Visual Hallucinations Are Characterized by Impaired Sensory Evidence Accumulation: Insights From Hierarchical Drift Diffusion Modeling in Parkinson's Disease

Modelling phenomenological differences in aetiologically distinct visual hallucinations using deep neural networks

Visual hallucinations (VHs) are perceptions of objects or events in the absence of the sensory stimulation that would normally support such perceptions. Although all VHs share this core characteristic, there are substantial phenomenological differences between VHs that have different aetiologies, such as those arising from Neurodegenerative conditions, visual loss, or psychedelic compounds. Here, we examine the potential mechanistic basis of these differences by leveraging recent advances in visualising the learned representations of a coupled classifier and generative deep neural network-an approach we call 'computational (neuro)phenomenology'. Examining three aetiologically distinct populations in which VHs occur-Neurodegenerative conditions (Parkinson's Disease and Lewy Body Dementia), visual loss (Charles Bonnet Syndrome, CBS), and psychedelics-we identified three dimensions relevant to distinguishing these classes of VHs: realism (veridicality), dependence on sensory input (spontaneity), and complexity. By selectively tuning the parameters of the visualisation algorithm to reflect influence along each of these phenomenological dimensions we were able to generate 'synthetic VHs' that were characteristic of the VHs experienced by each aetiology. We verified the validity of this approach experimentally in two studies that examined the phenomenology of VHs in Neurodegenerative and CBS patients, and in people with recent psychedelic experience. These studies confirmed the existence of phenomenological differences across these three dimensions between groups, and crucially, found that the appropriate synthetic VHs were rated as being representative of each group's hallucinatory phenomenology. Together, our findings highlight the phenomenological diversity of VHs associated with distinct causal factors and demonstrate how a neural network model of visual phenomenology can successfully capture the distinctive visual characteristics of hallucinatory experience.

Neural Substrates of the Drift-Diffusion Model in Brain Disorders

Many studies on the drift-diffusion model (DDM) explain decision-making based on a unified analysis of both accuracy and response times. This review provides an in-depth account of the recent advances in DDM research which ground different DDM parameters on several brain areas, including the cortex and basal ganglia. Furthermore, we discuss the changes in DDM parameters due to structural and functional impairments in several clinical disorders, including Parkinson's disease, Attention Deficit Hyperactivity Disorder (ADHD), Autism Spectrum Disorders, Obsessive-Compulsive Disorder (OCD), and schizophrenia. This review thus uses DDM to provide a theoretical understanding of different brain disorders.

Impaired sensory evidence accumulation and network function in Lewy body dementia

Deficits in attention underpin many of the cognitive and neuropsychiatric features of Lewy body dementia. These attention-related symptoms remain difficult to treat and there are many gaps in our understanding of their neurobiology. An improved understanding of attention-related impairments can be achieved via mathematical modelling approaches, which identify cognitive parameters to provide an intermediate level between observed behavioural data and its underlying neural correlate. Here, we apply this approach to identify the role of impaired sensory evidence accumulation in the attention deficits that characterize Lewy body dementia. In 31 people with Lewy body dementia (including 13 Parkinson's disease dementia and 18 dementia with Lewy bodies cases), 16 people with Alzheimer's disease, and 23 healthy controls, we administered an attention task whilst they underwent functional 3 T MRI. Using hierarchical Bayesian estimation of a drift-diffusion model, we decomposed task performance into drift rate and decision boundary parameters. We tested the hypothesis that the drift rate-a measure of the quality of sensory evidence accumulation-is specifically impaired in Lewy body dementia, compared to Alzheimer's disease. We further explored whether trial-by-trial variations in the drift rate related to activity within the default and dorsal attention networks, to determine whether altered activity in these networks was associated with slowed drift rates in Lewy body dementia. Our results revealed slower drift rates in the Lewy body dementia compared to the Alzheimer's disease group, whereas the patient groups were equivalent for their decision boundaries. The patient groups were reduced relative to controls for both parameters. This highlights sensory evidence accumulation deficits as a key feature that distinguishes attention impairments in Lewy body dementia, consistent with impaired ability to efficiently process information from the environment to guide behaviour. We also found that the drift rate was strongly related to activity in the dorsal attention network across all three groups, whereas the Lewy body dementia group showed a divergent relationship relative to the Alzheimer's disease and control groups for the default network, consistent with altered default network modulation being associated with impaired evidence accumulation. Together, our findings reveal impaired sensory evidence accumulation as a specific marker of attention problems in Lewy body dementia, which may relate to large-scale network abnormalities. By identifying impairments in a specific sub-process of attention, these findings will inform future exploratory and intervention studies that aim to understand and treat attention-related symptoms that are a key feature of Lewy body dementia.

