Dialogical Temporal Chair Technique

ABSTRACT

The Dialogical Temporal Chair Technique (DTCT) is a method based on Hermans’s dialogical self theory combined with Gestalt and cognitive–behavioural therapeutic techniques. The Dialogical Temporal Chair Technique is a method of activating temporal voices. Three chairs symbolising the past, present and future are used to help subjects create a temporal dialogue. Participants are asked to take a specific I-position and to construct a temporal self-dialogue by changing chair (and hence I-position). The DTCT can be used to solve undefined problems or to explore new ways of thinking, but it may also be used to review important life decisions or enable integration of, or detachment from, ambivalent feelings and attitudes.     

Step by step: A microgenetic study of the development of strategy choice in infancy

To examine patterns of strategy choice and discovery during problem‐solving of a novel locomotor task, 13.5‐ and 18‐month‐old infants were placed at the top of a staircase and encouraged to descend. Spontaneous stair descent strategy choices were documented step by step and trial by trial to provide a microgenetic account of problem‐solving in action. Younger infants tended to begin each trial walking, were more likely to choose walking with each successive step, and were more likely to lose their balance and have to be rescued by an experimenter. Conversely, older infants tended to begin each trial scooting, were more likely to choose scooting with each successive step, and were more likely to use a handrail to augment balance on stairs. Documenting problem‐solving microgenetically across age groups revealed striking similarities between younger infants' strategy development and older children's behaviour on more traditionally cognitive tasks, including using alternative strategies, mapping prior experiences with strategies to a novel task, and strengthening new strategies. As cognitive resources are taxed during a challenging task, resources available for weighing alternatives or inhibiting a well‐used strategy are reduced. With increased motor experience, infants can more easily consider alternative strategies and maintain those solutions over the course of the trial.     

Assessing Mathematics Misunderstandings via Bayesian Inverse Planning

Online educational technologies offer opportunities for providing individualized feedback and detailed profiles of students' skills. Yet many technologies for mathematics education assess students based only on the correctness of either their final answers or responses to individual steps. In contrast, examining the choices students make for how to solve the equation and the ways in which they might answer incorrectly offers the opportunity to obtain a more nuanced perspective of their algebra skills. To automatically make sense of step-by-step solutions, we propose a Bayesian inverse planning model for equation solving that computes an assessment of a learner's skills based on her pattern of errors in individual steps and her choices about what sequence of problem-solving steps to take. Bayesian inverse planning builds on existing machine learning tools to create a generative model relating (mis)-understandings to equation solving choices. Two behavioral experiments demonstrate that the model can interpret people's equation solving and that its assessments are consistent with those of experienced teachers. A third experiment uses this model to tailor guidance for learners based on individual differences in misunderstandings, closing the loop between assessing understanding, and using that assessment within an educational technology. Finally, because the bottleneck in applying inverse planning to a new domain is in creating the model of possible student misunderstandings, we show how to combine inverse planning with an existing production rule model to make inferences about student misunderstandings of fraction arithmetic.     

Erroneous gambling-related beliefs emerge from broader beliefs during problem-solving: a critical review and classification scheme

Abstract

Erroneous gambling-related beliefs (EGRBs) can be defined as beliefs that imply a failure to recognise how commercial gambling activities are designed to generate a guaranteed loss to players. In theorising about how EGRBs develop, previous reviews have proposed that EGRBs are extensions of decision-making heuristics and associated biases. We propose an alternative generative mechanism: one in which gambling games make substantial wins seem possible through problem-solving and eventual correct strategic action. EGRBs are then beliefs in the possibility of correct strategic action (illusions of control) that develop as players trial candidate strategies—strategies selected based on various broader beliefs. We further propose that EGRBs can be classified based on what is theorised in cognitive science about categories of general human beliefs about the world. For example, it has been theorised that human beliefs about supernatural forces and randomness have certain similarities across cultures, and so we propose that there exists a category of supernatural EGRBs, as well as a category of EGRBs based on broader beliefs about the nature of randomness. We review evidence for this classification scheme and discuss how it can be applied in researching and treating gambling disorder.     

