Perceptual Representations and the Vividness of Stimulus-Triggered and Stimulus-Independent Experiences

Abstract

In recent years, researchers from independent subfields have begun to engage with the idea that the same cortical regions that contribute to on-line perception are recruited during and underlie off-line activities such as information maintenance in working memory, mental imagery, hallucinations, dreaming, and mind wandering. Accumulating evidence suggests that in all of these cases the activity of posterior brain regions provides the contents of experiences. This article is intended to move one step further by exploring specific links between the vividness of experiences, which is a characteristic feature of consciousness regardless of its actual content, and certain properties of the content-specific neural-activity patterns. Investigating the mechanisms that underlie mental imagery and its relation to working memory and the processes responsible for mind wandering and its similarities to dreaming form two clusters of research that are in the forefront of the recent scientific study of mental phenomena, yet communication between these two clusters has been surprisingly sparse. Here our aim is to foster such information exchange by articulating a hypothesis about the fine-grained phenomenological structure determining subjective vividness and its possible neural basis that allows us to shed new light on these mental phenomena by bringing them under a common framework.

Keywords

consciousness perception mental imagery dreaming mind wandering vividness intensity specificity

Recent efforts comparing conscious experiences that arise in different conditions (e.g., during wakeful perception, mental imagery, mind wandering, or dreaming) have typically concentrated on finding what is common in the underlying brain processes to identify the possible neural substrate of having conscious experiences in general, that is, to home in on the neural correlate of consciousness. In this article our aim is to move one step beyond this approach and investigate the individual phenomenological characteristics of such conscious experiences and the possible features of brain activity that might underlie these qualities.

The phenomenological characteristic that we focus on is the vividness of experiences. Regardless of their kind and content, experiences come with a certain level of vividness. Although vividness is a quality in terms of which different experiences are routinely compared, it is a vague and ambiguous notion without a clear definition. Intensity, liveliness, salience, prominence, clarity, and detailedness are but a few examples of those terms that vividness is often claimed to be synonymous with. It is thus also our specific goal to clarify what standard psychological measures of vividness ultimately tell us.

Background: Shared Content-Specific Activity in Stimulus-Triggered and Stimulus-Independent Experiences

Whether the “seat of consciousness” is in the back or in the front of the brain is hotly debated. On the one hand, theoretical approaches that emphasize the importance of cognition in giving rise to conscious experience attribute the critical role to frontal regions (Brown, Lau, & LeDoux, 2019; Dehaene & Changeux, 2004, 2011; Dehaene, Changeux, Naccache, Sackur, & Sergent, 2006; Dehaene, Charles, King, & Marti, 2014; Dehaene, Kerszberg, & Changeux, 1998; Dehaene, Lau, & Kouider, 2017; Dehaene & Naccache, 2001; Lau & Rosenthal, 2011; Sergent, 2018). On the other hand, accounts claiming that the major contribution is made by local perceptual processing locate the neural correlates of consciousness in posterior areas (Block, 2005, 2007; Lamme, 2006, 2010, 2018; Lamme & Roelfsema, 2000). Lesion, electric stimulation, and neuroimaging studies have been cited to support this latter view (Boly et al., 2017; Koch, Massimini, Boly, & Tononi, 2016). However, the correct interpretation of these findings is a matter of dispute (Odegaard, Knight, & Lau, 2017; Phillips, 2018; Pitts, Lutsyshyna, & Hillyard, 2018).

Be that as it may, there is a relatively broad consensus about the essential role of the posterior areas (low- and high-level sensory areas, precuneus, posterior cingulate, retrosplenial cortex) in determining the contents of conscious experiences regardless of the presence or absence of matching stimuli (Boly et al., 2017; Siclari et al., 2017).

In the case of stimulus-triggered on-line perception, these occipital, temporal, and parietal regions process the incoming information and form content-specific neural representations that are selectively activated by colors, faces, places, and so on (Dehaene & Changeux, 2011; Dehaene et al., 2006; Lamme, 2006). According to recent sensory-recruitment theories of working memory, the very same regions are also responsible for maintaining information after stimulus offset; that is, the neural representations that are active during the processing of incoming information are also active during the maintenance of this information in working memory (Christophel, Klink, Spitzer, Roelfsema, & Haynes, 2017; D’Esposito & Postle, 2015; Emrich, Riggall, LaRocque, & Postle, 2013; Postle, 2015; Riggall & Postle, 2012).

This information-maintenance component of working memory (Fazekas & Nemeth, 2018) has recently been tightly linked to mental imagery by a study that found evidence of common representations in early visual areas shared by mental imagery and visual working memory (Albers, Kok, Toni, Dijkerman, & de Lange, 2013) and by the subsequent interpretation that the phenomenon of mental imagery is, in fact, the dynamic component of visual working memory (Tong, 2013). Moreover, several recent studies have found a large neural overlap between perception and mental imagery and have shown in particular that the content of mental imagery is subserved by the activation of the same posterior areas that are active during on-line perception (Dijkstra, Bosch, & van Gerven, 2017, 2019; Ishai, Ungerleider, & Haxby, 2000; Johnson & Johnson, 2014; Naselaris, Olman, Stansbury, Ugurbil, & Gallant, 2015; O’Craven & Kanwisher, 2000; Reddy, Tsuchiya, & Serre, 2010; Stokes, Thompson, Cusack, & Duncan, 2009).

The perceptual contents of conscious experiences occurring during dreaming have also recently been associated with the activity of the posterior regions in question. Horikawa, Tamaki, Miyawaki, and Kamitani (2013) accurately identified the broad categories of the content elements of sleep-onset mentations using decoding models that had been trained on functional MRI (fMRI)-measured stimulus-induced brain activity in visual cortical areas. The authors demonstrated that the neural representations underlying the contents of experiences were shared by stimulus-triggered perception and sleep-onset mentations. Siclari and colleagues (2017) extended this link to experiences that occur during rapid-eye-movement (REM) sleep by showing that dream experiences with faces, a definite spatial setting, a sense of movement, and speech correlated with increased high-frequency electroencephalographic (EEG) activity in regions that are associated with these content types during wakeful perception.

That is, the claim that the contents of conscious experiences are subserved by neural activity in posterior areas is well supported by findings from many independent strands of research. This claim serves as the starting point for the rest of our article. Given that it is known where one should look for the neural activity underlying specific contents of consciousness, our question is whether it is possible to move one step further and try to link features of this neural activity with phenomenological features of conscious experiences.

The Vividness of Experiences

The phenomenological feature of consciousness that we focus on is the subjective vividness of experiences. Vividness is a central phenomenological feature of a broad range of (if not all) experiences: For instance, pains; felt emotions; and visual, auditory, olfactory, and tactile experiences all come with a certain level of vividness. The vividness of these experiences plays an important role in structuring our phenomenological lives (Morales, 2019) and has significant cognitive effects. For example, vivid dream contents are easier to remember (Cohen & MacNeilage, 1974), vivid mental imagery elicits incidental recall (D’Angiulli et al., 2013) and can even lead to false remembering (Gonsalves et al., 2004), metaconsciousness in mind wandering is more sensitive to vivid experiences (Schooler, 2002), and vivid hallucinations feel more realistic (Stephan-Otto et al., 2017).

What is vividness?

A good starting point for seeing how subjective vividness is operationalized in psychological investigations is provided by the questionnaires that are designed and used to measure the level of vividness in different contexts.

Versions of the Vividness of Visual Imagery Questionnaire (VVIQ) have been widely used since 1973 to measure the vividness of conscious experiences occurring during mental-imagery tasks (Marks, 1973). In its original form, the VVIQ asks subjects to rate the vividness of their imagery on a 5-point scale (1 = perfectly clear and as vivid as normal vision, 2 = clear and reasonably vivid, 3 = moderately clear and vivid, 4 = vague and dim, 5 = no image at all). Although routinely used in experimental practice, the notion of vividness that the VVIQ tries to measure (i.e., VVIQ vividness) is notoriously problematic because it is only intuitively defined with the use of other concepts such as clarity, detail, brightness, intensity, and so on, which themselves are then left unexplained (see, e.g., Cornoldi et al., 1991; Denis, 1995; Kind, 2017; McKelvie, 1995a, 1995b). Even whether VVIQ vividness is supposed to be a single feature of experience or a construct with more than one component is debated (McKelvie, 1995a).