The computational neurology of movement under active inference

We propose a computational neurology of movement based on the convergence of theoretical neurobiology and clinical neurology. A significant development in the former is the idea that we can frame brain function as a process of (active) inference, in which the nervous system makes predictions about its sensory data. These predictions depend upon an implicit predictive (generative) model used by the brain. This means neural dynamics can be framed as generating actions to ensure sensations are consistent with these predictions-and adjusting predictions when they are not. We illustrate the significance of this formulation for clinical neurology by simulating a clinical examination of the motor system using an upper limb coordination task. Specifically, we show how tendon reflexes emerge naturally under the right kind of generative model. Through simulated perturbations, pertaining to prior probabilities of this model's variables, we illustrate the emergence of hyperreflexia and pendular reflexes, reminiscent of neurological lesions in the corticospinal tract and cerebellum. We then turn to the computational lesions causing hypokinesia and deficits of coordination. This in silico lesion-deficit analysis provides an opportunity to revisit classic neurological dichotomies (e.g. pyramidal versus extrapyramidal systems) from the perspective of modern approaches to theoretical neurobiology-and our understanding of the neurocomputational architecture of movement control based on first principles.

Systematic Parameter Reviews in Cognitive Modeling: Towards a Robust and Cumulative Characterization of Psychological Processes in the Diffusion Decision Model

Parametric cognitive models are increasingly popular tools for analyzing data obtained from psychological experiments. One of the main goals of such models is to formalize psychological theories using parameters that represent distinct psychological processes. We argue that systematic quantitative reviews of parameter estimates can make an important contribution to robust and cumulative cognitive modeling. Parameter reviews can benefit model development and model assessment by providing valuable information about the expected parameter space, and can facilitate the more efficient design of experiments. Importantly, parameter reviews provide crucial-if not indispensable-information for the specification of informative prior distributions in Bayesian cognitive modeling. From the Bayesian perspective, prior distributions are an integral part of a model, reflecting cumulative theoretical knowledge about plausible values of the model's parameters (Lee, 2018). In this paper we illustrate how systematic parameter reviews can be implemented to generate informed prior distributions for the Diffusion Decision Model (DDM; Ratcliff and McKoon, 2008), the most widely used model of speeded decision making. We surveyed the published literature on empirical applications of the DDM, extracted the reported parameter estimates, and synthesized this information in the form of prior distributions. Our parameter review establishes a comprehensive reference resource for plausible DDM parameter values in various experimental paradigms that can guide future applications of the model. Based on the challenges we faced during the parameter review, we formulate a set of general and DDM-specific suggestions aiming to increase reproducibility and the information gained from the review process.

Pareidolia in Schizophrenia and Bipolar Disorder

While there are many studies on pareidolia in healthy individuals and patients with schizophrenia, to our knowledge, there are no prior studies on pareidolia in patients with bipolar disorder. Accordingly, in this study, we, for the first time, measured pareidolia in patients with bipolar disorder (N = 50), and compared that to patients with schizophrenia (N = 50) and healthy controls (N = 50). We have used (a) the scene test, which consists of 10 blurred images of natural scenes that was previously found to produce illusory face responses and (b) the noise test which had 32 black and white images consisting of visual noise and 8 images depicting human faces; participants indicated whether a face was present on these images and to point to the location where they saw the face. Illusory responses were defined as answers when observers falsely identified objects that were not on the images in the scene task (maximum illusory score: 10), and the number of noise images in which they reported the presence of a face (maximum illusory score: 32). Further, we also calculated the total pareidolia score for each task (the sum number of images with illusory responses in the scene and noise tests). The responses were scored by two independent raters with an excellent congruence (kappa > 0.9). Our results show that schizophrenia patients scored higher on pareidolia measures than both healthy controls and patients with bipolar disorder. Our findings are agreement with prior findings on more impaired cognitive processes in schizophrenia than in bipolar patients.

Effortful control is associated with executive attention: A computational study

INTRODUCTION

Effortful control (EC) is the self-regulatory aspect of temperament that is thought to reflect the efficiency of executive attention (EA). Findings on relationship between EC and performance on EA tasks among adults are still contradictory. This study used a computational approach to clarify whether greater self-reported EC reflects better EA.

METHODS

Four hundred twenty-seven healthy subjects completed the Adult Temperament Questionnaires and the Attention Network Task-revised, a conflict resolution task that gauges EA as the flanker effect (FE), that is, the difference in performances between incongruent and congruent trials. Here we also employed a drift-diffusion model in which parameters reflecting the actual decisional process (drift rate) and the extra-decisional time are extracted for congruent and incongruent trials.

