2019
DOI: 10.1016/j.tins.2019.02.001
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Exploration, Inference, and Prediction in Neuroscience and Biomedicine

Abstract: The last decades saw dramatic progress in brain research. These advances were often buttressed by probing single variables to make circumscribed discoveries, typically through null hypothesis significance testing. New ways for generating massive data fueled tension between the traditional methodology, used to infer statistically relevant effects in carefully-chosen variables, and pattern-learning algorithms, used to identify predictive signatures by searching through abundant information. In this article, we d… Show more

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Cited by 170 publications
(138 citation statements)
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References 63 publications
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“…This raises the question if the specificity of a Riemannian model could be enhanced in a similar way. Ultimately, what model to prefer, therefore, clearly depends on the strategic goal of the analysis (Bzdok et al, 2018;Bzdok and Ioannidis, 2019) and cannot be globally decided.…”
Section: Resultsmentioning
confidence: 99%
“…This raises the question if the specificity of a Riemannian model could be enhanced in a similar way. Ultimately, what model to prefer, therefore, clearly depends on the strategic goal of the analysis (Bzdok et al, 2018;Bzdok and Ioannidis, 2019) and cannot be globally decided.…”
Section: Resultsmentioning
confidence: 99%
“…Null-hypothesis models isolate variables deemed important above a relatively arbitrary P value, and can be incongruent with the variables that maximize predictions in new settings. 49 Significant differences are usually considered more useful for scientific inference than prediction. Predictive studies train models on a subset of the data and test performance on the rest of the data not used for training and therefore give insight into how well they may generalize to new individuals.…”
Section: Speech As An Automated Biomarker For Mental Healthmentioning
confidence: 99%
“…Both types of models are built using automatically extracted acoustic features. Null‐hypothesis models isolate variables deemed important above a relatively arbitrary P value, and can be incongruent with the variables that maximize predictions in new settings . Significant differences are usually considered more useful for scientific inference than prediction.…”
Section: Introductionmentioning
confidence: 99%
“…Yet, there is evidently a broad spectrum of conceptually and in particular technically different methods that are currently being used in medical applications including imaging neuroscience. For a more detailed overview, we would like to point the reader to other, more specialized publications (Bishop, ; Bzdok & Ioannidis, ; Choudhary & Gianey, ; James, Witten, Hastie, & Tibshirani, ; Jordan & Mitchell, ). What is critical to note in the current context, though, is that there is generally an inverse relation between the potential accuracy or performance of machine‐learning algorithms on one hand and their interpretability on the other.…”
Section: State Of the Art In Medical Aimentioning
confidence: 99%