Generalization of tacting actions in children with autism

This intervention compared the effects of two procedures on the generalization of a tacting repertoire (labeling) in 6 children with autism spectrum disorder. In one procedure the verbal antecedent stimulus “What is she doing?” appeared together with a person performing an action; in the other procedure, the antecedent stimulus was just the presence of the action. In initial tests, children emitted tacts only when the action was presented with the verbal antecedent. Thereafter, they learned to tact an action without the verbal antecedent and received tests to evaluate generalization to another action. Results indicated that in order to obtain generalization of tacting actions, it was necessary to learn to tact other actions without the verbal antecedent as well as learning to tact the action with the verbal antecedent. These findings have relevance for generalization of tacting actions from control by verbal antecedents to natural conditions and the production of spontaneous language.     

A Bayesian-optimal principle for learner-friendly adaptation in learning games

Adaptive learning games should provide opportunities for the student to learn as well as motivate playing until goals have been reached. In this paper, we give a mathematically rigorous treatment of the problem in the framework of Bayesian decision theory. To quantify the opportunities for learning, we assume that the learning tasks that yield the most information about the current skills of the student, while being desirable for measurement in their own right, would also be among those that are efficient for learning. Indeed, optimization of the expected information gain appears to naturally avoid tasks that are exceedingly demanding or exceedingly easy as their results are predictable and thus uninformative. Still, tasks that are efficient for learning may be experienced as too challenging, and the resulting failures can lower motivation. Therefore, in addition to quantifying the expected informational benefit for learning of any prospective task to be presented next, we also model the expected motivational cost of its presentation, measured simply as the estimated probability of failure in our example model. We propose a “learner-friendly” adaptation algorithm that chooses the learning tasks by optimizing the expected benefit divided by the expected cost. We apply this algorithm to a Rasch-like student model implemented within a real-world application and present initial results of a pilot experiment.     

Rate of Change of Angular Bearing as the Relevant Property in a Horizontal Interception Task During Locomotion

The authors ran 3 experiments to investigate how catchers deal with the horizontal component of the ball's trajectory in an interception task during locomotion. The experiments were built upon the finding that velocity adaptations are based upon changes in the horizontal angular position or velocity of the ball with respect to the observer (M. Lenoir, M. Janssens, E. Musch, E. Thiery, & J. Uyttenhove, 1999); a potential underlying information source for that strategy is described. In Experiment 1, actor (N = 10 participants) and ball approached each other along the legs of a V-shaped track. When the velocity and the initial angular bearing of the ball were varied, the observed behavior fitted with nulling the horizontal angular velocity of the ball: A positive or negative angular velocity was compensated by a velocity change. Evidence was obtained that those adaptations are modulated by a critical change in, rather than by a critical state of, the environment-actor system. In Experiment 2, the distance between the head and an artificial end-effector was varied. Irrespective of that distance, participants (N = 7) accelerated and decelerated in order to keep the angular velocity of the ball with respect to the end-effector close to constant. The ecological relevance of that constant bearing angle strategy was confirmed in Experiment 3: Participants (N = 7) in that experiment freely ran to catch fly balls. The present results support the concept that one can explain with a limited number of control variables an actor's behavior in an interception task during self-motion.     

The eclipse of heritability and the foundations of intelligence

It is well known that theory in human cognitive ability or ‘intelligence’ is not well developed, especially with regard to sources of trait variation. Roots of theory have been sought in biology, and it is now widely accepted, on the basis of twin studies, and statistical analysis of variance, that at least half of the normal trait variation can be attributed to genetic variation, a correlation known as the trait ‘heritability’. Since the 1990s, methods in molecular biology have been adopted to go ‘beyond’ this mere statistical attribution to the identification of individual genes responsible for trait variation. More than a decade of intense effort, however, has failed to produce unambiguous, replicable findings; explanations for the ‘missing heritability’ are now being demanded; and calls for new perspectives on the roles of genes and environments in development and trait variation are being demanded. Here, I propose a dynamic systems perspective indicating how the processes in which heritability becomes missing are the very ones that provide the roots of new intelligence theory.     

History and Analysis of Delayed Discovery Statutes of Limitations in Adult Survivor Litigation

Summary

In this chapter, attorney Mary R. Williams examines how while Childhood Sexual Abuse (CSA) cases share many of the types of facts for which delayed discovery, mental incapacity, equitable estoppel and other legal statutes of limitations doctrines were created, the combination of factors of CSA cases, including unusual social dynamics and long-term psychological effects, does not fit exactly with any of these legal doctrines. She explores the historical development of delayed discovery statutes of limitations, evaluates the different types of statutes and case law, and analyzes the problematic use of the concept of repression in legislation. This chapter interprets her ground-breaking state-by-state tabulations of delayed discovery statutes of limitations and reported appellate decisions on limitations in CSA actions, published in this volume as appendices A, B, and C.     

