Subjects: Psychology >> Cognitive Psychology submitted time 2024-05-03
Abstract： While previous studies have primarily focused on exploring the sources of time perception bias at an individual level, limited research has been conducted on understanding the mechanisms behind time perception bias in social contexts. To fill this gap, the present study combined a joint action paradigm with a time perception paradigm to investigate time perception in social contexts and further examine the mechanisms of co-representation and/or social facilitation in joint temporal perception through three experiments.
In general, the study utilized a between-subject 2 x 2 experimental design, with the factors of context (individual vs. joint) and duration distribution (short intervals vs. long intervals). The stimulus durations were either 400 ms or 1000 ms for the short interval group, and 1000 ms or 1600 ms for the long interval group. The different intervals were filled with either yellow or green circles. Participants first completed a learning task alone and then were randomly assigned to either an individual or joint context and completed a practice task. In the joint condition, two participants not knowing each other practiced in different temporal intervals and completed the experiment together. In the individual condition, one participant sat alone on the left or right side of the screen and completed the experiment. During the learning phase, participants were familiarized with the short- and long-interval stimuli. During the practice phase, orange solid circles of different durations (ranging from 400 ms to 1000 ms, in steps of 100 ms) or green solid circles (ranging from 1000 ms ~ 1600 ms, in steps of 100 ms) were randomly presented on the screen. Participants judged whether the duration of the stimulus was short or long according to the criteria formed during the learning phase.
In Experiment 1, we discovered that individuals’ subjective equivalence points were significantly altered, and their sensitivity to time perception was notably reduced in joint situations compared to individual situations. In Experiment 2, we stimulated co-representation by manipulating beliefs, and the absence of peers weakened the strength of social inhibition. The results indicated that individuals exhibited similar shifts in subjective equivalence points as observed in joint situations, but there was no significant change in temporal perceptual sensitivity. In Experiment 3, the effect of co-representation was attenuated by manipulating the peer’s task goal to be a non-temporal estimation task, while the presence of peers elicited social inhibition. The findings demonstrated a significant decrease in individuals’ time-perception sensitivity compared to the individual situation, but no significant shift in subjective equivalence points.
In summary, the present study suggests that individuals in joint action contexts represent their peers’ task information through the mechanism of co-representation, which introduces bias in time estimation. Additionally, the presence of others competes for attentional resources, leading to a reduction in individuals’ sensitivity to time perception in joint action contexts.
Peer Review Status:
Awaiting Review
Subjects: Psychology >> Psychological Measurement submitted time 2024-04-27
Abstract： Process data encompasses the human-computer interaction data captured in computer-based learning and assessment systems, reflecting participants’ problem-solving processes. Among various types of process data, action sequences stand out as a quintessential type, delineating participants’ step-by-step problem-solving processes. However, the non-standardized format of action sequences, characterized by varying data lengths among participants, presents challenges for the direct application of traditional psychometric models like diagnostic classification models (DCM). Extending psychometric models applicable to standardized structured data to process data analysis often necessitates key-action encoding – determining if each participant’s data contains essential problem-solving actions and encoding them (e.g., “1” for contains and “0” for does not contain ). Zhan and Qiao (2022) proposed a key-action encoding method facilitating the application of DCM to process data analysis for identifying participants’ mastery of problem-solving skills. Nevertheless, their approach overlooks the adverse impact of misconceptions on problem-solving. To this end, this study introduces a key-action encoding approach incorporating misconceptions and explores its utility in diagnostic classification analysis of process data. This new encoding method integrates both problem-solving skills and misconceptions, extending Zhan and Qiao’s (2022) approach.
An illustrative example is provided to compare the performance of the proposed encoding approach with Zhan and Qiao’s (2022) approach using a real-world interactive assessment item, “Tickets,” from PISA 2012. For the proposed approach, eight attributes (four problem-solving skills and four misconceptions) and 28 phantom items (i.e., key actions) were defined based on the scoring rule and assessment framework of the interactive assessment item. In contrast, Zhan and Qiao’s approach defined four attributes (problem-solving skills) and 10 phantom items. Four DCMs – DINA, DINO, ACDM, and GDINA models – were employed for data analysis. The relative fit metrics for model-data comparison were selected from AIC, BIC, CAIC, and SABIC. Additionally, a chi-square test was employed to evaluate whether there existed a significant difference in the fit to the data between GDINA and each of the constrained models. For assessing absolute fit between the model and the data, the SRMSR metric was utilized. Moreover, item quality was evaluated using the item differentiation index (IDI), while classification reliability was determined by calculating the classification accuracy index.
