Awareness shaping or shaped by prediction and postdiction: Editorial

In the flash-lag effect (FLE) and in representational momentum (RM), the represented position of a moving target is displaced in the direction of motion. Effects of numerous variables on the FLE and on RM are briefly considered. In many cases, variables appear to have the same effect on the FLE and on RM, and this is consistent with a hypothesis that displacements in the FLE and in RM result from overlapping or similar mechanisms. In other cases, variables initially appear to have different effects on the FLE and on RM, but accounts reconciling those apparent differences with a hypothesis of overlapping or similar mechanisms are suggested. Given that RM is simpler and accounts for a wider range of findings (i.e., RM involves a single stimulus rather than the relationship between two stimuli, RM accounts for displacement in absolute position of a single stimulus and for differences in relative position of two stimuli), it is suggested that (at least some cases of) the FLE might be a special case of RM in which the position of the target is assessed relative to the position of another stimulus (i.e., the flashed object) rather than relative to the actual position of the target.

One major problem in the empirical investigation of consciousness is to identify a so-called objective measure of the presence or absence of a specific conscious experience. An objective measure, in this context, refers to a measure of how well a subject is able to solve a task or to a report, given by the subject, which does not explicity refer to his or her own conscious experience. Such task performance or report may be influenced by conscious as well as unconscious processes. Subjective measures, on the other hand, are defined as reports (verbal or other kinds) made by a subject directly about his or her conscious experience. The paper by Busch, Fründ, and Herrmann (2009) is an important and interesting suggestion of how to find neural correlates involved in change detection and change blindness, but it also claims to infer knowledge about conscious experiences from its data. This commentary will focus on this last claim.In their study, the authors present a change blindness experiment in which they investigated whether change detection (sensing) and change identification (seeing) rely on different or similar neural processes. The authors successfully identified some ERP components that were similar for both conditions (the VAN and the P3) and some other components that specifically occurred for changes that were identified (the change-related positivity and the N2pc). In a second experiment (visual search), the authors showed that the N2pc reflected selective attention, whereas the change-related positivity was specific for change identification. They conclude that sensing and seeing a change rely on different neural processes. These results are based on signal detection theory (SDT) according to which data are analyzed and interpreted.SDT is a model of how systems detect signals among noise, and it has repeatedly proven applicable to the human perceptual system (Green & Swets, 1966). SDT provides an objective measure of a subject's capacity to detect stimuli (d′) and a criterion for detection (C). Thus, the theory suggests a way to obtain data about a subject's perceptual capacity that is not based on subjective verbal reports but on “objective reports,” that is, their task performance.There is, however, an important conceptual and empirical distinction between signals and reports (Overgaard, 2009). In this terminology, different from the SDT terminology, signals refer to the “uncontrolled behaviors such as reflexes” of a subject (Overgaard, 2009, p. 16)—that is, the observation of behavior that is not as such intended to inform an observer yet may be of use as data to analyze some cognitive process. Reports, in contrast, are communications from the subject; this may be a verbal statement describing a complex scenery or something as simple as a button press when a target is present—the important part being that the subject is intending a communication using a report.SDT makes no such demands that reports are based on intended communications or on reports directly about consciousness. Accordingly, a signal in SDT is not necessarily an expression of what a subject has perceived consciously.In order to measure d′ and C, subjects are performing a task, for example, a visual detection task. One problem that has previously been identified is that task performance is not a good guide to conscious experiences because unconscious factors might also be involved (Lau, 2008). To illustrate this point, Lau (2007) describes an experiment where the same d′ is constant while reports of experience differed in subjects over time. Therefore, d′ as well as C seem blind to the conscious experience the subjects has while performing the task. They seem to be of use when describing those cognitive events that make an overt behavior possible (such as responding to the presence of a target), that is, the so-called objective aspects of perception. However, Busch et al. (2009) are more ambitious than that. They claim explicitly to be investigating the subjective aspects as well using SDT—an approach that we, as argued above, find problematic as a matter of principle.The experiments by Busch et al. (2009) do however contain two important deviations from what may be considered a “standard” SDT design. Those deviations, it could be argued, may better the case for the experiments' ability to give information about conscious experience. First, whenever the subjects scored a hit, they were asked to identify the changing object (reporting either the object in the first or the second display) out of eight possible objects. Second, subjects were asked not to guess about detecting a change or the identity of the changing object (but instead answer “not sure”) if they were uncertain.Busch et al. (2009) claim that a “full blown visual experience [is] required for [object] identification”—that is, in order to complete the additional task, subjects must as a matter of principle have a visual experience of the object. However, the task is simply selecting the right object out of small number of possible objects. Similar approaches have been used previously to show the exact opposite by adding a subjective scale of conscious experience: the presence of subliminal perception. Several experiments in cognitive science and, for example, the blindsight literature are classically interpreted to show that the performance of such identification tasks in the absence of reports about experience demonstrate that unconscious visual identification is possible (Overgaard & Timmermans, in press; Trevathan, Saharie, & Weiskrantz, 2007). Accordingly, we find it problematic that Busch et al. use no measure of conscious experience yet still use an exact opposite interpretation of this kind of observation without further argument.This further argument could then theoretically be that because the subjects are asked not to guess but only to respond when they are certain, they are in fact conscious of the changing object when they make a positive identification, as high confidence ratings such as “certain” are often associated with awareness (Dienes, Altmann, Kwan, & Goode, 1995). However, drawing an exact line when to “be guessing” and when to “respond to a vague experience” may not be a simple task to the subjects who, more likely than not, will use different criteria to solve this situation. Theory aside, the argument is in fact in conflict with the actual data. The problem is that subjects are only correct around 70% of the time when trying to detect a change (chance is 50%) and 54% of the time when identifying a changing object (chance is 25%). These numbers indicate that the subjects are guessing, at least to some extent. The simple instruction not to guess, in other words, seems not to be sufficient.In conclusion, we believe that our relatively simple arguments above support conclusions from several other recent publications (e.g., Seth, Dienes, Cleeremans, Overgaard, & Pessoa, 2008; Slagter, Lutz, Greischar, Nieuwenhuis, & Davidson, 2009) that the application of objective methods only to study conscious experience in all cases ends up in self-contradiction and methodological pitfalls.Reprint requests should be sent to Morten Overgaard, CNRU, Hammel Neurorehabilitation and Research Center, Aarhus University Hospital, Voldbyvej 15, 8450 Hammel, Denmark, or via e-mail: mortover@rm.dk.