In his enormously rich review and analysis, Stuart McKelvie (1995a, 1995b) argues that VVIQ vividness is a combination of two factors, clarity and liveliness; the former characterizes the sharpness and detailedness of the content of experience and the latter characterizes its illumination, brightness, and intensity. McKelvie also emphasizes that, as suggested by the fact that the maximum value of VVIQ vividness is related to the clarity and liveliness of normal vision, the VVIQ asks subjects to measure their imagery against their full-blown visual experiences; that is, the VVIQ is indicative of the degree of similarity between internal imagery and normal (stimulus-triggered) visual experience.

Vividness as the quality of experience

In the context of whether consciousness is an all-or-nothing phenomenon or comes in degrees, the Perceptual Awareness Scale (PAS) has been used to measure how degraded people’s conscious experiences of a masked stimulus can be (Ramsøy & Overgaard, 2004; Sandberg & Overgaard, 2015; Sandberg, Timmermans, Overgaard, & Cleeremans, 2010). By using the PAS, subjects can rate on a 4-point scale whether they had no impression of the stimulus (not seen), whether they have a feeling that something had been shown but cannot specify any features of the stimulus (weak glimpse), whether they had an ambiguous experience of the stimulus with more vivid impressions of some stimulus aspects and less vivid impression of others (almost clear experience), or whether they had a clear, specific, unambiguous experience of the stimulus (clearly seen).

Comparing the descriptions of the PAS and VVIQ reveals that what the PAS measures in the context of stimulus-triggered visual experiences is very similar to what the VVIQ measures in the context of mental imagery: whether experiences of masked stimuli (in the former case) and of mental imagery (in the latter case) are as clear and as vivid (lively) as full-blown visual experiences are. That is, in line with McKelvie’s analysis, the VVIQ and PAS measure the similarity between specific experiences that are degraded for some reason and normal, full-blown experiences (i.e., how reduced these specific experiences in question are in quality).

According to a recently proposed theoretical model, consciousness can be reduced in quality along many different dimensions (Fazekas & Overgaard, 2016, 2018a, 2018b). The way in which the content elements that enter consciousness appear in a conscious experience—what is called the quality of the experience in this context—is determined by a number of factors, many of which are independent. In fact, these different factors form factor families that determine high-level characteristics of experiences such as their subjective intensity, specificity, and stability. Subjective intensity is determined by how much the content element in question stands out from the perceived background. More intense content elements have more strength (Morales, 2019) and more liveliness (McKelvie, 1995a). Subjective specificity is determined by how distinguishable a content element is from other content elements. A less specific experience of a content element is more generic, more vague, and more ambiguous. Subjective stability is determined by how long a content element is present in the experience. Less stable content elements occur in an experience, only for a shorter period of time (Fazekas & Overgaard, 2016, 2018a, 2018b).

As common characteristics of factor families, these features can change in many different ways. For example, a content element can visually appear in an experience with a higher subjective intensity if its contrast is increased, its saturation is higher, or its brightness is amplified. Likewise, a content element’s subjective specificity can change along many different subdimensions: It can be less ambiguous by being highly precise (specific shade of red vs. generic red), sharper and less blurry, or rich in details (photograph vs. line drawing). Note that these are independent factors that can be modulated either individually or in any combination.

Standard measures of vividness such as the VVIQ and PAS conflate these different factors. What they measure is the quality of the conscious experience in question, but their resolution is not fine-grained enough to uncover the different levels of degradation along these different dimensions. This conclusion is supported by studies aiming to find the neural underpinnings of subjective vividness ratings.

The Neural Bases of the Factors of Vividness

The neural mechanisms underlying at least some of the different factors that affect the vividness (quality) of experiences are well known. In the sections that follow, we focus on subjective intensity and subjective specificity. For more discussion of the temporal factor, see Fazekas and Overgaard (2018a).

Subjective intensity

Subjective intensity in the contrast sense (i.e., apparent contrast) is associated with the strength of the response of edge detectors: Allocating attention to a Gabor patch increases the apparent contrast of the stimulus by inducing an increase in the amplitude of the response function of the corresponding population of orientation-sensitive neurons (Carrasco, Ling, & Read, 2004). Attention also has a similar effect on subjective intensity in the saturation sense (i.e., apparent saturation; Carrasco, 2011; Carrasco & Barbot, 2019; Fazekas & Nanay, 2018): Allocating attention to a color patch increases the apparent saturation of the color by inducing an increase in the amplitude of the response function of the corresponding population of hue-sensitive neurons, which associates apparent saturation with the strength of the response of hue detectors (Fuller & Carrasco, 2006). In a similar vein, subjective intensity in the brightness sense has been associated with the strength of the response of neurons representing object surfaces: Changes in apparent brightness can be evoked by modulating the luminance of the surroundings of the object surface in question that induces a phase-shifted change in the strength of the neural response of the population representing the object surface (Rossi & Paradiso, 1999).

These findings suggest that factors of subjective intensity—contrast, saturation, and brightness, all prothetic dimensions with meaningful zero values and inherent directionality (Fuller & Carrasco, 2006; Stevens & Galanter, 1957)—are neurally encoded in a similar fashion by the strength of the response functions of populations of neurons that represent specific metathetic features (with qualitative differences and without inherent less-to-more directionality) such as orientation, hue, and object surfaces. As increased strength of neural response functions means increased firing rates, these findings predict that variance in subjective intensity along these subdimensions (individually or combined) could in theory be detected as modulations in the blood-oxygenation-level-dependent (BOLD) signal and in high-frequency EEG activity (Le Van Quyen et al., 2010; Masuda & Doiron, 2007; Panzeri, Macke, Gross, & Kayser, 2015).

Subjective specificity

The neural underpinnings of the different factors of subjective specificity are more diverse. Allocating attention to a particular hue (e.g., crimson) increases the subjective specificity of the apparent color in the precision sense, that is, it decreases the ambiguity between different shades of red by sharpening the population response function (decreasing its variance) and thus increasing the precision of the population code (Martinez-Trujillo & Treue, 2004; Maunsell & Treue, 2006), thereby providing more unique neural representations that code for the feature in question (Carrasco, 2011; Fazekas & Nanay, 2018). This suggests a link between the subjective precision of the experience and the precision of the underlying neural representation.

Subjective specificity in the blurriness sense has been associated with information in high spatial-frequency channels (i.e., information about fine-grained edges). Such information might not be available for at least three different reasons: stimulus blur, when the stimulus itself is blurry (as in the case of using of a low-pass filter to produce the stimulus); optical blur, when optical problems with the eye (scattering and optical aberrations) result in blurry vision; and neural blur (i.e., relatively low levels of neuronal activity with small receptive fields), which can result from low neural sensitivity to higher spatial frequencies in the case of stimulus-triggered perception or from a low level of recruitment of early visual representations in the case of stimulus-independent percepts (Webster, Georgeson, & Webster, 2002; Webster & Marcos, 2017). As in the case of precision, the allocation of attention can modulate apparent blur as it increases visual acuity by shrinking the effective receptive field of neurons, thereby enhancing their spatial resolution and increasing their sensitivity to high spatial frequencies (Abrams, Barbot, & Carrasco, 2010; Fazekas & Nanay, 2018; Gobell & Carrasco, 2005).

Relatedly, subjective specificity in the detailedness sense (i.e., the amount of fine-grained detail in the experience) is determined by the processing of high-spatial-frequency information that is associated with activity at the lower levels of the visual hierarchy in the occipital cortex (Lu et al., 2018). The rapid processing of low-spatial-frequency (i.e., coarse-grained) information along the dorsal visual stream results in a coarse parsing of the visual scene that then, via feedback to the primary visual cortex, guides the slower analysis of high-spatial-frequency information along the ventral stream (Kauffmann, Ramanoël, & Peyrin, 2014; Musel et al., 2014).

