Client Versus Clinicians’ Standards of Clinically Meaningful Change and the Effects of Treatment Expectations on Therapeutic Outcomes in Individuals With Posttraumatic Stress Disorder

There is limited research on the concordance between client perceptions and clinician standards of the degree of symptom change required to achieve meaningful therapeutic improvement. This was investigated in an adult sample (N = 147) who received trauma-focused cognitive-behavioral therapies for posttraumatic stress disorder (PTSD). We examined whether clients’ benchmarks of change were related to actual outcomes and the relationship between client expectations and their treatment outcomes. Clients completed measures indexing the level of symptom reduction required (in their view) to reflect a benefit or recovery from treatment and treatment expectations. Actual PTSD severity was indexed pre- and posttreatment via self-report and clinician-administered interview. Results demonstrated that the amount of change clients said they required to experience a benefit or recovery was significantly larger than typical clinical research standards. Nonetheless, the majority of client benchmarks of change (79.7–81.8%) were consistent with clinical research standards of what constitutes benefit or recovery. Client benchmarks were generally positively correlated with their actual outcomes. Clients’ belief that treatment would be successful was associated with greater reductions in PTSD symptoms. These findings provide preliminary evidence that the standards used to determine clinically significant change are somewhat consistent with clients’ own perceptions of required symptom change.     

A Single-Session Online Cognitive Bias Modification of Interpretations Modified for Adults With Anxiety and Depressive Symptoms

Anxiety and depression are common, co-occurring, and costly mental health disorders. Cognitive bias modification aims to modify biases to reduce associated symptoms. Few studies have targeted multiple biases associated with both anxiety and depression, and those that have lacked a control condition. This study piloted a single-session online cognitive bias modification (known as CBM-IA) designed to target two biases associated with anxiety and depression—interpretation bias and attribution style—in adults with varying levels of anxiety and depressive symptoms. Participants (18–26 years) with at least mild levels of anxiety/stress and depressive symptoms on the DASS-21 were randomly allocated to an intervention (n = 23) or a control (n = 22) condition. The training consisted of a single-session online CBM-IA to encourage positive interpretations and a positive attribution style.Interpretation bias, attribution style, anxious and depressive mood states, and anxiety, stress and depressive symptoms improved at posttraining and at follow-up, irrespective of condition. Changes in interpretation bias from pre- to posttraining were significantly associated with changes in anxious mood state. CBM-IA, as implemented in this single-session pilot study, did not significantly reduce targeted biases and symptoms compared to a control condition. This adds to the mixed evidence on the efficacy of single-session CBM-I for altering biases and symptoms.     

Interpretational Biases in Emotional Psychopathology

Many psychological disorders are characterized by interpretational processing biases, and Aaron T. Beck's work provided a crucial theoretical foundation for assessing, treating, and researching them. The present commentary aims to sketch a brief historical overview of how this work evolved and developed, and how it has been translated into concrete assessment techniques for use in treatment and led to innovative research lines in clinical research.     

Understanding the immediate aftereffects of road traffic crashes in Argentina: The role of injury

The current paper reports a field study of 132 Argentinian individuals who experienced a road traffic crash (RTC), focusing on the role of physical injury in the early aftermath of the crash. Three groups of participants were studied: 1) individuals who were <1 month post-RTC who were not injured (<1 mo, Not Injured, n = 89), 2) individuals who were <1 month post-RTC who were injured (<1 mo, Injured, n = 15), and 3) individuals who were 1–3 months post-RTC who were injured (1–3 mo, Injured, n = 28). Examination of PTSD symptoms indicated that with several exceptions, most PTSD symptoms were endorsed at higher levels by the two injured groups, relative to the not injured group. Consideration of post-RTC cognitive and interpersonal factors indicated that the two injured groups reported lower self-efficacy and higher levels of rumination, relative to the Not Injured group. Additionally, the 1–3 mo Injured group reported being more socially isolated, relative to the two other groups. Results are discussed in light of related literature, with elaboration of potential public health interventions designed to target injured survivors of RTCs.     

The influence of personality and cognitive failures on distracted driving behaviors among young adults

Distracted driving is a major safety concern. This paper explores the role personality traits and self-reported cognitive failures play in the propensity towards distracted driving behavior (DDB) among young adults in the United States. Two independent time-separated studies (study 1 with 522 participants; study 2 with 314 participants) confirm the role of cognitive failures as a mediator between specific personality traits and DDB propensity among young adult drivers. The results of this study suggest drivers’ personality traits such as extraversion, conscientiousness, and neuroticism have a direct impact on DDB propensity. In addition, there is evidence that cognitive failure mediates the relationship between these three personality traits and DDB propensity. Lastly, based on the results, agreeableness moderates the relationship between cognitive failure and DDB propensity. Together, these findings suggest that personality traits should be considered in conjunction with driver’s cognitive failure in explaining DDB propensity. Future research using a combination of self-reported, naturalistic and simulation studies may provide additional insight into the relationships between personality traits, cognitive failures, and the propensity towards DDB.     

