perceptual decision-making tasks while being scanned in a 3T MRI scanner: an orientation discrimination task ('was the grating tilted clockwise or anticlockwise?'), and a detection task ('was any grating presented at all?'; see Figure 1). The discrimination and detection tasks were performed in separate blocks each lasting 40 trials. At the end of each trial, participants rated their confidence in the accuracy of their decision on a 6-point scale. We adjusted the difficulty of the two tasks in a preceding behavioral session to achieve equal performance of around 70% accuracy. At scanning, 10 discrimination and detection blocks were presented in 5 scanner runs. Figure 1 Download asset Open asset Experimental design for discrimination and detection trials. Perceptual decisions were reported using the right index and middle fingers, and confidence ratings were reported using the left thumb. (A) In discrimination blocks, participants indicated the orientation of a visual grating (clockwise or anticlockwise). (B) In detection blocks, participants indicated whether a grating was embedded in the random noise, or not (yes or no). Confidence ratings were made by varying the size and color of a circle, with 6 options ranging from small and red to big and blue. For half of the subjects, high confidence was mapped to a small, red circle. For the other half, high confidence was mapped to a big, blue circle. The initial size and color of the circle was determined randomly at the beginning of the confidence rating phase. Participants performed 10 interleaved 40-trial detection and discrimination blocks inside a 3T MRI scanner. Behavioral results Task performance was similar for detection (75% accuracy, d'=1.48) and discrimination blocks (76% accuracy, d'=1.50). Repeated measures t-tests failed to detect a difference between tasks both in mean accuracy (t(34) = −0.90, p=0.37, BF01 = 5.15), and d' ( t(34) = −0.30, p=0.76, BF01=7.29), indicating that performance was well matched. Responses were also balanced for the two tasks. The probability of responding yes (target present) in the detection task was 0.49 ± 0.11, and not significantly different from 0.5 (t(34) = −0.39, p=0.70, BF01=7.07). The probability of responding clockwise in the discrimination task was 0.50 ± 0.08, and not significantly different from 0.5 (t(34) = 0.22, p=0.83, BF01=7.43). The distribution of confidence ratings was generally similar between the two tasks and four responses. For all four responses, participants were most likely to report the highest confidence rating compared to any other option. Within detection, a significant difference in mean confidence was observed between yes (target present) and no (target absent) responses, such that participants were more confident in their yes responses (t(34) = −4.85, p<0.0001; see Figure 2). This difference in mean confidence was mostly driven by the higher proportion of maximum confidence ratings in yes responses compared to no responses (46% of all yes responses compared to 26% of all no responses, t(34)=5.63, p<0.00001), but persisted even when ignoring the highest ratings (t(34)=2.39, p<0.05). Figure 2 Download asset Open asset Upper panels: response conditional type-2 ROC curves. In parentheses: the mean area under the curve. Lower panels: distribution of confidence ratings for the two tasks and four responses. Right panel: Mean accuracy for both tasks. Error bars represent the standard error of the mean. Metacognitive sensitivity, quantified as the area under the type-II ROC curve, was significantly higher for yes compared to no responses (t(34) = 7.83, p<10–8; see Figure 2), as expected (Meuwese et al., 2014). In other words, confidence ratings about the presence of a target stimulus were more diagnostic of accuracy than ratings about target absence, even though both sets of ratings tended to cover the full range of the scale, from low to high confidence. Taking metacognitive sensitivity following discrimination responses as a baseline, we found that this effect was driven by a decrease in metacognitive sensitivity for no responses (t(34) = −4.89, p<0.0001), whereas a quantitative increase in metacognitive sensitivity for yes responses compared to discrimination was not significant (t(34)=1.84, p=0.07). No difference was observed in metacognitive sensitivity between the two discrimination responses (clockwise and anticlockwise; t(34) = 0.06, p=0.95, BF01=7.6). Taken together, these results are consistent with the previously reported selective asymmetry in the fidelity of metacognitive evaluation following judgments about target absence (Meuwese et al., 2014; Kanai et al., 2010). Response times were faster on average for correct responses (849 ± 79 milliseconds) compared to incorrect responses (938 ± 95 milliseconds; t(34)=10.59, p<10-11 for a paired t-test on the log-transformed response times). Within the detection task, yes responses were significantly faster than no responses (850 ± 90 milliseconds and 896 ± 103 milliseconds, respectively; t(34)=3.16, p<0.005 for a paired t-test on the log-transformed response times). Imaging results Parametric effect of confidence We next turned to our fMRI data to ask whether confidence-related responses were similar or distinct across tasks (detection/discrimination) and response (target present: yes/target absent: no). We first established