The latency of a response is one of the most frequently reported parameters when describing the characteristics of a motor system. Such measurement provides important information both to the basic researcher investigating the neural circuitry of the underlying physiological system and to the clinician gathering information for diagnosing a patient. Our concern here is that when the latency of a response is determined on experimentally recorded data by using the most commonly referenced techniques to find the onset of a motor response, the resulting figure encompasses both the neural processing time and the dynamics of the system producing the response (e.g., the musculoskeletal apparatus). Therefore, the resulting latency measurement cumulates information relative to two substantially different sources and thus having different implications. The goal of our study is that of suggesting a technique allowing the separation of the relative contributions of neural transmission and processing time from that of the dynamics of the motor system. This is accomplished by using a technique based on fitting a model to the experimentally recorded response, thus allowing to exploit as much as is known with regards to the dynamics of the studied motor system (e.g., model order and constraints on the values of the model parameters). The optimization of the model parameters for fitting the experimental data is carried out using a real-valued genetic algorithm, allowing to avoid trapping in local, suboptimal minima. The use of this approach allows to estimate the pure delay in the response introduced by neural processing more accurately than the traditional latency detection techniques based on adaptive thresholds.

A.E. Hayes, J.J. Freyd, Representational Momentum When Attention is Divided. M. Nagai, K. Kazai, A. Yagi, Larger Forward Memory Displacement in the Direction of Gravity. N.G. Vinson, Sources of Object-specific Effects in Representational Momentum. D. Kerzel, A Matter of Design: No Representational Momentum Without Expectancy. C. Senior, J. Ward, A.S. David, Representational Momentum and the Brain: An Investigation into the Functional Necessity of V5/MT. H. Intraub, Anticipatory Spatial Representation of Natural Scenes: Momentum Without Movement? J. Musseler, S. Stork, D. Kerzel, Comparing Mislocalizations with Moving Stimuli: The Frohlich effect, the Flash-lag effect and Representational Momentum. D. Whitney, P. Cavanagh, Surrounding Motion Affects the Perceived Locations of Moving Stimuli. T.L. Hubbard, S.E. Ruppel, A Possible Role of Naive Impetus in Michottes's Launching Effect: Evidence from Representational Momentum. M.P. Munger, J.H. Minchew, Parallels Between Remembering and Predicting an Object's Location. M. Bertamini, Representational Momentum, Internalized Dynamics and Perceptual Adaptation. K. Verfaillie, A. Daems, Representing and Anticipating Human Actions in Vision. G. Wallis, The Role of Object Motion in Forging Long-term Representations of Objects. Z. Kourtzi, K. Nakayama, Distinct Mechanisms for the Representation of Moving and Static Objects.

