Multinomial process tree models of control and automaticity in weapon misidentification

When primed with a Black face, people are more likely to misidentify a non-weapon as a weapon. Weapon misidentification may hinge on the distinction between controlled and automatic processes. Various relationships between controlled and automatic processes are cast in the form of five multinomial process models, which are illustrated and compared. It is shown that variants of the traditional Process Dissociation model and the Stroop model are nested within the Quad-Model. Across four different studies, various complexity corrected model performance measures converged to support the Process Dissociation account. This account suggests that the automatic association between race and weapons is subordinate to controlled processing. More generally, these results suggest that the weapon-bias might be alleviated without interventions that directly target stereotypes.     

A Meta‐Analysis of the ELM's Argument Quality × Processing Type Predictions

One of the key dual‐process model predictions is that audiences will be more persuaded by strong persuasive arguments than weak and that this difference in persuasiveness will be larger when they are processing centrally rather than peripherally. A series of meta‐analyses were conducted (k = 134) to assess this claim and explore moderators. The data were generally consistent with the hypothesized interaction. The effects tended to be smaller when pre‐post designs were used rather than post‐test only. Assessments of the strength of the inductions did not tend to be associated with the size of the effects associated with those inductions.     

An intelligent facial expression recognition system with emotion intensity classification

Facial expressions play a crucial role in emotion recognition as compared to other modalities. In this work, an integrated network, which is capable of recognizing emotion intensity levels from facial images in real time using deep learning technique is proposed. The cognitive study of facial expressions based on expression intensity levels are useful in applications such as healthcare, coboting, Industry 4.0 etc. This work proposes to augment emotion recognition with 2 other important parameters, valence and emotion intensity. This helps in better automated responses by a machine to an emotion. The valence model helps in classifying emotion as positive and negative emotions and discrete model classifies emotions as happy, anger, disgust, surprise and neutral state using Convolution Neural Network (CNN). Feature extraction and classification are carried out using CMU Multi-PIE database. The proposed architecture achieves 99.1% and 99.11% accuracy for valence model and discrete model respectively for offline image data with 5-fold cross validation. The average accuracy achieved in real time for valance model and discrete model is 95% & 95.6% respectively. Also, this work contributes to build a new database using facial landmarks, with three intensity levels of facial expressions which helps to classify expressions into low, mild and high intensities. The performance is also tested for different classifiers. The proposed integrated system is configured for real time Human Robot Interaction (HRI) applications on a test bed consisting of Raspberry Pi and RPA platform to assess its performance.     

Attention in risky choice

Previous research on the processes involved in risky decisions has rarely linked process data to choice directly. We used a simple measure based on the relative amount of attentional deployment to different components (gains/losses and their probabilities) of a risky gamble during the choice process, and we related this measure to the actual choice. In an experiment we recorded the decisions, decision times, and eye movements of 80 participants who made decisions on 11 choice problems. We used the number of eye fixations and fixation transitions to trace the deployment of attention during the choice process and obtained the following main results. First, different components of a gamble attracted different amounts of attention depending on participants' actual choice. This was reflected in both the number of fixations and the fixation transitions. Second, the last-fixated gamble but not the last-fixated reason predicted participants' choices. Third, a comparison of data obtained with eye tracking and data obtained with verbal protocols from a previous study showed a large degree of convergence regarding the process of risky choice. Together these findings tend to support dimensional decision strategies such as the priority heuristic.     

Narrative innovation in life design counseling: The case of Ryan

The main aim of this research is to study the patterns of narrative change in a case of life design counseling (LDC). The innovative moments coding system (IMCS) was used to conduct an intensive analysis of the career counseling sessions. This coding system is grounded in a narrative conception of the self, and suggests that narrative change results from the elaboration and development of narrative exceptions to a client's core problematic self-narrative. The results supports the adequacy of the IMCS to the study of change in career counseling and reveal that the intervention promotes a pattern of change characterized by three types of innovative moments: action, reflection, and protest. The results are discussed taking into account their implications for career counseling theory, research and practice.     

