Elsevier

Consciousness and Cognition

Volume 18, Issue 3, September 2009, Pages 665-678
Consciousness and Cognition

Implicit working memory

https://doi.org/10.1016/j.concog.2009.04.003Get rights and content

Abstract

Working Memory (WM) plays a crucial role in many high-level cognitive processes (e.g., reasoning, decision making, goal pursuit and cognitive control). The prevalent view holds that active components of WM are predominantly intentional and conscious. This conception is oftentimes expressed explicitly, but it is best reflected in the nature of major WM tasks: All of them are blatantly explicit. We developed two new WM paradigms that allow for an examination of the role of conscious awareness in WM. Results from five studies show that WM can operate unintentionally and outside of conscious awareness, thus suggesting that the current view should be expanded to include implicit WM.

Introduction

Working memory (WM) has long attracted the attention of experimental psychologists and neuroscientists who are interested in how people reason, solve problems, pursue goals, make decisions, and achieve cognitive control (Baddeley, 2003, Baddeley and Logie, 1999, Cowan, 1999, Dudai, 2004, Shah and Miyake, 1999, Smith and Jonides, 1995). The conception of WM grew out of the literature on short-term memory (STM) in the mid 1970s (Baddeley & Hitch, 1974), and soon became what Baars and Franklin (2003) described as “the most influential empirical model of cognitive functions to date” (p. 170). Alan Baddeley, one of the leading researchers of WM, wondered in a recent exploration of the WM literature “what else is needed for a theory of working memory,” and he replied that, among other things, “the proposed link between working memory and conscious awareness also represents a lively and exciting interface” (Baddeley, 2003, Miyake and Shah, 1999). The current paper addresses this issue directly. It presents five studies that examine the relation between working memory and conscious awareness, with findings strongly suggestive of nonconscious, implicit WM.

There seems to be a widespread consensus regarding the functions that WM serves. This consensus was nicely described by Miyake and Shah (1999) who, after having reviewed major theories of WM, concluded that “working memory is not for ‘memorizing’ per se but, rather, it is in the service of complex cognitive activities such as language processing, visuospatial thinking, reasoning and problem solving, and decision making” (p. 445; cf. Unsworth, Heitz, & Engle, 2005).

A review of the models of WM is beyond the scope of the current paper (see Baddeley, 2007, Miyake and Shah, 1999 for overviews). However, an inspection of these models and the tasks used to examine WM (e.g., Daneman and Carpenter, 1980, O’reilly et al., 1999, Smith and Jonides, 1999, Turner and Engle, 1989) reveals that the consensus regarding the functions of WM is matched by a general agreement regarding the processes that underlie it. These include: (1) active maintenance of ordered information for relatively short periods of time; (2) context-relevant updating of information, and goal-relevant computations involving active representations; (3) rapid biasing (control) of task-relevant cognitions and behaviors, in the service of currently pursued goals (Hassin, 2005, O’reilly et al., 1999). The latter processes include attending and inhibiting, scheduling, monitoring and planning (cf. Smith & Jonides, 1999).

The consensus among WM- and consciousness researchers is that the psychological processes that underlie WM, attention and awareness are closely intertwined (e.g., Baars and Franklin, 2003, Dudai, 2004, Kintsch et al., 1999). The nature of these relations, however, is less consensual.

Some researchers adhere to what Kintsch et al. (1999) referred to as the “subset hypothesis,” which argues that a subset of the information that is actively maintained in working memory is that of which we are aware (cf. Cowan, 1999, O’reilly et al., 1999). Baddeley, who historically was reluctant about exploring the relations between working memory and consciousness (Baddeley, 1992), suggested that conscious awareness may be one of the functions of the central executive component of WM (Baddeley, 1993). In a more recent development of his model Baddeley (2000) explicated his proposal and suggested that awareness is inherent in the interaction between the executive and the episodic buffer.

Baars (1997) went one step further by postulating that conscious awareness is involved in all WM input, output, and voluntary operations, as in explicit problem solving. Similarly, in a more recent paper Baars and Franklin argued that “all active components of classical working memory are conscious: input, rehearsal, visuospatial operations, recall and report.” (Baars & Franklin, 2003, p. 170).

While divergent, these views seem to have two common themes. First, they share the idea that some components, processes or contents of WM are conscious. The contents may be the representations in the focus of attention (Cowan, 1999), or those that are involved in the interaction between the executive and the episodic buffer (Baddeley, 2000), and the processes may include input, rehearsal, and visuospatial operations (Baars & Franklin, 2003). We adopt Baars & Franklin’s terminology (2003; see above), and refer to these as active contents or processes. Second, none of these views suggests that people have conscious access to everything that goes on within WM (e.g., we may not have conscious access to the processes that underlie our ability to rehearse information, or to those that underlie our ability to update information in WM.)

