冲突水平的变化诱发冲突适应

冲突适应是重要的认知控制现象, 反映了认知控制的动态过程。然而, 目前为止对于冲突水平上的差异能否诱发冲突适应尚不清楚。本研究采用字母Flanker的变式, 通过改变目标-分心物的一致性操纵冲突水平, 探究冲突水平的变化对认知控制调整的影响。结果发现被试的反应时间随冲突水平的提高而增加; 而且先前试次的一致性影响当前试次的干扰效应, 无冲突和低冲突、无冲突和高冲突、低冲突和高冲突条件之间均出现了典型的冲突适应。本研究结果表明, 除了冲突的有无, 冲突水平的变化也能诱发冲突适应, 支持更大的冲突驱动更强的认知控制, 而且冲突诱发的认知控制的功能可能通过注意聚焦实现。这一发现为冲突监测理论的注意调节机制提供了直接的支持证据, 对于促进冲突适应的相关研究具有重要意义。     

Dynamics Versus Development in Numerosity Estimation: A Computational Model Accurately Predicts a Developmental Reversal

Perceptual judgments result from a dynamic process, but little is known about the dynamics of number-line estimation. A recent study proposed a computational model that combined a model of trial-to-trial changes with a model for the internal scaling of discrete numbers. Here, we tested a surprising prediction of the model—a situation in which children's estimates of numerosity would be better than those of adults. Consistent with the model simulations, task contexts led to a clear developmental reversal: children made more adult-like, linear estimates when to-be-estimated numbers were descending over trials (i.e., backward condition), whereas adults became more like children with logarithmic estimates when numbers were ascending (i.e., forward condition). In addition, adults’ estimates were subject to inter-trial differences regardless of stimulus order. In contrast, children were not able to use the trial-to-trial dynamics unless stimuli varied systematically, indicating the limited cognitive capacity for dynamic updates. Together, the model adequately predicts both developmental and trial-to-trial changes in number-line tasks.     

Linguistic Distributional Knowledge and Sensorimotor Grounding both Contribute to Semantic Category Production

The human conceptual system comprises simulated information of sensorimotor experience and linguistic distributional information of how words are used in language. Moreover, the linguistic shortcut hypothesis predicts that people will use computationally cheaper linguistic distributional information where it is sufficient to inform a task response. In a pre-registered category production study, we asked participants to verbally name members of concrete and abstract categories and tested whether performance could be predicted by a novel measure of sensorimotor similarity (based on an 11-dimensional representation of sensorimotor strength) and linguistic proximity (based on word co-occurrence derived from a large corpus). As predicted, both measures predicted the order and frequency of category production but, critically, linguistic proximity had an effect above and beyond sensorimotor similarity. A follow-up study using typicality ratings as an additional predictor found that typicality was often the strongest predictor of category production variables, but it did not subsume sensorimotor and linguistic effects. Finally, we created a novel, fully grounded computational model of conceptual activation during category production, which best approximated typical human performance when conceptual activation was allowed to spread indirectly between concepts, and when candidate category members came from both sensorimotor and linguistic distributional representations. Critically, model performance was indistinguishable from typical human performance. Results support the linguistic shortcut hypothesis in semantic processing and provide strong evidence that both linguistic and grounded representations are inherent to the functioning of the conceptual system. All materials, data, and code are available at https://osf.io/vaq56/ .     

Keyboard dynamics discrepancies between baseline and deceptive eyewitness narratives

Information manipulation and cognitive load imposition make the production of deceptive narratives difficult. But little is known about the production of deception, and how its mechanisms may help distinguish truthful from deceitful narratives. This study focuses on the measurement of keystroke dynamics while typing truthful and deceptive eyewitness testimonies after a baseline assessment. While typing their narrative, some participants would undergo an auditory cognitive load. Results show that liars typed their story slower, and in less time than the truthful participants when compared to their respective baselines. The imposition of the auditory cognitive load showed adverse results, enhancing the amount of keystrokes and the time necessary to type the narrative. Classification shows better results for deceptive narrative when no auditory cognitive load is imposed. These results are discussed in terms of expanding current models to include the cognition of linguistic production and writing strategies.     

What could be My Next Job? Using Flat Information Structures to Generate Creative Future Career Ideas

In a dynamic labor market, it is important to help people combine information and generate creative solutions to cope with complex career challenges. In the present research, we apply the theory of information structure to creative career idea generation and hypothesize that flat information structures—that is, structures in which the information is disorganized—are more conducive to creativity than hierarchical information structures—that is, structures in which information is organized in higher‐order categories. In two experimental studies, participants had to combine career information related to self and work preferences that was presented either in flat or hierarchical structures. We found that flat information structures, compared with hierarchical information structures, led to future career ideas that were more creative on average. Our results suggest that cognitive flexibility explains the effect of information structure on the creativity of career ideas. Theoretical implications and suggestions for career management practices are discussed.     

