Daw-An Wu

Rapid Assimilation of External Objects Into the Body Schema

When a warrior picks up a sword for battle, do sword and soldier become one? The notion of an extended sense of the body has been the topic of philosophical discussion for more than a century and more recently has been subjected to empirical tests by psychologists and neuroscientists. We used a unique afterimage paradigm to test if, and under what conditions, objects are integrated into an extended body sense. Our experiments provide empirical support for the notion that objects can be integrated into an extended sense of the body. Our findings further indicate that this extended body sense is highly plastic, quickly assimilating objects that are in physical contact with the observer. Finally, we show that this extended body sense is limited to first-order extensions, thus constraining how far one can extend oneself into the environment.

Discrete color filling beyond luminance gaps along perceptual surfaces

Perceived color at a point in space is not determined simply by the color directly stimulating the corresponding retinal position. Surface color is informed by flanking edge signals, which also serve to inhibit the intrusion of signals from neighboring surfaces. Spatially continuous local interactions among color and luminance signals have been implicated in a propagation process often referred to as filling-in. Here, we report a phenomenon of discrete color filling whereby color jumps over luminance gaps filling into disconnected regions of the stimulus. This color filling is found to be blocked at boundaries defined by texture. The color filling is also highly specific to the elements belonging to a common perceptual surface, even when multiple surfaces are transparently overlaid. Our results indicate that color filling can be governed by a host of visual cues outside the realm of first-order color and brightness, via their impact on perceptual surface segmentation and segregation.

Electronically Switchable Sham Transcranial Magnetic Stimulation (TMS) System

Transcranial magnetic stimulation (TMS) is increasingly being used to demonstrate the causal links between brain and behavior in humans. Further, extensive clinical trials are being conducted to investigate the therapeutic role of TMS in disorders such as depression. Because TMS causes strong peripheral effects such as auditory clicks and muscle twitches, experimental artifacts such as subject bias and placebo effect are clear concerns. Several sham TMS methods have been developed, but none of the techniques allows one to intermix real and sham TMS on a trial-by-trial basis in a double-blind manner. We have developed an attachment that allows fast, automated switching between Standard TMS and two types of control TMS (Sham and Reverse) without movement of the coil or reconfiguration of the setup. We validate the setup by performing mathematical modeling, search-coil and physiological measurements. To see if the stimulus conditions can be blinded, we conduct perceptual discrimination and sensory perception studies. We verify that the physical properties of the stimulus are appropriate, and that successive stimuli do not contaminate each other. We find that the threshold for motor activation is significantly higher for Reversed than for Standard stimulation, and that Sham stimulation entirely fails to activate muscle potentials. Subjects and experimenters perform poorly at discriminating between Sham and Standard TMS with a figure-of-eight coil, and between Reverse and Standard TMS with a circular coil. Our results raise the possibility of utilizing this technique for a wide range of applications.

A Causal Role for Posterior Medial Frontal Cortex in Choice-Induced Preference Change

After a person chooses between two items, preference for the chosen item will increase and preference for the unchosen item will decrease because of the choice made. In other words, we tend to justify or rationalize our past behavior by changing our attitude. This phenomenon of choice-induced preference change has been traditionally explained by cognitive dissonance theory. Choosing something that is disliked or not choosing something that is liked are both cognitively inconsistent and, to reduce this inconsistency, people tend to change their subsequently stated preference in accordance with their past choices. Previously, human neuroimaging studies identified posterior medial frontal cortex (pMFC) as a key brain region involved in cognitive dissonance. However, it remains unknown whether the pMFC plays a causal role in inducing preference change after cognitive dissonance. Here, we demonstrate that 25 min, 1 Hz repetitive transcranial magnetic stimulation applied over the pMFC significantly reduces choice-induced preference change compared with sham stimulation or control stimulation over a different brain region, demonstrating a causal role for the pMFC.

Shared Visual Attention Reduces Hindsight Bias

Hindsight bias is the tendency to retrospectively think of outcomes as being more foreseeable than they actually were. It is a robust judgment bias and is difficult to correct (or “debias”). In the experiments reported here, we used a visual paradigm in which performers decided whether blurred photos contained humans. Evaluators, who saw the photos unblurred and thus knew whether a human was present, estimated the proportion of participants who guessed whether a human was present. The evaluators exhibited visual hindsight bias in a way that matched earlier data from judgments of historical events surprisingly closely. Using eye tracking, we showed that a higher correlation between the gaze patterns of performers and evaluators (shared attention) is associated with lower hindsight bias. This association was validated by a causal method for debiasing: Showing the gaze patterns of the performers to the evaluators as they viewed the stimuli reduced the extent of hindsight bias.

Changing the mind? Not really—activity and connectivity in the caudate correlates with changes of choice

Changes in preference are inherently subjective and internal psychological events. We have identified brain events that presage ultimate (rather than intervening) choices, and signal the finality of a choice. At the first exposure to a pair of faces, caudate activity reflected the face of final choice, even if an initial choice was different. Furthermore, the orbitofrontal cortex and hippocampus exhibited correlations only when the subject had made a choice that would not change.

