The Projective Consciousness Model: Projective Geometry at the Core of Consciousness and the Integration of Perception, Imagination, Motivation, Emotion, Social Cognition and Action.

Consciousness has been described as acting as a global workspace that integrates perception, imagination, emotion and action programming for adaptive decision making. The mechanisms of this workspace and their relationships to the phenomenology of consciousness need to be further specified. Much research in this area has focused on the neural correlates of consciousness, but, arguably, computational modeling can better be used toward this aim. According to the Projective Consciousness Model (PCM), consciousness is structured as a viewpoint-organized, internal space, relying on 3D projective geometry and governed by the action of the Projective Group as part of a process of active inference. The geometry induces a group-structured subjective perspective on an encoded world model, enabling adaptive perspective taking in agents. Here, we review and discuss the PCM. We emphasize the role of projective mechanisms in perception and the appraisal of affective and epistemic values as tied to the motivation of action, under an optimization process of Free Energy minimization, or more generally stochastic optimal control. We discuss how these mechanisms enable us to model and simulate group-structured drives in the context of social cognition and to understand the mechanisms underpinning empathy, emotion expression and regulation, and approach-avoidance behaviors. We review previous results, drawing on applications in robotics and virtual humans. We briefly discuss future axes of research relating to applications of the model to simulation- and model-based behavioral science, geometrically structured artificial neural networks, the relevance of the approach for explainable AI and human-machine interactions, and the study of the neural correlates of consciousness.

The integrated information theory of consciousness: Unmasked and identified.

In our response to a truly diverse set of commentaries, we first summarize the principal topical themes around which they cluster, then address two "outlier" positions (the problem of consciousness has been solved vs. is intractable). Next, we address ways in which commentaries by non-integrated information theory (IIT) authors engage with the specifics of our IIT critique, turning finally to the four commentaries by IIT authors.

The integrated information theory of consciousness: A case of mistaken identity.

Giulio Tononi's integrated information theory (IIT) proposes explaining consciousness by directly identifying it with integrated information. We examine the construct validity of IIT's measure of consciousness, phi (Φ), by analyzing its formal properties, its relation to key aspects of consciousness, and its co-variation with relevant empirical circumstances. Our analysis shows that IIT's identification of consciousness with the causal efficacy with which differentiated networks accomplish global information transfer (which is what Φ in fact measures) is mistaken. This misidentification has the consequence of requiring the attribution of consciousness to a range of natural systems and artifacts that include, but are not limited to, large-scale electrical power grids, gene-regulation networks, some electronic circuit boards, and social networks. Instead of treating this consequence of the theory as a disconfirmation, IIT embraces it. By regarding these systems as bearers of consciousness ex hypothesi, IIT is led toward the orbit of panpsychist ideation. This departure from science as we know it can be avoided by recognizing the functional misattribution at the heart of IIT's identity claim. We show, for example, what function is actually performed, at least in the human case, by the cortical combination of differentiation with integration that IIT identifies with consciousness. Finally, we examine what lessons may be drawn from IIT's failure to provide a credible account of consciousness for progress in the very active field of research concerned with exploring the phenomenon from formal and neural points of view.

Lesion network mapping demonstrates that mind-wandering is associated with the default mode network.

Functional neuroimaging research has consistently associated brain structures within the default mode network (DMN) and frontoparietal network (FPN) with mind-wandering. Targeted lesion research has documented impairments in mind-wandering after damage to the medial prefrontal cortex (mPFC) and hippocampal regions associated with the DMN. However, no lesion studies to date have applied lesion network mapping to identify common networks associated with deficits in mind-wandering. In lesion network mapping, resting-state functional connectivity data from healthy participants are used to infer which brain regions are functionally connected to each lesion location from a sample with brain injury. In the current study, we conducted a lesion network mapping analysis to test the hypothesis that lesions affecting the DMN and FPN would be associated with diminished mind-wandering. We assessed mind-wandering frequency on the Imaginal Processes Inventory (IPI) in participants with brain injury (n = 29) and healthy comparison participants without brain injury (n = 19). Lesion network mapping analyses showed the strongest association of reduced mind-wandering with the left inferior parietal lobule within the DMN. In addition, traditional lesion symptom mapping results revealed that reduced mind-wandering was associated with lesions of the dorsal, ventral, and anterior sectors of mPFC, parietal lobule, and inferior frontal gyrus in the DMN (p < 0.05 uncorrected). These findings provide novel lesion support for the role of the DMN in mind-wandering and contribute to a burgeoning literature on the neural correlates of spontaneous cognition.

