Traditional theories of moral development emphasize the role of controlled cognition in mature moral judgment, while a more recent trend emphasizes intuitive and emotional processes. Here we test a dual-process theory synthesizing these perspectives. More specifically, our theory associates utilitarian moral judgment (approving of harmful actions that maximize good consequences) with controlled cognitive processes and associates non-utilitarian moral judgment with automatic emotional responses. Consistent with this theory, we find that a cognitive load manipulation selectively interferes with utilitarian judgment. This interference effect provides direct evidence for the influence of controlled cognitive processes in moral judgment, and utilitarian moral judgment more specifically.

A growing body of evidence from functional neuroimaging and computational modeling studies indicates that the anterior cingulate cortex (ACC) detects the presence of response conflict and conveys this information to other brain regions, enabling subsequent adjustments in cognitive control. The present study examined previous empirical findings of increased ACC for low-frequency stimuli across three distinct speeded response tasks (two-alternative forced choice, go/no-go, and oddball). Simulations conducted in a neural network model incorporating sequential priming mechanisms (developed in Cho et al., 2002) confirmed that a computational measure of response conflict was higher on low-frequency trials across all three tasks. In addition, the model captured detailed aspects of behavioral reaction time and accuracy data, predicted the dynamics of ACC activity related to trial sequence effects, and provided evidence for the functional role of conflict information in performance monitoring and optimization. The results indicate that the conflict-monitoring hypothesis, augmented by mechanisms for encoding stimulus history, can explain key phenomena associated with performance in sequential speeded response tasks.

The anterior cingulate cortex (ACC), on the medial surface of the frontal lobes of the brain, is widely believed to be involved in the regulation of attention1,2. Beyond this, however, its specific contribution to cognition remains uncertain. One influential theory has interpreted activation within the ACC as reflecting ‘selection-for-action’3,4,5, a set of processes that guide the selection of environmental objects as triggers of or targets for action. We have proposed an alternative hypothesis, in which the ACC serves not to exert top-down attentional control but instead to detect and signal the occurrence of conflicts in information processing6,7,8. Here, to test this theory against the selection-for-action theory, we used functional magnetic resonance imaging to measure brain activation during performance of a task where, for a particular subset of trials, the strength of selection-for-action is inversely related to the degree of response conflict. Activity within the ACC was greater during trials featuring high levels of conflict (and weak selection-for-action) than during trials with low levels of conflict (and strong selection-for-action), providing evidence in favour of the conflict-monitoring account of ACC function.

Multivariate pattern analysis (MVPA) is a relatively recent innovation in functional magnetic resonance imaging (fMRI) methods. MVPA is increasingly widely used, as it is apparently more effective than classical general linear model analysis (GLMA) for detecting response patterns or representations that are distributed at a fine spatial scale. However, we demonstrate that widely used approaches to MVPA can systematically admit certain confounds that are appropriately eliminated by GLMA. Thus confounds rather than distributed representations may explain some cases in which MVPA produced positive results but GLMA did not. The issue is that it is common practice in MVPA to conduct group tests on single-subject summary statistics that discard the sign or direction of underlying effects, whereas GLMA group tests are conducted directly on single-subject effects themselves. We describe how this common MVPA practice undermines standard experiment design logic that is intended to control at the group level for certain types of confounds, such as time on task and individual differences. Furthermore, we note that a simple application of linear regression can restore experimental control when using MVPA in many situations. Finally, we present a case study with novel fMRI data in the domain of rule representations, or flexible stimulus–response mappings, which has seen several recent MVPA publications. In our new dataset, as with recent reports, standard MVPA appears to reveal rule representations in prefrontal cortex regions, whereas GLMA produces null results. However, controlling for a variable that is confounded with rule at the individual-subject level but not the group level (reaction time differences across rules) eliminates the MVPA results. This raises the question of whether recently reported results truly reflect rule representations, or rather the effects of confounds such as reaction time, difficulty, or other variables of no interest.