Revealing psychological inertia in mode shift behavior and its quantitative influences on commuting trips

The inertia effects stemmed from repeated past behavior have been investigated by both psychology and transportation studies because of its bearing on explaining human mobility and forecasting travel demand. However, the existing literature from psychology does not strictly control potential endogeneity due to ignorance of detailed level-of-service (LOS) variables of alternatives and rational preference in the analysis. Quantitative transportation studies are insufficient in providing explicit behavior mechanisms. This paper aims to fill the gaps by empirically examining the effects of irrational psychological inertia in mode shift behavior with controlling potential endogeneity. A specific-designed comparison experiment is conducted to demonstrate the existence of psychological inertia in mode shift behavior. The effects of dominance in LOS variables and rational preference towards a certain transport mode are controlled to eliminate potential endogeneity in the analysis. The results demonstrate that after controlling the above-mentioned endogeneity, both car and metro users show significantly and substantially larger predilections to previously used transport mode in mode shift scenarios without overturning travel contexts than those in new context mode choice scenarios with noticeable changes in travel contexts. The results support that psychological inertia plays a significant role in mode shift behavior after controlling potential endogeneity. Moreover, this study utilizes hybrid choice modeling to quantitatively measure the effect of psychological inertia. The relationships between travelers’ characteristics and strength of psychological inertia are analyzed as well to shed light on heterogeneity in the strength of psychological inertia. The findings provide solid evidence of psychological inertia in mode shift behavior by a novel method and provide an approach to measure the quantitative effects of psychological inertia along with empirical studies.     

基于机器学习的古典诗词作者的判别研究

主要基于机器学习的NaveBaye等方法,对诗词文本采用向量空间模型来表示,首次提出了中国古代诗人李白和杜甫作品判别计算模型,并采用了信息增益作为特征选择的依据,结合爬山法完善了计算模型,该模型已经在精典语料《全唐诗》的机器学习基础上得到实现,获得较好的诗人作品判别效果,准确度可达到98.3%,该方法已成功地推广到古典诗词的风格流派的机器判别上[1],准确度达到88.5%。本文提出了古典诗词研究的技术方向。     

Accounting for the Theory of Planned Behaviour in departure time choice

Motivating people to change their departure time could play a key role in reducing peak-hour congestion, which remains one of the most prevalent transport problems in large urban areas. To achieve this behavioural change, it is necessary to better understand the factors that influence departure time choice. So far departure time choice modelling focussed mainly on objective factors, such as time and costs as main behavioural determinants. In this study, we derived psychological factors based on the Theory of Planned Behaviour, estimated them based on structural equation modelling, and included them into a discrete choice model. The psychological factors were measured based on an online questionnaire addressed to car commuters to the city centre of Copenhagen (N = 286). The questionnaire additionally included a travel diary and a stated preference experiment with nine departure time choice scenarios. All psychological factors had a significant effect on departure time choice and could improve the model as compared to a basic discrete choice model without latent constructs. As expected, the effects of the psychological factors were different depending on framework conditions: for people with fixed starting times at work, the intention to arrive at work on time (as estimated by subjective norm, attitude, perceived behavioural control) had the strongest effect; for people with flexible working hours, the attitude towards short travel time was most relevant. Limitations, the inclusion of additional psychological factors and their possible interactions are discussed.     

What are the factors determining user intentions to use AV while impaired?

In this study, we employ the Integrated Choice Latent Variable (ICLV) framework to model the public’s intention of using Autonomous Vehicles (AVs) while impaired under the influence of alcohol, medicine, or fatigue. We identify five latent constructs from psychometric indicators that define respondent’s perception and attitudes towards AVs which are i) perceived benefits, ii) perceived risks, iii) enjoy driving, iv) wheels public transport attitude, and v) rails public transport attitude. We use these latent variables along with explanatory variables to study user intentions regarding delegation of vehicle control from human driving to autonomous driving. The study uses survey data collected from 1,065 Czech residents between 2017 and 2018. Our findings indicate that user intentions are primarily defined by attitudes rather than socio-demographic attributes. However, the inclusion of both types of variables is crucial in evaluating user intentions. Despite a positive outlook towards AVs, people were found to be reluctant in using AVs while impaired which can be attributed to distrust towards the technology. Our analysis shows that with appropriate efforts from policymakers, the public’s attitude can be changed to promote adoption. The efforts will have to be emphasized towards building positive attitudes (such as perceived benefits) and diminishing existing negative attitudes (such as perceived risks).     

A Bayesian hierarchical model for the measurement of working memory capacity

Working memory is the memory system that allows for conscious storage and manipulation of information. The capacity of working memory is extremely limited. Measurements of this limit, and what affects it, are critical to understanding working memory. Cowan (2001) and Pashler (1988) suggested applying multinomial tree models to data from change detection paradigms in order to estimate working memory capacity. Both Pashler and Cowan suggested simple formulas for estimating capacity with these models. However, in many cases, these simple formulas are inadequate, and may lead to inefficient or biased estimation of working memory capacity. I propose a Bayesian hierarchical alternative to the Pashler and Cowan formulas, and show that the hierarchical model outperforms the traditional formulas. The models are easy to use and appropriate for a wide range of experimental designs. An easy-to-use graphical user interface for fitting the hierarchical model to data is available.     