Brief Cognitive-Behavioral Therapy for Suicidal Inpatients

Suicide risk is elevated in psychiatric patients following discharge from inpatient care. Despite this vulnerability, there has been limited research investigating suicide prevention protocols that take into account the unique system characteristics of this setting (e.g., short lengths of stay, crisis stabilization treatment model, multidisciplinary team coordination). Cognitive-behavioral therapy (CBT) has demonstrated efficacy for improving suicide risk in outpatients, but has not been validated with inpatients. The current study was a treatment development and pilot feasibility open trial that modified brief cognitive-behavioral therapy (BCBT) for an inpatient setting (BCBT-I). Key treatment modifications included administering up to 10 sessions (depending on patient length of stay), daily, and in a standardized order, with core crisis management skills introduced during the first three sessions. In addition, coordination with the inpatient treatment team was included in BCBT-I implementation. Six adult inpatients with a recent suicide attempt enrolled and completed an average of 4.67 BCBT-I sessions (SD = 1.36). The treatment was highly acceptable (Client Satisfaction Questionnaire total score M = 3.49, SD = 0.73). Pre- to posttreatment effect sizes demonstrated improvements in suicidal ideation (d = 0.97), depression (d = 1.33), and suicidal implicit associations (d = 1.28). All but one of the participants (83%) completed follow-up assessments 1-, 2-, and 3-months postdischarge. Over follow-up, two participants reported suicidal ideation (both without intent), and none reported suicide attempts, preparatory acts or behaviors, or nonsuicidal self-injury. This study provided preliminary evidence supporting the feasibility of CBT to treat suicidal inpatients. Future research is needed to validate BCBT-I in a larger, randomized controlled trial to determine whether BCBT-I reduces suicide risk beyond that afforded by inpatient treatment alone.     

Adaptations in driver deceleration behaviour with automatic incident detection: A naturalistic driving study

Traffic congestion and crash rates can be reduced by introducing variable speed limits (VSLs) and automatic incident detection (AID) systems. Previous findings based on loop detector measurements have revealed that drivers reduce their speeds while approaching traffic congestion when the AID system is active. Notwithstanding these behavioural effects, most microscopic traffic flow models assessing the impact of VSLs do not describe driver response accurately.This study analyses the main factors that influence driver deceleration behaviour while approaching traffic congestion with and without VSLs. The Dutch VSL database was linked to the driver behaviour data collected in the UDRIVE naturalistic driving study. Driver engagement in secondary tasks and glance behaviour were extracted from the video data. Linear mixed-effects models predicting the characteristics of deceleration events were estimated.The results show that the maximum deceleration is high when approaching a slower leader, when driving at high speeds and short distance headways, and close to the beginning of traffic congestion. The minimum time headway is short when driving at high speeds and changing lanes. Certain drivers showed higher decelerations and shorter time headways than others. Controlled for these main factors, smaller maximum decelerations were found when the VSLs were present and visible, and when the gantries were within close proximity. These factors could be incorporated into microscopic traffic simulations to evaluate the impact of AID systems on traffic congestion more realistically. Further research is needed to clarify the link between engagement in secondary tasks, glance behaviour and deceleration behaviour.     

Trust and intention to use autonomous vehicles: Manufacturer focus and passenger control

Autonomous Vehicle (AV) research has focused on public acceptance of and intention to use AVs, with trust emerging as important to these variables. Research on AV trust has centered on trust in vehicle performance. However, trust evaluation may include AV manufacturers and developers, and regulation pertaining to AVs; thus, we expand our measurement of trust to include beliefs based on manufacturers and regulation. In this experiment, we manipulate information regarding manufacturer focus (i.e., an emphasis on standards, regulations, and research (SRR) versus speed to market) to determine its effect on trust. When information focused on SRR, we found higher levels of trust in AV performance and in manufacturers, compared to when it focused on speed to market. In addition, information regarding passenger control (i.e., the ability to take over for the vehicle and to determine privacy settings) was manipulated to yield either high or low passenger control conditions. Behavioral Intention (BI) to use AVs was lowest when speed to market was emphasized and when passenger control was low. Furthermore, trust was tested as a mediator between perceived risk and BI. Trust in AV performance partially mediated the relationship between perceived AV performance risk and BI. In addition, trust in AV regulation partially mediated the relationship between AV privacy/security risk and BI. Researchers should continue to examine trust beyond the AV itself to encompass trust in manufacturers and regulations. Those designing and marketing AVs should carefully consider decisions that influence manufacturer/developer reputation and passenger control due to their effect on intention to use AVs.     

Learning algorithm for an intelligent decision making system based on multi-agent neurocognitive architectures

The paper presents the formalism of an intelligent decision-making system based on multi-agent neurocognitive architectures, which has an architectural similarity to the human brain. An invariant of the organizational and functional structure of the intellectual decision-making process based on the multi-agent neurocognitive architecture is developed. An algorithm for teaching intelligent decision-making systems based on the self-organization of the invariant of multi-agent neurocognitive architectures is presented. Using this algorithm, an intelligent agent was trained and the architecture of the learning process was built on the basis of an invariant of neurocognitive architecture. Further research is related to training an intelligent agent in more complex behavior and expanding the capabilities of an intelligent decision-making system based on multi-agent neurocognitive architectures.     

Cogmic space for narrative-based world representation

Representing a world or a physical/social environment in an agent’s cognitive system is essential for creating human-like artificial intelligence. This study takes a story-centered approach to this issue. In this context, a story refers to an internal representation involving a narrative structure, which is assumed to be a common form of organizing past, present, future, and fictional events and situations. In the artificial intelligence field, a story or narrative is traditionally treated as a symbolic representation. However, a symbolic story representation is limited in its representational power to construct a rich world. For example, a symbolic story representation is unfit to handle the sensory/bodily dimension of a world. In search of a computational theory for narrative-based world representation, this study proposes the conceptual framework of a Cogmic Space for a comic strip-like representation of a world. In the proposed framework, a story is positioned as a mid-level representation, in which the conceptual and sensory/bodily dimensions of a world are unified. The events and their background situations that constitute a story are unified into a sequence of panels. Based on this structure, a representation (i.e., a story) and the represented environment are connected via an isomorphism of their temporal, spatial, and relational structures. Furthermore, the framework of a Cogmic Space is associated with the generative aspect of representations, which is conceptualized in terms of unconscious- and conscious-level processes/representations. Finally, a proof-of-concept implementation is presented to provide a concrete account of the proposed framework.