The Moon illusion explained by the projective consciousness model.

Models of consciousness should account for the phenomenology of subjective experience, including perceptual illusions. The Moon Illusion is a paradigmatic example that has yet to be accounted for. The Moon often appears larger near the perceptual horizon and smaller high in the sky, though the visual angle subtended is invariant. We show how this illusion can result from the optimization of a 3D projective geometrical frame through free energy minimization, following the principles of the Projective Consciousness Model. The model accounts for all documented modulations of the illusion without anomalies (e.g., the "size-distance paradox"), surpasses other theories in explanatory power, makes sense of inter- and intra-subjective variability vis-à-vis the illusion, and yields new quantitative and qualitative predictions. Empirical data from a virtual reality experiment support the predictions of the model. We also discuss how the model suggests explanations for other relevant illusions, concerning objects both at far and nearer distances, including the sky dome illusion, illusions of perceived size observed in the context of crowding experiments, and the Ames Room illusion.

The practice of meditation is not associated with improved interoceptive awareness of the heartbeat.

Meditation is commonly assumed to be associated with enhanced interoceptive accuracy. We previously found that experienced meditators did not exhibit a greater ability than nonmeditators to detect heartbeat sensations at rest, despite the meditators' reported subjective ratings of higher accuracy and lower difficulty. Here, attempting to overcome previous methodological limitations, we assessed interoceptive awareness of heartbeat and breathing sensations across physiological arousal levels using infusions of isoproterenol, a beta-adrenergic agonist similar to adrenaline. We hypothesized that meditators would display greater interoceptive awareness than nonmeditators, as evidenced by higher interoceptive detection rates, increased interoceptive accuracy, and differences in localization of heartbeat sensations. We studied 15 meditators and 15 nonmeditators, individually matched on age, gender, and body mass index, using randomized, double-blinded, and placebo-controlled bolus infusions of isoproterenol. Participants reported their experience of heartbeat and breathing sensations using a dial during infusions and the location of heartbeat sensations on a two-dimensional manikin afterward. There was no evidence of higher detection rates or increased accuracy across any dose, although meditators showed a tendency to report cardiorespiratory sensation changes sooner at higher doses. Relative to nonmeditators, meditators exhibited prominent geographical differences in heartbeat localization, disproportionally reporting sensations throughout central regions of the chest, abdomen, neck, back, and head. To further assess indications of potential differences in cardiac interoceptive accuracy between meditators and nonmeditators, we conducted a meta-analysis including 724 participants and found little evidence for such differences. We conclude that the practice of meditation is not associated with improved cardiac interoceptive awareness.

Probabilistic functional tractography of the human cortex revisited.

In patients with pharmaco-resistant focal epilepsies investigated with intracranial electroencephalography (iEEG), direct electrical stimulations of a cortical region induce cortico-cortical evoked potentials (CCEP) in distant cerebral cortex, which properties can be used to infer large scale brain connectivity. In 2013, we proposed a new probabilistic functional tractography methodology to study human brain connectivity. We have now been revisiting this method in the F-TRACT project (f-tract.eu) by developing a large multicenter CCEP database of several thousand stimulation runs performed in several hundred patients, and associated processing tools to create a probabilistic atlas of human cortico-cortical connections. Here, we wish to present a snapshot of the methods and data of F-TRACT using a pool of 213 epilepsy patients, all studied by stereo-encephalography with intracerebral depth electrodes. The CCEPs were processed using an automated pipeline with the following consecutive steps: detection of each stimulation run from stimulation artifacts in raw intracranial EEG (iEEG) files, bad channels detection with a machine learning approach, model-based stimulation artifact correction, robust averaging over stimulation pulses. Effective connectivity between the stimulated and recording areas is then inferred from the properties of the first CCEP component, i.e. onset and peak latency, amplitude, duration and integral of the significant part. Finally, group statistics of CCEP features are implemented for each brain parcel explored by iEEG electrodes. The localization (coordinates, white/gray matter relative positioning) of electrode contacts were obtained from imaging data (anatomical MRI or CT scans before and after electrodes implantation). The iEEG contacts were repositioned in different brain parcellations from the segmentation of patients' anatomical MRI or from templates in the MNI coordinate system. The F-TRACT database using the first pool of 213 patients provided connectivity probability values for 95% of possible intrahemispheric and 56% of interhemispheric connections and CCEP features for 78% of intrahemisheric and 14% of interhemispheric connections. In this report, we show some examples of anatomo-functional connectivity matrices, and associated directional maps. We also indicate how CCEP features, especially latencies, are related to spatial distances, and allow estimating the velocity distribution of neuronal signals at a large scale. Finally, we describe the impact on the estimated connectivity of the stimulation charge and of the contact localization according to the white or gray matter. The most relevant maps for the scientific community are available for download on f-tract. eu (David et al., 2017) and will be regularly updated during the following months with the addition of more data in the F-TRACT database. This will provide an unprecedented knowledge on the dynamical properties of large fiber tracts in human.

