The human brain has the capacity to produce intelligent and creative behavior in a complex and dynamic environment. However, the complexity by which this is achieved also bears the risk of behavioral errors (Reason, 1990). To guarantee an efficient
performance nevertheless, the cognitive system has mechanisms at its disposal which continually monitor ongoing behavior. The present work investigates how these behavior monitoring mechanisms enable error detection, and how this information can be used for initiating behavioral adjustments to reduce and/or compensate for unwanted consequences of errors.
Behavior monitoring is often described as a feedback loop. According to this idea, ongoing behavior is continually monitored for critical events. When the system detects the occurrence of a critical event, control processes are triggered which counteract the occurrence of further critical events. A prominent example of such a theory is the so-called conflict monitoring theory (Botvinick, Braver, Barch, Carter, & Cohen, 2001).
According to this theory, critical events are indicated by the presence of response conflict.
The following every day situation illustrates this process. Assume that your intention is to go straight ahead in a car at a crossroad. There are two traffic lights: One for the lane heading straight ahead, and another one located just besides for a lane heading to the right. Both traffic lights are red and you have to stop and wait. Suddenly, the traffic light for the lane heading to the right turns green. The correct response to the relevant stimulus – the red traffic light for the lane heading straight ahead – is to stand still.
Nevertheless, the irrelevant stimulus – the green traffic light for the lane heading to the right – triggers the response tendency to start the car. This creates a conflict between the two opposing response tendencies. When the system detects such a conflict between two or more response tendencies, processes are initiated which counteract this conflict.
In our example, an enhancement of selective attention on the relevant stimulus could amplify the correct response tendency to stand still and thus avoid the occurrence of an error.
The concept of behavior monitoring as a feedback loop was applied also to error monitoring. Here, the monitoring system detects an error in ongoing behavior and then initiates behavioral adjustments. However, there are different views as to which
mechanisms enable error detection. Again, a possible explanation is offered by the conflict monitoring theory. This theory states that error detection is enabled by the detection of a post-response conflict which arises because the correct response becomes activated during continued processing of the stimulus after an error (Yeung, Botvinick,
& Cohen, 2004).
In the present work, the conflict monitoring theory is to be contrasted with the response monitoring account as an alternative theory of error detection. This account posits that error detection is enabled by the detection of internal error corrections. These internal error corrections occur when the activation of the correct response reaches a certain threshold during continued processing after an error (Rabbitt & Vyas, 1981;
Steinhauser, Maier, & Hübner, 2008). To test which account is better suited to explain error detection, study [1] investigated behavioral measures of conscious error detection and implementations of both accounts within the computational model by Yeung et al.
(2004).
Furthermore, the question was investigated as to how behavioral measures of error detection are related to an electrophysiological correlate of error processing, the so-called error negativity/error-related negativity (Ne/ERN). This negative deflection in the event-related potential (ERP) peaks shortly after erroneous responses over
fronto-central brain areas (Falkenstein, Hohnsbein, Hoormann, & Blanke, 1990; 1991;
Gehring, Goss, Coles, Meyer, & Donchin, 1993) and is probably generated in the anterior cingulate cortex (ACC; Dehaene, Posner, & Tucker, 1994; Ullsperger & von Cramon, 2001; van Veen & Carter, 2002a). However, there is a controversial debate as to which processes the Ne/ERN represents.
In the present work, two classes of Ne/ERN theories will be distinguished, error detection theories of the Ne/ERN and error evaluation theories of the Ne/ERN. Error detection theories of the Ne/ERN hold that the Ne/ERN represents the information necessary for error detection. An important member of this class is the conflict
monitoring theory (Yeung et al., 2004), which assumes that the Ne/ERN represents the amount of post-response conflict and thus the same information that also enables error
detection. Another member of this class is the mismatch hypothesis (Bernstein,
Scheffers, & Coles, 1995; Falkenstein, Hoormann, Christ, & Hohnsbein, 2000), which posits that the Ne/ERN reflects the amount of mismatch between actual and intended behavior. This amount of mismatch also serves as indicator that an error has occurred.
From these error detection theories of the Ne/ERN, it can be predicted that the Ne/ERN amplitude correlates with the detectability of errors, because both are based on the same source of information.
Error evaluation theories of the Ne/ERN assume that the Ne/ERN reflects the significance of errors for behavioral adjustments. One representative of this class of theories is, for instance, the so-called reinforcement learning theory (Holroyd & Coles, 2002). This theory holds that errors are detected by certain mid brain structures like the basal ganglia. These convey dopaminergic error signals to the ACC which then guides motor structures to optimize task performance through reinforcement learning. The Ne/ERN is regarded as a correlate of this reinforcement learning signal by the ACC (Holroyd & Coles, 2002; Holroyd & Coles, 2008). Another error evaluation account posits that the Ne/ERN reflects the significance of errors for ongoing behavior (Hajcak, Moser, Yeung, & Simons, 2005). From error evaluation theories, one would not predict that the Ne/ERN reflects the detectability of errors, because an error can well be
difficult to detect and at the same time highly relevant for the optimization of ongoing behavior.
To test the prediction of error detection theories of the Ne/ERN that the Ne/ERN reflects error detectability, studies [2] and [3] compared behavioral measures of error detection with the Ne/ERN. The evidence raises considerable objections against error detection theories of the Ne/ERN. However, the results of the present work are in accordance with error evaluation theories of the Ne/ERN.