Control adjustment costs limit goal flexibility: Empirical evidence and a computational account.

A cornerstone of human intelligence is the ability to flexibly adjust our cognition and behavior as our goals change. For instance, achieving some goals requires efficiency, while others require caution. Adapting to these changing goals require corresponding adjustments in cognitive control (e.g., levels of attention, response thresholds). However, adjusting our control to meet new goals comes at a cost: we are better at achieving a goal in isolation than when transitioning between goals. The source of these control adjustment costs remains poorly understood, and the bulk of our understanding of such costs comes from settings in which participants transition between discrete task sets, rather than performance goals. Across four experiments, we show that adjustments in continuous control states incur a performance cost, and that a dynamical systems model can explain the source of these costs. Participants performed a single cognitively demanding task under varying performance goals (e.g., to be fast or to be accurate). We modeled control allocation to include a dynamic process of adjusting from one's current control state to a target state for a given performance goal. By incorporating inertia into this adjustment process, our model accounts for our empirical findings that people under-shoot their target control state more (i.e., exhibit larger adjustment costs) when (a) goals switch rather than remain fixed over a block (Study 1); (b) target control states are more distant from one another (Study 2); (c) less time is given to adjust to the new goal (Study 3); and (d) when anticipating having to switch goals more frequently (Study 4). Our findings characterize the costs of adjusting control to meet changing goals, and show that these costs can emerge directly from cognitive control dynamics. In so doing, they shed new light on the sources of and constraints on flexibility in human goal-directed behavior.

Learning when effort matters: neural dynamics underlying updating and adaptation to changes in performance efficacy.

To determine how much cognitive control to invest in a task, people need to consider whether exerting control matters for obtaining rewards. In particular, they need to account for the efficacy of their performance-the degree to which rewards are determined by performance or by independent factors. Yet it remains unclear how people learn about their performance efficacy in an environment. Here we combined computational modeling with measures of task performance and EEG, to provide a mechanistic account of how people (i) learn and update efficacy expectations in a changing environment and (ii) proactively adjust control allocation based on current efficacy expectations. Across 2 studies, subjects performed an incentivized cognitive control task while their performance efficacy (the likelihood that rewards are performance-contingent or random) varied over time. We show that people update their efficacy beliefs based on prediction errors-leveraging similar neural and computational substrates as those that underpin reward learning-and adjust how much control they allocate according to these beliefs. Using computational modeling, we show that these control adjustments reflect changes in information processing, rather than the speed-accuracy tradeoff. These findings demonstrate the neurocomputational mechanism through which people learn how worthwhile their cognitive control is.

Guiding spatial attention by multimodal reward cues.

Our attention is constantly captured and guided by visual and/or auditory inputs. One key contributor to selecting relevant information from the environment is reward prospect. Intriguingly, while both multimodal signal processing and reward effects on attention have been widely studied, research on multimodal reward signals is lacking. Here, we investigated this using a Posner task featuring peripheral cues of different modalities (audiovisual/visual/auditory), reward prospect (reward/no-reward), and cue-target stimulus-onset asynchronies (SOAs 100-1,300 ms). We found that audiovisual and visual reward cues (but not auditory ones) enhanced cue-validity effects, albeit with different time courses (Experiment 1). While the reward-modulated validity effect of visual cues was pronounced at short SOAs, the effect of audiovisual reward cues emerged at longer SOAs. Follow-up experiments exploring the effects of visual (Experiment 2) and auditory (Experiment 3) reward cues in isolation showed that reward modulated performance only in the visual condition. This suggests that the differential effect of visual and auditory reward cues in Experiment 1 is not merely a result of the mixed cue context, but confirms that visual reward cues have a stronger impact on attentional guidance in this paradigm. Taken together, it seems that adding an auditory reward cue to the inherently dominant visual one led to a shift/extension of the validity effect in time - instead of increasing its amplitude. While generally being in line with a multimodal cuing benefit, this specific pattern highlights that different reward signals are not simply combined in a linear fashion but lead to a qualitatively different process.

Decomposing the motivation to exert mental effort.

Achieving most goals demands cognitive control, yet people vary widely in their success at meeting these demands. While motivation is known to be fundamental to determining these successes, what determines one's motivation to perform a given task remains poorly understood. Here, we describe recent efforts towards addressing this question using the Expected Value of Control model, which simulates the process by which people weigh the costs and benefits of exerting mental effort. By functionally decomposing this cost-benefit analysis, this model has been used to fill gaps in our understanding of the mechanisms of mental effort and to generate novel predictions about the sources of variability in real-world performance. We discuss the opportunities the model provides for formalizing hypotheses about why people vary in their motivation to perform tasks, as well as for understanding limitations in our ability to test these hypotheses based on a given measure of performance.

Dynamic Interplay between Reward and Voluntary Attention Determines Stimulus Processing in Visual Cortex.

Reward enhances stimulus processing in the visual cortex, but the mechanisms through which this effect occurs remain unclear. Reward prospect can both increase the deployment of voluntary attention and increase the salience of previously neutral stimuli. In this study, we orthogonally manipulated reward and voluntary attention while human participants performed a global motion detection task. We recorded steady-state visual evoked potentials to simultaneously measure the processing of attended and unattended stimuli linked to different reward probabilities, as they compete for attentional resources. The processing of the high rewarded feature was enhanced independently of voluntary attention, but this gain diminished once rewards were no longer available. Neither the voluntary attention nor the salience account alone can fully explain these results. Instead, we propose how these two accounts can be integrated to allow for the flexible balance between reward-driven increase in salience and voluntary attention.