According to the emerging picture, higher levels of subjective vividness in both the blurriness and the detailedness sense thus require broader involvement of early visual areas (i.e., a higher level of recruitment of early visual processing).

The Neural Signatures of Vividness in Stimulus-Triggered and Stimulus-Independent Experiences

The links between features of neural-activity patterns and factors of subjective vividness established above allow us to move beyond the resolution that standard interpretations of empirical data offer with regard to how vividness changes during different varieties of stimulus-triggered and stimulus-independent experiences.

On-line perception

In the case of stimulus-triggered on-line perception, the relation between brain activity and vividness as measured by subjective PAS ratings has been extensively explored. In an early study, Christensen, Ramsøy, Lund, Madsen, and Rowe (2006) investigated how the fMRI-detected BOLD signal changed with different levels of subjective vividness (measured on a 3-point PAS-like scale). They found that the characteristics of the activity in areas that subserve the content of consciousness correlate with the phenomenological features of how the specific content elements appeared in one’s conscious experience. In particular, they identified a correlation between the level of subjective vividness of the experience and the intensity of the activity of these areas: Vivid experiences were accompanied by a significantly higher level of brain activity than nonvivid experiences (both in a direct comparison and in comparisons to no experiences). This finding thus suggests that masked stimuli appear in conscious experience with a degraded level of vividness in the sense of having a lower level of subjective intensity.

More recent studies point toward a similar conclusion and provide more accurate localization of the relevant neural activity both in time and in space (relative to the content of the conscious percept). Andersen and colleagues showed that in the case of simple geometrical figures the different levels of vividness reported using the PAS could be decoded from magnetoencephalographic signals from the occipital lobe within the time window of the visual awareness negativity (VAN) using multivariate classification algorithms (Andersen, Pedersen, Sandberg, & Overgaard, 2016). Others also found an association between the intensity of neural signals and the PAS-measured vividness of corresponding experiences which reinforces the idea that the intensity of VAN activity reflects the level of vividness (Fu et al., 2017; Tagliabue, Mazzi, Bagattini, & Savazzi, 2016).

Note that although these findings do establish a correlation between vividness and the level of neural activity, they cannot provide information about the more fine-grained structure of the factors of vividness. Andersen et al. (2016) conclude that differences between different PAS scores “are best explained by the conglomerate activity of the neurons in the occipital lobe during the VAN time range” (p. 2685). This result, however, is compatible with two scenarios. First, higher PAS ratings are underlain by a greater area of activation in the occipital lobe (i.e., the intensity of the activity does not change). Second, higher PAS ratings are underlain by more intense activity in occipital-lobe subregions (i.e., the region of activity does not change). That is, these results are indecisive with regard to whether variations of PAS scores reflect variations in subjective intensity or in subjective specificity.

Working memory maintenance

Moving away from stimulus-triggered experiences, consider the maintenance of perceptual information in working memory after stimulus offset. In an experiment in which subjects were asked to retain the orientation of a full-contrast square-wave grating presented for 1 s in memory and to report it by turning a probe grating after a 12-s delay interval, Ester and colleagues, relying on fMRI data from areas V1 and V2 and using a forward-encoding model of orientation selectivity (Brouwer & Heeger, 2009, 2011), generated a set of orientation-selective response functions (tuning profiles) to evaluate the features of the neural representations maintained by populations of neurons during the delay period (Ester, Anderson, Serences, & Awh, 2013). These tuning profiles showed the amplitudes of the population response as a function of orientation, and they peaked around the orientation stored in working memory. It was found that individual differences in the dispersion (precision), but not the amplitude (intensity), of the tuning profiles were robust predictors of the participants’ mean recall error (the difference between the orientation of the target and the orientation of the probe they set). That is, broader tuning profiles (i.e., less precise neural representations of the target stimulus maintained during working memory storage) were associated with greater error. The delay period signals from areas V1 and V2 used to estimate the tuning profiles disappeared when subjects received a cue at stimulus offset instructing them to “drop” the presented item (i.e., that no recall test would be run with that particular item), indicating that these signals were indeed results of working memory storage and not simply lingering effects of stimulus encoding (see also D’Esposito & Postle, 2015).

This finding supports a link between the precision of the distributed neural representation maintained during working memory storage and the precision of the mental representation that is assumed to support the recall performance (Postle, 2015). Although whether the content of this mental representation was conscious was not directly evaluated in the original study, keeping a target orientation in mind is a conscious effort, and working memory representations are typically considered to be conscious (for work on unconscious working memory representations, see, e.g., Bergström & Eriksson, 2018; King, Pescetelli, & Dehaene, 2016; Persuh, LaRock, & Berger, 2018). It is thus a probable conclusion that higher precision neural representations maintained during the delay period were accompanied by experiences with higher subjective specificity in the precision sense.

Mental imagery

The parallel research domains of mental imagery and visual working memory have recently been tightly linked by a study that found evidence of common representations in early visual areas shared by mental imagery and visual working memory and the subsequent interpretation that the phenomenon of mental imagery is, in fact, the dynamic component of visual working memory (Albers et al., 2013; Dijkstra et al., 2019; Tong, 2013).

Mental-imagery studies using the VVIQ have established that at the trait level—measuring how vivid someone’s mental imagery usually is—vividness correlated with the average of the relative visual cortex signal measured by fMRI in visualization tasks (i.e., that people who had more vivid visual imagery showed higher activity in the visual cortex during imagery; Amedi, Malach, & Pascual-Leone, 2005; Cui, Jeter, Yang, Montague, & Eagleman, 2007). This association has been further reinforced at the level of trial-by-trial variations (measuring how vivid a particular mental-imagery experience is) as well. It was found that subjective mental-imagery vividness correlates with “imagery strength,” that is, the priming effect of mental imagery on the dominant stimulus in a succeeding binocular rivalry presentation (Bergmann, Genç, Kohler, Singer, & Pearson, 2016; Pearson, Rademaker, & Tong, 2011; Rademaker & Pearson, 2012), which can be seen as an indicator of the intensity of the neural code underlying the imagined content (James, Humphrey, Gati, Menon, & Goodale, 2000; Pearson & Brascamp, 2008; Pearson, Clifford, & Tong, 2008; Wiggs & Martin, 1998).

Even more directly, in experimental paradigms in which subjects had to rate the vividness of their experienced mental imagery (on a 4-point scale: 1 = not vivid at all; 4 = very vivid), it was found after each trial of an imagery task that variations in the moment-to-moment experienced vividness of visual imagery correlated with the intensity of the neural activity (strength of simultaneously recorded fMRI signal) in a series of posterior brain regions. For instance, Dijkstra, Bosch, and van Gerven (2017) found a correlation between experienced imagery vividness and increased activity in the early visual cortex, precuneus, right parietal cortex, and medial frontal cortex. Likewise, Fulford and colleagues (2018) reported that subjective imagery vividness judged image by image correlated positively with activations of the precuneus, posterior cingulate, and higher order visual-association cortex. As we have seen, these correlations suggest that in these mental-imagery tasks experienced vividness changes along the subjective intensity dimension.

In addition to these findings, it has also been reported that VVIQ measures of vividness correlate with the overlap between the activity in visual areas during mental imagery and perception: A larger overlap between the activation of the occipital cortex during imagery and perception predicts a more vivid imagery experience (Albers et al., 2013; Cui et al., 2007; Dijkstra, Bosch, & van Gerven, 2017). Moreover, studying directional connectivity shows that vividness modulates top-down connectivity to early visual areas: The vividness of visual mental imagery positively correlates with the strength of top-down recruitment of early visual areas (Dijkstra et al., 2019; Dijkstra, Zeidman, Ondobaka, van Gerven, & Friston, 2017). These findings suggest that experienced vividness of mental imagery changes along the subjective specificity (blurriness/detailedness) dimension as well.