Resting-State Networks of Adolescents Experiencing Depersonalization-Like Illusions: Cross-sectional and Longitudinal Findings.

The mirror-gazing task (MGT) experimentally induces illusions, ranging from simple color changes in the specular image of oneself, to depersonalization-like anomalous self-experiences (ASE) as in experiencing one's specular image as someone else. The objective was to characterize how connectivity in resting-state networks (RSNs) differed in adolescents reporting such depersonalization-like ASEs during the MGT, in a cross-sectional (Y1) and in a longitudinal manner (a year after). 75 adolescents were recruited; for the cross-sectional analysis, participants were split into 2 groups: those who reported depersonalization-like ASEs on the MGT (ASE), and those who did not (NoASE). For the longitudinal analysis, participants were split into 3 groups whether they experienced MGT depersonalization-like ASEs: only at Y1 (Remitters), both times (Persisters), or never (Controls). Participants also filled out self-reports assessing schizotypal personality (Schizotypal Personality Questionnaire [SPQ]), and underwent resting-state functional MRI procedure (rs-fMRI). A group level Independent Component Analysis (ICA) was conducted and voxel-wise inter-group differences within RSNs were examined. The rs-fMRI analysis revealed lower connectivity of specific visual areas within the primary visual network (PVN), and higher connectivity of regions within the Default Mode Network (DMN) when contrasting the ASE and NoASE groups. The areas that were atypically connected within the PVN further presented differential pattern of connectivity in the longitudinal analysis. Atypical connectivity of visual area within the DMN at Y1 was associated with higher scores on the disorganized dimension of schizotypy at the second evaluation. The present study uncovers a subtle signature in the RSNs of non-clinical adolescents who experienced task-induced ASEs.

The Projective Consciousness Model and Phenomenal Selfhood.

We summarize our recently introduced Projective Consciousness Model (PCM) (Rudrauf et al., 2017) and relate it to outstanding conceptual issues in the theory of consciousness. The PCM combines a projective geometrical model of the perspectival phenomenological structure of the field of consciousness with a variational Free Energy minimization model of active inference, yielding an account of the cybernetic function of consciousness, viz., the modulation of the field's cognitive and affective dynamics for the effective control of embodied agents. The geometrical and active inference components are linked via the concept of projective transformation, which is crucial to understanding how conscious organisms integrate perception, emotion, memory, reasoning, and perspectival imagination in order to control behavior, enhance resilience, and optimize preference satisfaction. The PCM makes substantive empirical predictions and fits well into a (neuro)computationalist framework. It also helps us to account for aspects of subjective character that are sometimes ignored or conflated: pre-reflective self-consciousness, the first-person point of view, the sense of minenness or ownership, and social self-consciousness. We argue that the PCM, though still in development, offers us the most complete theory to date of what Thomas Metzinger has called "phenomenal selfhood."

A mathematical model of embodied consciousness.

We introduce a mathematical model of embodied consciousness, the Projective Consciousness Model (PCM), which is based on the hypothesis that the spatial field of consciousness (FoC) is structured by a projective geometry and under the control of a process of active inference. The FoC in the PCM combines multisensory evidence with prior beliefs in memory and frames them by selecting points of view and perspectives according to preferences. The choice of projective frames governs how expectations are transformed by consciousness. Violations of expectation are encoded as free energy. Free energy minimization drives perspective taking, and controls the switch between perception, imagination and action. In the PCM, consciousness functions as an algorithm for the maximization of resilience, using projective perspective taking and imagination in order to escape local minima of free energy. The PCM can account for a variety of psychological phenomena: the characteristic spatial phenomenology of subjective experience, the distinctions and integral relationships between perception, imagination and action, the role of affective processes in intentionality, but also perceptual phenomena such as the dynamics of bistable figures and body swap illusions in virtual reality. It relates phenomenology to function, showing the computational advantages of consciousness. It suggests that changes of brain states from unconscious to conscious reflect the action of projective transformations and suggests specific neurophenomenological hypotheses about the brain, guidelines for designing artificial systems, and formal principles for psychology.