The findings reveal that: (1) considering both problem-solving skills and misconceptions enables more nuanced participant classification, facilitating identification of specific factors influencing problem-solving success and failure and offering targeted remedial suggestions for personalized instruction; (2) the introduction of misconceptions slightly enhances diagnostic classification reliability; (3) a moderate-to-high negative correlation exists between participants’ mastery of misconceptions and raw scores, indicating misconceptions diminish students’ overall problem-solving performance.
In summary, this study proposes a key-action encoding approach incorporating misconceptions and explores its application in diagnostic classification analysis of process data, specifically action sequences. The proposed approach aids researchers in pinpointing specific factors influencing problem-solving outcomes and provides methodological support for targeted interventions. To enhance participants’ problem-solving performance, beyond improving their skills, addressing misconceptions’ adverse effects merits consideration.
Peer Review Status:Awaiting Review
Subjects: Psychology >> Social Psychology submitted time 2023-12-19
Abstract： Smoking behavior and cessation are significantly influenced by self-exempting beliefs, which are considered to be rationalizations. Previous studies have recognized the process of cognitive dissonance underlying the emergence and formation of self-exempting beliefs. However, little is known about the reasons for the specific selection of self-exempting beliefs rather than other types of rationalization among smokers. The formation of self-exempting beliefs among smokers should involve three processes: cognitive dissonance and rationalization, highlighting self-specificity, and belief competition and stability. Given this situation, specific interventions for self-exempting beliefs, including hypocrisy induction, motivational interviewing, and question-based smoking warnings, could be conducted for smokers. Future researchers should conduct additional indigenous studies that focus on the characteristics of Chinese smokers and explore the mechanisms underlying the influence of self-exempting beliefs on intentions to quit smoking, the factors that impact the emergence and formation of self-exempting beliefs, and effective interventions for addressing self-exempting beliefs.
Subjects: Psychology >> Psychological Measurement submitted time 2023-01-05
Abstract： Process data refers to the human-computer or human-human interaction data recorded in computerized learning and assessment systems that reflect respondents’ problem-solving processes. Among the process data, action sequences are the most typical data because they reflect how respondents solve the problem step by step. However, the non-standardized format of action sequences (i.e., different data lengths for different participants) also poses difficulties for the direct application of traditional psychometric models. Han et al. (2021) proposed the SRM by combining dynamic Bayesian networks with the nominal response model (NRM) to address the shortcomings of existing methods. Similar to the NRM, the SRM uses multinomial logistic modeling, which in turn assigns different parameters to each possible action sequence in the task, leading to high model complexity. Given that action sequences in problem-solving tasks have correct and incorrect outcomes rather than equivalence relations without quantitative order, this paper proposes two action sequence models based on binary logistic modeling with relatively low model complexity: the one- and two-parameter action sequence models (1P and 2P-ASM). Unlike the SRM, which applies the NRM migration to action sequence analysis, the 1P-ASM and 2P-ASM migrate the simpler one- and two-parameter IRT models to action sequence analysis, respectively. An illustrated example was provided to compare the performance of SRM and two ASMs with a real-world interactive assessment item, “Tickets,” in the PISA 2012. The results mainly showed that: (1) the latent ability estimates of two ASMs and the SRM had high correlation; (2) ASMs took less computing time than that of SRM; (3) participants who are solving the problem correctly tend to continue to present the correct action sequences, and vice versa; and (4) compared with the fixed discrimination parameter of the SRM, the free estimated discrimination parameter of the 2P-ASM helped us to better understand the task. A simulation study was further designed to explore the psychometric performance of the proposed model in different test scenarios. Two factors were manipulated: sample size (including 100, 200, and 500) and average problem state transition sequence length (including short and long). The SRM was used to generate the state transition sequences in the simulation study. The problem-solving task structure from the empirical study was used. The results showed that: (1) two ASMs could provide accurate parameter estimates even if they were not the data-generation model; (2) the computation time of both ASMs was lower than that of SRM, especially under the condition of a small sample size; (3) the problem-solving ability estimates of both ASMs were in high agreement with the problem-solving ability estimate of the SRM, and the agreement between 2P-ASM and SRM is relatively higher; and (4) the longer the problem state transition sequence, the better the recovery of problem solving ability parameter for both ASMs and SRM. Overall, the two ASMs proposed in this paper based on binary logistic modeling can achieve effective 6 analysis of action sequences and provide almost identical estimates of participants' problem-solving ability to SRM while significantly reducing the computational time. Meanwhile, combining the results of simulation and empirical studies, we believe that the 2P-ASM has better overall performance than the 1P-ASM; however, the more parsimonious 1P-ASM is recommended when the sample size is small (e.g., 100 participants) or the task is simple (fewer operations are required to solve the problem).
Peer Review Status:Awaiting Review
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