Money and Ethics

Money and Ethics: The consequences of market-oriented mindset evoked by money for obeying or violating ethical standards. (Research grant financed by National Science Centre in Poland; PI: Tomasz Zaleskiewicz)

The main goal of the present project is to experimentally investigate positive and negative ethical consequences of being in the market-oriented state of mind. On the basis of previous research, we assume that being exposed to money leads to perceiving interpersonal relations in terms of characteristics typical for the market mode and initiates behaviors that have either positive or negative ethical connotations. When developing our theoretical assumptions, we refer to the distinction between two qualitatively different mindsets or types of relationships: communal-oriented and market-oriented. The canonical example of being in the market-oriented mindset is when people pay with money or are paid with money. Money is such a strong marquee symbol of the market that even subtle cues of money can bring behavior in line with market mode and hampers communal-oriented approach.

Mental imagery

Mental imagery, emotions, and decisions under risk and uncertainty: Visualizing the future as an aid to decision making. (Research grant financed by National Science Centre in Poland; PI: Tomasz Zaleskiewicz)

This project introduces a novel theoretical approach for understanding and predicting people’s behavior in common everyday situations under risk and uncertainty. It posits that the interplay between mental images, affective responses, and evaluations of threats and benefits provides critical inputs to decisions in uncertain circumstances. Our theoretical idea differs from both the classical decision theories and the neuroeconomic models at least in a twofold way. Firstly, prior models assume sequential relations between phases of the decision process, and not circular associations as postulated in our theory. Secondly, the present model points at mental imagery as an important psychological mechanism reinforcing the process of decision making.

Excessive buying

Excessive buying as boredom-triggered behavior (Research grant financed by National Science Centre in Poland; PI: Agata Gasiorowska)

The aim of the present project is to empirically investigate the relation between boredom and excessive buying, which might serve both as a stimulating activity and a compensatory tool aimed at retrieving the sense of meaning when one experiences boredom. The feeling of boredom is an inseparable part of human lives, often perceived as a fairly trivial and temporary discomfort that can be eased by a simple change in circumstances. However, boredom can also be a chronic and pervasive stressor with significant psychosocial consequences (Eastwood et al., 2012). Empirical evidence reveals that boredom and the propensity to experience boredom are linked to a wide range of psychosocial problems, such as drug and alcohol abuse, problem gambling, or overeating and binge eating (e.g., LePera, 2011; Mercer & Eastwood, 2010; Moynihan et al., 2015). Such behaviors might occur because a bored person wants to engage in stimulating activity to escape this unpleasant state. However, other research suggests that boredom may lead to ostensibly unrelated effects, like an increase in prosocial behaviors, an appeal to political extremes, or an elevated affirmation of heroes, an appeal to political extremes (Coughlan et al., 2017; Van Tilburg & Igou, 2016, 2017). Researchers conclude that in all these cases, individuals turn toward those behaviors and tendencies because they need and strive for compensation of deficits in the sense of meaning caused by experiencing the feeling of boredom.

The labeling effect in prosocial behavior toward out-group members in the example of people diagnosed with depression

Research grant financed by National Science Centre in Poland; PI: Katarzyna Kulwicka-Durmowicz

The aim of the present project is to empirically investigate the labeling effect on pro-social behavior toward members of an out-group, by the example of people diagnosed with depression and with using different methods: declarations about one’s behavior, behavior in economic games, and behavior in laboratory, and field experiment. Following Link (1987) we define labeling effect as the effect of use of psychiatric diagnosis as a label while referring to or describing a person who suffers from mental disorder that leads to greater social distance and negative attitude toward this person (Martin, Pescosolido, & Tuch, 2000). Firstly, we will investigate the labeling effect in lay people’s declarations of behavior in different types of social situations. Next, we will investigate laypeople’s declared and actual behavior toward individuals described with depression symptoms or labeled as having depression in two simple economic games – The Dictator Game and The Reverse Dictator Game. Finally, we will also conduct laboratory experiment in order to investigate labeling effect in actual helping behavior. In each of the aforementioned situation we expect that labeling effect will lead to lower prosociality in declared and in actual behaviors.

Trust in artificial intelligence

Trust in artificial intelligence (Research grant financed by National Science Centre in Poland; PI: Katarzyna Samson)

Artificial intelligence (AI) is not just a new technology. It is a powerful force that is reshaping daily practices, personal and professional interactions, and the social environment we live in. It presents us with unparalleled opportunities but also entails novel, sometimes unknown challenges. One of the major challenges related to the development of AI is establishing harmonious human-AI relations necessary for harnessing AI’s potential to use its power for good. To address this challenge, the current project focuses on trust—the critical building block of any society. The main goals of the project are to: (a) discern the differences between trust in humans and in AI agents; (b) identify the key psychological factors that determine the development of trust towards AIs; (c) determine the role of moral and competence components in the perception of AI (vs. human) trustworthiness; and (d) compare the relative weights of deeds and their consequences when making moral judgments about AIs (vs. humans).