A computational theory of the subjective experience of flow.

Flow is a subjective state characterized by immersion and engagement in one's current activity. The benefits of flow for productivity and health are well-documented, but a rigorous description of the flow-generating process remains elusive. Here we develop and empirically test a theory of flow's computational substrates: the informational theory of flow. Our theory draws on the concept of mutual information, a fundamental quantity in information theory that quantifies the strength of association between two variables. We propose that the mutual information between desired end states and means of attaining them - [Formula: see text] - gives rise to flow. We support our theory across five experiments (four preregistered) by showing, across multiple activities, that increasing [Formula: see text] increases flow and has important downstream benefits, including enhanced attention and enjoyment. We rule out alternative constructs including alternative metrics of associative strength, psychological constructs previously shown to predict flow, and various forms of instrumental value.

Using dynamic monitoring of choices to predict and understand risk preferences.

Navigating conflict is integral to decision-making, serving a central role both in the subjective experience of choice as well as contemporary theories of how we choose. However, the lack of a sensitive, accessible, and interpretable metric of conflict has led researchers to focus on choice itself rather than how individuals arrive at that choice. Using mouse-tracking-continuously sampling computer mouse location as participants decide-we demonstrate the theoretical and practical uses of dynamic assessments of choice from decision onset through conclusion. Specifically, we use mouse tracking to index conflict, quantified by the relative directness to the chosen option, in a domain for which conflict is integral: decisions involving risk. In deciding whether to accept risk, decision makers must integrate gains, losses, status quos, and outcome probabilities, a process that inevitably involves conflict. Across three preregistered studies, we tracked participants' motor movements while they decided whether to accept or reject gambles. Our results show that 1) mouse-tracking metrics of conflict sensitively detect differences in the subjective value of risky versus certain options; 2) these metrics of conflict strongly predict participants' risk preferences (loss aversion and decreasing marginal utility), even on a single-trial level; 3) these mouse-tracking metrics outperform participants' reaction times in predicting risk preferences; and 4) manipulating risk preferences via a broad versus narrow bracketing manipulation influences conflict as indexed by mouse tracking. Together, these results highlight the importance of measuring conflict during risky choice and demonstrate the usefulness of mouse tracking as a tool to do so.

Construal level shifts integration and segregation of the brain network.

The ability to expand and contract one's mental horizons allows people to regulate toward ends that are both distant and near. One challenge that people face when regulating toward distant relative to near ends is the lack of information about detailed specifics. In response, construal level theory (CLT) proposes that people engage in high-level construal-a representational process that highlights the essential properties of events that are invariant across potential instantiations. To tailor responses to more immediate events, however, CLT proposes that people engage in low-level construal-a representational process that highlights idiosyncratic specifics that distinguish events from one another. The present article uses network neuroscience to investigate the neurocognitive mechanisms for these representational processes. While undergoing fMRI, participants were instructed to think about the distant versus near future, and completed tasks that directly manipulated high-level versus low-level construal. Thinking about the distant future and engaging in high-level construal both promoted integration across the network (indexed by global efficiency). Thinking about the near future and engaging in low-level construal promoted segregation within the network (indexed by clustering coefficient). These are the first findings to document how the brain reconfigures to support the expansion versus contraction of one's mental horizons, and provides new insight into the neural mechanisms that help people regulate toward distant versus near ends. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

A consistent organizational structure across multiple functional subnetworks of the human brain.

A recurrent theme of both cognitive and network neuroscience is that the brain has a consistent subnetwork structure that maps onto functional specialization for different cognitive tasks, such as vision, motor skills, and attention. Understanding how regions in these subnetworks relate is thus crucial to understanding the emergence of cognitive processes. However, the organizing principles that guide how regions within subnetworks communicate, and whether there is a common set of principles across subnetworks, remains unclear. This is partly due to available tools not being suited to precisely quantify the role that different organizational principles play in the organization of a subnetwork. Here, we apply a joint modeling technique - the correlation generalized exponential random graph model (cGERGM) - to more completely quantify subnetwork structure. The cGERGM models a correlation network, such as those given in functional connectivity, as a function of activation motifs - consistent patterns of coactivation (i.e., connectivity) between collections of nodes that describe how the regions within a network are organized (e.g., clustering) - and anatomical properties - relationships between the regions that are dictated by anatomy (e.g., Euclidean distance). By jointly modeling all features simultaneously, the cGERGM models the unique variance accounted for by each feature, as well as a point estimate and standard error for each, allowing for significance tests against a random graph and between graphs. Across eight functional subnetworks, we find remarkably consistent organizational properties guiding subnetwork architecture, suggesting a fundamental organizational basis for subnetwork communication. Specifically, all subnetworks displayed greater clustering than would be expected by chance, but lower preferential attachment (i.e., hub use). These findings suggest that human functional subnetworks follow a segregated highway structure, in which tightly clustered subcommunities develop their own channels of communication rather than relying on hubs.

Minding One's Reach (To Eat): The Promise of Computer Mouse-Tracking to Study Self-Regulation of Eating.

In this review, we present the case for using computer mouse-tracking techniques to examine psychological processes that support (and hinder) self-regulation of eating. We first argue that computer mouse-tracking is suitable for studying the simultaneous engagement of-and dynamic interactions between-multiple perceptual and cognitive processes as they unfold and interact over a fine temporal scale (i.e., hundreds of milliseconds). Next, we review recent work that implemented mouse-tracking techniques by measuring mouse movements as participants chose between various food items (of varying nutritional content). Lastly, we propose next steps for future investigations to link behavioral features from mouse-tracking paradigms, corresponding neural correlates, and downstream eating behaviors.

How Mouse-tracking Can Advance Social Cognitive Theory.

