Decision processes in human performance monitoring

Abstract

The ability to detect and compensate for errors is crucial in producing effective, goal-directed behavior. Human error processing is reflected in two event-related brain potential components, the error-related negativity (Ne/ERN) and error positivity (Pe), but the functional significance of both components remains unclear. Our approach was to consider error detection as a decision process involving an evaluation of available evidence that an error has occurred against an internal criterion. This framework distinguishes two fundamental stages of error detection--accumulating evidence (input), and reaching a decision (output)--that should be differentially affected by changes in internal criterion. Predictions from this model were tested in a brightness discrimination task that required human participants to signal their errors, with incentives varied to encourage participants to adopt a high or low criterion for signaling their errors. Whereas the Ne/ERN was unaffected by this manipulation, the Pe varied consistently with criterion: A higher criterion was associated with larger Pe amplitude for signaled errors, suggesting that the Pe reflects the strength of accumulated evidence. Across participants, Pe amplitude was predictive of changes in behavioral criterion as estimated through signal detection theory analysis. Within participants, Pe amplitude could be estimated robustly with multivariate machine learning techniques and used to predict error signaling behavior both at the level of error signaling frequencies and at the level of individual signaling responses. These results suggest that the Pe, rather than the Ne/ERN, is closely related to error detection, and specifically reflects the accumulated evidence that an error has been committed.

Figure 1.

Sequence of stimulus events in a typical trial. Participants were first required to indicate which of two boxes in the stimulus was brighter. Following the error prompt, they pressed a signaling key if they judged that their primary task response was incorrect.

Figure 2.

A simple decision model of error detection. A, Hypothetical error signals in a sequence of trials. The error detector takes decision evidence as input and determines the decision output by applying a decision criterion. B, Effects of criterion shift on decision evidence. Trials are now sorted left to right according to size of error signal, with hypothetical low (L) and high (H) criterion values shown in the left and right panels, respectively. The overall strength of error evidence (solid line) is unchanged, but stronger evidence is required for signaled errors (dashed line) in the high criterion condition. C, Effects of criterion shift on decision output. More errors are signaled in the low criterion case, so that overall decision output (solid line) is greater here than in the high criterion condition.

Figure 3.

Mean ERP waveforms at electrode FCz for errors and correct responses (upper row) and difference waves for errors minus correct responses (lower row), separately for the low criterion and high criterion conditions. Left column presents waveforms averaged across all trials. Right column presents averaged waveforms including data only from correctly signaled trials (hits and correct rejections). Shaded area indicates the time interval associated with the error negativity (Ne/ERN). Black arrows indicate the latency of the primary task response.

Figure 4.

Mean ERP waveforms at electrode CPz for errors and correct responses (top row) and difference waves for errors minus correct responses (bottom row), separately for the low criterion and high criterion conditions. Left column presents waveforms averaged across all trials. Right column presents averaged waveforms including data only from correctly signaled trials (hits and correct rejections). Shaded area indicates the time interval associated with the error positivity (Pe). Black arrows indicate the latency of the primary task response.

Figure 5.

Time course of spatial distribution of the difference between errors and correct responses, separately for all trials and correctly signaled trials for each criterion condition and for the difference between the two criterion conditions. Crit., Criterion.

Figure 6.

Correlations between the Pe contrast representing the criterion effect for correctly signaled trials minus the criterion effect for all trials, and behavioral estimates of criterion shift (left) and sensitivity shift (right). Scatter plots illustrate correlations at channel Pz.

Figure 7.

Time course of the extracted component of error-related brain activity, showing sensitivity (Az) of the classifier for discriminating errors and correct responses. Each time point represents the application of the analysis to a moving window of 100 ms width. Time windows close to the baseline interval (−150 to 50 ms) produced implausible values and were omitted. Orange and red points indicate Az classification values significantly above chance (orange: p < 0.05, red: p < 0.01). The topographies represent the distribution of component activity predicted by the classifier for the marked time periods representing the Ne/ERN (left) and Pe (right).

Figure 8.

Recovered distribution of error signals estimated by the prediction value of the classifier (top row) and raw ERP voltage at channel CPz (bottom row) for each trial. Left column, Separate distributions for correct trials and errors were constructed. Vertical lines indicate the estimated criterion values for the low criterion (L) and high criterion (H) conditions. Right column, Empirical (“data”) and predicted (“model”) frequencies of false alarms (FA), correct rejections (CRj), hits, and misses, for each condition.

Figure 9.

Classifier-based estimates of Pe amplitude for error trials sorted by value (signal strength), separately for each participant. Light gray indicates hits, dark gray indicates misses. The number in the upper left corner refers to the discrimination sensitivity between hits and misses (Az). Vertical lines are behaviorally estimated low (L) and high (H) criterion values. Horizontal dashed lines indicate mean of error signal.