Stimulation of subgenual cingulate area decreases limbic top-down effect on ventral visual stream: A DBS-EEG pilot study.

Deep brain stimulation (DBS) of the subgenual cingulate gyrus (area CG25) is beneficial in treatment resistant depression. Though the mechanisms of action of Cg25 DBS remain largely unknown, it is commonly believed that Cg25 DBS modulates limbic activity of large networks to achieve thymic regulation of patients. To investigate how emotional attention is influenced by Cg25 DBS, we assessed behavioral and electroencephalographic (EEG) responses to an emotional Stroop task in 5 patients during ON and OFF stimulation conditions. Using EEG source localization, we found that the main effect of DBS was a reduction of neuronal responses in limbic regions (temporal pole, medial prefrontal and posterior cingulate cortices) and in ventral visual areas involved in face processing. In the dynamic causal modeling (DCM) approach, the changes of the evoked response amplitudes are assumed to be due to changes of long range connectivity induced by Cg25 DBS. Here, using a simplified neural mass model that did not take explicitly into account the cytoarchitecture of the considered brain regions, we showed that the remote action of Cg25 DBS could be explained by a reduced top-down effective connectivity of the amygdalo-temporo-polar complex. Overall, our results thus indicate that Cg25 DBS during the emotional Stroop task causes a decrease of top-down limbic influence on the ventral visual stream itself, rather than a modulation of prefrontal cognitive processes only. Tuning down limbic excitability in relation to sensory processing might be one of the biological mechanisms through which Cg25 DBS produces positive clinical outcome in the treatment of resistant depression.

Functional Connectivity's Degenerate View of Brain Computation.

Brain computation relies on effective interactions between ensembles of neurons. In neuroimaging, measures of functional connectivity (FC) aim at statistically quantifying such interactions, often to study normal or pathological cognition. Their capacity to reflect a meaningful variety of patterns as expected from neural computation in relation to cognitive processes remains debated. The relative weights of time-varying local neurophysiological dynamics versus static structural connectivity (SC) in the generation of FC as measured remains unsettled. Empirical evidence features mixed results: from little to significant FC variability and correlation with cognitive functions, within and between participants. We used a unified approach combining multivariate analysis, bootstrap and computational modeling to characterize the potential variety of patterns of FC and SC both qualitatively and quantitatively. Empirical data and simulations from generative models with different dynamical behaviors demonstrated, largely irrespective of FC metrics, that a linear subspace with dimension one or two could explain much of the variability across patterns of FC. On the contrary, the variability across BOLD time-courses could not be reduced to such a small subspace. FC appeared to strongly reflect SC and to be partly governed by a Gaussian process. The main differences between simulated and empirical data related to limitations of DWI-based SC estimation (and SC itself could then be estimated from FC). Above and beyond the limited dynamical range of the BOLD signal itself, measures of FC may offer a degenerate representation of brain interactions, with limited access to the underlying complexity. They feature an invariant common core, reflecting the channel capacity of the network as conditioned by SC, with a limited, though perhaps meaningful residual variability.

Stimulation artifact correction method for estimation of early cortico-cortical evoked potentials.

BACKGROUND: Effective connectivity can be explored using direct electrical stimulations in patients suffering from drug-resistant focal epilepsies and investigated with intracranial electrodes. Responses to brief electrical pulses mimic the physiological propagation of signals and manifest as cortico-cortical evoked potentials (CCEP). The first CCEP component is believed to reflect direct connectivity with the stimulated region but the stimulation artifact, a sharp deflection occurring during a few milliseconds, frequently contaminates it. NEW METHOD: In order to recover the characteristics of early CCEP responses, we developed an artifact correction method based on electrical modeling of the electrode-tissue interface. The biophysically motivated artifact templates are then regressed out of the recorded data as in any classical template-matching removal artifact methods. RESULTS: Our approach is able to make the distinction between the physiological responses time-locked to the stimulation pulses and the non-physiological component. We tested the correction on simulated CCEP data in order to quantify its efficiency for different stimulation and recording parameters. We demonstrated the efficiency of the new correction method on simulations of single trial recordings for early responses contaminated with the stimulation artifact. The results highlight the importance of sampling frequency for an accurate analysis of CCEP. We then applied the approach to experimental data. COMPARISON WITH EXISTING METHOD: The model-based template removal was compared to a correction based on the subtraction of the averaged artifact. CONCLUSIONS: This new correction method of stimulation artifact will enable investigators to better analyze early CCEP components and infer direct effective connectivity in future CCEP studies.

