Abstract
Human memory does not always retain accurate mental representations that precisely correspond to the exceedingly rich contents in natural vision. This functional limit can be attributed to a reduction in precision of internal representations from visual perception to visual short-term memory (VSTM). The mechanism underlying this bottleneck in representational precision remains a topic of controversy. One class of theories attributes VSTM precision to neural noise in sustained neural activities that support VSTM retention. In contrast, another class of theories maintains that VSTM retention and mnemonic precision are supported by dissociable and independent neural mechanisms. For example, the level of neural noise in sustained neural activities for VSTM, which manifests as mnemonic precision of VSTM, may be determined by hippocampal pattern separation, a computational process that orthogonalizes similar memories into non-overlapping representations. To test this hippocampal pattern separation hypothesis, the present study adopted Harrison and Tong's (2009) orientation VSTM paradigm with high-resolution fMRI. Using the inverted decoding model, we decoded item-specific information from the hippocampal dentate gyrus (DG) and CA3 subfield, a brain region previously implicated in pattern separation, during the delay interval of the VSTM task. In contrast, item-specific information could not be reliably decoded from the hippocampal CA1 subfield or the amygdala. A whole-brain searchlight analysis revealed some additional areas in occipital, posterior parietal, and prefrontal cortices that carry item-specific information, replicating some previous findings. Furthermore, Granger causality analyses identified a feedback projection from the hippocampal DG/CA3 to visual cortices during the delay interval, potentially linking hippocampal pattern separation to sensory reactivation of precise representation. Overall, these findings support a novel hippocampal pattern separation hypothesis for mnemonic precision, which is central to the ongoing debate on the nature of the limits in VSTM.
Meeting abstract presented at VSS 2018