From Individual to Collective Cognition: Applications to Climate Solutions

Our research investigates the social and cognitive processes shaping individuals’ and collectives’ memories, beliefs, and behaviors, with direct applications to the climate crisis response. Following a unifying theoretical framework (Vlasceanu, Enz, Coman, 2018; Vlasceanu, Dyckovsky, & Coman, 2023), our lab employs a large array of methods ranging from behavioral experiments and social network analysis, to megastudies and international collaborations, with the goal of uncovering avenues through which behavioral sciences can stimulate climate awareness and action at the (1) individual, (2) collective, and (3) systemic levels.

1.     Individual level

1.1.  Belief in climate change

Even though more and more people around the world are beginning to express concern about climate change (Pew, 2021), climate denial discourse remains prevalent, especially on social media platforms (CCDH, 2022). This growing polarization of belief is often cited as a major barrier to individual, collective, and system level climate action (Pew, 2016). Thus, a critical step to stimulating climate action is addressing climate denial through belief change interventions.

Early steps in understanding psychological mechanisms by which beliefs can be changed, for example to strengthen belief in climate change, have identified rational evidence and motivations (Lund, 1925) as well as irrational factors (James, 1895) as pathways to belief change (Connors & Halligan, 2014). In our work, we constructed a theoretical framework under which additional such processes can be programmatically identified (Vlasceanu et al., 2018; Vlasceanu et al., 2023). Following this framework, we found that mnemonic accessibility (Vlasceanu & Coman, 2018; Vlasceanu, Morais, Duker, & Coman, 2020), emotional arousal (Vlasceanu, Goebel, & Coman, 2020), prediction errors (Vlasceanu, Morais, & Coman, 2021), and social norms (Vlasceanu & Coman, 2021, 2022) are viable sociocognitive mechanisms that can be leveraged in interventions aimed at changing beliefs and thus decreasing climate-related misinformation. However, reducing climate change misinformation by changing false beliefs is not enough to stimulate the degree of climate action needed to address the climate crisis (Aron, 2022).

1.2.  From belief to action

Beliefs have long been known to be a meaningful predictor of behavior (Ajzen, 1985; Hochbaum, 1958). Indeed, even on polarized ideological topics such as climate change, people’s beliefs tend to predict their behaviors (van der Linden et al., 2015; 2019; Vlasceanu, McMahon, Van Bavel, & Coman, 2023). However, despite this prevalent association in the literature, researchers have also documented contexts in which beliefs and behaviors do not align (e.g., Paluck, 2009; Vlasceanu et al., 2023). In additional empirical support for this belief-behavior incongruity, data collected from 60 countries and over 50,000 participants (Vlasceanu et al., 2023), also reveals a stark disconnect between believing in climate change and engaging in mitigation behaviors. For instance, while climate change beliefs are highly polarized between liberals and conservatives, pro-climate behaviors are not, suggesting that climate action interventions, unlike climate awareness interventions, might be able to effectively reach people across ideological divides. In future work combining empirical with computational approaches, we aim to disentangle the complex relationship between beliefs and behaviors by identifying processes moderating the impact climate beliefs have on climate action.

1.3. Individual climate action

The solutions to the climate crisis are undoubtedly dependent on promoting sustainable behaviors through large-scale implementation of psychological theories into climate action interventions (Allcott & Mullainathan, 2010; Jenny & Betsch, 2022). Accordingly, a burgeoning body of research has been investigating strategies aimed at boosting sustainable behaviors such as recycling, public transportation use, or household energy saving (Gifford, Kormos, & McIntyre, 2011; Kastner & Stern, 2015). For instance, showing people information about the public health costs of electricity production led them to consume 8% less electricity (Asensio & Delmas, 2015). In another experimental investigation, a descriptive social norm emphasis using neighborhood comparisons was successful at reducing water consumption (Datta et al., 2015). However, testing theories independently in different social and cultural contexts, and across different metrics, hinders the ability to make direct comparisons between strategies, a major barrier to scientific implementation into policy. To address this concern, recent work in behavioral science has proposed the adoption of the megastudy approach–an experimental paradigm designed to evaluate many theories on the same outcome, in the same large-scale experiment (Milkman et al., 2021).

            Building on this new approach, we led an international megastudy aimed at empirically revealing the best behavioral intervention to stimulate climate action around the world. In this project involving 257 collaborators from 60 countries, we tested the most promising 11 psychological theories (selected by surveying 188 behavioral scientists) at stimulating pro-climate behaviors (Vlasceanu et al., 2023). In future work we are interested in exploring the long-term effects of these interventions, which might have different rates of decay, as well as the effects of combining these interventions for optimal impact.

2.     Collective level

2.1. From individuals to social networks and group dynamics

Recognizing that the effects of cognition are not limited to individuals, but instead they are dependent on social contexts, in our lab we are also exploring the emergent properties of individual phenomena at a collective level. For instance, in past work we explored the effects conversational interactions (Vlasceanu & Coman, 2022) and network structures (Vlasceanu et al., 2021) on the formation of collective beliefs (Vlasceanu et al., 2020). In future work we aim to investigate emergent properties of collective behaviors.

2.2. Collective climate action

Most climate action interventions to date have focused on private mitigation efforts based on individual decision-making (Steg & Vlek, 2009). However, framing climate change as an individual level problem with individual solutions has been found to backfire, leading to feelings of helplessness and concerns of free riding (Salomon, Preston, & Tannenbaum, 2017). A collective mindset on the other hand, has been suggested to prevent from such outcomes, mobilizing collective action (Masson & Fritsche, 2021). Moreover, action at the individual and family level is estimated to only account for 14% of the emissions reductions needed to reach net zero by 2050, whereas collective action could account for up to 64% of reductions in emissions needed. Yet even when the public intends to act proenvironmentally, only 20% of their behaviors are explained by beliefs and intentions, the rest likely being a function of the systems and structures in which people operate. Accordingly, in a new megastudy, we aim to identify and test the top behavioral interventions that could most successfully promote climate advocacy behaviors.

3.     System level

While individual and collective climate awareness and climate action are critical components in addressing the climate crisis, top-down climate solutions from the system level are also needed to complement bottom-up efforts. Accordingly, our research also explores avenues of intervening at this level. For example, in a recent project, we investigated the role of prevalent artificial intelligence algorithms such as Google Search in driving climate awareness or climate denial around the world. Following procedures by Vlasceanu and Amodio (2022), in a sample of 47 nations, we found that the sentiment elicited by climate change algorithmic outputs is associated with preexisting climate concerns in a nation, but not with objective climate impacts in that nation. In follow-up experimental studies, we then found that these algorithmic outputs causally influence climate policy support, suggesting a cycle of sentiment propagation between preexisting climate attitudes and AI, and pointing to promising top-down intervention opportunities to increase climate awareness through AI (Berkebile-Weinberg, Tang, & Vlasceanu, in review).