In today's hyperconnected world, smartphones have become an indispensable part of our lives, offering instant access to information, communication, and entertainment. However, this pervasiveness has also led to concerns about excessive smartphone use, a phenomenon often referred to as "constant checking." Characterized by the habitual and often uncontrollable urge to check one's smartphone for new notifications, messages, or updates, constant checking can have detrimental impacts on individuals' well-being and productivity.
Across our studies, run on CloudResearch and other platforms, our salient victim effect occurred when participants considered positive outcomes from their own lives, when they were in interpersonal interactions, and when they imagined a situation at work. Beneficiaries of bias were responsive to the salient victim across a variety of reasons for the bias (i.e., participants were White, they were similar to the decision maker, or they were related to the decision maker) and regardless of whether a specific victim was mentioned or not. In short, it seemed like the salient victim had a powerful effect. In real life, too, there seem to be some examples of when people will take action to correct bias after they reflect on the victims.
Think about a person who, one day, was late to work, twisted her ankle because she had to run to catch the bus, and almost got fired. She blamed it all on her phone alarm that didn’t sound—she judged that single cause (the alarm) as responsible for many consequences. Well, it turns out these kinds of judgments (what social psychologists call “causal attributions”) have to do with our thinking styles — whether we are more holistic or analytic individuals. Our thinking styles affect how we make sense of the world around us.
We are excited to announce a new chapter in the growth of our Connect platform. Our journey began with a mission to provide researchers with a seamless, high-quality online research experience, and today, we're thrilled to take the next step in that journey.
Ever since ChatGPT 3 made its entrance in November 2022, it has been used for a diverse array of tasks, from creating meal plans and vacation itineraries to troubleshooting code. LLMs are advanced AI programs trained to understand and generate human-like text based on vast amounts of data. They can answer open-ended text questions, write in various styles, and assist with a wide range of tasks by analyzing patterns in the text they were trained on. But behavioral scientists have been concerned about the ways that LLMs may impact research.
While there are plenty of papers demonstrating that high-quality data can be collected effortlessly and affordably, we decided to write a white paper to demonstrate the underlying mechanics and principles that drive Connect’s success. Our white paper delves into the comprehensive design, innovation, and commitment that have shaped Connect.