To summarize, recent efforts aimed at detecting the neural correlates of visual-imagery vividness seem to support the claim that the VVIQ does indeed conflate the different factors—subjective intensity and subjective specificity—that together determine the vividness of the experiences occurring during mental imagery. By focusing on the distinct neural signatures of these different factors, however, it becomes possible to provide a more fine-grained analysis of how vividness changes in different conditions.

Using the Neural Signatures of Vividness to Form Alternative Interpretations

Over and above providing access to a more fine-grained picture with regard to the factors determining the vividness of experiences, the foregoing analysis also allows us to shed new light on conscious experiences occurring during dreaming and mind wandering.

The vividness of dreams

The visual qualities of dreams vary along similar dimensions that determine the overall vividness of conscious experiences. In a now classic strand of studies, dream experiences were investigated by using variations of a single photograph, in which each variation was reduced along one or more dimensions such as brightness, contrast, color saturation, figure clarity, background clarity, overall hue, and so on (Antrobus, Hartwig, Rosa, Reinsel, & Fein, 1987; Antrobus, Kondo, Reinsel, & Fein, 1995; Antrobus & Wamsley, 2009; Fosse, 2000; Kerr, 1993; Rechtschaffen & Buchignani, 1983, 1992). Subjects had to find that element of a set of manipulated photographs that best matched the appearance of a preceding dream experience. It was found that REM dreams were generally more intense (higher brightness and contrast) and more specific (higher clarity) than non-REM mentations (Antrobus, 1991; Kerr, 1993; Rechtschaffen & Buchignani, 1992). In addition, better quality dream experiences were reported from phasic than from tonic periods of REM sleep (Foulkes & Pope, 1973; Kahn, Pace-Schott, & Hobson, 1997; Molinari & Foulkes, 1969; Pivik, 1991; Rechtschaffen & Buchignani, 1992). It has also been suggested that enhanced-quality dream experiences are underlain by increased neural activity (Antrobus et al., 1995; Nielsen, 2017; Rechtschaffen & Buchignani, 1992).

However, up until very recently, little has been known about the qualitative characteristics of individual dreams or about the fine-grained changes of such content characteristics as the vividness of dream experiences. An important example of the prevalence of categorical thinking and coarse-grained characterization in this regard is the case of white dreams (which occur when subjects report that they are certain that they had a dream but cannot recall any details; Cohen, 1972; De Gennaro & Violani, 1990; Monday, Montplaisir, & Malo, 1987; Montplaisir, Cote, Laverdiere, & St-Hilaire, 1985; Strauch & Meier, 1996). White dreaming is an intriguing phenomenon, even more so because it is quite frequent: Approximately 30% of postawakening reports described white dreams (Cohen, 1972; Siclari, LaRocque, Postle, & Tononi, 2013).

White dreams are traditionally interpreted as forgotten dreams resulting from problems with the retrieval of dream experiences: Subjects undergo “proper” dream experiences, and these experiences are then stored in memory, but the subjects cannot recall the content of these dreams because they cannot access the stored memory traces (Cohen, 1974; Nir & Tononi, 2010). Alternatively, it has recently been proposed that white dreams might be “contentless” or “imageless” in the sense that subjects experience only a minimal form of conscious presence with no narrative structure and no specific percepts, bodily sensations, or thoughts occurring during such experiences (Windt, Nielsen, & Thompson, 2016).

A recent high-dimensional EEG study (Siclari et al., 2017) directly addressing this question for the first time provided both content-specific and neural-activity-specific data about dreaming with high spatial resolution. The study found a difference between white dreams and dreams with recallable content in high-frequency EEG activity over the medial and lateral frontal areas associated with memory encoding. Consequently, the authors proposed that white dreams are “normal” dream experiences (just like the reportable ones), but because of problems with memory encoding, their content is unavailable for the awakened subjects (Siclari et al., 2017).

However, a closer examination of the data revealed that the content-specific neural activity occurring during dreaming was also different in the white-dream and normal “contentful”-dream conditions (Fazekas, Nemeth, & Overgaard, 2019). The content of dreams is underlain by neural activity in content-specific posterior regions (Horikawa et al., 2013; Siclari et al., 2017). In the case of white dreams (compared with no dream experiences), the high-frequency component of the EEG signal showed a local increase over posterior content-specific regions (similar to the ones indicated by the mental-imagery studies of Dijkstra, Bosch, & van Gerven, 2017, and Fulford et al., 2018; see above), which was smaller than the local increase that was found in the case of contentful dreams (Fazekas et al., 2019). That is, white dreams turn out to be associated with existing (nonzero) posterior brain activity that is diminished in intensity compared with the activity characteristic of contentful dreams. It follows that white dreams are neither really contentless (cf. Windt et al., 2016) nor full-fledged dream experiences that later become forgotten (cf. Cohen, 1974; Siclari et al., 2017). Rather, on the basis of the link between subjective intensity and the level of activity of associated brain areas, white dreams are experiences with degraded vividness (quality) along the subjective intensity dimension. Dream experiences during white dreams are thus less vivid than during contentful dreams. This reduced vividness results in lower meta-awareness, which might be responsible for the inability to report these experiences (Schooler, 2002).

The fact that both white and contentful dreams are reported from the same sleep stages (Siclari et al., 2013, 2017) indicates that the vividness of dreams fluctuates, that is, changes on (at least) a dream-by-dream basis—similar to how mental-imagery vividness changes on a trial-by-trial basis.

Vividness during mind wandering

When the mind freely wanders—similar to dreaming and unlike the intentional construction of mental imagery—the self-generated images occurring in conscious experience can spontaneously change as the process of image construction is less constrained (Christoff, Irving, Fox, Spreng, & Andrews-Hanna, 2016); this is true both in the case of mind wandering without intention and in the case when mind wandering is intentionally initiated (see Seli, Risko, Smilek, & Schacter, 2016). Instead of a top-down control characteristic of voluntary mental imagery and the typically bottom-up, salience-driven nature of on-line perception, the internal generation of the contents of mind wandering is under the influence of a part of the default-mode network that increases its activity in the absence of tasks or stimuli requiring externally oriented cognitive efforts (Raichle, 2015; Raichle et al., 2001). According to a recent neural model (Christoff et al., 2016), the source of the variability in the content of mind wandering is a subsystem of the default-mode network that includes the hippocampal formation, parahippocampal cortex, retrosplenial cortex, ventral medial prefrontal cortex, and posterior inferior parietal lobule.

Thought-like content is prevalent in mind wandering (Perogamvros et al., 2017), yet a significant proportion of spontaneous mental activity unfolds in the form of mental images (Andrews-Hanna et al., 2013; Chou et al., 2017; Delamillieure et al., 2010). Although during mind wandering the primary sensory areas are decoupled from the default-mode network, which may underlie the relative independence of this self-generated activity from perception (Schooler et al., 2011), higher order sensory-association areas are nevertheless very much active in supporting the image-like nature of the contents of the wandering mind (Andrews-Hanna, Irving, Fox, Spreng, & Christoff, 2018; Fox, Nijeboer, Solomonova, Domhoff, & Christoff, 2013; Fox, Spreng, Ellamil, Andrews-Hanna, & Christoff, 2015).

In a now classic study, Mason et al. (2007) identified key brain areas in which activity correlated with the self-reported frequency of mind wandering. Periods of high-incidence mind wandering were established by training subjects on certain working memory tasks. fMRI-measured brain activity during the performance of practiced task sequences (high-incidence condition) was contrasted with brain activity during the performance of similar but novel tasks (low-incidence condition). This data set was then compared with a questionnaire-based score (Singer & Antrobus, 1972) of the subjects’ general mind-wandering proclivity. Mason et al. (2007) found that the frequency of mind wandering positively correlated with greater activity in the medial prefrontal cortex, anterior cingulate, posterior cingulate, precuneus, left angular gyrus, insula, and areas in the superior and middle temporal gyri (for a recent confirmation of the increased activity of these regions by a meta-analytic review, see Fox et al., 2015).

