System structure and cognitive ability as predictors of performance in dynamic system control tasks

Hundertmark, Jan; Holt, Daniel V.; Fischer, Andreas; Said, Nadia; Fischer, Helen

doi:10.11588/jddm.2015.1.26416

Cited by 9 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Performance Goal Orientation had a positive effect on performance in less complex CPS task variants in Birney et al (2018). In Hundertmark et al (2015), Cognitive Reflection was a stronger predictor of overall CPS performance than g but this effect was reduced in more complex variants of the task.…”

Section: Intellectual Investment Traitsmentioning

confidence: 88%

“…The literature reveals small relationships between CPS performance and several intellectual investment traits. These include Goal Orientation (Birney et al, 2018;Cripps et al, 2016;Vollmeyer & Rheinberg, 1999), Implicit Theories of Ability (Birney et al, 2018;Cripps et al, 2016), Cognitive Reflection (Hundertmark et al, 2015), and Need for Cognition (NFC) (Rudolph et al, 2018). Only two of these studies found that the traits significantly influenced CPS performance when controlling for g or Gf.…”

Section: Intellectual Investment Traitsmentioning

confidence: 99%

See 1 more Smart Citation

Motivated, Willing, and Able: Non-Cognitive Factors Influence Complex Problem Solving Performance beyond Cognitive Ability

Vaughan,

Birney,

Fayn

2022

Preprint

View full text Add to dashboard Cite

Complex problem-solving (CPS) tasks have become an increasingly popular tool for understand-ing and assessing cognitive ability. These tasks have been repeatedly shown to be predictors of academic and workplace success above and beyond traditional measures of general intelligence and fluid intelligence. To date, there has been little exploration of the underlying mechanisms that drive this additional predictive utility. In this study, we examined the role of a variety of non-cognitive personality and investment traits that could drive performance on CPS tasks. Adult participants (n = 152) were recruited via M-Turk and completed a battery of personality and in-vestment trait measures, a measure of general mental ability, and a 61-trial microworlds-style CPS task. Generalised linear mixed-effects models revealed a wide variety of personality and in-vestment traits influenced task performance above and beyond general mental ability. Specifical-ly, two clusters of traits emerged as important determinants of performance: one cluster that in-fluenced the capacity to deal with the introduction of system randomness (Conscientiousness and Extraversion) and one cluster that influenced the capacity to deal with the introduction of system delays (NFC, Learning Goal Orientation, and Intellect). These findings suggest that CPS tasks do capture more than just general mental ability and may be good predictors of academic and workplace success because they tap into both cognitive ability and the motivation and willingness to engage in cognitive exploration and mental effort.

show abstract

Section: Intellectual Investment Traitsmentioning

confidence: 88%

Section: Intellectual Investment Traitsmentioning

confidence: 99%

Motivated, Willing, and Able: Non-Cognitive Factors Influence Complex Problem Solving Performance beyond Cognitive Ability

Vaughan,

Birney,

Fayn

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Similarly, learning from knowledge maps has been shown to increase recall of more central ideas, particularly for individuals with low verbal abilities ( O’Donnell et al, 2002 ), and for ill-defined and complex subject-matter content ( Tergan, 2005 ). Moreover, capturing the complexity of mental models may be challenging in interviews because of its high demand on working memory, which has shown to be a restraining factor for complex systems ( Hundertmark et al, 2015 ).…”

Section: Discussionmentioning

confidence: 99%

Evaluating the Application of the Mental Model Mapping Tool (M-Tool)

et al. 2021

Self Cite

View full text Add to dashboard Cite

Mental models influence how individuals think and act in relation to their external environment and have been identified as leverage points to address sustainability challenges. Given the importance of mental models, a new tool to assess mental models has been developed: the Mental Model Mapping Tool (M-Tool). M-Tool was designed to have a standardized format and to be user-friendly for low literacy populations, using pictograms and audio instructions. In this paper, we evaluate M-Tool’s application in two studies with Tanzanian fishers. In Study 1, we investigated M-tool’s convergent validity compared to standard interviewing methods (n = 30). Study 2 investigated M-Tool’s construct validity by relating mental model complexity to participants’ education level (n = 185), a relationship that has been well established. The findings show that (1) mental models produced with M-Tool are more complex than mental models obtained through interviewing techniques; (2) model composition is similar across the two methods; and (3) participants with higher levels of education tend to produce more complex mental models with M-Tool than participants with lower levels of education, in line with previous research. These findings suggest that M-Tool can successfully capture mental models among diverse participants. This tool offers researchers and practitioners an instrument to map and compare perceptions of (conservation) challenges across groups.

show abstract

“…While a great deal of work has studied human performance on this triad of prediction (e.g., Bates et al, 2015 ; Uppal et al, 2020 ), explanation (e.g., Dorfman et al, 2019 ; Gerstenberg et al, 2021 ; Wojtowicz & DeDeo, 2020 ), and control (e.g., Brehmer, 1992 ; Hundertmark et al, 2015 ; Osman, 2010 ), it has tended to focus on one ability in isolation from the others. There is, however, growing evidence that these abilities interact in complex ways.…”

Section: Introductionmentioning

confidence: 99%

Prediction, Explanation, and Control: The Use of Mental Models in Dynamic Environments

Tikhonov,

DeDeo

2023

Open Mind

View full text Add to dashboard Cite

The abilities to predict, explain, and control might arise out of operations on a common underlying representation or, conversely, from independent cognitive processes. We developed a novel experimental paradigm to explore how individuals might use probabilistic mental models in these three tasks, under varying levels of complexity and uncertainty. Participants interacted with a simple chatbot defined by a finite-state machine, and were then tested on their ability to predict, explain, and control the chatbot’s responses. When full information was available, performance varied significantly across the tasks, with control proving most robust to increased complexity, and explanation being the most challenging. In the presence of hidden information, however, performance across tasks equalized, and participants demonstrated an alternative neglect bias, i.e., a tendency to ignore less likely possibilities. A second, within-subject experimental design then looked for correlations between abilities. We did not find strong correlations, but the challenges of the task for the subjects limited our statistical power. To understand these effects better, a final experiment investigated the possibility of cross-training, skill transfer, or “zero-shot” performance: how well a participant, explicitly trained on one of the three tasks, could perform on the others without additional training. Here we found strong asymmetries: participants trained to control gained generalizable abilities to both predict and explain, while training on either prediction or explanation did not lead to transfer. This cross-training experiment also revealed correlations in performance; most notably between control and prediction. Our findings highlight the complex role of mental models, in contrast to task-specific heuristics, when information is partially hidden, and suggest new avenues for research into situations where the acquisition of general purpose mental models may provide a unifying explanation for a variety of cognitive abilities.

show abstract

System structure and cognitive ability as predictors of performance in dynamic system control tasks

Cited by 9 publications

References 0 publications

Motivated, Willing, and Able: Non-Cognitive Factors Influence Complex Problem Solving Performance beyond Cognitive Ability

Motivated, Willing, and Able: Non-Cognitive Factors Influence Complex Problem Solving Performance beyond Cognitive Ability

Evaluating the Application of the Mental Model Mapping Tool (M-Tool)

Prediction, Explanation, and Control: The Use of Mental Models in Dynamic Environments

Contact Info

Product

Resources

About