The Timescale of Control: A Meta-Control Property that Generalizes across Tasks but Varies between Types of Control

Abstract

Prominent models of control assume that conflict and the probability of conflict are signals used by control processes that regulate attention. For example, when conflict is frequent across preceding trials (i.e., high probability of conflict), control processes bias attention toward goal-relevant information on subsequent trials. An important but underspecified question regards the metacontrol property of timescale-that is, how far back does the control system "look" to determine the probability of conflict? To address this question, Aben, Verguts, and Van den Bussche ( 2017) developed a statistical model quantifying the timescale of control. In a flanker task, they observed short timescales for lists with a low probability of conflict (which induce reactive control) and long timescales for lists with a high probability of conflict (which induce proactive control). To investigate the domain generality of these timescales, we applied their model to two additional conflict tasks that manipulated the list-wide probability of conflict. Our findings replicated Aben et al. suggesting meta-control may be task general with respect to timescales operating on the list level. We subsequently modified their model to examine timescale differences for items in the same list that differed in their probability of conflict but not the type of control engaged. We failed to detect a difference in timescales between items. Collectively, the findings demonstrate that differences in the timescale of control are task general and suggest that timescale differences are driven by the type of control engaged and not by the probability of conflict per se.

Meta-control: From psychology to computational neuroscience

“…Participants are assumed to engage in reactive or proactive control if the overall likelihood of response conflict is low or high, respectively. Dey and Bugg (2021) show that participants integrate more recent experience into their behavior (i.e., adopt a higher learning rate) when they are expected to engage reactive control. Conversely, the authors show that participants consider a longer history of trials (i.e., adopt a smaller learning rate) in task environments that promote proactive control.…”

“…Thus, the stabilityflexibility dilemma also applies to the integration of information over time: Higher learning rates allow for the efficient integration of recent experiences at the expense of forgetting (overlearning) older information. Dey and Bugg (2021) propose that different strategies of control-in this case, reactive versus proactive control-rely on different time scales for integrating past information, resulting in different learning rates (Dey & Bugg, 2021). They deploy a statistical model (Aben et al, 2017) to analyze published data of three Stroop experiments in which the probability of conflict was manipulated.…”

“…For example, we may have to adjust how much control to deploy depending on the frequency of response conflict in the Stroop task (for a review see Bugg, 2012), considering that exerting cognitive control is associated with a cost (Shenhav et al, 2017). Similarly, in task environments that require frequent switching between tasks, participants may invoke control strategies that promote cognitive flexibility at the expense of cognitive stability (Braem, 2017;Dreisbach & Fröber, 2019;Goschke, 2000;Mayr, 2006;Monsell & Mizon, 2006;Musslick et al, 2019). Borrowing terminology from machine learning and artificial intelligence (AI), we define the subset of control mechanisms responsible for the monitoring and regulation of cognitive control itself as meta-control (Fig.…”

“…Note that meta-control processes can operate over different timescales and may be subject to other meta-control processes themselves: one meta-control process (e.g., the regulation of the amount of control applied to a single trial of the Stroop task) may be the target of another meta-control process (e.g., the regulation of the overall amount of cognitive stability versus flexibility across an experiment). Furthermore, as suggested by some of the work featured in this special issue (Bustamante et al, 2021;Dey & Bugg, 2021;Siqi-Liu & Egner, 2020), meta-control itself can be learned from experience (e.g., based on contextual features), without the requirement for external regulation.…”

“…The target of regulation corresponds to the subject of meta-control, i.e., the set of control processes being monitored and regulated. For instance, in the Stroop task, participants may invoke meta-control to regulate the amount of proactive engagement in a control-dependent behavior (Bustamante et al, 2021;Dey & Bugg, 2021;Lieder & Iwama, 2021), whereas in a prospective memory task, meta-control is needed to regulate the trade-off between the requirement to shield a current task-goal from interfering stimuli and the requirement to monitor the environment for cues signaling that one should switch to a different task (Goschke & Dreisbach, 2008;Möschl et al, 2020). However, meta-control also may regulate more complex control policies that determine exploratory versus exploitative behavior (Marković et al, 2021).…”