Multimodal travel behaviour,attitudes, and cognitive dissonance

Multimodal travel behaviour, also termed multimodality, refers to as the phenomenon of an individual using more than one mode of transport in a given period. Studies indicate that encouraging multimodality may provide a solution to induce modal shifts towards sustainable transport. In this research, we investigate the distribution of mode-specific attitudes and attitude-mode use incompatibilities across clusters and levels of multimodality using the Netherlands Mobility Panel. We find that the most positive attitude does not necessarily correspond to the mode with the highest level of use. Attitudes towards car use are most positive, independent of the cluster membership and levels of multimodality. We also find that multimodal public transport users (compared with car-dominant users) and those with a higher level of multimodality are more likely to be attitudinally incompatible with frequently-used modes and the composition of their existing mode sets of travelling. This suggests that multimodal individuals may tend to experience cognitive dissonance with their mode use. Our findings also help uncover the psychological mechanism underlying a recent important finding that multimodal individuals are inclined to change their mode use patterns over time.     

Bio-inspired task-rule retrieval model with auditory sorting test

Cognitive architectures (CAs) are currently used to bring the behavior of computer systems closer to human behavior. One of the main capacities of humans is the ability to plan and make decisions. Thus, part of the human behavior is based on rules associated with the relevant environmental stimuli. Rule management can be divided into six needed processes: rule learning, rule retrieving, rule coding, rule updating, rule reinforcement, and rule changing. A key aspect of rule processing is retrieval, which involves the use of information extracted from memory. This work deals with how rules are stored in coded form in the brain, retrieved and used as the need arises. The proposed model takes inspiration mainly from the processes in the VLPFC and MTL brain areas to extract rules from memory and create a rule-set that is sent to the DLPFC. The DLPFC and VLPFC prefrontal area operations are also described, and a process is proposed to select the appropriate rule and give a response. The experimentation of the implementation of our proposal with different configuration parameters gives rise to different approaches to human behavior in rule retrieval. Our conclusion is that after a virtual entity is endowed with this proposal, it computes in a way similar to human behavior.     

Unveiling the reasoning processes of robots through introspective dialogues in a storytelling system: A study on the elicited empathy

The work studies the empathy elicited by a storytelling system in which the characters in the story are interpreted by humanoid robots and modelled as cognitive agents. The ACT-R architecture is exploited to shape the characters’ personalities and equip them with knowledge and behaviours typical of social practices. The narration is enriched with gestures and emotional expressions obtained by setting parameters that can be correlated to some emotions, such as the pitch and speech rate, the LEDs colour and the head inclination. The system has been evaluated by comparing a simple narrative modality with an enhanced one, where an introspective dialogue is adopted to explain and let transparent the internal reasoning processes of the characters. The obtained results show that storytelling affected the cognitive component of empathy, especially through the advanced narrative mode.     

Human vs. supervised machine learning: Who learns patterns faster?

The capabilities of supervised machine learning (SML), especially compared to human abilities, are being discussed in scientific research and in the usage of SML. This study provides an answer to how learning performance differs between humans and machines when there is limited training data. We have designed an experiment in which 44 humans and three different machine learning algorithms identify patterns in labeled training data and have to label instances according to the patterns they find. The results show a high dependency between performance and the underlying patterns of the task. Whereas humans perform relatively similarly across all patterns, machines show large performance differences for the various patterns in our experiment. After seeing 20 instances in the experiment, human performance does not improve anymore, which we relate to theories of cognitive overload. Machines learn slower but can reach the same level or may even outperform humans in 2 of the 4 of used patterns. However, machines need more instances compared to humans for the same results. The performance of machines is comparably lower for the other 2 patterns due to the difficulty of combining input features.     

Positive affect moderates inhibitory control and positive affect following a single bout of self-select aerobic exercise

PurposeResearch suggests that individual differences in baseline cognitive performance moderates subsequent cognitive benefits following a single bout of exercise. The present study seeks to evaluate additional moderating mental states – specifically positive affect – on inhibitory control and affect following exercise.MethodsUsing a within- and between-participants pre-post cross-over design, eighty university students (54 females; 21.7 ± 2.7 years old) completed a flanker task and affect measures before and after a single bout of aerobic exercise at a self-selected intensity or studying for class (15-min) on separate days. Groups of high-positive affect (HPA; n = 41) and low-positive affect (LPA; n = 39) were determined based on a median split of positive affect measures prior to the exercise bout.ResultsThe HPA group revealed shorter reaction time (RT) from before to after exercise and rest with no difference observed between exercise and rest. The LPA group revealed shorter RT after exercise compared to before exercise and after rest. For accuracy, the LPA group improved performance during the exercise session compared to the rest session to a level comparable to the HPA group. Lastly, positive affect decreased in the LPA and HPA groups from before to after rest; however, only the LPA group’s positive affect increased from before to after exercise.ConclusionIndividuals with low positive affect experience greater cognitive and positive affect improvements following acute aerobic exercise at a self-select intensity, further supporting intraindividual differences in mental states as a mechanism for subsequent cognitive and affective benefits encompassing healthy behaviors of exercise.