the presence of linear confidence-related effects in our a priori ROIs, both across tasks and response types and across correct and incorrect responses, in line with previous findings of 'generic' or task-invariant confidence signals in these regions (Morales et al., 2018). Specifically, higher confidence ratings were associated with increased activation in the ventromedial prefrontal cortex (vmPFC), the ventral striatum, and the precuneus. Conversely, activations in the posterior medial frontal cortex (pMFC) were negatively correlated with confidence (see Figure 3). For the confidence effect pattern obtained from the Global-Confidence Design Matrix (GC-DM), see Appendix 3—figure 1. Figure 3 Download asset Open asset Univariate parametric effect of confidence. (a) Glass brain visualization of global effect of confidence, thresholded at the single voxel level for visualization (p<0.001, uncorrected). Negative confidence effect appears in blue, and positive effect in red. (b) Whole brain contrast between confidence in 'target present' (yes) and 'target absent' (no) detection responses, corrected for family-wise error rate at the cluster level (p<0.05) with a cluster defining threshold of p<0.001, uncorrected. (c) Upper panel: BOLD signal in the rTPJ cluster from panel b as a function of response and confidence. lower panel: mean coefficients of response- and subject-specific multiple linear regression models, predicting rTPJ activation as a linear and quadratic function of confidence. * - p<0.05; uncorrected for multiple comparisons across the four tests. Comparison lines above and below the x axis indicate main effect of response and task, respectively. Interaction of linear confidence effects with task and response We next asked whether the linear parametric relationship between confidence and BOLD activity differed as a function of task (discrimination vs. detection) and response type (yes vs. no in detection). In the pMFC, vmPFC, ventral striatum and precuneus ROIs, the parametric effect of confidence failed to show a significant difference between the two tasks (all p-values>0.3), between the two discrimination responses (all p-values>0.24), or between the two detection responses (all p-values>0.09). Similarly, no cluster within the pre-specified frontopolar ROI showed a differential effect of confidence as a function of task or response. We show below that this absence of a linear interaction should not be taken as evidence of absence of differences between detection and discrimination, due to the presence of nonlinear interaction effects. In the next section we first explain the analysis steps we took to uncover nonlinear effects of confidence. Interaction of nonlinear confidence effects with task and response An exploratory whole brain analysis (p<0.05, corrected for multiple comparisons at the cluster-level) revealed no differential confidence effect as a function of task anywhere in the brain. However, within detection, whole-brain analysis revealed that the linear effect of confidence was significantly more negative for no compared to yes responses in the right temporo-parietal junction (rTPJ: 101 voxels, peak voxel: [54,-46, 26], z = 5.10). To further characterize the nature of the interaction between confidence and response in the rTPJ, we fitted a new design matrix for each task (Categorical-Confidence Design Matrices (post-hoc analysis; CC-DM)) where confidence was represented as a categorical variable with 6 levels instead of one parametric modulator. In contrast to our original design matrix (Main Design Matrix (DM-1)) that assumed a linear effect of confidence, this analysis is agnostic as to the functional form of the confidence effect. We then plotted the mean activation level for each combination of response and confidence level in the rTPJ cluster (see Figure 3, panel c). The categorical-confidence design matrix revealed a positive quadratic effect of confidence on activation levels in the rTPJ, with stronger activation levels for the two extremities of the confidence scale. We confirmed the presence of a significant quadratic effect of confidence in this region by fitting a second-order polynomial to the response-specific confidence curve of each participant (see Materials and methods). This analysis revealed a main quadratic effect of confidence in this region (t(34) = 5.21, p<0.00001), an effect which was stronger in detection compared to discrimination (t(34)=2.06, p<0.05, d = 0.35). Importantly, the linear interaction of confidence with detection responses remained significant for this quadratic model, establishing that this response-specific effect is not explained by an overall quadratic pattern (t(33)=2.09, p<0.05, d = 0.36 ; see Figure 3). More generally, these analyses make clear that linear effects of parametric modulators and their interactions are not exhaustive in their characterization of the confidence-related BOLD response – in this region and potentially in our other ROIs too. To formally test for such nonlinear differences in the activation profile of other ROIs, we extracted the coefficients from the categorical model for each ROI, and fitted a second-order polynomial to the ensuing confidence-related response. Within our a priori ROIs, no quadratic effect of confidence was observed in the pMFC, the precuneus, the ventral striatum, or the vmPFC (Appendix 5—figure 1). In contrast, in all three anatomical subregions of the frontopolar cortex, we found a positive quadratic effect of confidence, with stronger activations for the two extremities of the confidence scale. Strikingly, in both the FPl and the FPm, this positive quadratic effect of confidence was entirely driven by the detection task (FPm: t(34)=3.04, p<0.005, d = 0.51; FPl: t(34)=3.90, p<0.001, d = 0.66; see Figure 4). Confidence ratings for the discrimination task however showed a quadratic effect that was not statistically different from zero (FPm: t(34)=-0.54, p=0.59, d = −0.09, BF01=6.61; FPl: t(34)=1.42, p=0.16, d = 0.24, BF01=2.92). In the FPm, the linear effect of confidence was more negative for detection than for discrimination (t(34) = −2.11, d = −0.36, p<0.05), and within detection, more negative for confidence in judgments about absence (no responses; t(34) = 2.10, d = −0.36, p<0.05). Figure 4 Download asset Open asset Confidence effect as a function of response in the frontopolar cortex separated into its three anatomical subcomponents: FPm, FPl, and BA 46. Same conventions as in Figure 3c. * - p<0.05; uncorrected for multiple comparisons. Comparison lines above and below the x axis indicate main effect of response and task, respectively. Finally, to test for similar quadratic effects of confidence at the whole-brain level, we constructed a new design matrix (in a departure to our pre-registered analysis plan) in which confidence was modeled by a parametric modulator with a polynomial expansion of 2 (Quadratic-Confidence Design Matrix (post-hoc analysis; QC-DM)). Three clusters showed a significantly stronger quadratic effect of confidence in detection compared to discrimination (Figure 5). These were located in the right superior temporal sulcus (72 voxels, peak voxel: [60,-43,2], Z = 3.99), pre-SMA (130 voxels, peak voxel: [0,35,47], Z = 4.07), and right frontopolar cortex, overlapping with our FPl and FPm frontopolar anatomical subregions (51 voxels, peak voxel: [9,65,-10], Z = 4.00). Importantly, no region showed stronger quadratic effects of confidence in discrimination compared to detection. Figure 5 Download asset Open asset Left, top panel: a glass-brain representation of a contrast between the quadratic effects of confidence in detection and in discrimination, whole-brain corrected for family-wise error rate at the cluster-level (p<0.05) with a cluster-defining threshold of p<0.001, uncorrected. Remaining panels: mean betas from the categorical model for each of the four responses and six confidence ratings, for the three indicated clusters. The second-order polynomial coefficients for these estimates are presented below each plot. Significance is only indicated for the linear effects, which are orthogonal to the quadratic contrast used to select the clusters. * - p<0.05; ** - p<0.01. Comparison lines above and below the x axis indicate main effect of response and task, respectively. To visualize activity patterns in these regions, we extracted the mean coefficients from the categorical model for these three clusters, and fitted a second-order polynomial separately to each response estimate (see Figure 5). In addition to the effect of task on the quadratic effect of confidence in all three clusters, the linear effect of confidence in the right frontopolar cluster was significantly more negative for detection, compared to discrimination (t(34)=-3.13, d = −0.53, p<0.005). For both tasks, inter-subject variability in metacognitive efficiency (measured as meta-d'/d'; Maniscalco and Lau, 2012) was not reliably correlated with linear or quadratic parametric effect of confidence in any of the three regions (see Appendix 7). Computational models We next considered alternative computational-level explanations for the detection-specific quadratic activation profile. Specifically, we evaluated how latent model variables or belief states change non-linearly as a function of confidence in three candidate model architectures (see Figure 6): a static 'Signal Detection' model, a 'Dynamic Criterion' model where policy changes as a function of previous perceptual samples, and an 'Attention Monitoring' model in which beliefs about fluctuations in attention inform decisions and confidence judgments. A detailed formal description of the three models is available in the appendix (sections 9, 10 and 11). Figure 6 Download asset Open asset The three models (left) and their prediction for confidence effects (right). Top panel: In Signal Detection Theory, perceptual decisions and confidence ratings are generated by comparing the sensory evidence to a fixed set of criteria. In detection the 'signal' distribution is assumed to have higher variance. Plotting the absolute value of the log likelihood ratio as a function of decision and confidence results in a linear curve for discrimination, and a pronounced quadratic effect for yes responses in detection, an effect that is specific to unequal-variance SDT. Middle panel: In a Dynamic Criterion model beliefs about the mean and variance of the perceptual distributions are updated as a function of incoming samples (plotted as circles) and the decision criterion is shifted accordingly. Plotting the absolute change in criterion placement as a function of decision and confidence results in a quadratic effect of confidence for detection