The Experience of Insight Follows Incubation in the Compound Remote Associates Task

The phenomenon of insight is frequently characterized by the experience of a sudden and certain solution. Anecdotal accounts suggest that insight frequently occurs after the problem solver has taken some time away from the problem (i.e., incubation). However, the mechanism by which incubation may facilitate insight problem‐solving remains unclear. Here, we used compound remote associates problems to explore the likely mechanisms by which incubation may facilitate problem‐solving. First, we manipulated problem fixation to explore whether forgetting can explain incubation effects. Second, leveraging previous work linking the experience of insight to unconscious semantic integration, we asked participants to report their experience of insight after each problem solution, including problems solved after a period of distracted incubation. We hypothesized that incubation was not principally important for forgetting but rather frequently causes a shift to a more unconscious semantic integration strategy. Consistent with this we found that initial problem fixation did not predict the improvement in problem‐solving after incubation and that participants were more likely to report insight on problems solved after incubation. Our findings suggest that incubation may facilitate insight problem‐solving leading to a mind‐set shift to a more unconscious problem‐solving strategy involving semantic integration.     

Set as an Instance of a Real‐World Visual‐Cognitive Task

Complex problem solving is often an integration of perceptual processing and deliberate planning. But what balances these two processes, and how do novices differ from experts? We investigate the interplay between these two in the game of SET. This article investigates how people combine bottom‐up visual processes and top‐down planning to succeed in this game. Using combinatorial and mixed‐effect regression analysis of eye‐movement protocols and a cognitive model of a human player, we show that SET players deploy both bottom‐up and top‐down processes in parallel to accomplish the same task. The combination of competition and cooperation of both types of processes is a major factor of success in the game. Finally, we explore strategies players use during the game. Our findings suggest that within‐trial strategy shifts can occur without the need of explicit meta‐cognitive control, but rather implicitly as a result of evolving memory activations.     

Representational dynamics in the domain of iterated mental paper folding

Successful problem solving relies on the availability of suitable mental representations of the task domain. Especially for more complex problems, there might be a wide variety of possible problem representations, and it might even be beneficial to change them during problem solving. In a first part, we argue that investigating the dynamics of understanding in terms of dynamically changing problem representations is an underexplored aspect of problem solving research, and that most classic tasks even preclude the opportunity of such dynamics to occur. Continuing this theoretical discussion, as an illustrative example of a task designed for the exploration of such representational dynamics, the second part of the paper discusses a novel, complex spatial transformation and problem solving task. In this task, one is asked to repeatedly mentally cross-fold a sheet of paper, and to predict the resulting sheet geometry without the use of external aids. Through its deliberate openness and difficulty, this task requires finding new and more efficient representations – ranging from kinaesthetic and visuospatial imagery to symbolic notions. We present an overview of the task domain and discuss various ways of representing the domain as well as potential dynamics between them.     

Extending problem-solving procedures through reflection

A large-sample (n = 75) fMRI study guided the development of a theory of how people extend their problem-solving procedures by reflecting on them. Both children and adults were trained on a new mathematical procedure and then were challenged with novel problems that required them to change and extend their procedure to solve these problems. The fMRI data were analyzed using a combination of hidden Markov models (HMMs) and multi-voxel pattern analysis (MVPA). This HMM–MVPA analysis revealed the existence of 4 stages: Encoding, Planning, Solving, and Responding. Using this analysis as a guide, an ACT-R model was developed that improved the performance of the HMM–MVPA and explained the variation in the durations of the stages across 128 different problems. The model assumes that participants can reflect on declarative representations of the steps of their problem-solving procedures. A Metacognitive module can hold these steps, modify them, create new declarative steps, and rehearse them. The Metacognitive module is associated with activity in the rostrolateral prefrontal cortex (RLPFC). The ACT-R model predicts the activity in the RLPFC and other regions associated with its other cognitive modules (e.g., vision, retrieval). Differences between children and adults seemed related to differences in background knowledge and computational fluency, but not to the differences in their capability to modify procedures.