Making Warnings about Misleading Advertising and Product Recalls more Effective: An Implicit Attitude Perspective

The authors tested whether image-based information is more effective than text in changing implicit attitudes from positive to negative, even when both forms similarly change explicit attitudes. They studied corrective information (i.e., warnings about misleading advertising and product recall notices) because it is a common, important effort to change consumer attitudes. Corrective information in the form of pictures or imagery-evoking text, as well as direct instructions to imagine the scene, changed implicit attitudes more than plain, descriptive text, which is currently the most common warning method. Image-based stimuli can change implicit attitudes because they evoke vivid visual mental imagery of counterattitudinal valence (Experiments 1–2). Conditions that hindered the formation of visual mental imagery blocked implicit attitude change, whereas cognitive busyness did not (Experiment 3). In short, imagery-based information changed both explicit and implicit attitudes, whereas materials not based on imagery changed only explicit attitudes. Managers and regulators who aim to protect consumers from claims and products that could do harm should use image-based campaigns to best convey the message effectively.

The role of suspiciousness in understanding others’ goals

An experiment examined goal understanding and how perceivers’ suspiciousness was associated with the accuracy, valence, and certainty of their inferences about a pursuer’s goal. In initial interactions, one dyad member was randomly assigned as the pursuer, and the other was the perceiver. The congruency of the perceiver’s and the pursuer’s conversation goals (i.e., discordant, identical, or concordant) and the perceiver’s cognitive busyness were manipulated. Results confirmed that accuracy decreased as perceivers’ suspiciousness increased only for not-busy perceivers in the goal-discord condition because perceivers’ inferences were negatively valenced. Results also supported the hypotheses that certainty decreased as perceivers’ suspiciousness increased only for not-busy perceivers in the goal-discord condition and that certainty increased as perceivers’ suspiciousness increased both for not-busy perceivers in the identical-goal condition and for busy perceivers in the goal-discord condition.

Social learning in design teams: The importance of direct and indirect communications

This paper discusses the effects of direct and indirect communications on social learning and task coordination in design teams. The findings reported in this paper are based on a computational model that simulates the formation of transactive memory (TM) through social learning from direct and indirect communications. Direct communications are explicit information exchanged between team members whereas indirect communication may be opportunistic and coincidental, resulting in learning and information gained through observations of the actions of others. However, team structure mediates opportunities for communication. Three types of team structures are studied, which are differentiated on the basis of their constraints on and opportunities for direct and indirect communications across the team. The differences across the team structures are investigated through a series of simulations in which team member retention, cognitive busyness of team members, and task complexity are additional moderating variables, and task coordination and formation of TM are the dependent variables. Fewer communications to coordinate the same tasks are taken as the measure of efficient task coordination. Findings suggest that reduction in communication and learning opportunities are more detrimental to the task coordination in flat teams as compared to functional teams. Indirect communications contribute more to the formation of TM than to task coordination. Flat teams facilitate the formation of TM, whereas functional teams are more appropriate for efficient task coordination, indicating that the role of TM in mediating task coordination varies with team structure.

Artificial Intelligence as a positive and negative factor in global risk

By far the greatest danger of Artificial Intelligence (AI) is that people conclude too early that they understand it. Of course, this problem is not limited to the field of AI. Jacques Monod wrote: ‘A curious aspect of the theory of evolution is that everybody thinks he understands it’ (Monod, 1974). The problem seems to be unusually acute in Artificial Intelligence. The field of AI has a reputation for making huge promises and then failing to deliver on them. Most observers conclude that AI is hard, as indeed it is. But the embarrassment does not stem from the difficulty. It is difficult to build a star from hydrogen, but the field of stellar astronomy does not have a terrible reputation for promising to build stars and then failing. The critical inference is not that AI is hard, but that, for some reason, it is very easy for people to think they know far more about AI than they actually do. It may be tempting to ignore Artificial Intelligence because, of all the global risks discussed in this book, AI is probably hardest to discuss. We cannot consult actuarial statistics to assign small annual probabilities of catastrophe, as with asteroid strikes. We cannot use calculations from a precise, precisely confirmed model to rule out events or place infinitesimal upper bounds on their probability, as with proposed physics disasters. But this makes AI catastrophes more worrisome, not less. The effect of many cognitive biases has been found to increase with time pressure, cognitive busyness, or sparse information. Which is to say that the more difficult the analytic challenge, the more important it is to avoid or reduce bias. Therefore I strongly recommend reading my other chapter (Chapter 5) in this book before continuing with this chapter. When something is universal enough in our everyday lives, we take it for granted to the point of forgetting it exists. Imagine a complex biological adaptation with ten necessary parts. If each of the ten genes is independently at 50% frequency in the gene pool – each gene possessed by only half the organisms in that species – then, on average, only 1 in 1024 organisms will possess the full, functioning adaptation.