Working memory (WM) has been a major focus of recent schizophrenia research, driven by robust behavioral evidence of patient impairment and neuroimaging evidence suggesting abnormalities in neural activity during the performance of WM tasks.1–4
This clinical literature has been motivated by basic cognitive science models suggesting that WM is a critical building block of many higher cognitive functions.5,6
Further, there is an extensive basic neuroscience literature suggesting that WM involves dopaminergic activity in prefrontal cortex, and the known abnormalities in dopaminergic function in schizophrenia would seem to be consistent with deficits in WM. 4, 7–11
More recently, findings from post-mortem neuropathological studies of patients with schizophrenia as well as genetic findings have implicated abnormalities in the neural circuitry involved in WM. 12–15
Several investigators have recently proposed integrative theoretical accounts of the biological origins of cognitive impairment in schizophrenia. Each account involves an effort to translate the behavioral implications of basic biological findings. Lisman et al 12
provide a circuit-based account of the implications of genetic findings involving the dopamine, glutamate, and GABA systems. They emphasize the cascading impact of reductions in inhibitory function needed to tune and focus cortical processing, with a particular focus on memory and sensory processing. Durstewitz and Seamans16
explicitly address WM and propose that D1 hypofunction would result in “highly unstable representations” leading to “an inability to hold and manipulate information.” Rolls et al17
address much of the same evidence from the standpoint of computational modeling, concluding that NMDA receptor hypofunction would result in a neural environment where the “stability of the attractor state is reduced, resulting in difficulty maintaining a short-term memory.” 17p701
Further, reductions in prefrontal dopamine function “could be measured as a decreased signal to noise ratio and impaired short-term memory performance”. 17p707
While these accounts primarily address basic biological mechanisms, they lead to testable predictions about the types of cognitive impairment that would be expected in schizophrenia. Further, it is much easier to test these behavioral predictions than the predictions these models make about cellular activity in patients. For example, both Durstewitz and Rolls imply that WM representations should be prone to accelerated decay due to network instability. Further, Rolls, Durstewitz and perhaps Lisman suggest that WM representations in patients will have a poor signal-to-noise ratio, which should be evident behaviorally in the form of reduced memory precision. Here we ask whether these theoretically motivated claims, rooted in neurobiological evidence, accurately reflect the WM performance of schizophrenia patients. To preview, we will argue that these theoretical accounts are largely at odds with the accumulated behavioral literature, and we will present evidence from a new paradigm that provides direct evidence that visual WM representations are neither less precise nor more prone to decay in schizophrenia. Instead, patients exhibit a reduction in the number of items they can concurrently maintain in WM.
The overall pattern of WM findings in the schizophrenia literature does not provide much support for the idea that WM representations are less stable in patients, leading to faster decay. In a meta-analytic review of the WM literature, including 65 separate effect-size estimates with retention intervals that ranged from one to 30 seconds, Lee and Park2
concluded that the extent of patient impairment did not vary with length of delay interval. That is, the WM impairment in schizophrenia is just as pronounced at a one-second delay as it is at longer delays, arguing against instability of the representations during the retention interval. However, relatively few studies have parametrically varied the retention interval, and these conclusions rely on comparisons across studies. Moreover, most studies used categorical response alternatives (e.g., same vs. different), which may have made it difficult to observe gradual declines in precision over time. Thus, it is possible that the methods employed have not been optimal to document representational instability.
A few studies have provided evidence of reduced WM precision in schizophrenia patients.18–23
In these studies, perceptual parameters or encoding durations were adjusted at a short retention interval to equate patient and control performance. Patients required more discriminable stimuli to reach the same level of performance at short retention intervals, which may indicate that their WM representations were less precise. In addition, some of these studies found greater rates of decline in the patients as the retention interval increased.18–20, 24
However, the threshold estimation procedures in these studies can lead to biased threshold estimates when subjects occasionally fail to encode the stimuli, either due to attention lapses or low WM capacity25
. Thus, the findings of these studies may reflect a higher rate of all-or-none failures of encoding rather than instability or imprecision of the WM representations.
To provide a powerful test of WM instability in schizophrenia, a task must be able to directly measure the precision of WM representations, the number of representations that are stored in WM, and the decline in the number and/or precision of these representations with increasing delays. A new paradigm and analytic approach developed by Zhang and Luck 25,26
can separately measure each of these aspects of WM performance. As illustrated in , participants are first shown a sample array of 3–4 different colors for 500 ms. After a 1- or 4-second blank delay interval, one of the previous color locations is cued. Participants then indicate the color previously presented at the cued location by clicking on a color wheel displaying the entire range of possible colors.
Figure 1 A. Stimuli from the color recall task. B. Model of performance. When the probed item is present in memory, the reported color is most likely to be at the original value, and the probability declines with distance from the original value. When the probed (more ...)
If the cued item is present in WM, the recalled color should be close to the color of the originally presented item, with a bell-shaped distribution of errors (see ). If the cued item was not stored in WM, however, the response will be a random guess, leading to a flat distribution of errors. The observed data represent a mixture of these two types of trials, but it is possible to decompose this mixture, yielding two parameters that represent the two critical performance dimensions: (1) Pm (probability in memory) represents the probability that the cued item was stored in WM and was available at time of test; (2) SD (standard deviation) represents the width of the bell curve, which is inversely related to the precision of the WM representation for trials on which it was actually present in memory. Thus, reductions in WM capacity should be evident in lower Pm values, whereas reduced WM precision should be reflected in larger SD values. Most critically, a significant reduction in Pm in the absence of a difference in SD would indicate that the capacity reduction in schizophrenia cannot be explained on the basis of impaired WM precision. It should be noted that Pm would also be reduced if subjects accidentally reported the color of one of the uncued items; the frequency of this type of error can be assessed by examining the distribution of responses around each of the uncued colors.
The inclusion of two delay intervals also makes it possible to determine whether WM representations are less stable in patients than in controls, which would yield a reduction in Pm or an increase in SD over time. We chose delay intervals of 1 and 4 seconds because healthy young adults begin to show a decline in performance sometime between 4 and 10 seconds.25
If WM representations are unstable in patients, they should exhibit a decline at an earlier delay than do control subjects. We did not go beyond 4 seconds because longer delays may lead to an inability of patients to stay on task, artifactually producing the appearance of a WM decline.
In our view, recent theoretical accounts lead to strong predictions that patients should demonstrate reduced WM precision (i.e., an increased SD) and that the patient impairment of Pm and/or SD should be amplified as delay interval increases: each of these predictions is contradicted by the data presented here.