PT  - JOURNAL ARTICLE
AU  - Thomas C. Sprague
AU  - Kirsten C. S. Adam
AU  - Joshua J. Foster
AU  - Masih Rahmati
AU  - David W. Sutterer
AU  - Vy A. Vo
TI  - Inverted Encoding Models Assay Population-Level Stimulus Representations, Not Single-Unit Neural Tuning
AID  - 10.1523/ENEURO.0098-18.2018
DP  - 2018 May 11
TA  - eneuro
PG  - ENEURO.0098-18.2018
4099  - http://www.eneuro.org/content/early/2018/05/11/ENEURO.0098-18.2018.short
4100  - http://www.eneuro.org/content/early/2018/05/11/ENEURO.0098-18.2018.full
AB  - Significance Statement Inverted encoding models (IEMs) are a powerful tool for reconstructing population-level stimulus representations from aggregate measurements of neural activity (e.g., fMRI or EEG). In a recent report, Liu et al., (2018) tested whether IEMs can provide information about the underlying tuning of single units. Here, we argue that using stimulus reconstructions to infer properties of single neurons, such as neural tuning bandwidth, is an ill-posed problem with no unambiguous solution. Instead of interpreting results from these methods as evidence about single-unit tuning, we emphasize the utility of these methods for assaying population-level stimulus representations. These can be compared across task conditions to better constrain theories of large-scale neural information processing across experimental manipulations, such as changing sensory input or attention.