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BACKGROUND: Cognitive control mechanisms enable an individual to regulate, coordinate, and sequence thoughts and actions in order to obtain desirable outcomes. Cognitive control is typically conceptualized as dual processes that occur in frontoparietal regions: proactive control, which uses sustained activation to enact anticipatory planning and goal maintenance, and reactive control, which entails retrieving information as presently needed (Braver, 2012), with patients with schizophrenia being especially susceptible to proactive control impairments (Poppe et al., 2016). However, nonhuman primate research suggests most prefrontal neurons ‘switch,’ firing during both proactive and reactive control, implying overlap between neural encoding of these two processes (Blackman et al., 2016). We sought to examine the overlapping neural circuitry of proactive and reactive control in healthy and patient populations using the Dot Pattern Expectancy Task (DPX). METHODS: 47 patients with schizophrenia (SZ) and 56 matched healthy controls (HC) completed 4 blocks of the DPX through the Cognitive Neuroscience Test Reliability and Clinical applications for Serious mental illness (CNTRaCS). During a 3-Tesla fMRI scan, participants followed the ‘X-then-Y’ rule, in which they were to press one button whenever an A cue was followed by an X probe, and another button for any other non-target stimulus sequence. Dissimilarity between proactive and reactive activation was evaluated within bilateral regions implicated in cognitive control: the medial frontal gyrus, superior frontal gyrus, and anterior cingulate cortex. Neuroimaging data was processed with FMRIB Software Library (FSL) packages. Probe accuracy and reaction time data were divided into ‘first half’ and ‘second half’ groups, depending on the block during which it occurred. RESULTS: Behavioral data analysis showed HC subjects showed a greater proclivity to engaging in proactive control across the study length than SZ subjects. HC subjects were also faster than SZ subjects in trials that required successful marshalling of proactive control. However, there was no within-subject increase in proactive proclivity or speed across the study procedure, complicating recent findings that suggest proactive control increases as a function of trial set length (Janowich & Cavanagh, 2018). ROI activation analysis showed no significant difference between HC and SZ proactive – reactive dissimilarity. Interestingly, within-ROI activation levels were significantly negative for both subject groups, implying these regions may be slightly more active during reactive processes. DISCUSSION: Results point to a between-group difference of relative strengths and weaknesses in proactive control, despite shared neural substrates. The lack of distinct ROI preference for proactive control offers support for the malleable nature of regions implicated in human cognitive control. Future analysis may investigate the association between proactive—reactive ROI dissimilarity and clinical and real-world functioning measures among patients.