Briefly Cuing Memories Leads to Suppression of Their Neural Representations

Abstract

Previous studies have linked partial memory activation with impaired subsequent memory retrieval (e.g., Detre et al., 2013) but have not provided an account of this phenomenon at the level of memory representations: How does partial activation change the neural pattern subsequently elicited when the memory is cued? To address this question, we conducted a functional magnetic resonance imaging (fMRI) experiment in which participants studied word-scene paired associates. Later, we weakly reactivated some memories by briefly presenting the cue word during a rapid serial visual presentation (RSVP) task; other memories were more strongly reactivated or not reactivated at all. We tested participants' memory for the paired associates before and after RSVP. Cues that were briefly presented during RSVP triggered reduced levels of scene activity on the post-RSVP memory test, relative to the other conditions. We used pattern similarity analysis to assess how representations changed as a function of the RSVP manipulation. For briefly cued pairs, we found that neural patterns elicited by the same cue on the pre-and post-RSVP tests (preA-postA; preB-postB) were less similar than neural patterns elicited by different cues (preA-postB; preB-postA). These similarity reductions were predicted by neural measures of memory activation during RSVP. Through simulation, we show that our pattern similarity results are consistent with a model in which partial memory activation triggers selective weakening of the strongest parts of the memory.

Recognition-induced forgetting is not due to category-based set size

“…This property refers to the idea that the degree to which practiced items are strengthened is not predictive of the degree of retrieval-induced forgetting (e.g., previous work on retrievalinduced forgetting has also dissociated the size of the remembering and forgetting effects of retrieval practice; Aslan & Bäuml, 2011;Hanslmayr, Staudigl, Aslan, & Bäuml, 2010;Hulbert, Shivde, & Anderson, 2012;Kuhl, Dudukovic, Kahn, & Wagner, 2007;Macrae & MacLeod, 1999;. Such strength independence is mimicked herein as increased memory for practiced objects was not directly predictive of forgetting of related objects, consistent with work by Norman and colleagues suggesting that moderate activation of a memory is sufficient for weakening of that memory representation, irrespective of the strength of competing items (Detre, Natarajan, Gershman, & Norman, 2013;Poppenk & Norman, 2014).…”

Neural pattern change during encoding of a narrative predicts retrospective duration estimates

“…Fischl et al, 2004; Poppenk and Norman, 2014). Briefly, this processing includes removal of non-brain tissue using a hybrid watershed/surface deformation procedure (Ségonne et al, 2004), automated Talairach transformation, intensity normalization (Sled et al, 1998), tessellation of the grey matter / white matter boundary, automated topology correction (Fischl et al, 2001; Segonne et al, 2007), surface deformation following intensity gradients (Fischl and Dale, 2000), parcellation of cortex into units based on gyral and sulcal structure (Desikan et al, 2006; Fischl et al, 2004), and creation of a variety of surface-based data, including maps of curvature and sulcal depth.…”

Multiple-object Tracking as a Tool for Parametrically Modulating Memory Reactivation

“…In addition, a functional localizer was collected to assist with pattern classification analysis (Phase 3). This design was modeled after that used by Poppenk and Norman (2014), but it incorporated a novel method for reactivating memories (Phase 5), as well as a novel procedure for training a classifier sensitive to memory reactivation (Phase 3). We employed an MOT task in which participants tracked moving MOT target dots among a set of identically-colored moving lure dots (Pylyshyn & Storm, 1988) while centrally fixating on a verbal memory cue.…”

“…According to the non-monotonic plasticity hypothesis (Newman & Norman, 2010), such partial memory reactivations can weaken memory representations, even though full reactivations can strengthen them. Non-monotonic learning is supported by various lines of evidence: for example, the large and growing cognitive literature on retrieval-induced forgetting (Murayama, Miyatsu, Buchli, & Storm, 2014); neurophysiological evidence of moderate, but not high, levels of depolarization leading to weakening (Artola, Brocher, & Singer, 1990; Hansel, Artola, & Singer, 1996); neural models of synaptic plasticity (Norman, Newman, Detre, & Polyn, 2006); and impaired subsequent memory for events shown to be partially activated by EEG and fMRI (e.g., Detre, Natarajan, Gershman, & Norman, 2013; Kim, Lewis-Peacock, Norman, & Turk-Browne, 2014; Lewis-Peacock & Norman, 2014; Newman & Norman, 2010; Poppenk & Norman, 2014; Wimber, Alink, Charest, Kriegeskorte, & Anderson, 2015). But as empirical evidence for non-monotonic learning accumulates, what tools are needed to further advance the field?…”

“…In a recent study (Poppenk & Norman, 2014), we set out to parametrically modulate memory activation using an adaptation of an rapid serial visual presentation (RSVP) design that we called “The Great Fruit Harvest”. Participants associated word memory cues with pictures of bedrooms; these word memory cues were then embedded in an RSVP stream that participants were monitoring for fruit words (note that none of the word memory cues were themselves fruit words).…”