According to the original interpretation, the increased neural activity in these areas tracks with the subjects’ mind-wandering frequency, that is, with how often subjects find themselves being engaged in mind wandering (Mason et al., 2007). Note, however, that the areas implicated in the Mason et al. study include regions that in mental-imagery studies show increased activity with increased imagery vividness (Dijkstra, Bosch, & van Gerven, 2017; Fulford et al., 2018). Therefore, it might be the case that the increased activity in the same regions occurring during mind wandering, instead of being directly linked with the general frequency of self-generated mental activity, is, in fact, indicative of the vividness—in the sense of subjective intensity—of the conscious content of mind wandering.

Mason and colleagues already acknowledge that the questionnaire-based measure of the frequency of mind wandering might assess subjects’ meta-awareness regarding their mind-wandering episodes rather than their propensity to engage in such episodes. Tying the increased brain activity uncovered to the vividness (subjective intensity) of the content of mind wandering is compatible with this meta-awareness-linked interpretation given that more vivid content is more salient, captures attention to a greater extent, and thus elicits a higher level of meta-awareness and is easier to remember (Schooler, 2002). This interpretation also fits better with the idea that mind wandering is a psychological baseline emerging when the brain is otherwise unoccupied (Mason et al., 2007), as according to this understanding (as opposed to the frequency-based one), the mind does return to this baseline state automatically—the difference is that in the case of certain individuals the vividness (in the sense of subjective intensity) of the accompanying experiences are usually higher, which results in more robust memories about these states that are then reflected in their answers to retrospective questionnaires.

Future Directions

Shifting the focus from the neural correlates of consciousness per se to the correlates of more fine-grained characteristics of experiences sets the course of future research by suggesting novel research questions. In this article we have proposed hypothetical correspondence relations between phenomenological characteristics and neural features. In many contexts these hypothetical correspondence relations still need to be experimentally confirmed. Our discussion concentrated on the visual modality—the questions whether and how our hypotheses can be extended to other modalities need to be addressed by future investigations. Important additional questions also arise and require further empirical studies and more extensive analysis.

For instance, if visual subjective specificity in the detailedness sense is associated with primary visual cortex involvement, then does the fact that mind wandering is associated with sensory decoupling (attenuated sensory cortex activity; Schooler et al., 2011; Smallwood, Beach, Schooler, & Handy, 2008) mean that mind-wandering experiences in general are less vivid in the specificity (detailedness) sense than similar mental-imagery experiences?

Relatedly, it has been reported that mind-wandering frequency in individuals with Parkinson’s disease who have hallucinatory experiences is strongly associated with the coupling between the primary visual cortex and dorsal default-mode network (Walpola et al., 2020). Is this an indication of more vivid (detailed) phenomenology?

The vividness of hallucinations might be elevated in the sense of subjective intensity as well. It has been claimed that both visual and auditory hallucinations are underlain by the hyperactivation of the sensory cortices that provide the content of the hallucinatory experiences (Zmigrod & Hommel, 2011) and that the sensory strength of mental imagery predicts the frequency of visual hallucinations (Shine et al., 2015). The mental images formed during hallucinations might thus be more vivid (both in the intensity and specificity sense) than during nonhallucinatory mental imagery. This might be a reason why individuals with hallucinations do not question the reality of the contents of these mental images (Stephan-Otto et al., 2017). The vividness of conscious experiences might thus provide an important clue for metacognitive judgments about whether the contents presented in the experience are real or not.

Conclusion

Once it is known where to look for the neural underpinnings of the contents of conscious experiences, it becomes possible to start exploring potential correspondence relations between features of these content-specific neural-activity patterns and certain phenomenological characteristics of subjective experiences. As recent evidence converges on the claim that it is activity in posterior brain areas that correlates with the contents of consciousness, here our aim was to try to link features of this neural activity with the level of vividness with which different contents of consciousness appear in experiences. Vividness is a common characteristic of all experiences, regardless of their content. We have argued that vividness, neither from a phenomenological perspective nor in the sense of what existing psychological tools measure, is a monolithic phenomenon. We have distinguished between two major components of vividness, subjective intensity and subjective specificity, and identified further factors that determine these major components: contrast, saturation, and brightness in the case of intensity; and precision, sharpness, and detail in the case of specificity. At least some of the fine-grained structure of vividness can be mapped onto distinct neural-activity patterns; that is, some of the different factors that together determine the vividness of an experience have unique neural underpinnings. The factors determining subjective intensity correlate with the strength of the underlying neural activity, whereas the factors determining subjective specificity correlate with the precision of the corresponding neural population codes and with the recruitment of early visual areas. We used these specific neural signatures to shed new light on the more specific phenomenological counterparts of many recent empirical findings about on-line perception, working memory maintenance, and mental imagery. These correspondence relations also helped us formulate new interpretations and draw novel conclusions with regard to those kinds of experiences that are hard to access, such as the ones occurring during dreaming and mind wandering.

Footnotes

Transparency

Action Editor: Laura A. King

Editor: Laura A. King

ORCID iDs

Peter Fazekas

Georgina Nemeth

References

Abrams

Barbot

Carrasco

(2010). Voluntary attention increases perceived spatial frequency. Attention, Perception & Psychophysics, 72, 1510–1521.

Albers

A. M.

Kok

Toni

Dijkerman

H. C.

de Lange

F. P.

(2013). Shared representations for working memory and mental imagery in early visual cortex. Current Biology, 23, 1427–1431.

Amedi

Malach

Pascual-Leone

(2005). Negative BOLD differentiates visual imagery and perception. Neuron, 48, 859–872.

Andersen

L. M.

Pedersen

Sandberg

Overgaard

(2016). Occipital MEG activity in the early time range (< 300 ms) predicts graded changes in perceptual consciousness. Cerebral Cortex, 26, 2677–2688.

Andrews-Hanna

Irving

Z. C.

Fox

K. C. R.

Spreng

R. N.

Christoff

(2018). The neuroscience of spontaneous thought: An evolving, interdisciplinary field. In Fox

K. C. R.

Christoff

(Eds.), The Oxford handbook of spontaneous thought (pp. 143–164). New York, NY: Oxford University Press.

Andrews-Hanna

Kaiser

Turner

Reineberg

Godinez

Dimidjian

Banich

(2013). A penny for your thoughts: Dimensions of self-generated thought content and relationships with individual differences in emotional wellbeing. Frontiers in Psychology, 4, Article 900. doi:10.3389/fpsyg.2013.00900

Antrobus

(1991). Dreaming: Cognitive processes during cortical activation and high afferent thresholds. Psychological Review, 98, 96–121.

Antrobus

Hartwig

Rosa

Reinsel

Fein

(1987). Brightness and clarity of REM and NREM imagery: Photo response scale. Sleep Research, 16, 240.

Antrobus

Kondo

Reinsel

Fein

(1995). Dreaming in the late morning: Summation of REM and diurnial cortical activation. Consciousness and Cognition, 4, 275–299.

10.

Antrobus

Wamsley

E. J.

(2009). Sleep mentation in REM and NREM: A neurocognitive perspective. In Squire

L. R.

(Ed.), Encyclopedia of neuroscience (pp. 1021–1026). San Diego, CA: Academic Press.

11.

Bergmann

Genç

Kohler

Singer

Pearson

(2016). Smaller primary visual cortex is associated with stronger, but less precise mental imagery. Cerebral Cortex, 26, 3838–3850.

12.

Bergström

Eriksson

(2018). Neural evidence for non-conscious working memory. Cerebral Cortex, 28, 3217–3228.

13.

Block

(2005). Two neural correlates of consciousness. Trends in Cognitive Sciences, 9, 46–52.

14.

Block

(2007). Consciousness, accessibility, and the mesh between psychology and neuroscience. Behavioral & Brain Sciences, 30, 481–548.

15.

Boly

Massimini

Tsuchiya

Postle

Koch

Tononi

(2017). Are the neural correlates of consciousness in the front or in the back of the cerebral cortex? Clinical and neuroimaging evidence. The Journal of Neuroscience, 37, 9603–9613.

16.

Brouwer

G. J.

Heeger

D. J.

(2009). Decoding and reconstructing color from responses in human visual cortex. The Journal of Neuroscience, 29, 13992–14003.

