| Literature DB >> 32428044 |
Thomas P Reber, Marcel Bausch, Sina Mackay, Jan Boström, Christian E Elger, Florian Mormann.
Abstract
[This corrects the article DOI: 10.1371/journal.pbio.3000290.].Entities:
Year: 2020 PMID: 32428044 PMCID: PMC7236971 DOI: 10.1371/journal.pbio.3000753
Source DB: PubMed Journal: PLoS Biol ISSN: 1544-9173 Impact factor: 8.029
Fig 4Pattern classifier algorithms learn abstract semantic information.
(A–E) Classifiers were trained to classify the superordinate category from Z scored responses to half of the stimuli per category and tested out of sample on the other half. Classification performance on 100 random divisions of data into training and test set is indicated in box plots (Cohen’s κ). (B–I) Confusion matrices (rows: correct label; columns: predicted label). (F–K) Classifiers were trained on half of the trials per stimulus to predict individual stimulus identity and tested out of sample on the other half of trials. Colour codes extend to maximally 50% (B–E) and 10% (G–K) for display purposes. Values higher than these maxima (for example, squares on the main diagonal) are not resolved in favour of making the patterns in off-diagonal areas more clearly visible. Data and scripts underlying this figure are deposited here: https://github.com/rebrowski/abstractRepresentationsInMTL. AM, amygdala; Bi, birds; Cl, clothes; Co, computer; EC, entorhinal cortex; Fl, flowers; Fr, fruit; Fu, furniture; HC, hippocampus; In, insects; In, instruments; MF, manmade food; PHC, parahippocampal cortex; WA, wild animals.