A current challenge for decision-making research is in extending models of simple decisions to more complex and ecological choice situations. Conflict tasks (e.g., Simon, Stroop, Eriksen flanker) have been the focus of much interest, because they provide a decision-making context representative of everyday life experiences. Modeling efforts have led to an elaborated drift diffusion model for conflict tasks (DMC), which implements a superimposition of automatic and controlled decision activations. The DMC has proven to capture the diversity of behavioral conflict effects across various task contexts. This study combined DMC predictions with EEG and EMG measurements to test a set of linking propositions that specify the relationship between theoretical decision-making mechanisms involved in the Simon task and brain activity. Our results are consistent with a representation of the superimposed decision variable in the primary motor cortices. The decision variable was also observed in the EMG activity of response agonist muscles. These findings provide new insight into the neurophysiology of human decision-making. In return, they provide support for the DMC model framework.

The concept of “monitoring” refers to our ability to control our actions on-line. Monitoring involved in speech production is often described in psycholinguistic models as an inherent part of the language system. We probed the specificity of speech monitoring in two psycholinguistic experiments where electroencephalographic activities were recorded. Our focus was on a component previously reported in nonlinguistic manual tasks and interpreted as a marker of monitoring processes. The error negativity (Ne, or error-related negativity), thought to originate in medial frontal areas, peaks shortly after erroneous responses. A component of seemingly comparable properties has been reported, after errors, in tasks requiring access to linguistic knowledge (e.g., speech production), compatible with a generic error-detection process. However, in contrast to its original name, advanced processing methods later revealed that this component is also present after correct responses in visuomotor tasks. Here, we reported the observation of the same negativity after correct responses across output modalities (manual and vocal responses). This indicates that, in language production too, the Ne reflects on-line response monitoring rather than error detection specifically. Furthermore, the temporal properties of the Ne suggest that this monitoring mechanism is engaged before any auditory feedback. The convergence of our findings with those obtained with nonlinguistic tasks suggests that at least part of the monitoring involved in speech production is subtended by a general-purpose mechanism.

The ability to stop ongoing motor responses in a split-second is a vital element of human cognitive control and flexibility that relies in large part on prefrontal cortex. We used the stop-signal paradigm to elucidate the engagement of primary motor cortex (M1) in inhibiting an ongoing voluntary motor response. The stop-signal paradigm taps the ability to flexibly countermand ongoing voluntary behavior upon presentation of a stop signal. We applied single-pulse TMS to M1 at several intervals following the stop signal to track the time course of excitability of the motor system related to generating and stopping a manual response. Electromyography recorded from the flexor pollicis brevis allowed quantification of the excitability of the corticospinal tract and the involvement of intracortical GABA B ergic circuits within M1, indexed respectively by the amplitude of the motor-evoked potential and the duration of the late part of the cortical silent period (SP). The results extend our knowledge of the neural basis of inhibitory control in three ways. First, the results revealed a dynamic interplay between response activation and stopping processes at M1 level during stop-signal inhibition of an ongoing response. Second, increased excitability of inhibitory interneurons that drives SP prolongation was evident as early as 134 msec following the instruction to stop. Third, this pattern was followed by a stop-related reduction of corticospinal excitability implemented around 180 after the stop signal. These findings point to the recruitment of GABA B ergic intracortical inhibitory circuits within M1 in stop-signal inhibition and support the notion of stopping as an active act of control.

Our ability to detect and correct errors is essential for our adaptive behavior. The conflict-loop theory states that the anterior cingulate cortex (ACC) plays a key role in detecting the need to increase control through conflict monitoring. Such monitoring is assumed to manifest itself in an electroencephalographic (EEG) component, the “error negativity” ( N e or “error-related negativity” [ERN]). We have directly tested the hypothesis that the ACC monitors conflict through simulation and experimental studies. Both the simulated and EEG traces were sorted, on a trial-by-trial basis, as a function of the degree of conflict, measured as the temporal overlap between incorrect and correct response activations. The simulations clearly show that conflict increases as temporal overlap between response activation increases, whereas the experimental results demonstrate that the amplitude of the N e decreases as temporal overlap increases, suggesting that the ACC does not monitor conflict. At a functional level, the results show that the duration of the N e depends on the time needed to correct (partial) errors, revealing an “on-line” modulation of control on a very short time scale.