Change blindness, representations, and consciousness: Reply to Noë

This chapter describes the mechanisms behind conscious experiences, emphasizing the Neural Binding and Coherence (NBC) theory. It explores how neural representation, binding, coherence, and competition interact to create conscious phenomena. Using smell, hunger, loneliness, and self-awareness as examples, the chapter illustrates how these mechanisms contribute to component consciousness and compound experiences. The concept of ‘recurring emergence’ is central, highlighting how interactions within neural systems produce qualitative novelties like conscious states. The chapter also examines evolutionary functions, explaining how conscious experiences, such as smell aiding survival or hunger driving action, enhance biological fitness. Through detailed analyses, it connects abstract concepts like self-awareness to neural processes, arguing for consciousness as a product of neural mechanisms, in contrast to mystical, dualist, or panpsychist explanations.

When judging the position of a moving object, human observers do not perceive and memorize the moving object's correct position. There are two known phenomena in judged position errors of a moving object, representational momentum (RM) and the flash-lag effect (FLE), both of which we consider here.

When observers are asked to localize the onset or the offset position of a moving target, they typically make localization errors in the direction of movement. Similarly, when observers judge a moving target that is presented in alignment with a flash, the target appears to lead the flash. These errors are known as the Fröhlich effect, representational momentum, and flash-lag effect, respectively. This study compared the size of the three mislocalization errors. In Experiment 1, a flash appeared either simultaneously with the onset, the mid-position, or the offset of the moving target. Observers then judged the position where the moving target was located when the flash appeared. Experiments 2 and 3 are exclusively concerned with localizing the onset and the offset of the moving target. When observers localized the position with respect to the point in time when the flash was presented, a clear mislocalization in the direction of movement was observed at the initial position and the mid-position. In contrast, a mislocalization opposite to movement direction occurred at the final position. When observers were asked to ignore the flash (or when no flash was presented at all), a reduced error (or no error) was observed at the initial position and only a minor error in the direction of the movement occurred at the final position. An integrative model is proposed, which suggests a common underlying mechanism, but emphasizes the specific processing components of the Fröhlich effect, flash-lag effect, and representational momentum.

Our muscles are the primary means through which we affect the external world, and the sense of agency (SoA) over the action through those muscles is fundamental to our self-awareness. However, SoA research to date has focused almost exclusively on agency over action outcomes rather than over the musculature itself, as it was believed that SoA over the musculature could not be manipulated directly. Drawing on methods from human-computer interaction and adaptive experimentation, we use human-in-the-loop Bayesian optimization to tune the timing of electrical muscle stimulation so as to robustly elicit a SoA over electrically actuated muscle movements in male and female human subjects. We use time-resolved decoding of subjects' EEG to estimate the time course of neural activity which predicts reported agency on a trial-by-trial basis. Like paradigms which assess SoA over action consequences, we found that the late (post-conscious) neural activity predicts SoA. Unlike typical paradigms, however, we also find patterns of early (sensorimotor) activity with distinct temporal dynamics predicts agency over muscle movements, suggesting that the "neural correlates of agency" may depend on the level of abstraction (i.e., direct sensorimotor feedback versus downstream consequences) most relevant to a given agency judgment. Moreover, fractal analysis of the EEG suggests that SoA-contingent dynamics of neural activity may modulate the sensitivity of the motor system to external input.SIGNIFICANCE STATEMENT The sense of agency, the feeling of "I did that," when directing one's own musculature is a core feature of human experience. We show that we can robustly manipulate the sense of agency over electrically actuated muscle movements, and we investigate the time course of neural activity that predicts the sense of agency over these actuated movements. We find evidence of two distinct neural processes: a transient sequence of patterns that begins in the early sensorineural response to muscle stimulation and a later, sustained signature of agency. These results shed light on the neural mechanisms by which we experience our movements as volitional.

