Evaluating the boundaries of analytic efficiency and control: A consecutive controlled case series of 26 functional analyses

We conducted this study to determine if the efficiency of the functional analysis could be improved without detrimental effects on control. In Experiment 1, we reanalyzed functional analyses conducted for the problem behavior of 18 children. We analyzed rates of problem behavior during the first 5 min and first 3 min of the original 10-min sessions and evaluated if changes in the level of control over problem behavior by the programmed contingency were evident from the analyses of shorter session duration. In Experiment 2, we conducted 8 consecutive functional analyses with 3-min sessions to further evaluate the utility of brief session durations. We found that control over problem behavior was demonstrated when conducting functional analyses with sessions as brief as 3 min.     

A note on Dasgupta’s Generalism

Philosophical Studies - Dasgupta (Philos Stud Int J Philos Anal Tradit 145(1):35–67, 2009) has argued that material individuals, such as particles and laptops, are metaphysically...     

Prioritization within visual working memory reflects a flexible focus of attention

When motivated, people can keep nonrecent items in a list active during the presentation of new items, facilitating fast and accurate recall of the earlier items. It has been proposed that this occurs by flexibly orienting attention to a single prioritized list item, thus increasing the amount of attention-based maintenance directed toward this item at the expense of other items. This is manipulated experimentally by associating a single distinct feature with a higher reward value, such as a single red item in a list of black items. These findings may be more parsimoniously explained under a distinctiveness of encoding framework rather than a flexible attention allocation framework. The retrieval advantage for the prioritized list position may be because the incongruent feature stands out in the list perceptually and causes it to become better encoded. Across three visual working memory experiments, we contrast a flexible attention theory against a distinctiveness of encoding theory by manipulating the reward value associated with the incongruent feature. Findings from all three experiments show strong support in favor of the flexible attention theory and no support for the distinctiveness of encoding theory. We also evaluate and find no evidence that strategy use, motivation, or tiredness/fatigue associated with reward value can adequately explain flexible prioritization of attention. Flexible attentional prioritization effects may be best understood under the context of an online attentional refreshing mechanism.

     

The Cognitive Architecture of Perceived Animacy: Intention,Attention, and Memory

Human vision supports social perception by efficiently detecting agents and extracting rich information about their actions, goals, and intentions. Here, we explore the cognitive architecture of perceived animacy by constructing Bayesian models that integrate domain‐specific hypotheses of social agency with domain‐general cognitive constraints on sensory, memory, and attentional processing. Our model posits that perceived animacy combines a bottom–up, feature‐based, parallel search for goal‐directed movements with a top–down selection process for intent inference. The interaction of these architecturally distinct processes makes perceived animacy fast, flexible, and yet cognitively efficient. In the context of chasing, in which a predator (the “wolf”) pursues a prey (the “sheep”), our model addresses the computational challenge of identifying target agents among varying numbers of distractor objects, despite a quadratic increase in the number of possible interactions as more objects appear in a scene. By comparing modeling results with human psychophysics in several studies, we show that the effectiveness and efficiency of human perceived animacy can be explained by a Bayesian ideal observer model with realistic cognitive constraints. These results provide an understanding of perceived animacy at the algorithmic level—how it is achieved by cognitive mechanisms such as attention and working memory, and how it can be integrated with higher‐level reasoning about social agency.