Collaborative Communication Between Psychologists and Primary Care Providers

Psychologists frequently collaborate in the care of patients managed in primary care. Communication with a patient’s primary care team is important to ensure coordination and continuity of care. The communication is far from seamless. Although The Health Information Privacy and Portability Act (HIPPA) is designed to promote sharing of clinical information while protecting patient confidentiality, unique problems arise when mental health records are included. Mental health records are subject to different regulations to protect the patient’s confidentiality. Thus, what is communicated and how it will be accomplished are challenges. Further, psychologists and primary care providers often view documentation differently, resulting in different styles of documenting that may also impede coordinated care. Increasingly, health care systems are moving toward electronic medical records, creating greater opportunities for an integrated record. Improved communication through the record can keep other providers abreast of the mental health care being provided as well as suggestions they can use to reinforce the mental health care treatment plan.     

Learning by Arguing About Evidence and Explanations

Collaborative learning with cases characteristically involves discussing and developing shared explanations. We investigated the argumentation scheme which learners use in constructing shared explanations over evidence. We observed medical students attempting to explain how a judge had arrived at his verdict in a case of medical negligence. The students were learning within a virtual learning environment and their communication was computer mediated. We identify the dialogue type that these learners construct and show that their argumentation conforms with an abductive form of argumentation scheme (‘inference to the best explanation’). We also assessed the students’ learning and propose that it is related to particular features of this argumentation scheme.     

Reasoning serves argumentation in children

The argumentative theory of reasoning (Mercier & Sperber, in press-c) claims that reasoning evolved for argumentation: to find and evaluate arguments in dialogic contexts. The theory has drawn most of its supportive evidence from work with adults, leaving open the possibility that argumentive features of reasoning are in fact entirely learned. Evidence is reviewed here suggesting that the special relation between reasoning and argumentation holds at all ages. More specifically, it is argued that (a) children possess at least rudimentary argument skills, (b) they are able to reap the benefits of social reasoning from very early on, (c) confirmation bias is present as soon as they start to argue, and (d) children can be victims of the same biases that affect adults when they use reasoning in the wrong contexts. These claims strengthen the argumentative theory of reasoning and support a call for more research on the interactive features of reasoning in both adults and children.     

Coordinating cognition: the costs and benefits of shared gaze during collaborative search

Collaboration has its benefits, but coordination has its costs. We explored the potential for remotely located pairs of people to collaborate during visual search, using shared gaze and speech. Pairs of searchers wearing eyetrackers jointly performed an O-in-Qs search task alone, or in one of three collaboration conditions: shared gaze (with one searcher seeing a gaze-cursor indicating where the other was looking, and vice versa), shared-voice (by speaking to each other), and shared-gaze-plus-voice (by using both gaze-cursors and speech). Although collaborating pairs performed better than solitary searchers, search in the shared gaze condition was best of all: twice as fast and efficient as solitary search. People can successfully communicate and coordinate their searching labor using shared gaze alone. Strikingly, shared gaze search was even faster than shared-gaze-plus-voice search; speaking incurred substantial coordination costs. We conclude that shared gaze affords a highly efficient method of coordinating parallel activity in a time-critical spatial task.     

Motivated learning for the development of autonomous systems

A new machine learning approach known as motivated learning (ML) is presented in this work. Motivated learning drives a machine to develop abstract motivations and choose its own goals. ML also provides a self-organizing system that controls a machine’s behavior based on competition between dynamically-changing pain signals. This provides an interplay of externally driven and internally generated control signals. It is demonstrated that ML not only yields a more sophisticated learning mechanism and system of values than reinforcement learning (RL), but is also more efficient in learning complex relations and delivers better performance than RL in dynamically-changing environments. In addition, this paper shows the basic neural network structures used to create abstract motivations, higher level goals, and subgoals. Finally, simulation results show comparisons between ML and RL in environments of gradually increasing sophistication and levels of difficulty.     

THE END OF NEO-LIBERALISM AND THE BEGINNINGS OF INTEGRAL ECONOMICS

A burgeoning policy shift from neo-liberal economics is underway, with leadership by presidential candidate Dennis Kucinich (D-Ohio). His platform positions stem in part from his negative experiences with neo-liberalism when he was Mayor of Cleveland more than 30 years ago. Although his response as Mayor was based on confrontation politics, examples of community-based economies built on collaborative planning, ownership, and management have since become more widely known. We can now show that the successful Grameen Bank and the Mondragon Cooperatives were constructed on principles consistent with Integral Science.