The differences in opinion described above may result in a somewhat complex picture of the relations between WM and conscious awareness. The major WM tasks, however, are clearer and unequivocal on this question: In all of them participants are explicitly presented with materials that they are explicitly asked to manipulate (e.g., they are instructed to memorize, rehearse, compare, subtract, and so on). In other words, these tasks take as given that WM is conscious (see Berns, Cohen, and Mintun (1997), for a similar argument regarding novelty detection).

We suggest to view these tasks as implicit, or indirect, measures of researchers’ beliefs regarding WM. As such, they clearly show that researchers adhere to the view that active components and contents of WM are conscious.

A notable exception to the picture portrayed above is short-term memory: Results from two very different paradigms suggest that it can operate implicitly. McKone (1995, Study 1) used a lexical decision task to assess the effects of repetition priming on words and non-words at lags of 0, 1, 2, 3, 4, 5, 9, 23, and 1050 intervening items (where lag 0 is an immediate repetition condition, in which there are no intervening items between two presentations of a stimulus). Participants in these experiments were not asked to memorize the stimuli, and were largely unaware of the repetitions. Yet, the results showed that repetition priming for words had two components. First, a short term component that led to a relatively big reduction in RTs. This component decayed pretty smoothly until lag 4 (starting from ∼140 ms at lag 0, and asymptoting at approximately 50 ms at lag 4). Second, a relatively long term component, that led to a reduction of around 50 ms in RTs as a result of repetition priming. This component lasted at least until lag 23.

These two components were found in three more studies, leading Mckone (1995) to conclude that there are both short term implicit memory effects in word priming (lasting approximately 8 s in her studies), and long-term implicit memory effects (lasting at least 40 s in her studies.) The short term effects described above did not replicate with non-words. Thus, McKone’s interpretation of the results focused on short-term retention within system(s) of word recognition (McKone, 1995, McKone, 1998, McKone and Dennis, 2000; cf. Cowan, 2001).

In a similar vein, Maljkovic and her colleagues (Maljkovic and Martini, 2005, Maljkovic and Nakayama, 1994, Maljkovic and Nakayama, 2000) found evidence for implicit short-term memory in a visual search task. On each trial in this task several shapes appear on a computer’s screen, and participants are asked to decide whether the left or right side of one of these shapes is cut off. The target shape (i.e., the one about which the decision is made) is determined, on each trial, by popout. Thus, for example, on trial n participants may be presented with 10 stimuli, nine of which are green and one is red; in this trial response should be based on the red stimulus. On trial m, however, participants may be presented with nine red stimuli and only one green, and here response should be based on the green stimulus.

The consistent finding of Maljkovic and her colleagues is that when the popout feature is repeated (e.g., the “oddball”, popout color in trial n and n + 1 is red), responses are speeded. This effect lasts for 15–30 s, suggesting that the short term visual memory of the popout feature improves performance. Crucially, participants were unaware of the repetition and its effect (Maljkovic and Nakayama, 1994, Maljkovic and Nakayama, 2000), and hence the researchers concluded that this short term maintenance is implicit.

Whether these lines of investigation look at two different implicit mechanisms – reminiscent of the phonological loop and the visuospatial sketchpad (Baddeley & Hitch, 1974) – or at a more general short term implicit memory system(s) is still an open question. Regardless of the answer to this question we find these findings encouraging, because they establish the existence of implicit short-term memory.

The review above indicated that there is a wide consensus regarding the role of WM in many high-order cognitive processes (e.g., reasoning, problem solving, cognitive control, goal pursuit). It also indicated that, with the possible exception of short-term memory, active components of WM are generally perceived as intimately tied to conscious awareness. Given the acknowledged capacity limitations of conscious awareness (e.g., Kahneman, 1973), these two conclusions imply that we can only engage in a very limited number of these high-order cognitive processes at any given point in time. This conclusion stands in stark contradiction to the simple intuition that there are points in time in which we seem to be advancing multiple goals, decisions and plans (etc.), so the question that arises is – how can the two be reconciled?

One option is that it only seems as if we can pursue more than a handful of these processes at any given point in time, but in actuality when they are “out of sight” (i.e., unconscious) they are also “out of mind” (i.e., inactive). In other words, it may be the case that when processes of this sort are not in our current conscious focus they are idle. They do not do any work.

Yet another option is to reconsider the view that active components of WM are conscious, and to suggest that WM can operate implicitly1 (cf. Spelke, Hirst, & Neisser, 1976). While both options are logically possible, psychological considerations – mainly, the grave limitations on conscious resources (e.g., Kahneman, 1973) – suggest to us that the latter is more plausible.