Decision ambiguity is mediated by a late positive potential originating from cingulate cortex

&NA; People often make decisions in the face of ambiguous information, but it remains unclear how ambiguity is represented in the brain. We used three types of ambiguous stimuli and combined EEG and fMRI to examine the neural representation of perceptual decisions under ambiguity. We identified a late positive potential, the LPP, which differentiated levels of ambiguity, and which was specifically associated with behavioral judgments about choices that were ambiguous, rather than passive perception of ambiguous stimuli. Mediation analyses together with two further control experiments confirmed that the LPP was generated only when decisions are made (not during mere perception of ambiguous stimuli), and only when those decisions involved choices on a dimension that is ambiguous. A further control experiment showed that a stronger LPP arose in the presence of ambiguous stimuli compared to when only unambiguous stimuli were present. Source modeling suggested that the LPP originated from multiple loci in cingulate cortex, a finding we further confirmed using fMRI and fMRI‐guided ERP source prediction. Taken together, our findings argue for a role of an LPP originating from cingulate cortex in encoding decisions based on task‐relevant perceptual ambiguity, a process that may in turn influence confidence judgment, response conflict, and error correction. HighlightsA late positive potential encodes levels of perceptual ambiguity.The LPP is associated with ambiguity of decisions rather than identification.The LPP only arises in the context of ambiguous stimuli.fMRI‐informed EEG revealed a network of brain regions encoding ambiguity.

Where are you looking? Pseudogaze in afterimages

How do we know where we are looking? A frequent assumption is that the subjective experience of our direction of gaze is assigned to the location in the world that falls on our fovea. However, we find that observers can shift their subjective direction of gaze among different nonfoveal points in an afterimage. Observers were asked to look directly at different corners of a diamond-shaped afterimage. When the requested corner was 3.5° in the periphery, the observer often reported that the image moved away in the direction of the attempted gaze shift. However, when the corner was at 1.75° eccentricity, most reported successfully fixating at the point. Eye-tracking data revealed systematic drift during the subjective fixations on peripheral locations. For example, when observers reported looking directly at a point above the fovea, their eyes were often drifting steadily upwards. We then asked observers to make a saccade from a subjectively fixated, nonfoveal point to another point in the afterimage, 7° directly below their fovea. The observers consistently reported making appropriately diagonal saccades, but the eye movement traces only occasionally followed the perceived oblique direction. These results suggest that the perceived direction of gaze can be assigned flexibly to an attended point near the fovea. This may be how the visual world acquires its stability during fixation of an object, despite the drifts and microsaccades that are normal characteristics of visual fixation.

Does Adaptation of Motion-Direction Detectors Affect Bias or Sensitivity of Direction Judgments?

The question how channels tuned to different motion directions contribute to motion perception has been investigated by using motion adaptation to silence certain channels, and then measuring performance in a fine motion-discrimination task. To help constrain models of how the channels become integrated, we examined whether changes in performance stem from reduced accuracy (bias) or from reduced precision (sensitivity) in direction judgments. On a given trial, the observer first adapted to a field of dots moving coherently in a given direction (ranging ±180° from upward), then judged whether the motion of an ensuing test stimulus (ranging ±3°) was left or right of reference. Bias and sensitivity of the psychometric fits were computed for each adapter direction. Relative to baseline performance, post-adaptation judgments showed significant changes in sensitivity that were tightly correlated with overall performance. Meanwhile, bias shifts were found to be weaker and less systematic. Both performance and sensitivity suffered the largest losses at ±60°, with some enhancement at 180°. No similar trends were found in the domain of bias. A regression model, with precision as the sole predictor, captured 97% of the variation in performance; no gains were found in adding bias to the model. Our findings on fine motion-discrimination question the idealized notion of a pure feature detector, as the main impact of adaptation in such a system would be to bias direction judgments away from the adapted direction.

