SUMMARY
We described a simple, unifying framework for inhibitory control that serves as a comprehensive scaffold to integrate emotional
influences on cognitive processes. In our view, emotion can
be understood as additional context (e.g., interoceptive experience), which constrains and biases the computations in an
“ideal observer model” of inhibitory control. That is, the role
of affect in inhibitory control can be interpreted in terms
of well-understood computational aspects of cognition such
as beliefs, action valuation and choice. Thus, emotion may
affect inhibitory behavior by biasing (a) prior expectations
and associated changes in internal beliefs about various taskrelevant events, and (b) action/outcome valuation (see Figure 4).
Importantly, on the basis of behavioral and neural data, the
framework highlights a strong interdependence between the
appetitive/aversive nature of emotional states and basic action
tendencies that are intrinsic to inhibitory control. Thus, we surmise that the valence dimension may have primary influences
on action parameters associated with approach and inhibition
(action constraint), and exert valence congruent influences on
outcome valuation and expectancies. In contrast, arousal may
have a more selective role in biasing expectancies of action cancellation. In addition, we argue that higher levels of arousal
may more indirectly modulate the computational processes
supporting inhibitory function by redirecting attention away
from task-relevant information and generally impairing prefrontal function and related computational mechanisms. Our
theoretical framework has some limitations inherent to the
challenge of testing these hypotheses. For instance, the separate effect of valence and arousal are difficult to disentangle in both experimental settings and affective disorders. The
breadth of individual variability in the experience and regulation of emotion make these potential effects further difficult to
pinpoint.
With regard to the potential impact of emotion on sensory disambiguation, we have emphasized the contribution of outcome
and action expectancies (i.e., prior distributions associated with
valence congruent events and trial type). However, we should
note that more downstream effects of emotion have been documented. For instance, valence and arousal have been shown to
modulate visual processing style (i.e., global vs. detail) and selective attention (e.g., breadth of attentional focus; (Loftus et al.,
1987; Basso et al., 1996; Gasper and Clore, 2002). Although outside the scope of this review, modeling potential biases in sensory
input parameters (e.g., sensory input mixing factors) may capture additional aspects of the interaction between emotion and
inhibitory control.
Finally, an equally important aspect of such emotioncognition interactions is the iterative nature of any emotioncognitions interactions. That is behavioral performance and the
dynamic feedback received when engaged in inhibitory control
tasks are likely to modulate emotional state. As a consequence,
the nature and types of biases impacting inhibitory control are
likely to emerge from the dynamic interaction between Bayesian
computation of response costs, selection of actions, and reception
of outcomes, which subsequently affect the Bayesian updating of
beliefs. These dynamic processes might be particularly relevant in
psychopathological conditions, which emerge over longer periods
of time.
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