17.

Brouwer

G. J.

Heeger

D. J.

(2011). Cross-orientation suppression in human visual cortex. Journal of Neurophysiology, 106, 2108–2119.

18.

Brown

Lau

LeDoux

J. E.

(2019). Understanding the higher-order approach to consciousness. Trends in Cognitive Sciences, 23, 754–768.

19.

Carrasco

(2011). Visual attention: The past 25 years. Vision Research, 51, 1484–1525.

20.

Carrasco

Barbot

(2019). Spatial attention alters visual appearance. Current Opinion in Psychology, 29, 56–64.

21.

Carrasco

Ling

Read

(2004). Attention alters appearance. Nature Neuroscience, 7, 308–313.

22.

Chou

Y.-H.

Sundman

Whitson

H. E.

Gaur

Chu

M.-L.

Weingarten

C. P.

Madden

D. J.

. . . Chen

N.-K.

(2017). Maintenance and representation of mind wandering during resting-state fMRI. Scientific Reports, 7, Article 40722. doi:10.1038/srep40722

23.

Christensen

M. S.

Ramsøy

T. Z.

Lund

T. E.

Madsen

K. H.

Rowe

J. B.

(2006). An fMRI study of the neural correlates of graded visual perception. NeuroImage, 31, 1711–1725.

24.

Christoff

Irving

Z. C.

Fox

K. C. R.

Spreng

R. N.

Andrews-Hanna

J. R.

(2016). Mind-wandering as spontaneous thought: A dynamic framework. Nature Reviews Neuroscience, 17, 718–731.

25.

Christophel

T. B.

Klink

P. C.

Spitzer

Roelfsema

P. R.

Haynes

J.-D.

(2017). The distributed nature of working memory. Trends in Cognitive Sciences, 21, 111–124.

26.

Cohen

D. B.

(1972). Failure to recall dream content: Contentless vs dreamless reports. Perceptual and Motor Skills, 34, 1000–1002.

27.

Cohen

D. B.

(1974). Toward a theory of dream recall. Psychological Bulletin, 81, 138–154.

28.

Cohen

D. B.

MacNeilage

P. F.

(1974). A test of the salience hypothesis of dream recall. Journal of Consulting and Clinical Psychology, 42, 699–703.

29.

Cornoldi

De Beni

Giusberti

Marucci

Massironi

Mazzoni

(1991). The study of vividness of images. In Logie

R. H.

Denis

(Eds.), Mental images in human cognition (pp. 305–312). Amsterdam, the Netherlands: Elsevier.

30.

Cui

Jeter

C. B.

Yang

Montague

P. R.

Eagleman

D. M.

(2007). Vividness of mental imagery: Individual variability can be measured objectively. Vision Research, 47, 474–478.

31.

D’Angiulli

Runge

Faulkner

Zakizadeh

Chan

Morcos

(2013). Vividness of visual imagery and incidental recall of verbal cues, when phenomenological availability reflects long-term memory accessibility. Frontiers in Psychology, 4, Article 1. doi:10.3389/fpsyg.2013.00001

32.

D’Esposito

Postle

(2015). The cognitive neuroscience of working memory. Annual Review of Psychology, 66, 115–142.

33.

De Gennaro

Violani

(1990). White dreams: The relationship between the failure in dream recall and degree of hemispheric lateralization. Association for the Study of Dreams Newsletter, 7(5), 7.

34.

Dehaene

Changeux

(2004). Neural mechanisms for access to consciousness. In Gazzaniga

M. S.

(Ed.), The cognitive neurosciences (pp. 1145–1157). Cambridge, MA: MIT Press.

35.

Dehaene

Changeux

(2011). Experimental and theoretical approaches to conscious processing. Neuron, 70, 200–227.

36.

Dehaene

Changeux

Naccache

Sackur

Sergent

(2006). Conscious, preconscious, and subliminal processing: A testable taxonomy. Trends in Cognitive Sciences, 10, 204–211.

37.

Dehaene

Charles

King

J.-R.

Marti

(2014). Toward a computational theory of conscious processing. Current Opinion in Neurobiology, 25, 76–84.

38.

Dehaene

Kerszberg

Changeux

J.-P.

(1998). A neuronal model of a global workspace in effortful cognitive tasks. Proceedings of the National Academy of Sciences, USA, 95, 14529–14534.

39.

Dehaene

Lau

Kouider

(2017). What is consciousness, and could machines have it? Science, 358, 486–492.

40.

Dehaene

Naccache

(2001). Towards a cognitive neuroscience of consciousness: Basic evidence and a workspace framework. Cognition, 79, 1–37.

41.

Delamillieure

Doucet

Mazoyer

Turbelin

M.-R.

Delcroix

Mellet

. . . Joliot

(2010). The resting state questionnaire: An introspective questionnaire for evaluation of inner experience during the conscious resting state. Brain Research Bulletin, 81, 565–573.

42.

Denis

(1995). Vividness of visual imagery and the evaluation of its effects on performance. Journal of Mental Imagery, 19, 136–138.

43.

Dijkstra

Bosch

S. E.

van Gerven

M. A. J.

(2017). Vividness of visual imagery depends on the neural overlap with perception in visual areas. The Journal of Neuroscience, 37, 1367–1373.

44.

Dijkstra

Bosch

S. E.

van Gerven

M. A. J.

(2019). Shared neural mechanisms of visual perception and imagery. Trends in Cognitive Sciences, 23, 423–434.

45.

Dijkstra

Zeidman

Ondobaka

van Gerven

M. A. J.

Friston

(2017). Distinct top-down and bottom-up brain connectivity during visual perception and imagery. Scientific Reports, 7, Article 5677. doi:10.1038/s41598-017-05888-8

46.

Emrich

S. M.

Riggall

A. C.

LaRocque

Postle

(2013). Distributed patterns of activity in sensory cortex reflect the precision of multiple items maintained in visual short-term memory. Journal of Neuroscience, 33, 6516–6523.

47.

Ester

E. F.

Anderson

D. E.

Serences

J. T.

Awh

(2013). A neural measure of precision in visual working memory. Journal of Cognitive Neuroscience, 25, 754–761.

48.

Fazekas

Nanay

(2018). Attention is amplification, not selection. British Journal for the Philosophy of Science. Advance online publication. doi:10.1093/bjps/axy065

49.

Fazekas

Nemeth

(2018). Dream experiences and the neural correlates of perceptual consciousness and cognitive access. Philosophical Transactions of the Royal Society B: Biological Sciences, 373, Article 20170356. doi:10.1098/rstb.2017.0356

50.

Fazekas

Nemeth

Overgaard

(2019). White dreams are made of colours: What studying contentless dreams can teach about the neural basis of dreaming and conscious experiences. Sleep Medicine Reviews, 43, 84–91.

51.

Fazekas

Overgaard

(2016). Multidimensional models of degrees and levels of consciousness. Trends in Cognitive Sciences, 20, 715–716.

52.

Fazekas

Overgaard

(2018a). A multi-factor account of degrees of awareness. Cognitive Science, 42, 1833–1859.

53.

Fazekas

Overgaard

(2018b). Multiple factors and multiple mechanisms determine the quality of conscious experiences: A reply to Anzulewicz and Wierzchoń. Cognitive Science, 42, 2101–2103.

54.

Fosse

(2000). REM mentation in narcoleptics and normals: An empirical test of two neurocognitive theories. Consciousness and Cognition, 9, 488–509.

55.

Foulkes

Pope

(1973). Primary visual experience and secondary cognitive elaboration: A modest confirmation and an extension. Perceptual and Motor Skills, 37, 107–118.

56.

Fox

K. C. R.

Nijeboer

Solomonova

Domhoff

G. W.

Christoff

(2013). Dreaming as mind wandering: Evidence from functional neuroimaging and first-person content reports. Frontiers in Human Neuroscience, 7, Article 412. doi:10.3389/fnhum.2013.00412

57.