Single mechanism, divergent effects; multiple mechanisms, convergent effect

It is commonplace for a single physiological mechanism to seed multiple phenomena, and for multiple mechanisms to contribute to a single phenomenon. We propose that the flash-lag effect should not be considered a phenomenon with a single cause. Instead, its various aspects arise from the convergence of a number of different mechanisms proposed in the literature. We further give an example of how a neuron’s generic spatio-temporal response profile can form a physiological basis not only of “prediction,” but also of many of the other proposed flash-lag mechanisms, thus recapitulating a spectrum of flash-lag phenomena. Finally, in agreeing that such basic predictive mechanisms are present throughout the brain, we argue that motor prediction contributes more to biological fitness than visual prediction. It is likely that multiple mechanisms combine to create the flashlag phenomenon: persistence, priming, backward masking, temporal dilation, and even attention have all been demonstrated in one study or another (Bachmann & Poder 2001; Baldo & Namba 2002; Kanai et al. 2004; Krekelberg & Lappe 2001; Namba & Baldo 2004; Sheth et al. 2000). It seems that cleverly designed experiments can prove the importance of one’s favored model, but in vanishingly small parameter regimes. For example, experiments on the flash-terminated condition support extrapolation, but the results are limited to degraded, uncertain stimuli (Fu et al. 2004; Kanai et al. 2004). Other experiments support differential latency, but these use stimuli of much lower luminance (Patel et al. 2000; Purushothaman et al. 1998). We have previously argued that a very basic consideration of neuronal response profiles can recapitulate a wide array of flash-lag related mechanisms and effects (Kanai et al. 2004). As a stimulus moves in physical space, it maps out a topographically corresponding path in cortical space. At a given time instant, there are the following components: (A) cells at the “current” location of the stimulus are the most active; (B) cells in the immediate past path of the motion contain residual activity; (C) cells in the distant past path contain below-baseline activity caused by adaptation and intracortical inhibition; and (D) cells in the family of future motion paths have above-baseline subthreshold activity through intracortical excitation. This pattern of activity arises from the basic temporal response profile of a single neuron to input, and from the fact that lateral connections between neighboring neurons tend to cause net excitation to weakly firing neurons and net inhibition to strongly firing neurons (Henry et al. 1978; Levitt & Lund 1997; Somers et al. 1998; Stemmler et al. 1995). These four components of the spatiotemporal response profile have strengths that depend not only on factors intrinsic to the neuronal network, but also on stimulus parameters such as luminance, speed, and so on. These components can implement various mechanisms related to flash lag and motion processing. Component D could be descriptively labeled as priming, and if the activity in D is high enough to shift the centroid of the activity distribution forward, it could partially underlie a motion extrapolation mechanism. C could be a critical part of the neural basis for motion deblurring. When component B is prominent, differential latency for motion and flash arises: The spatiotemporal integral of the activity of AþB will reach perceptual threshold faster than a temporal integral of a stationary flash. Finally, stimulus conditions such as uncertainty will determine whether the activity in A alone suffices for awareness, or whether B needs to be added; this is a plausible neural basis for two different Bayesian estimators – conditional mean and maximum likelihood. Thus, the tuning of a simple neural mechanism can give rise to myriad psychophysical phenomena and high-level models. When distilled down to the idea of lateral propagation of cortical activity, we agree that prediction is intuitive and should be neurally omnipresent. The above properties of neurons are generic and found in almost all networks – sensory and motor. One question that arises then is: What is the relative contribution of sensory and motor prediction to successful behavior? We argue that prediction in the motor realm seems to be more effective and useful. First, visual prediction is applicable if a target moves with uniform velocity, but motion is hardly ever uniform in real life – physical (friction) and internal (attention, interest) factors often disrupt the smooth flow of motion. Second, motor prediction does not need to be as accurate as visual prediction. The agent can often over-compensate for the movements of the target, thus arriving at a common intersection point some time before the target. This allows the agent some slop, and with it, the flexibility to compensate for change in target speed, and for relatively small synaptic delays within its own nervous system. All delays – visual, synaptic, and of the muscle or tool-based effector – are available in a lump sum and are undifferentiated to the motor system as motor error. Motor systems routinely compensate for delays of the order of seconds, which arise from slow effectors. Such a system should be well-equipped to accommodate 100 msec of visual synaptic delay. Thus, the motor system seems to be the workhorse. Although this is but an isolated example, we note that prism adaptation begins in the motor system; one’s motor system compensates for errors weeks before one begins to correctly perceive the world. Visual prediction at the neural level is then just one of many important mechanisms in two senses: it is only one of the mechanisms which contribute to the flash-lag effect, and it is only one of the types of “neural prediction” which contribute to our biological fitness. In the case of flash-lag, variations in stimulus conditions can dictate the relative importance of visual prediction. In the case of biological fitness, it seems that visual prediction is just a small jumpstart – a small, subthreshold benefit to the organism in comparison to other predictive brain mechanisms. The mechanisms responsible for the flash-lag effect cannot provide the motor prediction that we need in daily life doi: 10.1017/S0140525X08003993 Jeroen B. J. Smeets and Eli Brenner Research Institute MOVE, Faculty of Human Movement Sciences, VU University, NL-1081 BT Amsterdam, The Netherlands. j.smeets@fbw.vu.nl e.brenner@fbw.vu.nl http://www.fbw.vu.nl/~JSmeets/ Abstract: The visual prediction that Nijhawan proposes cannot explain The visual prediction that Nijhawan proposes cannot explain why the flash-lag effect depends on what happens after the flash. Moreover, using a visual prediction based on retinal image motion to compensate for neuronal time delays will seldom be of any use for motor control, because one normally pursues objects with which one intends to interact with ones eyes. In his target article, Nijhawan proposes that early visual processing provides the prediction that is needed to deal with sensorymotor delays when we interact with moving objects, rather than such prediction arising from complex motor strategies as is generally assumed. He argues that the flash-lag effect and related phenomena illustrate the visual basis of such prediction. In his discussion of the extensive literature on this topic, he ignores several findings that show that the flash-lag effect cannot be Commentary/Nijhawan: Visual prediction BEHAVIORAL AND BRAIN SCIENCES (2008) 31:2 215