Anatomy of a Psychological Theory: Integrating Construct-Validation and Computational-Modeling Methods to Advance Theorizing.

Discussions about the replicability of psychological studies have primarily focused on improving research methods and practices, with less attention paid to the role of well-specified theories in facilitating the production of reliable empirical results. The field is currently in need of clearly articulated steps to theory specification and development, particularly regarding frameworks that may generalize across different fields of psychology. Here we focus on two approaches to theory specification and development that are typically associated with distinct research traditions: computational modeling and construct validation. We outline the points of convergence and divergence between them to illuminate the anatomy of a scientific theory in psychology-what a well-specified theory should contain and how it should be interrogated and revised through iterative theory-development processes. We propose how these two approaches can be used in complementary ways to increase the quality of explanations and the precision of predictions offered by psychological theories.

A computational perspective on the roles of affect in cognitive control.

Previous work has demonstrated that cognitive control can be influenced by affect, both when it is tied to the anticipated outcomes for cognitive performance (integral affect) and when affect is induced independently of performance (incidental affect). However, the mechanisms through which such interactions occur remain debated, in part because they have yet to be formalized in a way that allows experimenters to test quantitative predictions of a putative mechanism. To generate such predictions, we leveraged a recent model that determines cognitive control allocation by weighing potential costs and benefits in order to determine the overall Expected Value of Control (EVC). We simulated potential accounts of how integral and incidental affect might influence this valuation process, including whether incidental positive affect influences how difficult one perceives a task to be, how effortful it feels to exert control, and/or the marginal utility of succeeding at the task. We find that each of these accounts makes dissociable predictions regarding affect's influence on control allocation and measures of task performance (e.g., conflict adaptation, switch costs). We discuss these findings in light of the existing empirical findings and theoretical models. Collectively, this work grounds existing theories regarding affect-control interactions, and provides a method by which specific predictions of such accounts can be confirmed or refuted based on empirical data.

Motivation and cognitive control in depression.

Depression is linked to deficits in cognitive control and a host of other cognitive impairments arise as a consequence of these deficits. Despite of their important role in depression, there are no mechanistic models of cognitive control deficits in depression. In this paper we propose how these deficits can emerge from the interaction between motivational and cognitive processes. We review depression-related impairments in key components of motivation along with new cognitive neuroscience models that focus on the role of motivation in the decision-making about cognitive control allocation. Based on this review we propose a unifying framework which connects motivational and cognitive control deficits in depression. This framework is rooted in computational models of cognitive control and offers a mechanistic understanding of cognitive control deficits in depression.

Temperamental factors in remitted depression: The role of effortful control and attentional mechanisms.

BACKGROUND: Temperamental effortful control and attentional networks are increasingly viewed as important underlying processes in depression and anxiety. However, it is still unknown whether these factors facilitate depressive and anxiety symptoms in the general population and, more specifically, in remitted depressed individuals. METHODS: We investigated to what extent effortful control and attentional networks (i.e., Attention Network Task) explain concurrent depressive and anxious symptoms in healthy individuals (n = 270) and remitted depressed individuals (n = 90). Both samples were highly representative of the US population. RESULTS: Increased effortful control predicted a substantial decrease in symptoms of both depression and anxiety in the whole sample, whereas decreased efficiency of executive attention predicted a modest increase in depressive symptoms. Remitted depressed individuals did not show less effortful control nor less efficient attentional networks than healthy individuals. Moreover, clinical status did not moderate the relationship between temperamental factors and either depressive or anxiety symptoms. LIMITATIONS: Limitations include the cross-sectional nature of the study. CONCLUSIONS: Our study shows that temperamental effortful control represents an important transdiagnostic process for depressive and anxiety symptoms in adults.

Individual differences in cognitive control over emotional material modulate cognitive biases linked to depressive symptoms.

Deficient cognitive control over emotional material and cognitive biases are important mechanisms underlying depression, but the interplay between these emotionally distorted cognitive processes in relation to depressive symptoms is not well understood. This study investigated the relations among deficient cognitive control of emotional information (i.e. inhibition, shifting, and updating difficulties), cognitive biases (i.e. negative attention and interpretation biases), and depressive symptoms. Theory-driven indirect effect models were constructed, hypothesising that deficient cognitive control over emotional material predicts depressive symptoms through negative attention and interpretation biases. Bootstrapping analyses demonstrated that deficient inhibitory control over negative material was related to negative attention bias which in turn predicted a congruent bias in interpretation and subsequently depressive symptoms. Both shifting and updating impairments in response to negative material had an indirect effect on depression severity through negative interpretation bias. No evidence was found for direct effects of deficient cognitive control over emotional material on depressive symptoms. These findings may help to formulate an integrated understanding of the cognitive foundations of depressive symptoms.

Mapping the interplay among cognitive biases, emotion regulation, and depressive symptoms.

Cognitive biases and emotion regulation (ER) difficulties have been instrumental in understanding hallmark features of depression. However, little is known about the interplay among these important risk factors to depression. This cross-sectional study investigated how multiple cognitive biases modulate the habitual use of ER processes and how ER habits subsequently regulate depressive symptoms. All participants first executed a computerised version of the scrambled sentences test (interpretation bias measure) while their eye movements were registered (attention bias measure) and then completed questionnaires assessing positive reappraisal, brooding, and depressive symptoms. Path and bootstrapping analyses supported both direct effects of cognitive biases on depressive symptoms and indirect effects via the use of brooding and via the use of reappraisal that was in turn related to the use of brooding. These findings help to formulate a better understanding of how cognitive biases and ER habits interact to maintain depressive symptoms.