Memory decisions are predicted by temporally-asymmetric global similarity in parietal cortex

Poster Session E - Monday, April 15, 2024, 2:30 – 4:30 pm EDT, Sheraton Hall ABC

Zhifang Ye¹ (zhifangy@uoregon.edu), J. Benjamin Hutchinson¹, Brice A. Kuhl¹; ¹University of Oregon

The ability to recognize a previously-encountered stimulus (recognition memory) is one of the most well-studied forms of memory. According to an influential class of computational models, recognition memory decisions are based on the global similarity (sum of all similarities) between a current stimulus (a memory ‘probe’) and previously-encountered stimuli. These global matching models can explain a number of behavioral phenomena, including why stimuli are sometimes falsely recognized. Several fMRI studies have linked neural measures of global pattern similarity (nGPS) to recognition memory decisions. However, these studies share an important limitation: nGPS could either reflect the influence of previous episodic experiences or generic properties of a given memory probe (e.g., its typicality). Here, we leveraged a massive dataset (Allen et al., 2022), in which participants completed up to 40 fMRI sessions (30,000 trials) of a continuous recognition memory test. By computing nGPS between a given memory probe and events not only from the past, but also events in the future, we tested for temporally asymmetric relationships between nGPS and recognition memory decisions. For novel probes (new stimuli), we found that greater nGPS to past events vs. future events was associated with a higher probability of ‘old’ responses (false recognition). For old probes (repeated stimuli), the influence of nGPS on memory decisions critically depended on when the probe was previously encountered: if the prior encounter occurred recently, the influence of nGPS was markedly suppressed. Together, these results provide important evidence linking temporally-specific neural measures of global similarity to recognition memory decisions.

Topic Area: LONG-TERM MEMORY: Episodic