Baseline-dependent effect of dopamine’s precursor L-tyrosine on working memory gating but not updating

Abstract

Adaptive goal-directed behavior requires a dynamic balance between maintenance and updating within working memory (WM). This balance is controlled by an input-gating mechanism implemented by dopamine in the basal ganglia. Given that dopaminergic manipulations can modulate performance on WM-related tasks, it is important to gain mechanistic insight into whether such manipulations differentially affect updating (i.e., encoding and removal) and the closely-related gate opening/closing processes that respectively enable/prevent updating. To clarify this issue, 2.0 g of dopamine's precursor L-tyrosine was administered to healthy young adults (N = 45) in a double-blind, placebo-controlled, within-subjects study. WM processes were empirically distinguished using the reference-back paradigm, which isolates performance related to updating, gate opening, and gate closing. L-tyrosine had a selective, baseline-dependent effect only on gate opening, which was evidenced by markedly reduced variance across subjects in gate opening performance in the L-tyrosine compared with the placebo condition, whereas the whole-sample average performance did not differ between conditions. This indicates a pattern of results whereby low-performing subjects improved, whereas high-performing subjects were impaired on L-tyrosine. Importantly, this inverted U-shaped pattern was not explained by regression to the mean. These results are consistent with an inverted-U relationship between dopamine and WM, and they indicate that updating and gating are differentially affected by a dopaminergic manipulation. This highlights the importance of distinguishing these processes when studying WM, for example, in the context of WM deficits in disorders with a dopaminergic pathophysiology.

Cognitive Control of Working Memory: A Model-Based Approach

“…In this study, we performed the first detailed model-based analysis of behavioral costs in the reference-back task, which are assumed to reflect a set of cognitive control processes that support WM. Our data replicated the set of behavioral effects found in previous reference-back studies [11,12,14,[21][22][23][24][25], including the asymmetrical costs of opening and closing the gate to WM [7,11,12,14,21]. The DDM provided close fits to empirical choice-RT distributions for each design cell of the reference-back, and model selection indicated that, for most subjects, drift rate and non-decision time each played an important role in explaining the observed differences in accuracy and RT.…”

“…To summarize, the canonical reference-back cost measures were all associated with slower RT and, for gate opening and substitution, lower accuracy. In addition, the larger RT cost for gate closing versus gate opening replicates the asymmetric gating costs found in previous works [7,11,12,14,21]. These effects are illustrated in Figure 4.…”

“…Unlike the traditional n-back, in which multiple operations are confounded on each trial [11], the reference-back allows costs specific to each control process (e.g., updating and gating) to be decomposed by contrasting different trial types (described in Table 1). This feature of the reference-back paradigm has led to important insights into WM control processes, such as the effects of dopaminergic drugs on WM control, and identifying the neural basis of specific WM control processes [21][22][23][24][25]. Performing the reference-back task involves holding one of two stimuli (an 'X' or an 'O') in WM while deciding whether a series of probe stimuli are the same as or different to the item currently held in WM (called the reference item or referent).…”

“…The benchmark behavioral finding from the reference-back literature is that each cost measure is associated with poorer accuracy (i.e., more frequent errors) and slower RT [11,12,14,[21][22][23][24][25]. However, we currently do not have any idea about the underlying cognitive mechanisms that give rise to these costs.…”

“…Although necessary for successful WM, recent work with the novel reference-back paradigm has shown that these processes impose substantial performance costs on both the speed and accuracy of WM-based decisions [11,12,14,[21][22][23][24][25]. However, less is known about how such costs arise out of latent decision processes (e.g., processing rate, time outside of the decision stage, response threshold settings) as instantiated in leading computational theories of WM-based decision making [26,27].…”