Evaluating models of recognition memory using first- and second-choice responses

Swets, Tanner Jr., and Birdsall (1961) proposed a 4-alternative forced-choice task with two choices (4AFC-2R) for distinguishing between the Equal-Variance Signal Detection model and the One-High Threshold model. This task was recently implemented in the field of recognition memory (Parks & Yonelinas, 2009), a field in which several candidate models have been proposed. One advantage of the 4AFC-2R task is that it permits parameter estimation and goodness of fit testing, something which so far was only possible through the use of Receiver Operating Characteristic (ROC) functions for the more complex candidate models. The present article provides a thorough characterization and comparison of the main recognition memory models in the context of this task. Results are illustrated by a reanalysis of Parks and Yonelinas’ original data, revealing a preference for hybrid approaches to recognition memory, more specifically for the dual-process model (Yonelinas, 1997), whereas pure signal detection models performed poorly. The present analysis provides an assessment of the merits and limitations of this task, highlighting future research applications.     

OPTIMAL practice conditions enhance the benefits of gradually increasing error opportunities on retention of a stepping sequence task

IntroductionPhysical therapists should implement practice conditions that promote motor skill learning after neurological injury. Errorful and errorless practice conditions are effective for different populations and tasks. Errorful learning provides opportunities for learners to make task-relevant choices. Enhancing learner autonomy through choice opportunities is a key component of the Optimizing Performance through Intrinsic Motivation and Attention for Learning (OPTIMAL) theory of motor learning. The objective of this study was to evaluate the interaction between error opportunity frequency and OPTIMAL (autonomy-supportive) practice conditions during stepping sequence acquisition in a virtual environment.MethodsForty healthy young adults were randomized to autonomy-supportive or autonomy-controlling practice conditions, which differed in instructional language, focus of attention (external vs internal) and positive versus negative nature of verbal and visual feedback. All participants practiced 40 trials of 4, six-step stepping sequences in a random order. Each of the 4 sequences offered different amounts of choice opportunities about the next step via visual cue presentation (4 choices; 1 choice; gradually increasing [1-2-3-4] choices, and gradually decreasing [4-3-2-1] choices). Motivation and engagement were measured by the Intrinsic Motivation Inventory (IMI) and the User Engagement Scale (UES). Participants returned 1–3 days later for retention tests, where learning was measured by time to complete each sequence. No choice cues were offered on retention.ResultsParticipants in the autonomy-supportive group outperformed the autonomy-controlling group at retention on all sequences (mean difference 2.88s, p < .005, t[6835] = 3.42). Participants in both groups had the most difficulty acquiring the decreasing choice (4-3-2-1) sequence (p < .001, t[6835] = −4.26) and performed most poorly on the errorful (4 choice) sequence (p < .034, t[6835] = 2.65) at retention. Participants in the autonomy-supportive group performed best at retention on the increasing choice (1-2-3-4) sequence (p < .033, t[6835] = −2.7). Participants in both groups who reported greater attention to the task on the UES Average Focused Attention subscale during acquisition had poorer retention performance, particularly for the decreasing choice (4-3-2-1) sequence (p < .005, t(6835) = 3.39). Participants in the autonomy-supportive group reported significantly higher overall motivation (p = .007, t(38) = 0.728, d = 0.248) on the IMI as compared to the autonomy-controlling group.ConclusionIndividual benefits of errorless learning and autonomy-supportive practice conditions, with an interaction effect for practice that begins errorless but adds increasing error opportunities over time, suggest that participants relied on implicit learning strategies for this full body task and that feedback about successes minimized errors and reduced their potential information-processing benefits. Subsequent work will continue to examine how assigning a positive versus a negative quality to error provision influences the benefits of errorful learning in a variety of tasks.     

The complexity of satisfiability for fragments of hybrid logic—Part I

The satisfiability problem of hybrid logics with the downarrow binder is known to be undecidable. This initiated a research program on decidable and tractable fragments.In this paper, we investigate the effect of restricting the propositional part of the language on decidability and on the complexity of the satisfiability problem over arbitrary, transitive, total frames, and frames based on equivalence relations. We also consider different sets of modal and hybrid operators. We trace the border of decidability and give the precise complexity of most fragments, in particular for all fragments including negation. For the monotone fragments, we are able to distinguish the easy from the hard cases, depending on the allowed set of operators.     

NLP心理疗法——一种简单快速的心理咨询与治疗方法

NLP心理疗法是从完形疗法发展而来的一种,目前正被越来越多地应用于心理咨询与心理治疗的临床工作。本文主要介绍了NLP心理疗法的主要思想、基本原理和常用治疗技巧,以及把NLP疗法与精神分析进行了简要比较。     

A step forward: Bayesian hierarchical modelling as a tool in assessment of individual discrimination performance

Individual assessment of infants’ speech discrimination is of great value for studies of language development that seek to relate early and later skills, as well as for clinical work. The present study explored the applicability of the hybrid visual fixation paradigm (Houston et al., 2007) and the associated statistical analysis approach to assess individual discrimination of a native vowel contrast, /aː/ - /eː/, in Dutch 6 to 10-month-old infants. Houston et al. found that 80% (8/10) of the 9-month-old infants successfully discriminated the contrast between pseudowords boodup - seepug. Using the same approach, we found that 12% (14/117) of the infants in our sample discriminated the highly salient /aː/ -/eː/ contrast. This percentage was reduced to 3% (3/117) when we corrected for multiple testing. Bayesian hierarchical modeling indicated that 50% of the infants showed evidence of discrimination. Advantages of Bayesian hierarchical modeling are that 1) there is no need for a correction for multiple testing and 2) better estimates at the individual level are obtained. Thus, individual speech discrimination can be more accurately assessed using state of the art statistical approaches.