Artificial neural networks reveal individual differences in metacognitive monitoring of memory

Comparison of MPL-ANN and PLS-DA models for predicting the severity of patients with acute pancreatitis: An exploratory study

OBJECTIVE

Acute pancreatitis (AP) is a common inflammatory disorder that may develop into severe AP (SAP), resulting in life-threatening complications and even death. The purpose of this study was to explore two different machine learning models of multilayer perception-artificial neural network (MPL-ANN) and partial least squares-discrimination (PLS-DA) to diagnose and predict AP patients' severity.

METHODS

The MPL-ANN and PLS-DA models were established using candidate markers from 15 blood routine parameters and five serum biochemical indexes of 133 mild acute pancreatitis (MAP) patients, 167 SAP (including 88 moderately SAP) patients, and 69 healthy controls (HCs). The independent parameters and combined model's diagnostic efficiency in AP severity differentiation were analyzed using the area under the receiver operating characteristic curve (AUC).

RESULTS

The neutrophil to lymphocyte ratio (NLR) is the most useful marker in 20 parameters for screening AP patients [AUC = 0.990, 95% confidence interval (CI): 0.984-0.997, sensitivity 94.3%, specificity 98.6%]. The MPL-ANN model based on six optimal parameters exhibited better diagnostic and predict performance (AUC = 0.984, 95% CI: 0.960-1.00, sensitivity 92.7%, specificity 93.3%, accuracy 93.0%) than the PLS-DA model based on five optimal parameters (AUC = 0.912, 95% CI: 0.853-0.971, sensitivity 87.8%, specificity 84.4%, accuracy 84.8%) in discriminating MAP patients from SAP patients.

CONCLUSION

The results demonstrated that the MPL-ANN model based on routine blood and serum biochemical indexes provides a reliable and straightforward daily clinical practice tool to predict AP patients' severity.

Evidence for Age-Equivalent and Task-Dissociative Metacognition in the Memory Domain

Research suggests that metacognitive monitoring ability does not decline with age. For example, judgments-of-learning (JOL) accuracy is roughly equivalent between younger and older adults. But few studies have asked whether younger and older adults' metacognitive ability varies across different types of memory processes (e.g., for items vs. pairs). The current study tested the relationship between memory and post-decision confidence ratings at the trial level on item (individual words) and associative (word pairs) memory recognition tests. As predicted, younger and older adults had similar metacognitive efficiency, when using meta-d'/d', a measure derived from Signal Detection Theory, despite a significant age effect favoring younger adults on memory performance. This result is consistent with previous work showing age-equivalent metacognitive efficiency in the memory domain. We also found that metacognitive efficiency was higher for associative memory than for item memory across age groups, even though associative and item recognition memory (d') were statistically equivalent. Higher accuracy on post-test decision confidence ratings for associative recognition relative to item recognition on resolution accuracy itself (meta-d') and when corrected for performance differences (meta-d'/d') are novel findings. Implications for associative metacognition are discussed.

An Amazon stingless bee foraging activity predicted using recurrent artificial neural networks and attribute selection

Bees play a key role in pollination of crops and in diverse ecosystems. There have been multiple reports in recent years illustrating bee population declines worldwide. The search for more accurate forecast models can aid both in the understanding of the regular behavior and the adverse situations that may occur with the bees. It also may lead to better management and utilization of bees as pollinators. We address an investigation with Recurrent Neural Networks in the task of forecasting bees’ level of activity taking into account previous values of level of activity and environmental data such as temperature, solar irradiance and barometric pressure. We also show how different input time windows, algorithms of attribute selection and correlation analysis can help improve the accuracy of our model.