Allen Newell (1973) once observed that psychology researchers were playing “twenty questions with nature,” carving up human cognition into hundreds of individual phenomena but shying away from the difficult task of integrating these phenomena with unifying theories. We argue that research on cognitive control has followed a similar path, and that the best approach toward unifying theories of cognitive control is that proposed by Newell, namely developing theories in computational cognitive architectures. Threaded cognition, a recent theory developed within the ACT-R (...) cognitive architecture, offers promise as a unifying theory of cognitive control that addresses multitasking phenomena for both laboratory and applied task domains. (shrink)

Recently, an associative learning account of cognitive control has been suggested (Verguts & Notebaert, 2009). In this so-called adaptation by binding theory, Hebbian learning of stimulus–stimulus and stimulus–response associations is assumed to drive the adaptation of human behavior. In this study, we evaluated the validity of the adaptation-by-binding account for the case of implicit learning of regularities within a stimulus set (i.e., the frequency of specific unit digit combinations in a two-digit number magnitude comparison task) and their association with a (...) particular response. Our data indicated that participants indeed learned these regularities and adapted their behavior accordingly. In particular, influences of cognitive control were even able to override the numerical distance effect—one of the most robust effects in numerical cognition research. Thus, the general cognitive processes involved in two-digit number magnitude comparison seem much more complex than previously assumed. Multi-digit number magnitude comparison may not be automatic and inflexible but influenced by processes of cognitive control being highly adaptive to stimulus set properties and task demands on multiple levels. (shrink)

This commentary proposes that “cognitive control” is neither componential nor emergent, but a fundamental feature of behavior. The term “control” requires an operational definition. This is best provided by the negative feedback loop that utilizes behavior to control perception; it does not control behavior per se. In order to model complex cognitive control, Perceptual Control Theory proposes that loops are organized into a dissociable hierarchical network (PCT; Powers, Clark, & McFarland, 1960; Powers, 1973a, 2008). In this way, behavior is dynamically (...) adaptive to environmental disturbances, rather than being formed by, or superimposed upon, learned associations between stimulus and response. (shrink)

In this commentary, I will argue that the componential and emergent views of cognitive control as defined by Cooper (2010) do not necessarily oppose each other, and I will try to make a case for their interdependence. First, I will use the construct of cognitive inhibition—one of the main componential control functions mentioned in the target articles—to illustrate my line of reasoning. Then, I will comment on how some of the target articles, each from a different perspective, bring arguments in (...) favor of this integrative view. (shrink)

Several authors assume that evaluative conditioning (EC) relies on high-level propositional thinking. In contrast, the dual-process perspective proposes two processing pathways, one associative and the other propositional, contributing to EC. Dual-process theorists argue that attitudinal ambiguity resulting from these two pathways’ conflicting evaluations demonstrate the involvement of both automatic and controlled processes in EC. Previously, we suggested that amplitude variations of error-related negativity and error-positivity, two well-researched event-related potentials of performance monitoring, allow for the detection of attitudinal ambiguity at the (...) neural level. The present study utilises self-reported evaluation, categorisation performance, and neural correlates of performance monitoring to explore associative-propositional ambiguity during social attitude formation. Our results show that compared to associative-propositional harmony, attitudinal ambiguity correlates with more neutral subjective evaluations, longer response times, increased error commission, and diminished error-related negativity amplitudes. While our findings align with dual-process models, we aim to offer a propositional interpretation. We discuss dual-process theories in the context of evolutionary psychology, suggesting that associative processes may only represent a small piece of the EC puzzle. (shrink)

In this commentary, I focus on the difference between processes and representations and how this distinction relates to the question of what is controlled. Despite some views that task switching is a prototypical control process, the analysis concludes that task switching depends on the task goal representation and that control processes are there to prevent goal representations from disintegrating. Over time, these processes become obsolete, leaving behind a representation that automatically controls task performance. The distinction between processes and representations relates (...) to practice effects and automaticity and sheds light on what is meant by the phrase “automatic control.”. (shrink)

Cognitive control is not only componential, but those components may interact in complicated ways in the service of cognitive control tasks. This complexity poses a challenge for developing an ontological description, because the mapping may not be direct between our task descriptions and true component differences reflected in indicators. To illustrate this point, I discuss two examples: (a) the relationship between adaptive gating and working memory and (b) the recent evidence for a control hierarchy. From these examples, I argue that (...) an ontological program must simultaneously seek to identify component processes and their interactions within a broader processing architecture. (shrink)

In the past two decades, reinforcement learning has become a popular framework for understanding brain function. A key component of RL models, prediction error, has been associated with neural signals throughout the brain, including subcortical nuclei, primary sensory cortices, and prefrontal cortex. Depending on the location in which activity is observed, the functional interpretation of prediction error may change: Prediction errors may reflect a discrepancy in the anticipated and actual value of reward, a signal indicating the salience or novelty of (...) a stimulus, and many other interpretations. Anterior cingulate cortex has long been recognized as a region involved in processing behavioral error, and recent computational models of the region have expanded this interpretation to include a more general role for the region in predicting likely events, broadly construed, and signaling deviations between expected and observed events. Ongoing modeling work investigating the interaction between ACC and additional regions involved in cognitive control suggests an even broader role for cingulate in computing a hierarchically structured surprise signal critical for learning models of the environment. The result is a predictive coding model of the frontal lobes, suggesting that predictive coding may be a unifying computational principle across the neocortex. (shrink)