Fox

K. C. R.

Spreng

R. N.

Ellamil

Andrews-Hanna

J. R.

Christoff

(2015). The wandering brain: Meta-analysis of functional neuroimaging studies of mind-wandering and related spontaneous thought processes. NeuroImage, 111, 611–621.

58.

Liu

Y.-J.

Dienes

Chen

(2017). Neural correlates of subjective awareness for natural scene categorization of color photographs and line-drawings. Frontiers in Psychology, 8, Article 210. doi:10.3389/fpsyg.2017.00210

59.

Fulford

Milton

Salas

Smith

Simler

Winlove

Zeman

(2018). The neural correlates of visual imagery vividness—an fMRI study and literature review. Cortex,105, 26–40.

60.

Fuller

Carrasco

(2006). Exogenous attention and color perception: Performance and appearance of saturation and hue. Vision Research, 46, 4032–4047.

61.

Gobell

Carrasco

(2005). Attention alters the appearance of spatial frequency and gap size. Psychological Science, 16, 644–651.

62.

Gonsalves

Reber

P. J.

Gitelman

D. R.

Parrish

T. B.

Mesulam

M. M.

Paller

K. A.

(2004). Neural evidence that vivid imagining can lead to false remembering. Psychological Science, 15, 655–660.

63.

Horikawa

Tamaki

Miyawaki

Kamitani

(2013). Neural decoding of visual imagery during sleep. Science, 340(6132), 639–642.

64.

Ishai

Ungerleider

L. G.

Haxby

J. V.

(2000). Distributed neural systems for the generation of visual images. Neuron, 28, 979–990.

65.

James

T. W.

Humphrey

G. K.

Gati

J. S.

Menon

R. S.

Goodale

M. A.

(2000). The effects of visual object priming on brain activation before and after recognition. Current Biology, 10, 1017–1024.

66.

Johnson

(2014). Decoding individual natural scene representations during perception and imagery. Frontiers in Human Neuroscience, 8, Article 59. doi:10.3389/fnhum.2014.00059

67.

Kahn

Pace-Schott

E. F.

Hobson

(1997). Consciousness in waking and dreaming: The roles of neuronal oscillation and neuromodulation in determining similarities and differences. Neuroscience, 78, 13–38.

68.

Kauffmann

Ramanoël

Peyrin

(2014). The neural bases of spatial frequency processing during scene perception. Frontiers in Integrative Neuroscience, 8, Article 37. doi:10.3389/fnint.2014.00037

69.

Kerr

(1993). Mental imagery, dreams and perception. In Cavallero

Foulkes

(Eds.), Dreaming as cognition (pp. 18–37). Englewood Cliffs, NJ: Prentice Hall.

70.

Kind

(2017). Imaginative vividness. Journal of the American Philosophical Association, 3, 32–50.

71.

King

J.-R.

Pescetelli

Dehaene

(2016). Brain mechanisms underlying the brief maintenance of seen and unseen sensory information. Neuron, 92, 1122–1134.

72.

Koch

Massimini

Boly

Tononi

(2016). Neural correlates of consciousness: Progress and problems. Nature Reviews Neuroscience, 17, 307–321.

73.

Lamme

V. A. F.

(2006). Towards a true neural stance on consciousness. Trends in Cognitive Sciences, 10, 494–501.

74.

Lamme

V. A. F.

(2010). How neuroscience will change our view on consciousness. Cognitive Neuroscience, 1, 204–220.

75.

Lamme

V. A. F.

(2018). Challenges for theories of consciousness: Seeing or knowing, the missing ingredient, and how to deal with panpsychism. Philosophical Transactions of the Royal Society B: Biological Sciences, 373(1755), Article 20170344. doi:10.1098/rstb.2017.0344

76.

Lamme

V. A. F.

Roelfsema

P. R.

(2000). The distinct modes of vision offered by feedforward and recurrent processing. Trends in Neurosciences, 23, 571–579.

77.

Lau

Rosenthal

(2011). Empirical support for higher-order theories of conscious awareness. Trends in Cognitive Sciences, 15, 365–373.

78.

Le Van Quyen

Staba

Bragin

Dickson

Valderrama

Fried

Engel

(2010). Large-scale microelectrode recordings of high-frequency gamma oscillations in human cortex during sleep. The Journal of Neuroscience, 30, 7770–7782.

79.

Yin

Chen

Gong

Liu

Qian

. . . Wang

(2018). Revealing detail along the visual hierarchy: Neural clustering preserves acuity from V1 to V4. Neuron, 98, 417–428.

80.

Marks

D. F.

(1973). Visual imagery differences in the recall of pictures. British Journal of Psychology, 64, 17–24.

81.

Martinez-Trujillo

J. C.

Treue

(2004). Feature-based attention increases the selectivity of population responses in primate visual cortex. Current Biology, 14, 744–751.

82.

Mason

M. F.

Norton

M. I.

Van Horn

J. D.

Wegner

D. M.

Grafton

S. T.

Macrae

C. N.

(2007). Wandering minds: The default network and stimulus-independent thought. Science, 315, 393–395.

83.

Masuda

Doiron

(2007). Gamma oscillations of spiking neural populations enhance signal discrimination. PLOS Computational Biology, 3, 2348–2355.

84.

Maunsell

J. H. R.

Treue

(2006). Feature-based attention in visual cortex. Trends in Neurosciences, 29, 317–322.

85.

McKelvie

S. J.

(1995a). The VVIQ and beyond: Vividness and its measurement. Journal of Mental Imagery, 19, 197–252.

86.

McKelvie

S. J.

(1995b). The VVIQ as a psychometric test of individual differences in visual imagery vividness: A critical quantitative review and plea for direction. Journal of Mental Imagery, 19(3–4), 1–106.

87.

Molinari

Foulkes

(1969). Tonic and phasic events during sleep: Psychological correlates and implications. Perceptual and Motor Skills, 29, 343–368.

88.

Monday

Montplaisir

Malo

J.-L.

(1987). Dream process in asthmatic subjects with nocturnal attacks. American Journal of Psychiatry, 144, 638–640.

89.

Montplaisir

Cote

Laverdiere

St-Hilaire

(1985). Dream recall before and after partial commissurotomy. In Koella

Ruther

Schulz

(Eds.), Sleep ’84 (pp. 346–348). Stuttgart, Germany: Gustav Fischer Verlag.

90.

Morales

(2019). Mental strength: A theory of experience intensity. Manuscript in preparation.

91.

Musel

Kauffmann

Ramanoël

Giavarini

Guyader

Chauvin

Peyrin

(2014). Coarse-to-fine categorization of visual scenes in scene-selective cortex. Journal of Cognitive Neuroscience, 26, 2287–2297.

92.

Naselaris

Olman

C. A.

Stansbury

D. E.

Ugurbil

Gallant

J. L.

(2015). A voxel-wise encoding model for early visual areas decodes mental images of remembered scenes. NeuroImage, 105, 215–228.

93.

Nielsen

(2017). Microdream neurophenomenology. Neuroscience of Consciousness, 2017(1), Article nix001. doi:10.1093/nc/nix001

94.

Nir

Tononi

(2010). Dreaming and the brain: From phenomenology to neurophysiology. Trends in Cognitive Sciences, 14, 88–100.

95.

O’Craven

K. M.

Kanwisher

(2000). Mental imagery of faces and places activates corresponding stimulus-specific brain regions. Journal of Cognitive Neuroscience, 12, 1013–1023.

96.

Odegaard

Knight

Lau

(2017). Should a few null findings falsify prefrontal theories of conscious perception? The Journal of Neuroscience, 37, 9593–9602.

97.

Panzeri

Macke

J. H.

Gross

Kayser

(2015). Neural population coding: Combining insights from microscopic and mass signals. Trends in Cognitive Sciences, 19, 162–172.

98.

Pearson

Brascamp

(2008). Sensory memory for ambiguous vision. Trends in Cognitive Sciences, 12, 334–341.

99.

Pearson

Clifford

C. W. G.

Tong

(2008). The functional impact of mental imagery on conscious perception. Current Biology, 18, 982–986.

100.

