Attentional biases and daily game craving dynamics: An ecological momentary assessment study

Background and aims: Theories posit that the combination of external (e.g. cue exposure) and internal (e.g. attention biases) factors contributes to the development of game craving. Nevertheless, whether different components of attentional biases (namely, engagement bias and disengagement bias) play separate roles on game craving has not been fully elucidated. We aimed to examine the associations between two facets of attentional biases and game craving dynamics under a daily life setting. Methods: Participants (110 regular internet game players) accomplished the modified attentional assessment task in the laboratory, after which they entered a 10-day ecological momentary assessment (EMA) to collect data on their momentary game craving and occurrence of game-related events at five different time points per day. Results: We found that occurrence of game-related events was significantly associated with increased game craving. Moreover, attentional disengagement bias, instead of engagement bias, bore on the occasional level variations of game craving as moderating variables. Specifically, attentional disengagement bias, not engagement bias, was associated with a greater increase in game craving immediately after encountering a game-related event; however, neither attentional engagement bias nor disengagement bias was associated with the craving maintenance after a relatively long period. Discussion and conclusions: The present study highlights the specific attentional processes involved in game craving dynamics, which could be crucial for designing interventions for attentional bias modification (ABM) in Internet Gaming Disorder (IGD) populations

CharacterGLM: Customizing Chinese Conversational AI Characters with Large Language Models

In this paper, we present CharacterGLM, a series of models built upon ChatGLM, with model sizes ranging from 6B to 66B parameters. Our CharacterGLM is designed for generating Character-based Dialogues (CharacterDial), which aims to equip a conversational AI system with character customization for satisfying people's inherent social desires and emotional needs. On top of CharacterGLM, we can customize various AI characters or social agents by configuring their attributes (identities, interests, viewpoints, experiences, achievements, social relationships, etc.) and behaviors (linguistic features, emotional expressions, interaction patterns, etc.). Our model outperforms most mainstream close-source large langauge models, including the GPT series, especially in terms of consistency, human-likeness, and engagement according to manual evaluations. We will release our 6B version of CharacterGLM and a subset of training data to facilitate further research development in the direction of character-based dialogue generation.Comment: Work in progres

Visual Working Memory Capacity Does Not Modulate the Feature-Based Information Filtering in Visual Working Memory

Background: The limited capacity of visual working memory (VWM) requires us to select the task relevant information and filter out the irrelevant information efficiently. Previous studies showed that the individual differences in VWM capacity dramatically influenced the way we filtered out the distracters displayed in distinct spatial-locations: low-capacity individuals were poorer at filtering them out than the high-capacity ones. However, when the target and distracting information pertain to the same object (i.e., multiple-featured object), whether the VWM capacity modulates the featurebased filtering remains unknown. Methodology/Principal Findings: We explored this issue mainly based on one of our recent studies, in which we asked the participants to remember three colors of colored-shapes or colored-landolt-Cs while using two types of task irrelevant information. We found that the irrelevant high-discriminable information could not be filtered out during the extraction of VWM but the irrelevant fine-grained information could be. We added 8 extra participants to the original 16 participants and then split the overall 24 participants into low- and high-VWM capacity groups. We found that regardless of the VWM capacity, the irrelevant high-discriminable information was selected into VWM, whereas the irrelevant fine-grained information was filtered out. The latter finding was further corroborated in a second experiment in which the participants were required to remember one colored-landolt-C and a more strict control was exerted over the VWM capacity

Perspective Taking in Space

We aim to investigate whether spontaneous perspective taking occurs in an unconventional setting, a space station, where inverted human positions are commonly observed

Hierarchical Constraints on the Distribution of Attention in Dynamic Displays

Human vision is remarkably good at recovering the latent hierarchical structure of dynamic scenes. Here, we explore how visual attention operates with this hierarchical motion representation. The way in which attention responds to surface physical features has been extensively explored. However, we know little about how the distribution of attention can be distorted by the latent hierarchical structure. To explore this topic, we conducted two experiments to investigate the relationship between minimal graph distance (MGD), one key factor in hierarchical representation, and attentional distribution. In Experiment 1, we constructed three hierarchical structures consisting of two moving objects with different MGDs. In Experiment 2, we generated three moving objects from one hierarchy to eliminate the influence of different structures. Attention was probed by the classic congruent–incongruent cueing paradigm. Our results show that the cueing effect is significantly smaller when the MGD between two objects is shorter, which suggests that attention is not evenly distributed across multiple moving objects but distorted by their latent hierarchical structure. As neither the latent structure nor the graph distance was part of the explicit task, our results also imply that both the construction of hierarchical representation and the attention to that representation are spontaneous and automatic

Whether and how choice decision can be postdictively influenced by attention?

Our conscious perception of the world is not an instantaneous moment-by-moment construction, but rather the information subsequent in time seems to affect the percept of the prior event, which was known as postdictive effect. A recent study reported this postdictive effect could even occur in choice decision. The present study sought to test whether the striking postdictive effect of choice reflects the modulation of attention on choice, by directly and systematically manipulating attention in two experiments. Specifically, Experiment 1 revealed that the robust postdictive effect of choice was almost completely eliminated when the attentional bias triggered by a sudden color change was removed. More importantly, Experiment 2 demonstrated that the postdictive effect of choice could be modulated by directly manipulating participants’ attention with a spatial cue, in particular when the cue appeared at short time delays (less than 500 ms). These findings suggest that choice decision could be considerably postdicted by attention and this effect was most pronounced within a short time window wherein the decision was most likely in progress