The sense of agency refers to the feeling of control over one’s own actions, and through them, the external events. This study examined the effect of modified visual feedback on the sense of agency over one’s body movements using virtual reality in healthy individuals whose motor control was disturbed. Participants moved a virtual object using their right hand to trace a trajectory (Experiment 1) or a leading target (Experiment 2). Their motor control was disturbed by a delay in visual feedback (Experiment 1) or a 1-kg weight attached to their wrist (Experiment 2). In the offset conditions, the virtual object was presented at the median point between the desired position and the participants’ actual hand position. In both experiments, participants reported improved sense of agency in the offset condition compared to the aligned condition where the visual feedback reflected their actual body movements, despite their motion being less precise in the offset condition. The results show that sense of agency can be enhanced by modifying feedback to motor tasks according to the goal of the task, even when visual feedback is discrepant from the actual body movements. The present study sheds light on the possibility of artificially enhancing body agency to improve voluntary motor control.

If an observer sees a flashed (briefly presented) object that is aligned with a moving target, the perceived position of the flashed object usually lags the perceived position of the moving target. This has been referred to as the flash-lag effect, and the flash-lag effect has been suggested to reflect how an observer compensates for delays in perception that are due to neural processing times and is thus able to interact with dynamic stimuli in real time. Characteristics of the stimulus and of the observer that influence the flash-lag effect are reviewed, and the sensitivity or robustness of the flash-lag effect to numerous variables is discussed. Properties of the flash-lag effect and how the flash-lag effect might be related to several other perceptual and cognitive processes and phenomena are considered. Unresolved empirical issues are noted. Theories of the flash-lag effect are reviewed, and evidence inconsistent with each theory is noted. The flash-lag effect appears to involve low-level perceptual processes and high-level cognitive processes, reflects the operation of multiple mechanisms, occurs in numerous stimulus dimensions, and occurs within and across multiple modalities. It is suggested that the flash-lag effect derives from more basic mislocalizations of the moving target or flashed object and that understanding and analysis of the flash-lag effect should focus on these more basic mislocalizations rather than on the relationship between the moving target and the flashed object.

Because of the delays inherent in neural transmission, the brain needs time to process incoming visual information. If these delays were not somehow compensated, we would consistently mislocalize moving objects behind their physical positions. Twenty-five years ago, Nijhawan used a perceptual illusion he called the flash-lag effect (FLE) to argue that the brain's visual system solves this computational challenge by extrapolating the position of moving objects (Nijhawan, 1994). Although motion extrapolation had been proposed a decade earlier (e.g., Finke et al., 1986), the proposal that it caused the FLE and functioned to compensate for computational delays was hotly debated in the years that followed, with several alternative interpretations put forth to explain the effect. Here, I argue, 25 years later, that evidence from behavioral, computational, and particularly recent functional neuroimaging studies converges to support the existence of motion extrapolation mechanisms in the visual system, as well as their causal involvement in the FLE. First, findings that were initially argued to challenge the motion extrapolation model of the FLE have since been explained, and those explanations have been tested and corroborated by more recent findings. Second, motion extrapolation explains the spatial shifts observed in several FLE conditions that cannot be explained by alternative (temporal) models of the FLE. Finally, neural mechanisms that actually perform motion extrapolation have been identified at multiple levels of the visual system, in multiple species, and with multiple different methods. I outline key questions that remain, and discuss possible directions for future research.

Exploring the limits to our understanding of whether fish feel pain.

Minimal conditions for the emergence of a vicarious sense of agency toward artificial agents.

This chapter explores the role of time in conscious experience through the Neural Binding and Coherence (NBC) theory. It examines how neural mechanisms, such as time cells and memory units, create a biological representation of time, enabling the perception of duration, change, simultaneity, and causality. By binding time cells with sensory and abstract information, the brain forms temporal representations that contribute to understanding the present, past, and future. The chapter investigates how emotions, cultural contexts, and biological rhythms influence time perception, offering insights into phenomena like why time flies during enjoyable activities and drags during boredom. Addressing philosophical and scientific debates on the reality of time, the chapter argues that time, as experienced through consciousness, is rooted in neural and cognitive processes, integrating biology, culture, and physics.