Opening the black box: Presenting a model for evaluating organizational-level interventions

Organizational-level occupational health interventions are often recommended when improvements in working conditions, employee health, and well-being are sought within organizations. Research has revealed that these interventions result in inconsistent effects despite being based on theoretical frameworks. This inconsistency indicates that intervention studies need to be designed to examine directly how and why such interventions bring about change and why they sometimes fail. We argue that intervention studies should include a process evaluation that includes a close examination of the psychological and organizational mechanisms that hinder and facilitate desired intervention outcomes. By drawing on existing intervention literature we present an evidence-based model containing three levels of elements that appear to be crucial in process evaluation. We describe how this model may be applied and developed in future research to identify better the mechanisms that link intervention processes to intervention outcomes.     

Accountability and ideology: When left looks right and right looks left

Managers face hard choices between process and outcome systems of accountability in evaluating employees, but little is known about how managers resolve them. Building on the premise that political ideologies serve as uncertainty-reducing heuristics, two studies of working managers show that: (1) conservatives prefer outcome accountability and liberals prefer process accountability in an unspecified policy domain; (2) this split becomes more pronounced in a controversial domain (public schools) in which the foreground value is educational efficiency but reverses direction in a controversial domain (affirmative action) in which the foreground value is demographic equality; (3) managers who discover employees have subverted their preferred system favor tinkering over switching to an alternative system; (4) but bipartisan consensus arises when managers have clear evidence about employee trustworthiness and the tightness of the causal links between employee effort and success. These findings shed light on ideological and contextual factors that shape preferences for accountability systems.     

Reflections on research and learning from the patient: the art and science of what we do

Over three decades ago, John Bowlby argued for psychoanalysis to seek beyond its own parameters if it was to maintain its claim to be a science. Since then there has been a wealth of relevant research from various fields. While this has been instrumental in the development of my own work, this paper concerns learning from the patient. The paper begins with a premise: interpretative analytic work requires three‐dimensionality (self, other and object). Although interpretative work may be ingrained in our professional identity, this triangulation may or may not exist in our patients in any stable way. The paper continues with a brief developmental account of how early archetypally‐shaped shifts in the infant's field of interest establish the experiential components of three‐dimensionality. From there, observational and clinical material with a toddler and a young boy describe how early relational deficits hindered their capacities for three‐dimensionality. Yet both were able to engage with the therapist and to become active in the creation of three‐dimensionality within their own minds. Implied in this work are considerations for working with patients for whom interpretations do not work. Michael Fordham's comments on ‘working out of the self’ are linked with the art of what we do.     

Confirming,Validating, and Norming the Factor Structure of Systemic Therapy Inventory of Change Initial and Intersession

Progress or feedback research tracks and feeds back client progress data throughout the course of psychotherapy. In the effort to empirically ground psychotherapeutic practice, feedback research is both a complement and alternative to empirically supported manualized treatments. Evidence suggests that tracking and feeding back progress data with individual or nonsystemic feedback systems improves outcomes in individual and couple therapy. The research reported in this article pertains to the STIC^® (Systemic Therapy Inventory of Change)—the first client‐report feedback system designed to empirically assess and track change within client systems from multisystemic and multidimensional perspectives in individual, couple, and family therapy. Clients complete the STIC Initial before the first session and the shorter STIC Intersession before every subsequent session. This study tested and its results supported the hypothesized factor structure of the six scales that comprise both STIC forms in a clinical outpatient sample and in a normal, random representative sample of the U.S. population. This study also tested the STIC's concurrent validity and found that its 6 scales and 40 of its 41 subscales differentiated the clinical and normal samples. Lastly, the study derived clinical cut‐offs for each scale and subscale to determine whether and how much a client's score falls in the normal or clinical range. Beyond supporting the factorial and concurrent validity of both STIC forms, this research supported the reliabilities of the six scales (Omega_hierarchical) as well as the reliabilities of most subscales (alpha and rate–rerate). This article delineates clinical implications and directions for future research.