Multitask training promotes automaticity of a fundamental laparoscopic skill without compromising the rate of skill learning.

A defining characteristic of expertise is automated performance of skills, which frees attentional capacity to better cope with some common intraoperative stressors. There is a paucity of research on how best to foster automated performance by surgical trainees. This study examined the use of a multitask training approach to promote automated, robust laparoscopic skills.Eighty-one medical students completed training of a fundamental laparoscopic task in either a traditional single-task training condition or a novel multitask training condition. Following training, participants' laparoscopic performance was tested in a retention test, two stress transfer tests (distraction and time pressure) and a secondary task test, which was included to evaluate automaticity of performance. The laparoscopic task was also performed as part of a formal clinical examination (OSCE).The training groups did not differ in the number of trials required to reach task proficiency (p = .72), retention of skill (ps > .45), or performance in the clinical examination (p = .14); however, the groups did differ with respect to the secondary task (p = .016). The movement efficiency (number of hand movements) of single-task trainees, but not multitask trainees, was negatively affected during the secondary task test. The two stress transfer tests had no discernable impact on the performance of either training group.Multitask training was not detrimental to the rate of learning of a fundamental laparoscopic skill and added value by providing resilience in the face of a secondary task load, indicative of skill automaticity. Further work is needed to determine the extent of the clinical utility afforded by multitask training

Gerontological Society of America

Objectives: The aim of this study was to examine the association between conscious monitoring and control of movements (i.e., movement specific reinvestment) and visuo-motor control during walking by older adults. Method: The Movement Specific Reinvestment Scale (MSRS; Masters, Eves, & Maxwell, 2005) was administered to ninety-two community-dwelling older adults, aged 65-81 years, who were required to walk along a 4.8-meter walkway and step on the middle of a target as accurately as possible. Participants’ movement kinematics and gaze behavior were measured during approach to the target and when stepping on it. Results: High scores on the MSRS were associated with prolonged stance and double support times during approach to the stepping target, and less accurate foot placement when stepping on the target. No associations between MSRS and gaze behavior were observed. Discussion: Older adults with a high propensity for movement specific reinvestment seem to need more time to “plan” future stepping movements, yet show worse stepping accuracy than older adults with a low propensity for movement specific reinvestment. Future research should examine whether older adults with a higher propensity for reinvestment are more likely to display movement errors that lead to fallin

Distinguishing the Recent Past from the Complicated Present in Recognition Memory

In the study of verbal memory, a critical question is the extent to which recognition is influenced by the prior contexts in which items have appeared (‘context noise’), as opposed to competition from other items present within the immediate task context (‘item noise’). In a standard recognition task, subjects study a list of words, and at test, discriminate between studied items (targets) and novel items (foils). To disentangle the contributions of context and item noise, we systematically manipulated both the contexts in which critical items had been encountered prior to study, and the composition of the recognition list, varying semantic similarity among items. Our results suggest independent contributions of each factor, with word frequency and temporal lag as important mediating variables. These findings can be interpreted within both associative learning and memory paradigm

Visual Search as a Combination of Automatic and Attentive Processes

We present a model in which visual search behavior is assumed to result from a combination of controlled, serial search and automatic attraction of attention to target stimuli. The model provides a quantitative framework for how these different processes are combined, and despite a large number of constraints, it is highly successful in accounting for human search behavior at the level of full response time distributions and choice probabilities

Computational Models of Event Memory

This chapter discusses key features of computational models of event memory, also called “episodic” memory. Models aim to capture the representations and processes that enable us to perform a variety of episodic memory tasks, including recognition and free, cued, and serial recall. We review different ways in which models distinguish between the content and context of events; how different models represent event memories, including networks and vectors; and the types of processes that operate on memory representations to accomplish retrieval goals, including matching and search. Finally, we discuss how modeling serves broader scientific goals and can help bridge levels of explanation between the cognitive processes involved in memory and their neural instantiation

A Dynamic Approach to Recognition Memory

We present a dynamic model of memory that integrates the processes of perception, retrieval from knowledge, retrieval of events, and decision making as these evolve from one moment to the next. The core of the model is that recognition depends on tracking changes in familiarity over time from an initial baseline generally determined by context, with these changes depending on the availability of different kinds of information at different times. A mathematical implementation of this model leads to precise, accurate predictions of accuracy, response time, and speed-accuracy trade-off in episodic recognition at the levels of both groups and individuals across a variety of paradigms. Our approach leads to novel insights regarding word frequency, speeded responding, context reinstatement, short-term priming, similarity, source memory, and associative recognition, revealing how the same set of core dynamic principles can help unify otherwise disparate phenomena in the study of memory

A Dynamic Approach to Recognition Memory

We present a dynamic model of memory that integrates the processes of perception, retrieval from knowledge, retrieval of events, and decision making as these evolve from one moment to the next. The core of the model is that recognition depends on tracking changes in familiarity over time from an initial baseline generally determined by context, with these changes depending on the availability of different kinds of information at different times. A mathematical implementation of this model leads to precise, accurate predictions of accuracy, response time, and speed-accuracy trade-off in episodic recognition at the levels of both groups and individuals across a variety of paradigms. Our approach leads to novel insights regarding word frequency, speeded responding, context reinstatement, short-term priming, similarity, source memory, and associative recognition, revealing how the same set of core dynamic principles can help unify otherwise disparate phenomena in the study of memory

Function Estimation: Individual Differences in Quantitative Inference

Graphical perception is a crucial part of scientific endeavour, and the interpretation of graphical information is increasingly important among the lay public, who are often presented with graphs of data in support of different policy positions. However, graphs are multidimensional and data in graphs are comprised not only of overall global trends but also local perturbations. We presented a novel function estimation task in which scatterplots of noisy data that varied in the number of data points, the scale of the data, and the true generating function were shown to observers. Observers were asked to draw the function which they believe generated the data. Our results indicated not only a general influence of various aspects of the presented graph (e.g., increasing the number of data points results in smoother generated functions), but also clear individual differences, with some observers tending to generate functions which track the local changes in the data and others following global trends in the data

Recognition memory decisions made with short- and long-term retrieval

The present research explores the way retrieval from both short- and long-term memory is used to make recognition decisions about the contents of short-term memory. Pictures are presented in a short list followed by a test picture, either a target or foil. The research contrasts four paradigms: VM: target and foil responses to a given stimulus change from trial to trial; CM: the responses do not change from trial to trial; AN: every trial uses new stimuli; MIXED: combinations of VM, CN, and AN occur on each trial. These conditions vary the way long-term memory (traces from prior trials) affect current trial recognition decisions. A new paradigm is used in which a given picture is equally often presented and tested in both VM and CM. A model is presented that shows the way short- and long-term retrieval work together to produce the observed accuracy and response time performance. The model is used to predict published data from a traditional paradigm (Nosofsky et al., 2021) and the new data. It predicts quite well the accuracy and response time results from 376 conditions, while holding almost all parameter values constant across groups and conditions, lending support to its assumed processes