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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 — I ( M ; E ) — gives rise to flow. We support our theory across five experiments (four preregistered) by showing, across multiple activities, that increasing I ( M ; E ) 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. Flow is a desired but elusive state characterized by the subjective experience of immersion and engagement in an activity. Here, the authors develop and empirically validate a formal model that specifies variables and computations involved in the subjective experience of flow.

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.

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.

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. •cGERGM is a joint modeling framework for quantifying correlation networks.•We quantify the unique contribution of different features in network organization.•Across subnetworks, we find a consistent set of organizational principles.•These networks show greater clustering than expected by chance.•Networks show less preferential attachment (i.e., hub use) than expected by chance.

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.

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.

Research on thin slice judgment, or people's ability to make accurate judgments about a target based on very brief exposure, has largely focused on the detection of individual-level traits. The present studies extend this work to group-level characteristics, such as teamwork and cohesiveness, and demonstrate that these inferences can predict behavioral performance outcomes. In Study 1, judgments based on 10-s performance videos of rock bands predicted view-counts of the full performance videos posted on the Internet. In Study 2, judgments of Ultimate Frisbee teams based on 10-s warm-up videos predicted the teams' winning percentages. In Study 3, thin slice judgments based on pictures of boards of directors predicted the companies' success. The authors conclude that judgments of emergent group-level characteristics based on very brief exposures can predict important real-world outcomes. [PUBLICATION ABSTRACT]

Last week President Truman, in his speech before the Democratic women in Washington, challenged the Republicans--in fact dared them--to make an issue of his foreign policy.

What we call Americanism is based on the precept that all citizens stand equal before the law, equal in rights and equal in responsibilities.

While we Americans are tossing our millions about so casually, it is, perhaps, ungracious to say that we could use some of those millions right here at home to excellent purpose.