Pearson

Rademaker

R. L.

Tong

(2011). Evaluating the mind’s eye: The metacognition of visual imagery. Psychological Science, 22, 1535–1542.

101.

Perogamvros

Baird

Seibold

Riedner

Boly

Tononi

(2017). The phenomenal contents and neural correlates of spontaneous thoughts across wakefulness, NREM sleep, and REM sleep. Journal of Cognitive Neuroscience, 29, 1766–1777.

102.

Persuh

LaRock

Berger

(2018). Working memory and consciousness: The current state of play. Frontiers in Human Neuroscience, 12, Article 78. doi:10.3389/fnhum.2018.00078

103.

Phillips

(2018). The methodological puzzle of phenomenal consciousness. Philosophical Transactions of the Royal Society B: Biological Sciences, 373(1755), Article 20170347. doi:10.1098/rstb.2017.0347

104.

Pitts

M. A.

Lutsyshyna

L. A.

Hillyard

S. A.

(2018). The relationship between attention and consciousness: An expanded taxonomy and implications for “no-report” paradigms. Philosophical Transactions of the Royal Society B: Biological Sciences, 373(1755), Article 20170348. doi:10.1098/rstb.2017.0348

105.

Pivik

R. T.

(1991). Tonic states and phasic events in relation to sleep mentation. In Ellman

S. J.

Antrobus

(Eds.), The mind in sleep: Psychology and physiology (pp. 214–247). New York, NY: Wiley.

106.

Postle

(2015). The cognitive neuroscience of visual short-term memory. Current Opinion in Behavioral Sciences, 1, 40–46.

107.

Rademaker

Pearson

(2012). Training visual imagery: Improvements of metacognition, but not imagery strength. Frontiers in Psychology, 3, Article 224. doi:10.3389/fpsyg.2012.00224

108.

Raichle

M. E.

(2015). The brain’s default mode network. Annual Review of Neuroscience, 38, 433–447.

109.

Raichle

M. E.

MacLeod

A. M.

Snyder

A. Z.

Powers

W. J.

Gusnard

D. A.

Shulman

G. L.

(2001). A default mode of brain function. Proceedings of the National Academy of Sciences, USA, 98, 676–682.

110.

Ramsøy

Overgaard

(2004). Introspection and subliminal perception. Phenomenology and the Cognitive Sciences, 3(1), 1–23.

111.

Rechtschaffen

Buchignani

(1983). Visual dimensions and correlates of dream images. Sleep Research, 12, 189.

112.

Rechtschaffen

Buchignani

(1992). The visual appearance of dreams. In Antrobus

Bertini

(Eds.), The neuropsychology of sleep and dreaming (pp. 143–156). Hillside, NJ: Erlbaum.

113.

Reddy

Tsuchiya

Serre

(2010). Reading the mind’s eye: Decoding category information during mental imagery. NeuroImage, 50, 818–825.

114.

Riggall

A. C.

Postle

(2012). The relationship between working memory storage and elevated activity as measured with functional magnetic resonance imaging. The Journal of Neuroscience, 32, 12990–12998.

115.

Rossi

A. F.

Paradiso

M. A.

(1999). Neural correlates of perceived brightness in the retina, lateral geniculate nucleus, and striate cortex. The Journal of Neuroscience, 19, 6145–6156.

116.

Sandberg

Overgaard

(2015). Using the perceptual awareness scale (PAS). In Overgaard

(Ed.), Behavioural methods in consciousness research (pp. 181–196). Oxford, England: Oxford University Press.

117.

Sandberg

Timmermans

Overgaard

Cleeremans

(2010). Measuring consciousness: Is one measure better than the other? Consciousness and Cognition, 19, 1069–1078.

118.

Schooler

J. W.

(2002). Re-representing consciousness: Dissociations between experience and meta-consciousness. Trends in Cognitive Sciences, 6, 339–344.

119.

Schooler

J. W.

Smallwood

Christoff

Handy

T. C.

Reichle

E. D.

Sayette

M. A.

(2011). Meta-awareness, perceptual decoupling and the wandering mind. Trends in Cognitive Sciences, 15, 319–326.

120.

Seli

Risko

E. F.

Smilek

Schacter

D. L.

(2016). Mind-wandering with and without intention. Trends in Cognitive Sciences, 20, 605–617.

121.

Sergent

(2018). The offline stream of conscious representations. Philosophical Transactions of the Royal Society B: Biological Sciences, 373(1755), Article 20170349. doi:10.1098/rstb.2017.0349

122.

Shine

J. M.

Keogh

O’Callaghan

Muller Alana

Lewis Simon

J. G.

Pearson

(2015). Imagine that: Elevated sensory strength of mental imagery in individuals with Parkinson’s disease and visual hallucinations. Proceedings of the Royal Society B: Biological Sciences, 282(1798), Article 20142047. doi:10.1098/rspb.2014.2047

123.

Siclari

Baird

Perogamvros

Bernardi

LaRocque

J. J.

Riedner

. . . Tononi

(2017). The neural correlates of dreaming. Nature Neuroscience, 24, 872–878.

124.

Siclari

LaRocque

Postle

Tononi

(2013). Assessing sleep consciousness within subjects using a serial awakening paradigm. Frontiers in Psychology, 4, Article 542. doi:10.3389/fpsyg.2013.00542

125.

Singer

Antrobus

(1972). Daydreaming, imaginal processes, and personality: A normative study. In Sheehan

(Ed.), The function and nature of imagery (pp. 175–202). New York, NY: Academic Press.

126.

Smallwood

Beach

Schooler

J. W.

Handy

T. C.

(2008). Going AWOL in the brain: Mind wandering reduces cortical analysis of external events. Journal of Cognitive Neuroscience, 20, 458–469.

127.

Stephan-Otto

Siddi

Senior

Cuevas-Esteban

Cambra-Martí

M. R.

Ochoa

Brébion

(2017). Remembering verbally-presented items as pictures: Brain activity underlying visual mental images in schizophrenia patients with visual hallucinations. Cortex, 94, 113–122.

128.

Stevens

S. S.

Galanter

E. H.

(1957). Ratio scales and category scales on a dozen perceptual continua. Journal of Experimental Psychology, 54, 377–411.

129.

Stokes

Thompson

Cusack

Duncan

(2009). Top-down activation of shape-specific population codes in visual cortex during mental imagery. The Journal of Neuroscience, 29, 1565–1572.

130.

Strauch

Meier

(1996). In search of dreams: Results of Experimental Dream Research. Albany: State University of New York Press.

131.

Tagliabue

C. F.

Mazzi

Bagattini

Savazzi

(2016). Early local activity in temporal areas reflects graded content of visual perception. Frontiers in Psychology, 7, Article 572. doi:10.3389/fpsyg.2016.00572

132.

Tong

(2013). Imagery and visual working memory: One and the same? Trends in Cognitive Sciences, 17, 489–490.

133.

Walpola

I. C.

Muller

A. J.

Hall

J. M.

Andrews-Hanna

J. R.

Irish

Lewis

S. J. G.

. . . O’Callaghan

(2020). Mind-wandering in Parkinson’s disease hallucinations reflects primary visual and default network coupling. Cortex, 125, 233–245. doi:10.1016/j.cortex.2019.12.023

134.

Webster

M. A.

Georgeson

M. A.

Webster

S. M.

(2002). Neural adjustments to image blur. Nature Neuroscience, 5, 839–840.

135.

Webster

M. A.

Marcos

(2017). Neural adaptation to blur. In Artal

(Ed.), Handbook of visual optics (Vol. II, pp. 307–323). Boca Raton, FL: CRC Press.

136.

Wiggs

C. L.

Martin

(1998). Properties and mechanisms of perceptual priming. Current Opinion in Neurobiology, 8, 227–233.

137.

Windt

J. M.

Nielsen

Thompson

(2016). Does consciousness disappear in dreamless sleep? Trends in Cognitive Sciences, 20, 871–882.

138.

Zmigrod

Hommel

(2011). The relationship between feature binding and consciousness: Evidence from asynchronous multi-modal stimuli. Consciousness and Cognition, 20, 586–593.