responses only. Bottom: In the Attention Monitoring model, beliefs about overall attentiveness ('onTask' node) probabilistically reflect sensory precision. Plotting beliefs about overall attentiveness as a function of decision and confidence results in an overall quadratic effect of confidence, and an interaction between yes and no responses in detection. For a detailed specification of all three models see appendix sections 9, 10 and 11. First, we consider the static Signal Detection Theory (SDT) model. In SDT models of confidence formation, the log likelihood-ratio between the two competing hypotheses (L⁢L⁢R=log⁡p⁢(x|S1)p⁢(x|S2)) is a useful measure for determining the certainty with which one should commit to a choice. The mapping between the perceptual sample x and the LLR is linear for equal-variance SDT, which is often used to model discrimination, but quadratic for unequal-variance SDT, which is often used to model detection. It then follows that if confidence is proportional to the distance of the sample x from the decision criterion, neuronal populations that represent the relative likelihood of a choice being correct (be it LLR or an analogue quantity) will show a quadratic tuning function of confidence in detection and a linear tuning function in discrimination, similar to that observed in FPC, pre-SMA and STS. However, LLR is also expected to scale more strongly with confidence in yes responses (see simulation results in Figure 6, upper panel), which was not observed in these brain regions. This model also predicts a stronger quadratic effect of confidence in participants for which the variance ratio between the signal and noise distributions is particularly high. However, the variance ratio was not significantly correlated with the quadratic effect of confidence in any of these regions, as would be expected if they were representing LLR or a similar quantity (see Appendix 6—figure 1). For the next two models, confidence was assumed to be directly proportional to the LLR, with the measured signal representing internal beliefs about hidden model parameters. In the 'Dynamic Criterion' model, we considered whether a quadratic effect of confidence in detection may reflect the active tuning of decision policy in the absence of explicit feedback (Guggenmos et al., 2016; Ko and Lau, 2012). In the model, beliefs about the underlying distributions are updated on a trial-to-trial basis, and in turn affect the placement of decision criterion (for a formal description of the model, see Appendix section 10). The Dynamic Criterion model predicts that the magnitude of shift in decision criterion will display a positive quadratic relation to confidence (LLR) in detection but not discrimination (see simulation results in Figure 6, middle panel). This is because the problem is asymmetric in detection, and decision policy should depend on beliefs about both sensory precision (or the relative variance of the noise and signal distribution) and expected signal strength (mean of the signal distribution), which is not the case for a symmetric discrimination problem. Notably, the pattern of criterion shifts in the Dynamic Criterion model resembled the task-specific effect of confidence in the FPC, STS and pre-SMA. As a post-hoc test of a role for these regions in criterion adjustment, we examined sequential pairs of trials of the same stimulus category (for example, a signal present trial that was followed by a signal present trial), and contrasted 'repeat' trials with 'switch' trials (for example, [yes, yes] vs. [yes, no]). The Dynamic Criterion model predicts stronger activation in switch compared to stay trials in both detection and discrimination. The FPl showed a weak effect in this direction (t = 2.03, p=0.05, d = 0.34), whereas FPm, pre-SMA, right BA10 and STS did not (all p-values>0.15). Finally, we considered a higher-order 'Attention Monitoring' model in which beliefs about one's current attentional state (precision or inverse variance in SDT) are taken into account when making perceptual decisions and confidence ratings on detection trials. This model formalizes the notion that after not detecting a target the participant may ask 'Given my current attentional state, would I have missed the target?'. The Attention Monitoring model thus makes different predictions for confidence in detection 'target absent' (no) responses, where the participant is assumed to reflect on the detection-likelihood of hypothetical targets, compared to 'target present' (yes) responses, similar to the activation profile observed in the rTPJ. However, this model also predicts a pronounced quadratic confidence profile for all four responses, which we do not see in our data. Discussion Previous studies of the neural basis of human perceptual decision-making have tended to focus on discrimination judgments, such as sorting stimuli into category A or B. The general computational architecture supporting discrimination judgments can be naturally extended to support detection (for instance, within signal detection theory). However, computational considerations and behavioral findings suggest that forming confidence in detection judgments may rest on qualitatively distinct cognitive and neural processes in comparison to generating confidence in discrimination judgments. To test for such differences, here we acquired fun