Diagonals of the time-time pattern similarity matrices (see Figure 3) for within-item similarity (solid line) and within-category similarity (dotted line) in children (blue) and adults (black).
The human brain encodes information in neural activation patterns. While standard approaches to analyzing neural data focus on brain (de-)activation (e.g., regarding the location, timing, or magnitude of neural responses), multivariate neural pattern similarity analyses target the informational content represented by neural activity. In adults, a n...
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... group mean similarity matrices (averaged across all electrodes) and the diagonals of the mean similarity matrices can be plotted using step3_plot_sim_matrices (see Figure 3 and Figure 4). The diagonals show the similarity of the respective spectral patterns at identical time points. ...
Long-standing theories of cognitive aging suggest that memory decline is associated with age-related differences in the way information is neurally represented. Multivariate pattern similarity analyses enabled researchers to take a representational perspective on brain and cognition, and allowed them to study the properties of neural representations that support successful episodic memory. Two representational properties have been identified as crucial for memory performance, namely the distinctiveness and the stability of neural representations. Here, we review studies that used multivariate analysis tools for different neuroimaging techniques to clarify how these representational properties relate to memory performance across adulthood. While most evidence on age differences in neural representations involved stimulus category information , recent studies demonstrated that particularly item-level stability and specificity of activity patterns are linked to memory success and decline during aging. Overall, multivariate methods offer a versatile tool for our understanding of age differences in the neural representations underlying memory.