We argue, therefore, that WM can operate outside of conscious awareness. More specifically, we suggest that the processes that were identified above as underlying (or constituting) WM can be recruited without conscious intention, and that they can then go on to operate non-consciously. These include: (1) active maintenance of ordered information for relatively short periods of time; (2) context-relevant updating of information, and goal-relevant computations involving active representations; (3) rapid biasing (control) of task-relevant cognitions and behaviors, in the service of currently pursued goals. Furthermore, we contend that this is the case even when the representations on which these processes operate are inherent to the focal task. Lastly, we argue that the content that is task-relevant and that is modified by these processes may be unconscious too.

Since existing WM tasks take as given that WM is predominantly conscious, they cannot be used to systematically address questions regarding the role of conscious awareness in WM. We therefore developed two new working memory paradigms. In the next section we describe the paradigm that was used in four out of the five studies we report, and the following section examines whether it is indeed a working memory paradigm.

In this paradigm the computer screen is divided into cells by a 24 (columns) by 18 (rows) matrix. Small round disks (five millimeters in diameter) that are either empty (i.e., bagel-shaped), or full, appear in the different intersections of the matrix. The disks appear one at a time, and participants’ task is perceptual: They are asked to indicate, using two keys on the keyboard, whether the disk is full or empty. The disk remains on the screen until participants respond, and erroneous responses are followed by a short auditory feedback. After each response the disk disappears and 150 ms later the next disk appears.

The stimuli appear in sets of five, which are demarcated from each other by a fixation point (a small square) that appears in the center of the display for 1500 ms. There are three different kinds of sets, which define three conditions. In the Pattern sets condition the locations of all the disks in a set follow a pre-determined pattern. If one keeps in mind all of the disks in a set in their right order, and then mentally “draws the lines” between their locations, then many of these patterns are familiar (e.g., zigzags). Note, however, that the disks appear one at a time, and hence the input to the cognitive system is comprised of individual disks, not sets (for a full list of these patterns see Table 1). In the yoked Broken Pattern condition the locations of the first four disks in a set follow the same pattern (e.g., zigzag), but the location of the fifth disk does not2, and in the yoked Control condition the locations of the first three disks are randomly determined, but the fourth and fifth follow the pattern.

If participants extract the pattern during the first four disks of a Pattern set, they can correctly anticipate the location of the last disk in that particular set. This may allow them to speed up their response to this last disk. If they extract patterns during Broken Pattern sets, though, their anticipations will err systematically, thus potentially slowing down their responses. The dependent measure of interest is thus reaction time to the last disks in Pattern, Broken Pattern and Control sets. Because of the hypothesized pattern-driven anticipations of the disks’ locations, RTs to last disks in Pattern sets (where pattern-based anticipations are veridical) should be faster than RTs to the last disks in Broken Pattern sets (where pattern-based anticipations are systematically misleading), with Control sets falling somewhere in between.

Note that participants’ explicit task is merely to judge whether the disks are empty or full. Hence, any extraction of the patterns that govern disks’ locations is incidental. Furthermore, the nature of the disks (i.e., whether they are full or empty) is randomly determined throughout the study, and so extracting the patterns that determine disks’ locations cannot help participants predict the correct responses (although it can speed them up if it allows them to predict where will the next disk appear).

Each pattern, broken pattern and control set (see illustration in Fig. 1) appears only once in a block, and the remaining sets are Random, that is – the locations of all five disks are pseudo-randomly determined, with the constraint that two consecutive disks cannot be more than four matrix-units apart from each other (the average distance between disks in the experimental sets is 3.125). In four out of the five studies we report, each block contains 10 different patterns, and their 10 yoked broken patterns and 10 yoked controls. The remaining 70 sets of each block are random.

To answer this question we adopt a functional perspective. Thus, a working memory paradigm is a paradigm that taps the functions of WM. To answer this question, then, one needs to consider the mental operations that subserve extraction and use of patterns in this paradigm. First, the goals of being fast and accurate need to be in place. Second, one must mentally rehearse and actively update a list of locations of disks (within each set). Third, the locations of the disks must be maintained in order of their appearance. Fourth, mental computations must be conducted in order to extract the pattern. Fifth, the extracted patterns must lead to anticipations regarding the locations of the last disks, anticipations that affect the speed with which responses are made. In other words, an extracted pattern biases other concurrent cognitive processes and thus controls behavior.3

A comparison of these features with the list of WM processes given above reveals that the current paradigm is indeed a WM paradigm: It requires active maintenance of ordered information for relatively short periods of time; in addition, it requires context-relevant updating of information (with incoming disks) and goal-relevant computations (i.e., pattern extraction and anticipation formation). Lastly, the information is processed in the service of current goals (of being fast and accurate), and is readily available to bias cognition and behavior (thus speeding/slowing responses).