Segregation of anterior temporal regions critical for retrieving names of unique and non-unique entities reflects underlying long-range connectivity.

Lesion-deficit studies support the hypothesis that the left anterior temporal lobe (ATL) plays a critical role in retrieving names of concrete entities. They further suggest that different regions of the left ATL process different conceptual categories. Here we test the specificity of these relationships and whether the anatomical segregation is related to the underlying organization of white matter connections. We reanalyzed data from a previous lesion study of naming and recognition across five categories of concrete entities. In voxelwise logistic regressions of lesion-deficit associations, we formally incorporated measures of disconnection of long-range association fiber tracts (FTs) and covaried for recognition and non-category-specific naming deficits. We also performed fiber tractwise analyses to assess whether damage to specific FTs was preferentially associated with category-selective naming deficits. Damage to the basolateral ATL was associated with naming deficits for both unique (famous faces) and non-unique entities, whereas the damage to the temporal pole was associated with naming deficits for unique entities only. This segregation pattern remained after accounting for comorbid recognition deficits or naming deficits in other categories. The tractwise analyses showed that damage to the uncinate fasciculus (UNC) was associated with naming impairments for unique entities, while damage to the inferior longitudinal fasciculus (ILF) was associated with naming impairments for non-unique entities. Covarying for FT transection in voxelwise analyses rendered the cortical association for unique entities more focal. These results are consistent with the partial segregation of brain system support for name retrieval of unique and non-unique entities at both the level of cortical components and underlying white matter fiber bundles. Our study reconciles theoretic accounts of the functional organization of the left ATL by revealing both category-related processing and semantic hub sectors.

Preserved emotional awareness of pain in a patient with extensive bilateral damage to the insula, anterior cingulate, and amygdala.

Functional neuroimaging investigations of pain have discovered a reliable pattern of activation within limbic regions of a putative "pain matrix" that has been theorized to reflect the affective dimension of pain. To test this theory, we evaluated the experience of pain in a rare neurological patient with extensive bilateral lesions encompassing core limbic structures of the pain matrix, including the insula, anterior cingulate, and amygdala. Despite widespread damage to these regions, the patient's expression and experience of pain was intact, and at times excessive in nature. This finding was consistent across multiple pain measures including self-report, facial expression, vocalization, withdrawal reaction, and autonomic response. These results challenge the notion of a "pain matrix" and provide direct evidence that the insula, anterior cingulate, and amygdala are not necessary for feeling the suffering inherent to pain. The patient's heightened degree of pain affect further suggests that these regions may be more important for the regulation of pain rather than providing the decisive substrate for pain's conscious experience.

The effect of meditation on regulation of internal body states.

Meditation is commonly thought to induce physiologically quiescent states, as evidenced by decreased autonomic parameters during the meditation practice including reduced heart rate, respiratory rate, blood pressure, skin conductance, and increased alpha activity in the electroencephalogram. Preliminary empirical support for this idea was provided in a case report by Dimsdale and Mills (2002), where it was found that meditation seemed to regulate increased levels of cardiovascular arousal induced by bolus isoproterenol infusions. In that study, while meditating, a self-taught meditator exhibited unexpected decreases in heart rate while receiving moderate intravenous doses of the beta adrenergic agonist isoproterenol. This effect was no longer observed when the individual received isoproterenol infusions while not meditating. The current study was designed to explore this phenomenon empirically in a group of formally trained meditators. A total of 15 meditators and 15 non-meditators individually matched on age, sex, and body mass index were recruited. Participants received four series of infusions in a pseudorandomized order: isoproterenol while meditating (or during a relaxation condition for the non-meditators), isoproterenol while resting, saline while meditating (or during a relaxation condition for the non-meditators), and saline while resting. Heart rate was continuously measured throughout all infusions, and several measures of heart rate were derived from the instantaneous cardiac waveform. There was no evidence at the group or individual level suggesting that meditation reduced the cardiovascular response to isoproterenol, across all measures. These results suggest that meditation is not associated with increased regulation of elevated cardiac adrenergic tone.

Predicting functional connectivity from structural connectivity via computational models using MRI: an extensive comparison study.

