SGER: Efficient Strategy Selection-Predictors and Cognitive Mechanisms

这项研究代表了一项程序性研究计划的第一步，该计划旨在通过使用复杂的软件程序来确定与从新手到专家水平的表现转换相关的认知过程。这项研究的独特视角是调查社会影响(例如，观察某人使用不同的技术)如何促成这种转变。此外，PI将调查用户对使用有效策略的成本和收益的判断如何影响他们随后使用更有效方法的可能性。为了测试观察他人对后续有效策略使用的影响以及对成本和收益的权衡，PI将采用准实验相关设计，其中预测因子是参与者观察某人使用有效技术的次数，标准是他们随后使用有效技术的次数，以及他们对使用该技术的利弊进行权衡。为了在实验室中创造一个有人使用有效策略观察另一个人的情景，参与者将被分配到二元组。一名参与者将接受使用有效技术的培训，而另一名参与者将接受无关任务的培训。双方将共同完成一项任务，而接受过有效策略培训的参与者将操作计算机。训练有素的参与者使用有效策略的次数将通过摄像机观察、击键录音和实验者观察来记录。在共同完成任务后，两位参与者将单独完成一项类似于前一项任务的任务。同样，高效技术的使用次数也将被记录下来。PI的假设是，未受训练的参与者对有效策略的使用将与他们观察到受过训练的参与者使用该策略的次数有关。两位参与者还将被要求权衡使用更有效技术的利弊。这项研究的学术价值主要与它对一个老问题的新颖方法有关。尽管研究人员此前研究了人们如何通过使用软件程序达到专家水平的表现，但结果肯定是模棱两可的。在这个项目中，PI将具体调查如何通过观察其他人来影响有效微观策略(高效/专家用户的特征)的采用。广泛影响：这项研究的结果将表明是否有必要公开指导用户如何以及何时使用有效策略，或者观察他人是否提供了足够的信息来适当调整用户的选择规则，以使用更有效的方法。如果仅凭观察就足以改变选择规则，这将对与选择规则调整相关的认知机制产生重要影响。一旦知道了这些认知机制，选择规则中的这种变化可以用认知架构模型来建模，例如在ACT-R PM中，这特别令人兴奋，因为这种准确预测复杂程序任务从新手到专家水平转换的模型可以用于测试新设备和软件的训练协议和接口。

This study represents the first step in a plan of programmatic research designed to identify the cognitive processes associated with the transition from novice to expert levels of performance with the use of complex software programs. The unique perspective of this research is the investigation of how social influences (such as watching someone using different techniques) contribute to this transition. Furthermore, the PI will investigate how users' judgment of the costs and benefits of utilizing efficient strategies influences the likelihood of their subsequently utilizing the more efficient method. To test the effects of observing others on the subsequent use of efficient strategies as well as the weighing of the costs and benefits, the PI will employ a quasi-experimental correlational design, in which the predictor is the number of times participants observe someone using the efficient technique and the criteria are their subsequent use of the efficient technique and their weighing of the pros and cons of using that technique. To create a situation in the lab where someone is observing another person using an efficient strategy, participants will be assigned to dyads. One participant will be trained on the use of an efficient technique while the other will be trained on an unrelated task. The dyad will work together to complete a task and the participant trained on the efficient strategy will operate the computer. The number of times the trained participant uses the efficient strategy will be recorded through camera observations, keystroke recordings, and experimenter observations. Subsequent to performing the task together, both participants will individually complete a task similar to the previous task. Again, the number of times the efficient technique is used will be recorded. The PI's hypothesis is that the untrained participants' use of the efficient strategy will be related to the number of times they observed the trained participants use the strategy. Both participants will also be asked to weigh the pros and cons of using the more efficient technique. The intellectual merit of this study is primarily associated with its novel approach to an old problem. Although researchers have previously examined how people achieve expert levels of performance with the use of software programs, the results are certainly equivocal. In this project the PI will specifically investigate how the adoption of efficient micro-strategies (hallmarks of the efficient/expert user) is impacted through observing others.Broader Impacts: The results of this study will indicate whether it is necessary to overtly instruct users regarding how and when to use efficient strategies, or if observing others provides enough information to appropriately adjust users' selection rules to use the more efficient method. If observation alone turns out to be enough to change the selection rules, this will have important implications regarding the cognitive mechanisms associated with selection rule adjustment. Once these cognitive mechanisms are known, this change in selection rule could be modeled with a model of cognitive architecture such as that in ACT-R PM, which is particularly exciting because such a model that accurately predicted the transition from novice to expert levels of performance with complex procedural tasks could be used to test training protocols and interfaces for new devices and software.