Table 1.

Summary of various techniques used in RSA.

Technique	Description	Pros (+), Cons (−)	References
ANN-based RSA	Method of specifying RSA models based on artificial neural network representations.	+ more direct link between data and formal models + less computationally intensive than estimating unit-to-unit mappings − cannot predict response to novel conditions	• [82], see also [140] • [13,16]
Full-factorial RSA	Type of experimental approach that uses crossed factors and multiple regression to decompose neural coding.	+ efficiently boost model specificity + test interaction of representations + handles complex designs − number of trials, experimental time	• [63,108]; see also [122], discussion in [58]; see [124] for related technique
Single-trial RSA	Analytic procedure for fitting RSA models at single trial level.	+ test within-subject brain–behavior relationships + supports hierarchical or joint modelling − autocorrelation confounds	• [63,64] • [141-143] • e.g., [144]
Cross-task RSA	Type of analysis that examines similarity structure within battery of tasks.	+ assess “neural construct validity” + use to rigorously assess replication	• [88]; see also [95] • [99]
RSA fingerprinting	Method of assessing presence of stable individual differences in representational structure.	+ mitigates individual differences due to anatomical factors − requires repeated measures	• [114]; see also [54]
Cross-subject RSA	Method of assessing task-dependent similarity in response topographies.	+ useful when individuals can be meaningfully grouped (e.g., by genetic relation) − potential confounds with univariate activity	• [115] see discussion in [92]
Unbiased similarity measures	Type of similarity measure for which the expected value is not impacted by measurement error.	+ useful for unbalanced designs − increased variance − each condition must appear in >1 run	• techniques: [13,96,97,124] • unbalanced design: e.g., [75] • increased variance: [145]
Representational “connectivity” analysis	Type of analysis that examines covariation in coding strength (across region, timepoint). When constrained by RSA models, this covariation is assessed along specific coding variables.	+ test representational interactions (e.g., b/w PFC and downstream coding + constrained or unconstrained by RSA models − third variables, directionality	• PFC–downstream interactions: e.g., [3,146] • unconstrained: [60,147]; constrained: e.g., see interaction analyses in [108]