The relationship between structural connectivity (SC) and functional connectivity (FC) in the human brain can be studied using magnetic resonance imaging (MRI). However many of the underlying physiological mechanisms and parameters cannot be directly observed with MRI. This limitation has motivated the recent use of various computational models meant to bridge the gap. However their absolute and relative explanatory power and the properties that actually drive that power remain insufficiently characterized. We performed an extensive comparison of seven mainstream computational models predicting FC from SC. We investigated the extent to which simulated FC could predict empirical FC. We also applied graph theory to the entire set of simulated and empirical FCs in order to further characterize the relationships between the models and the MRI data. The comparison was performed at three different spatial scales. We found that (i) there were significant effects of scale and model on predictive power; (ii) among all models, the simplest model, the simultaneous autoregressive (SAR) model, was found to consistently perform better than the other models; (iii) the SAR also appeared more 'central' from a graph theory perspective; and (iv) empirical FC only appeared weakly correlated with simulated FCs, and was featured as 'peripheral' in the graph analysis. We conclude that the substantial differences existing between these computational models have little impact on their predictive power for FC and that their capacity to predict FC from SC appears to be both moderate and essentially underlined by a simple core linear process embodied by the SAR model.

Damage to the default mode network disrupts autobiographical memory retrieval.

Functional neuroimaging studies have implicated the default mode network (DMN) in autobiographical memory (AM). Convergent evidence from a lesion approach would help clarify the role of the DMN in AM. In this study, we used a voxelwise lesion-deficit approach to test the hypothesis that regions of the DMN are necessary for AM. We also explored whether the neural correlates of semantic AM (SAM) and episodic AM (EAM) were overlapping or distinct. Using the Iowa Autobiographical Memory Questionnaire, we tested AM retrieval in 92 patients with focal, stable brain lesions. In support of our hypothesis, damage to regions within the DMN (medial prefrontal cortex, mPFC; posterior cingulate cortex, PCC; inferior parietal lobule, IPL; medial temporal lobe, MTL) was associated with AM impairments. Within areas of effective lesion coverage, the neural correlates of SAM and EAM were largely distinct, with limited areas of overlap in right IPL. Whereas SAM deficits were associated with left mPFC and MTL damage, EAM deficits were associated with right mPFC and MTL damage. These results provide novel neuropsychological evidence for the necessary role of parts of the DMN in AM. More broadly, the findings shed new light on how the DMN participates in self-referential processing.

What affects detectability of lesion-deficit relationships in lesion studies?

Elucidating the brain basis for psychological processes and behavior is a fundamental aim of cognitive neuroscience. The lesion method, using voxel-based statistical analysis, is an important approach to this goal, identifying neural structures that are necessary for the support of specific mental operations, and complementing the strengths of functional imaging techniques. Lesion coverage in a population is by nature spatially heterogeneous and biased, systematically affecting the ability of lesion-deficit correlation methods to detect and localize functional associations. We have developed a simulator that allows investigators to model parameters in a lesion-deficit study and characterize the statistical bias in lesion deficit detection coverage that will result from specific assumptions. We used the simulator to assess the signal detection properties and localization accuracy of standard lesion-deficit correlation methods, under a simple truth model - that a critical region of interest (CR), when damaged, gives rise to a deficit. We considered voxel-based lesion-symptom mapping (VLSM) and proportional MAP-3 (PM3). Using regression analysis, we examined if the pattern of outcome statistics can be explained by simulation parameters, factors that are inherent to anatomic parcels, and lesion coverage of the population, which consisted of a representative sample of 351 subjects drawn from the Iowa Patient Registry. We examined the effect of using nonparametric versus parametric statistics to obtain thresholded maps and the effect of correcting for multiple comparisons using false discovery rate or cluster-based correction. Our results, which are derived from samples of realistic lesions, indicate that even a simple truth model yields localization errors that are systematic and pervasive, averaging 2 cm in the standard anatomic space, and tending to be directed towards areas of greater anatomic coverage. This displacement positions the center of mass of the detected region in a different anatomical region 87% of the time. This basic result is not affected by the choice of PM3 vs VLSM as the fundamental approach, nor is localization error ameliorated by incorporation of lesion size as a covariate in the VLSM approach, or by data distribution-driven approaches to controlling multiple spatial comparisons (false discovery rate or cluster-based correction approaches). Our simulations offer a quantitative basis for interpreting lesion studies in cognitive neuroscience. We suggest ways in which lesion simulation and analysis frameworks could be productively extended.