Addressing climate change with behavioral science: A global intervention tournament in 63 countries.

Effectively reducing climate change requires marked, global behavior change. However, it is unclear which strategies are most likely to motivate people to change their climate beliefs and behaviors. Here, we tested 11 expert-crowdsourced interventions on four climate mitigation outcomes: beliefs, policy support, information sharing intention, and an effortful tree-planting behavioral task. Across 59,440 participants from 63 countries, the interventions' effectiveness was small, largely limited to nonclimate skeptics, and differed across outcomes: Beliefs were strengthened mostly by decreasing psychological distance (by 2.3%), policy support by writing a letter to a future-generation member (2.6%), information sharing by negative emotion induction (12.1%), and no intervention increased the more effortful behavior-several interventions even reduced tree planting. Last, the effects of each intervention differed depending on people's initial climate beliefs. These findings suggest that the impact of behavioral climate interventions varies across audiences and target behaviors.

The Effect of Prediction Error on Belief Update Across the Political Spectrum.

Making predictions is an adaptive feature of the cognitive system, as prediction errors are used to adjust the knowledge they stemmed from. Here, we investigated the effect of prediction errors on belief update in an ideological context. In Study 1, 704 Cloud Research participants first evaluated a set of beliefs and then either made predictions about evidence associated with the beliefs and received feedback or were just presented with the evidence. Finally, they reevaluated the initial beliefs. Study 2, which involved a U.S. Census-matched sample of 1,073 Cloud Research participants, was a replication of Study 1. We found that the size of prediction errors linearly predicts belief update and that making large errors leads to more belief update than does not engaging in prediction. Importantly, the effects held for both Democrats and Republicans across all belief types (Democratic, Republican, neutral). We discuss these findings in the context of the misinformation epidemic.

The synchronization of collective beliefs: From dyadic interactions to network convergence.

Systems of beliefs organized around religion, politics, and health constitute the building blocks of human communities. One central feature of these collectively held beliefs is their dynamic nature. Here, we study the dynamics of belief endorsement in lab-created 12-member networks using a 2-phase communication model. Individuals first evaluate the believability of a set of beliefs, after which, in Phase 1, some networks listen to a public speaker mentioning a subset of the previously evaluated beliefs while other networks complete a distracter task. In Phase 2, all participants engage in conversations within their network to discuss the initially evaluated beliefs. Believability is then measured both post conversation and after one week. We find that the public speaker impacts the community's beliefs by altering their mnemonic accessibility. This influence is long-lasting and amplified by subsequent conversations, resulting in community-wide belief synchronization. These findings point to optimal sociocognitive strategies for combating misinformation in social networks. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Dynamics of excitatory and inhibitory networks are differentially altered by selective attention.

Inhibition and excitation form two fundamental modes of neuronal interaction, yet we understand relatively little about their distinct roles in service of perceptual and cognitive processes. We developed a multidimensional waveform analysis to identify fast-spiking (putative inhibitory) and regular-spiking (putative excitatory) neurons in vivo and used this method to analyze how attention affects these two cell classes in visual area V4 of the extrastriate cortex of rhesus macaques. We found that putative inhibitory neurons had both greater increases in firing rate and decreases in correlated variability with attention compared with putative excitatory neurons. Moreover, the time course of attention effects for putative inhibitory neurons more closely tracked the temporal statistics of target probability in our task. Finally, the session-to-session variability in a behavioral measure of attention covaried with the magnitude of this effect. Together, these results suggest that selective targeting of inhibitory neurons and networks is a critical mechanism for attentional modulation.

Global network influences on local functional connectivity.

A central neuroscientific pursuit is understanding neuronal interactions that support computations underlying cognition and behavior. Although neurons interact across disparate scales, from cortical columns to whole-brain networks, research has been restricted to one scale at a time. We measured local interactions through multi-neuronal recordings while accessing global networks using scalp electroencephalography (EEG) in rhesus macaques. We measured spike count correlation, an index of functional connectivity with computational relevance, and EEG oscillations, which have been linked to various cognitive functions. We found a non-monotonic relationship between EEG oscillation amplitude and spike count correlation, contrary to the intuitive expectation of a direct relationship. With a widely used network model, we replicated these findings by incorporating a private signal targeting inhibitory neurons, a common mechanism proposed for gain modulation. Finally, we found that spike count correlation explained nonlinearities in the relationship between EEG oscillations and response time in a spatial selective attention task.

Correlations in V1 are reduced by stimulation outside the receptive field.

The trial-to-trial response variability of nearby cortical neurons is correlated. These correlations may strongly influence population coding performance. Numerous studies have shown that correlations can be dynamically modified by attention, adaptation, learning, and potent stimulus drive. However, the mechanisms that influence correlation strength remain poorly understood. Here we test whether correlations are influenced by presenting stimuli outside the classical receptive field (RF) of visual neurons, where they recruit a normalization signal termed surround suppression. We recorded simultaneously the activity of dozens of cells using microelectrode arrays implanted in the superficial layers of V1 in anesthetized, paralyzed macaque monkeys. We presented annular stimuli that encircled--but did not impinge upon--the RFs of the recorded cells. We found that these "extra-classical" stimuli reduced correlations in the absence of stimulation of the RF, closely resembling the decorrelating effects of stimulating the RFs directly. Our results suggest that normalization signals may be an important mechanism for modulating correlations.

Variance in population firing rate as a measure of slow time-scale correlation.

Correlated variability in the spiking responses of pairs of neurons, also known as spike count correlation, is a key indicator of functional connectivity and a critical factor in population coding. Underscoring the importance of correlation as a measure for cognitive neuroscience research is the observation that spike count correlations are not fixed, but are rather modulated by perceptual and cognitive context. Yet while this context fluctuates from moment to moment, correlation must be calculated over multiple trials. This property undermines its utility as a dependent measure for investigations of cognitive processes which fluctuate on a trial-to-trial basis, such as selective attention. A measure of functional connectivity that can be assayed on a moment-to-moment basis is needed to investigate the single-trial dynamics of populations of spiking neurons. Here, we introduce the measure of population variance in normalized firing rate for this goal. We show using mathematical analysis, computer simulations and in vivo data how population variance in normalized firing rate is inversely related to the latent correlation in the population, and how this measure can be used to reliably classify trials from different typical correlation conditions, even when firing rate is held constant. We discuss the potential advantages for using population variance in normalized firing rate as a dependent measure for both basic and applied neuroscience research.