Thought control in daily working life: How the ability to stop thoughts protects self‐esteem

Tackling addictive behaviors through memory suppression: A scoping review and perspective

Forgetting is often viewed as a human frailty. However, over the years, it has been considered an adaptive process that allows people to avoid retrieval of undesirable memories, preventing them from suffering and discomfort. Evidence shows that the ability to suppress memories is affected by several psychopathological conditions characterized by persistent unwanted thoughts, including anxiety and posttraumatic stress disorders. Nevertheless, memory suppression (MS) mechanisms in addiction-a clinical condition characterized by recurrent drug-related thoughts that contribute to repeated drug use-have received little attention so far. Addiction theories reveal that drugs change behavior by working on memory systems, particularly on declarative memory, which is related to the retrieval and encoding of drug-related memories. In this review, the main behavioral and neurofunctional findings concerning the Think/No-Think task-an adaptation of the classical Go/No-Go tasks typically used to evaluate the suppression of motor response-are presented. We then show how the memory system can be involved in the craving or anticipation/preoccupation stage of the addiction cycle. Subsequently, the study of MS in the context of addictive behaviors is highlighted as a promising approach for gaining knowledge about the mechanisms contributing to the continuation of addiction. Finally, we discuss how interventions aiming to strengthen this ability could impact the anticipation/preoccupation stage by (i) reducing the accessibility of drug-related memories, (ii) decreasing craving and attention toward drug-related stimuli, and (iii) improving overall inhibition abilities. In conclusion, this review aims to illustrate how the study of MS may be a valuable approach to enhance our understanding of substance use disorders by unveiling the underlying cognitive and neural mechanisms involved, which could have important implications for addiction treatment.

Recent findings on organizational unlearning and intentional forgetting research (2019-2022)

This mini review aims at summarizing the current state-of-the-art of empirical unlearning and intentional forgetting (U/IF) research at the individual, team, and organizational level. It adds to an earlier review and incorporates 31 recent studies from 2019 to 2022. The review reveals that predictors based on the organization's adaptation context (e.g., competitive intensity), organization level (e.g., leadership exploration activities), individual task-related (e.g., features of the routines changed), and person-related level (e.g., cognitive control strategies) variables relate to process variables, such as the type of U/IF, the U/IF content (e.g., success beliefs or failure beliefs), and information processing variables (e.g., team information processing). The outcome variables are at the organizational level (e.g., cross-boundary innovation), team level performance level, the individual task performance level (e.g., errors), and person-related level (e.g., self-esteem). The analyzed studies at the team and organizational levels preferred cross-sectional study designs or in-depth qualitative methods, which severely limits the possibility of making causal statements. In contrast, at the individual-level studies use longitudinal designs as well to make temporal aspects of U/IF visible. But these individual level results are limited in terms of their generalizability to other levels. Even though all studies make valuable contribution to the understanding of antecedents and outcomes of U/IF, the temporal and process-related aspects of how U/IF unfolds at the different levels and subsequent options for its deliberate facilitation remain empirically little elaborated. It is proposed that in addition to studying the antecedents and consequences of U/IF in cross sectional designs, the topic needs more longitudinal designs to capture the nature of the U/IF processes in organizations.

Explanatory machine learning for justified trust in human-AI collaboration: Experiments on file deletion recommendations

In the digital age, saving and accumulating large amounts of digital data is a common phenomenon. However, saving does not only consume energy, but may also cause information overload and prevent people from staying focused and working effectively. We present and systematically examine an explanatory AI system (Dare2Del), which supports individuals to delete irrelevant digital objects. To give recommendations for the optimization of related human-computer interactions, we vary different design features (explanations, familiarity, verifiability) within and across three experiments (N ₁ = 61, N ₂ = 33, N ₃= 73). Moreover, building on the concept of distributed cognition, we check possible cross-connections between external (digital) and internal (human) memory. Specifically, we examine whether deleting external files also contributes to human forgetting of the related mental representations. Multilevel modeling results show the importance of presenting explanations for the acceptance of deleting suggestions in all three experiments, but also point to the need of their verifiability to generate trust in the system. However, we did not find clear evidence that deleting computer files contributes to human forgetting of the related memories. Based on our findings, we provide basic recommendations for the design of AI systems that can help to reduce the burden on people and the digital environment, and suggest directions for future research.