Challenges in Obtaining Valid Causal Effect Estimates with Machine Learning Algorithms.

Unlike parametric regression, machine learning (ML) methods do not generally require precise knowledge of the true data generating mechanisms. As such, numerous authors have advocated for ML methods to estimate causal effects. Unfortunately, ML algorithmscan perform worse than parametric regression. We demonstrate the performance of ML-based single- and double-robust estimators. We use 100 Monte Carlo samples with sample sizes of 200, 1200, and 5000 to investigate bias and confidence interval coverage under several scenarios. In a simple confounding scenario, confounders were related to the treatment and the outcome via parametric models. In a complex confounding scenario, the simple confounders were transformed to induce complicated nonlinear relationships. In the simple scenario, when ML algorithms were used, double-robust estimators were superior to single-robust estimators. In the complex scenario, single-robust estimators with ML algorithms were at least as biased as estimators using misspecified parametric models. Double-robust estimators were less biased, but coverage was well below nominal. The use of sample splitting, inclusion of confounder interactions, reliance on a richly specified ML algorithm, and use of doubly robust estimators was the only explored approach that yielded negligible bias and nominal coverage. Our results suggest that ML based singly robust methods should be avoided.

Memory and language improvements following cognitive control training.

Cognitive control refers to adjusting thoughts and actions when confronted with conflict during information processing. We tested whether this ability is causally linked to performance on certain language and memory tasks by using cognitive control training to systematically modulate people's ability to resolve information-conflict across domains. Different groups of subjects trained on 1 of 3 minimally different versions of an n-back task: n-back-with-lures (High-Conflict), n-back-without-lures (Low-Conflict), or 3-back-without-lures (3-Back). Subjects completed a battery of recognition memory and language processing tasks that comprised both high- and low-conflict conditions before and after training. We compared the transfer profiles of (a) the High- versus Low-Conflict groups to test how conflict resolution training contributes to transfer effects, and (b) the 3-Back versus Low-Conflict groups to test for differences not involving cognitive control. High-Conflict training-but not Low-Conflict training-produced discernable benefits on several untrained transfer tasks, but only under selective conditions requiring cognitive control. This suggests that the conflict-focused intervention influenced functioning on ostensibly different outcome measures across memory and language domains. 3-Back training resulted in occasional improvements on the outcome measures, but these were not selective for conditions involving conflict resolution. We conclude that domain-general cognitive control mechanisms are plastic, at least temporarily, and may play a causal role in linguistic and nonlinguistic performance. (PsycINFO Database Record

The effects of bilingualism on conflict monitoring, cognitive control, and garden-path recovery.

Bilinguals demonstrate benefits on non-linguistic tasks requiring cognitive control-the regulation of mental activity to resolve information-conflict during processing. This "bilingual advantage" has been attributed to the consistent management of two languages, yet it remains unknown if these benefits extend to sentence processing. In monolinguals, cognitive control helps detect and revise misinterpretations of sentence meaning. Here, we test if the bilingual advantage extends to parsing and interpretation by comparing bilinguals' and monolinguals' syntactic ambiguity resolution before and after practicing N-back, a non-syntactic cognitive-control task. Bilinguals outperformed monolinguals on a high-conflict but not a no-conflict version of N-back and on sentence comprehension, indicating that the advantage extends to language interpretation. Gains on N-back conflict trials also predicted comprehension improvements for ambiguous sentences, suggesting that the bilingual advantage emerges across tasks tapping shared cognitive-control procedures. Because the overall task benefits were observed for conflict and non-conflict trials, bilinguals' advantage may reflect increased cognitive flexibility.

Evidence for language transfer leading to a perceptual advantage for non-native listeners.

Phonological transfer from the native language is a common problem for non-native speakers that has repeatedly been shown to result in perceptual deficits vis-à-vis native speakers. It was hypothesized, however, that transfer could help, rather than hurt, if it resulted in a beneficial bias. Due to differences in pronunciation norms between Korean and English, Koreans in the U.S. were predicted to be better than Americans at perceiving unreleased stops-not only in their native language (Korean) but also in their non-native language (English). In three experiments, Koreans were found to be significantly more accurate than Americans at identifying unreleased stops in Korean, at identifying unreleased stops in English, and at discriminating between the presence and absence of an unreleased stop in English. Taken together, these results suggest that cross-linguistic transfer is capable of boosting speech perception by non-natives beyond native levels.