Note that this task (like many other WM tasks; cf. Baddeley, 2003, Waugh and Norman, 1965) may involve long term memory. This involvement may occur both at the level of the individual stimulus (e.g., the stimulus may seem to be a “ring” or a “bagel”) and at the level of sets, especially in those sets that may be used to create patterns that are familiar (e.g., zigzags). Recall, however, that each disk appears in isolation, separated in time and space from the previous and following disks. Thus, without active maintenance of ordered locations there are no sets of disks, only isolated ones. And without the goal-relevant computations that involve disk-related information (i.e., “connecting the dots”) there are no patterns, just sets of disks. In other words, one cannot identify (or recognize) a pattern in this paradigm without rehearsing the locations of the disks in a set, in their right order, and without mentally “drawing the lines” that “connect” the disks. Without this endeavor of WM the only relevant information in long term memory would be the information provided by each trial (i.e., the information of one disk) or, at most, the unordered, unprocessed, “raw” information of multiple trials. And hence, long term memory cannot generate the hypothesized effects by itself.

We conclude, then, that WM operations are necessary for “drawing” the visual patterns created by the various sets. This means that evidence which suggests that the hypothesized effect occurred is also evidence that these WM operations had taken place. Whether the processes of “naming” the patterns, creating anticipations for future locations, and controlling cognition, are done solely by WM or, like many other WM paradigms (Baddeley, 2003, Gathercole et al., 2001), involve the interaction of WM and long term memory, is an empirical question that we sadly leave for future research.

To answer this question it is important to reiterate that pattern- and broken pattern sets are equally likely. It is important, because this procedure prevents more gradual implicit learning per-se. To see why this is so consider the following conditional probability: Given four disks of a pattern/broken pattern set, where would the next disk appear? Recall, that up to the fourth disk a pattern and its broken pattern set are identical in terms of disks’ locations. Note, furthermore, that in each block we keep the probability of a pattern-following last move equal to the probability of a pattern breaking last move (=.5). Thus, one’s learning history during the experiment should lead to chance performance on the fifth disk. As far as learning is concerned, then, no differences should emerge between pattern-following and pattern-breaking sets. We conclude, then, that evidence for pattern extraction in this paradigm cannot be merely attributed to implicit learning.

This paper offers to extend our views of WM to include implicit working memory. Studies 1–3 use the paradigm that was described above with various tests of awareness (self report, immediate reconstruction, and immediate recognition). Study 4 again tests for awareness of pattern extraction, this time by contrasting the implicit version of this paradigm with an explicit version. Study 5 uses a conceptual replication of this paradigm in a non-visual domain. We conclude by discussing the current proposal in its wider context.

Section snippets

Participants

Twenty Hebrew University students participated in Study 1 in exchange for course credit or 15 Shekels (∼$3).

Materials and tools

These were described above in Section 1.5. The study comprised of two blocks of 100 sets, and it included 10 different patterns (the complete list of patterns is presented in Table 1). Each block was comprised of a single presentation of each pattern-, broken pattern- and control sets, and 70 random sets. The experimental sets were pseudo-randomly distributed within the block. The only

Study 2

Study 2 is a replication of Study 1 with one extension: awareness of pattern extraction was also assessed through an immediate reconstruction-probe method. Thus, the last set that participants saw was always a (randomly chosen) Pattern set. Immediately after giving their response to the last disk in this set participants were asked to reconstruct it. Awareness was determined by whether participants could do so.

Study 3

Study 3 is identical to the previous study, except for the method that was used to assess awareness. Like in the previous study, the last set participants saw was always a pattern-following set. Immediately following their last response to this set participants were given a recognition test.

Study 4

The first three studies established that participants can non-consciously and unintentionally extract patterns in a new working memory paradigm. The studies used three different measures of awareness – post-experimental questionnaire, immediate reconstruction, and immediate recognition. The current study examines the contention that pattern extraction is non-conscious in this paradigm in yet another way: Instead of measuring awareness and intention, it manipulates them. Thus, participants in

Study 5

The fifth study is a conceptual replication of the first four studies, in the domain of numbers. Participants saw sets of one-digit numbers that appeared, one at a time, on a computer screen. Unlike the previous studies, where the sets created visual patterns, here they create algebraic ones (e.g., 0, 2, 4, 6 for a pattern set vs. 0, 2, 4, 2 for a broken pattern set).

General discussion

The results of five studies show that the online extraction and application of patterns, a task that meets the requirements for a WM task, occurred in the absence of intention and conscious awareness. These data suggest, then, that WM can operate unintentionally and outside of conscious awareness, and hence – that current theorizing of WM should be expanded to include implicit working memory.

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