Resurgence of goal-directed actions and habits

This study investigated how goal-directed and habitual behaviors recover after extinction within the context of the resurgence effect, a form of relapse induced by the removal or worsening of alternative reinforcement. Rats were trained to press a target lever with one reinforcer (O1) for either minimal (4) or extended (16) sessions. An extinction test after the completion of O1 devaluation confirmed that minimal and extended training formed goal-directed and habitual behaviors, respectively. Then, pressing an alternative lever was reinforced with a second reinforcer (O2) while the target response was placed on extinction. When O2 was discontinued, the minimally trained target response resurged with goal-directed status as in the extinction test. However, the extinguished habitual behavior in the extensively trained rats did not recover as a habit but instead with goal-directed status, possibly due to the context specificity of habits or the introduction of a new response–reinforcer contingency. The critical finding that reinforcer devaluation consistently led to less resurgence regardless of the amount of acquisition training provides a clinical implication that coupling differential-reinforcement-of-alternative-behavior (DRA) treatments with the devaluation of the associated reinforcer of problematic behavior could effectively diminish its recurrence.     

Make‐or‐Break: Chasing Risky Goals or Settling for Safe Rewards?

Humans regularly pursue activities characterized by dramatic success or failure outcomes where, critically, the chances of success depend on the time invested working toward it. How should people allocate time between such make‐or‐break challenges and safe alternatives, where rewards are more predictable (e.g., linear) functions of performance? We present a formal framework for studying time allocation between these two types of activities, and we explore optimal behavior in both one‐shot and dynamic versions of the problem. In the one‐shot version, we illustrate striking discontinuities in the optimal time allocation policy as we gradually change the parameters of the decision‐making problem. In the dynamic version, we formulate the optimal strategy—defined by a giving‐up threshold—which adaptively dictates when people should stop pursuing the make‐or‐break goal. We then show that this strategy is computationally inaccessible for humans, and we explore boundedly rational alternatives. We compare the performance of the optimal model against (a) a myopic giving‐up threshold that is easier to compute, and even simpler heuristic strategies that either (b) only decide whether or not to start pursuing the goal and never give up or (c) consider giving up at a fixed number of control points. Comparing strategies across environments, we investigate the cost and behavioral implications of sidestepping the computational burden of full rationality.     

Making Sense of Dynamic Systems: How Our Understanding of Stocks and Flows Depends on a Global Perspective

Stocks and flows (SF) are building blocks of dynamic systems: Stocks change through inflows and outflows, such as our bank balance changing with withdrawals and deposits, or atmospheric CO₂ with absorptions and emissions. However, people make systematic errors when trying to infer the behavior of dynamic systems, termed SF failure, whose cognitive explanations are yet unknown. We argue that SF failure appears when people focus on specific system elements (local processing), rather than on the system structure and gestalt (global processing). Using a standard SF task (n = 148), SF failure decreased by (a) a global as opposed to local task format; (b) individual global as opposed to local processing styles; and (c) global as opposed to local perceptual priming. These results converge toward local processing as an explanation for SF failure. We discuss theoretical and practical implications on the connections between the scope of attention and understanding of dynamic systems.     

Anxiety Influences the Perceptual-Motor Calibration of Visually Guided Braking to Avoid Collisions

We investigated whether anxiety influences perceptual-motor calibration in a braking to avoid a collision task. Participants performed either a discrete braking task (Experiment 1) or a continuous braking task (Experiment 2), with the goal of stopping before colliding with a stop sign. Half of participants performed the braking task after an anxiety induction. We investigated whether anxiety reduced the frequency of crashing and if it influenced the calibration of perception (visual information) and action (brake pressure) dynamically between-trials in Experiment 1 and within-trials in Experiment 2. In the discrete braking task, anxious participants crashed less often and made larger corrective adjustments trial-to-trial after crashing, suggesting that the influence of anxiety on behavior did not occur uniformly, but rather dynamically with anxiety amplifying the reaction to previous crashes. However, when performing continuous braking, anxious participants crashed more often, and their within-trial adjustments of deceleration were less related to visual information compared to controls. Taken together, these findings suggest that the timescale and nature of the task mediates the influence of anxiety on the performance of goal-directed actions.     

A Limited Information Estimator for Dynamic Factor Models

Structural equation modeling (SEM) is an increasingly popular method for examining multivariate time series data. As in cross-sectional data analysis, structural misspecification of time series models is inevitable, and further complicated by the fact that errors occur in both the time series and measurement components of the model. In this article, we introduce a new limited information estimator and local fit diagnostic for dynamic factor models within the SEM framework. We demonstrate the implementation of this estimator and examine its performance under both correct and incorrect model specifications via a small simulation study. The estimates from this estimator are compared to those from the most common system-wide estimators and are found to be more robust to the structural misspecifications considered.