Mouse-tracking - measuring computer-mouse movements made by participants while they choose between response options - is an emerging tool that offers an accessible, data-rich, and real-time window into how people categorize and make decisions. In the present article we review recent research in social cognition that uses mouse-tracking to test models and advance theory. In particular, mouse-tracking allows examination of nuanced predictions about both the nature of conflict (e.g., its antecedents and consequences) as well as how this conflict is resolved (e.g., how decisions evolve). We demonstrate how mouse-tracking can further our theoretical understanding by highlighting research in two domains - social categorization and self-control. We conclude with future directions and a discussion of the limitations of mouse-tracking as a method.

Statistical Modeling of the Default Mode Brain Network Reveals a Segregated Highway Structure.

We investigate the functional organization of the Default Mode Network (DMN) - an important subnetwork within the brain associated with a wide range of higher-order cognitive functions. While past work has shown the whole-brain network of functional connectivity follows small-world organizational principles, subnetwork structure is less well understood. Current statistical tools, however, are not suited to quantifying the operating characteristics of functional networks as they often require threshold censoring of information and do not allow for inferential testing of the role that local processes play in determining network structure. Here, we develop the correlation Generalized Exponential Random Graph Model (cGERGM) - a statistical network model that uses local processes to capture the emergent structural properties of correlation networks without loss of information. Examining the DMN with the cGERGM, we show that, rather than demonstrating small-world properties, the DMN appears to be organized according to principles of a segregated highway - suggesting it is optimized for function-specific coordination between brain regions as opposed to information integration across the DMN. We further validate our findings through assessing the power and accuracy of the cGERGM on a testbed of simulated networks representing various commonly observed brain architectures.

Resisting Temptation: Tracking How Self-Control Conflicts Are Successfully Resolved in Real Time.

Across four studies, we used mouse tracking to identify the dynamic, on-line cognitive processes that underlie successful self-control decisions. First, we showed that individuals display real-time conflict when choosing options consistent with their long-term goal over short-term temptations. Second, we found that individuals who are more successful at self-control-whether measured or manipulated-show significantly less real-time conflict in only self-control-relevant choices. Third, we demonstrated that successful individuals who choose a long-term goal over a short-term temptation display movements that are smooth rather than abrupt, which suggests dynamic rather than stage-based resolution of self-control conflicts. These findings have important implications for contemporary theories of self-control.

Neurological evidence for the role of construal level in future-directed thought.

The ability to mentally represent future events is a significant human psychological achievement. A challenge that people encounter is that they often lack detailed specifics about distant relative to near future events. Construal level theory proposes that people represent distant future events by their abstract and essential features-a process referred to as high-level construal. As events become temporally proximal, people represent events by their increasingly available and reliable concrete and idiosyncratic features-a process referred to as low-level construal. The present fMRI experiment provides direct neural evidence for these assertions. Using the why-how localizer as a measure of construal level, results revealed brain regions associated with both temporal distance and high-level construal (medial prefrontal cortex), as well as temporal proximity and low-level construal (precuneus). We discuss the implications of these findings for the neuroscience of mental time travel and cognitive representation.

Neural dissociations in attitude strength: Distinct regions of cingulate cortex track ambivalence and certainty.

People's behaviors are often guided by valenced responses to objects in the environment. Beyond positive and negative evaluations, attitudes research has documented the importance of attitude strength--qualities of an attitude that enhance or attenuate its impact and durability. Although neuroscience research has extensively investigated valence, little work exists on other related variables like metacognitive judgments about one's attitudes. It remains unclear, then, whether the various indicators of attitude strength represent a single underlying neural process or whether they reflect independent processes. To examine this, we used functional MRI (fMRI) to identify the neural correlates of attitude strength. Specifically, we focus on ambivalence and certainty, which represent metacognitive judgments that people can make about their evaluations. Although often correlated, prior neuroscience research suggests that these 2 attributes may have distinct neural underpinnings. We investigate this by having participants make evaluative judgments of visually presented words while undergoing fMRI. After scanning, participants rated the degree of ambivalence and certainty they felt regarding their attitudes toward each word. We found that these 2 judgments corresponded to distinct brain regions' activity during the process of evaluation. Ambivalence corresponded to activation in anterior cingulate cortex, dorsomedial prefrontal cortex, and posterior cingulate cortex. Certainty, however, corresponded to activation in unique areas of the precuneus/posterior cingulate cortex. These results support a model treating ambivalence and certainty as distinct, though related, attitude strength variables, and we discuss implications for both attitudes and neuroscience research.

Valence asymmetries in the human amygdala: task relevance modulates amygdala responses to positive more than negative affective cues.

Organisms must constantly balance appetitive needs with vigilance for potential threats. Recent research suggests that the amygdala may play an important role in both of these goals. Although the amygdala plays a role in processing motivationally relevant stimuli that are positive or negative, negative information often appears to carry greater weight. From a functional perspective, this may reflect the fact that threatening stimuli generally require action, whereas appetitive stimuli can often be safely ignored. In this study, we examine whether amygdala activation to positive stimuli may be more sensitive to task goals than negative stimuli, which are often related to self-preservation concerns. During fMRI, participants were presented with two images that varied on valence and extremity and were instructed to focus on one of the images. Results indicated that negative stimuli elicited greater amygdala activity regardless of task relevance. In contrast, positive stimuli only led to a relative increase in amygdala activity when they were task relevant. This suggests that the amygdala may be more responsive to negative stimuli regardless of their relevance to immediate goals, whereas positive stimuli may only elicit amygdala activity when they are relevant to the perceivers' goals. This pattern of valence asymmetry in the human amygdala may help balance approach-related goal pursuit with chronic self-preservation goals.