Memory Monitoring:

Despite the vast amount of information stored in the human brain, we are remarkably adept at judging whether a particular piece of information is likely to be or has been successfully retrieved. There is broad agreement that such metamemorial judgments are closely linked to the process of memory retrieval itself. While an extensive literature has focused on the cognitive neuroscience of successful memory retrieval, relatively less attention has been paid to the neurocognitive basis of the processes that support predictions about the outcome of a retrieval attempt. Such predictions are critical in determining the effort and strategy that will be applied to a memory search. In a set of studies, we examined the neural basis of predictive retrieval judgments. This included a study of patients with a broad spectrum of damage to frontal cortex and matched controls. Lesion analysis of the patients with clear FOK impairment revealed an overlapping region of damage in ventral medial prefrontal cortex (VMPC) which we proposed plays a critical role in assessment of memory contents (Schnyer et al., 2004, Neuropsychologia). This study was followed by one utilizing fMRI in young healthy normals (Schnyer, Nicholls, & Verfaellie, 2005, Journal of Cognitive Neuroscience,), which indicated that VMPC is engaged during accurate FOK judgments and retrieval rating modulates its activation. Structural equations modeling supported the notion that VMPC, as part of a broader left hemisphere network involved in memory retrieval, monitors and evaluates the output of the retrieval process. Finally, in a recent paper, we have examined how a specific genetic profile interacts with the memory monitoring changes seen in aging. In this study, having a specific polymorphism of the 5HTTLPR gene contributed to better memory monitoring performance and this improved performance was associated with prefrontal compensatory activity (Pacheco, J., Beevers, C.G., McGeary, J.E., & Schnyer, D.M., Neuropsychologia, 2012). We are continuing our interest in explicit memory monitoring in the elderly by examining the influence of sleep and circadian rhythm patterns on the changes in performance seen in aging.

Implicit Memory:

A previous encounter with an item will often result in changes in a person’s ability to identify, produce or classify that item. Referred to as repetition priming, the existence of this facilitation effect, even in the absence of explicit recall of the prior event has led to the well accepted view that memory consists of multiple systems and/or processes. One of the most ubiquitous aspects of priming is that simple changes in presentation format or task demands between exposures can have significant effects on the level of facilitation gained through repetition. The nature and type of these specificity effects may be critical in helping to identify the representational level of dissociable components that contribute to behavioral facilitation.

I first began exploring a purely perceptual component of repetition priming by being the first to demonstrate unique neural signatures of masked word priming. The technique of masked word priming eliminates subjective awareness of the prime and putatively any long-term memory trace and provides an excellent model of pure “perceptual facilitation”. Beginning with human electrophysiology (Schnyer, Allen, & Forster, Neuropsychology, 1997; Schnyer, Allen, Kaszniak & Forster, Neuropsychology, 1999), and then later with fMRI (Schnyer, D.M., Ryan, L., Forster, K.I., & Trouard, T., NeuroReport, 2002; Eddy, M., Schnyer, D.M., Schmidt, A., & Holcomb, P., Neuroimage, 2007), these studies revealed clear electrophysiological and hemodynamic signatures associated with changes in perceptual systems that were not contaminated with post-identification processes that are dependent on the conscious perception of the prime. This work provided the foundation for work in collaboration with Dr. Chad Marsolek, were we explored the role that “maintenance relearning” within perceptual representations plays in repetition priming. Combining computation modeling, work in normal humans and amnesiac patients (Marsolek, C.J., Schnyer, D.M., Deason, R.G., Ritchey, M., & Verfaellie, M., Cognitive, Affective, & Behavioral Neuroscience, 2006) and neuroimaging (fMRI & ERPs; Marsolek, C.J., Deason, R.G., Ketz, N.A., Ramanathan, P., Bernat, E.M., Steele, V.R., Patrick, C.J., Verfaellie, M., Schnyer, D.M., Neuroimage, 2010), we demonstrated that repetition priming is associated with a cost (antipriming) that reflects changes in “baseline” characteristics of visual object representations. One intriguing aspect of this work was the finding that repetition related changes in the ventral stream processing might reflect an altered level of processing of baseline items rather than those that are repeated. The Neuroimage paper was awarded the Editor’s Choice Award in 2010 and cited for being a strong example of how to focus multiple Cognitive Neuroscience methodologies on a single research question.

In collaboration primarily with Dr. Ian Dobbins, we have explored the role that rapid decision or response learning plays in repetition priming (Dobbins, Schnyer, Verfaellie, & Schacter, Nature, 2004; Schnyer, Dobbin, Nicholls, Schacter, & Verfaellie, Neuropsychologia, 2005; Schnyer, Dobbins, Nicholls, Verfaellie, & Schacter, Memory & Cognition, 2007; Ghuman, Bar, Dobbins, & Schnyer, Proceedings of the National Academy of Sciences, 2008; Saggar, M., Miikkulainen, R.P. & Schnyer, D.M., Brain Research, 2010). These studies have demonstrated that the behavioral facilitation and neural activity reductions, both in regions associated with early visual processing and in those associated with later classification and decision making processes, result from the rapid learning of prior responses or decisions. This learning enables observers to bypass controlled or deliberative classification processes. This contrasts with a prominent view in Neuroscience, which proposes that the facilitation resulting from repetition reflects changes within object representational systems. Furthermore, we documented that amnesic patients with damage to the medial temporal lobes failed to show such learning (Schnyer et al., 2005). This finding is consistent with the idea that rapid response or decision learning reflects an MTL dependent associative learning mechanism whereby an item becomes directly associated with a particular response or decision outcome. Finally, results from our fMRI and MEG studies reliably demonstrate that the changes in left prefrontal cortex associated with repetition begin immediately following object recognition, proceed changes seen elsewhere in posterior regions and correlate directly with behavioral facilitation. The fMRI and MEG data provide clear neural signatures of the transition from algorithmic to a more automatic processing reflective of decision learning.

Finally, since arriving at UT Austin, I have worked closely with Dr. Todd Maddox to better understand the neural basis of category and prototype learning. We have examined the neural basis of protype learning using fMRI (Zeithamova, D., Maddox, W.T., & Schnyer, D.M., Journal of Neuroscience, 2008) and patients with lesions to prefrontal cortex (Schnyer, D.M., Maddox, W.T., Ell, S., Davis, S., Pacheco, J., & Verfaellie, M., Neuropsychologia, 2009). Furthermore, though work in aging (Maddox, W. T., Pacheco, J., Reeves, M., Zhu, B., & Schnyer, D. M., Neuropsychologia, 2010; Glass, B.D., Chotibut, T., Pacheco, J., Schnyer, D.M. & Maddox, W.T., Psychology of Aging, 2011) and sleep deprivation (Maddox, W.T., Glass, B.C., Wolosin, S.M., Savarie, Z.R., Bowen, C., Mathews, M.D., & Schnyer, D.M., Sleep, 2009; Maddox, W.T., Glass, B.C., Zeithamova, D., Savarie, Z.R., Bowen, C., Mathews, M.D., & Schnyer, D.M., Sleep, 2011) we have examined how changes in executive and attentional control processes influence category and prototype learning.