Process overlap theory (POT) is a new theoretical framework designed to account for the general factor of intelligence (g). According to POT, g does not reflect a general cognitive ability. Instead, g is the result of multiple domain-general executive attention processes and multiple domain-specific processes that are sampled in an overlapping manner across a battery of intelligence tests. POT explains several benchmark findings on human intelligence. However, the precise nature of the executive attention processes underlying g remains unclear. In the current paper, we discuss challenges associated with building a theory of individual differences in attention and intelligence. We argue that the conflation of psychological theories and statistical models, as well as problematic inferences based on latent variables, impedes research progress and prevents theory building. Two studies designed to illustrate the unique features of POT relative to previous approaches are presented. In Study 1, a simulation is presented to illustrate precisely how POT accounts for the relationship between executive attention processes and g. In Study 2, three datasets from previous studies are reanalyzed (N = 243, N = 234, N = 945) and reveal a discrepancy between the POT simulated model and the unity/diversity model of executive function. We suggest that this discrepancy is largely due to methodological problems in previous studies but also reflects different goals of research on individual differences in attention. The unity/diversity model is designed to facilitate research on executive function and dysfunction associated with cognitive and neural development and disease. POT is uniquely suited to guide and facilitate research on individual differences in cognitive ability and the investigation of executive attention processes underlying g.

The domain of cognitive control has been a major focus of experimental, neuroscience, and individual differences research. Currently, however, no theory of cognitive control successfully unifies both experimental and individual differences findings. Some perspectives deny that there even exists a unified psychometric cognitive control construct to be measured at all. These shortcomings of the current literature may reflect the fact that current cognitive control paradigms are optimized for the detection of within-subject experimental effects rather than individual differences. In the current study, we examine the psychometric properties of the Dual Mechanisms of Cognitive Control (DMCC) task battery, which was designed in accordance with a theoretical framework that postulates common sources of within-subject and individual differences variation. We evaluated both internal consistency and test-retest reliability, and for the latter, utilized both classical test theory measures (i.e., split-half methods, intraclass correlation) and newer hierarchical Bayesian estimation of generative models. Although traditional psychometric measures suggested poor reliability, the hierarchical Bayesian models indicated a different pattern, with good to excellent test-retest reliability in almost all tasks and conditions examined. Moreover, within-task, between-condition correlations were generally increased when using the Bayesian model derived estimates, and these higher correlations appeared to be directly linked to the higher reliability of the measures. In contrast, between-task correlations remained low regardless of theoretical manipulations or estimation approach. Together, these findings highlight the advantages of Bayesian estimation methods, while also pointing to the important role of reliability in the search for a unified theory of cognitive control.

Cognitive control serves a crucial role in human higher mental functions. The Dual Mechanisms of Control (DMC) account provides a unifying theoretical framework that decomposes cognitive control into two qualitatively distinct mechanisms – proactive control and reactive control. While prior behavioral and neuroimaging work has demonstrated the validity of individual tasks in isolating these two mechanisms of control, there has not been a comprehensive, theoretically-guided task battery specifically designed to tap into proactive and reactive control across different domains of cognition. To address this critical limitation and provide useful methodological tools for future investigations, the Dual Mechanisms of Cognitive Control (DMCC) task battery was developed to probe these two control modes, as well as their intra-individual and inter-individual differences, across four prototypical domains of cognition: selective attention, context processing, multi-tasking, and working memory. We present this task battery, along with detailed descriptions of the experimental manipulations used to encourage shifts to proactive or reactive control in each of the four task domains. We rigorously evaluate the group effects of these manipulations in primary indices of proactive and reactive control, establishing the validity of the DMCC task battery in providing dissociable yet convergent measures of the two cognitive control modes.

The domain of cognitive control has been a major focus of experimental, neuroscience, and individual differences research. Currently, however, no theory of cognitive control successfully unifies both experimental and individual differences findings. Some perspectives deny that there even exists a unified psychometric cognitive control construct to be measured at all. These shortcomings of the current literature may reflect the fact that current cognitive control paradigms are optimized for the detection of within-subject experimental effects rather than individual differences. In the current study, we examine the psychometric properties of the Dual Mechanisms of Cognitive Control (DMCC) task battery, which was designed in accordance with a theoretical framework that postulates common sources of within-subject and individual differences variation. We evaluated both internal consistency and test–retest reliability, and for the latter, utilized both classical test theory measures (i.e., split-half methods, intraclass correlation) and newer hierarchical Bayesian estimation of generative models. Although traditional psychometric measures suggested poor reliability, the hierarchical Bayesian models indicated a different pattern, with good to excellent test–retest reliability in almost all tasks and conditions examined. Moreover, within-task, between-condition correlations were generally increased when using the Bayesian model-derived estimates, and these higher correlations appeared to be directly linked to the higher reliability of the measures. In contrast, between-task correlations remained low regardless of theoretical manipulations or estimation approach. Together, these findings highlight the advantages of Bayesian estimation methods, while also pointing to the important role of reliability in the search for a unified theory of cognitive control.

Cognitive control serves a crucial role in human higher mental functions. The Dual Mechanisms of Control (DMC) account provides a unifying theoretical framework that decomposes cognitive control into two qualitatively distinct mechanisms – proactive control and reactive control. While prior behavioral and neuroimaging work has demonstrated the validity of individual tasks in isolating these two mechanisms of control, there has not been a comprehensive, theoretically-guided task battery specifically designed to tap into proactive and reactive control across different domains of cognition. To address this critical limitation and provide useful methodological tools for future investigations, the Dual Mechanisms of Cognitive Control (DMCC) task battery was developed to probe these two control modes, as well as their intra-individual and inter-individual differences, across four prototypical domains of cognition: selective attention, context processing, multi-tasking, and working memory. We present this task battery, along with detailed descriptions of the experimental manipulations used to encourage shifts to proactive or reactive control in each of the four task domains. We rigorously evaluate the group effects of these manipulations in primary indices of proactive and reactive control, establishing the validity of the DMCC task battery in providing dissociable yet convergent measures of the two cognitive control modes.