RESULTS

EC was not correlated with the FE computed with the classic approach, but correlated positively with drift rate for the incongruent trials, even when controlling for the drift rate in the congruent condition and the extra-decisional time in the incongruent condition.

CONCLUSION

This study demonstrates an association between self-reported EC and EA among adults. Specifically, EC is not associated with overall response facilitation but specifically with a greater ability to make goal-oriented decisions when facing conflicting information.

Visuo-Perceptual and Decision-Making Contributions to Visual Hallucinations in Mild Cognitive Impairment in Lewy Body Disease: Insights from a Drift Diffusion Analysis

Background: Visual hallucinations (VH) are a common symptom in dementia with Lewy bodies (DLB); however, their cognitive underpinnings remain unclear. Hallucinations have been related to cognitive slowing in DLB and may arise due to impaired sensory input, dysregulation in top-down influences over perception, or an imbalance between the two, resulting in false visual inferences. Methods: Here we employed a drift diffusion model yielding estimates of perceptual encoding time, decision threshold, and drift rate of evidence accumulation to (i) investigate the nature of DLB-related slowing of responses and (ii) their relationship to visuospatial performance and visual hallucinations. The EZ drift diffusion model was fitted to mean reaction time (RT), accuracy and RT variance from two-choice reaction time (CRT) tasks and data were compared between groups of mild cognitive impairment (MCI-LB) LB patients (n = 49) and healthy older adults (n = 25). Results: No difference was detected in drift rate between patients and controls, but MCI-LB patients showed slower non-decision times and boundary separation values than control participants. Furthermore, non-decision time was negatively correlated with visuospatial performance in MCI-LB, and score on visual hallucinations inventory. However, only boundary separation was related to clinical incidence of visual hallucinations. Conclusions: These results suggest that a primary impairment in perceptual encoding may contribute to the visuospatial performance, however a more cautious response strategy may be related to visual hallucinations in Lewy body disease. Interestingly, MCI-LB patients showed no impairment in information processing ability, suggesting that, when perceptual encoding was successful, patients were able to normally process information, potentially explaining the variability of hallucination incidence.

A Review of Multimodal Hallucinations: Categorization, Assessment, Theoretical Perspectives, and Clinical Recommendations

Hallucinations can occur in different sensory modalities, both simultaneously and serially in time. They have typically been studied in clinical populations as phenomena occurring in a single sensory modality. Hallucinatory experiences occurring in multiple sensory systems-multimodal hallucinations (MMHs)-are more prevalent than previously thought and may have greater adverse impact than unimodal ones, but they remain relatively underresearched. Here, we review and discuss: (1) the definition and categorization of both serial and simultaneous MMHs, (2) available assessment tools and how they can be improved, and (3) the explanatory power that current hallucination theories have for MMHs. Overall, we suggest that current models need to be updated or developed to account for MMHs and to inform research into the underlying processes of such hallucinatory phenomena. We make recommendations for future research and for clinical practice, including the need for service user involvement and for better assessment tools that can reliably measure MMHs and distinguish them from other related phenomena.

Reinforcement learning as an intermediate phenotype in psychosis? Deficits sensitive to illness stage but not associated with polygenic risk of schizophrenia in the general population

BACKGROUND

Schizophrenia is a complex disorder in which the causal relations between risk genes and observed clinical symptoms are not well understood and the explanatory gap is too wide to be clarified without considering an intermediary level. Thus, we aimed to test the hypothesis of a pathway from molecular polygenic influence to clinical presentation occurring via deficits in reinforcement learning.

METHODS

We administered a reinforcement learning task (Go/NoGo) that measures reinforcement learning and the effect of Pavlovian bias on decision making. We modelled the behavioural data with a hierarchical Bayesian approach (hBayesDM) to decompose task performance into its underlying learning mechanisms. Study 1 included controls (n = 29, F|M = 0.81), At Risk Mental State for psychosis (ARMS, n = 23, F|M = 0.35) and FEP (First-episode psychosis, n = 26, F|M = 0.18). Study 2 included healthy adolescents (n = 735, F|M = 1.06), 390 of whom had their polygenic risk scores for schizophrenia (PRSs) calculated.

RESULTS

Patients with FEP showed significant impairments in overriding Pavlovian conflict, a lower learning rate and a lower sensitivity to both reward and punishment. Less widespread deficits were observed in ARMS. PRSs did not significantly predict performance on the task in the general population, which only partially correlated with measures of psychopathology.

CONCLUSIONS

Reinforcement learning deficits are observed in first episode psychosis and, to some extent, in those at clinical risk for psychosis, and were not predicted by molecular genetic risk for schizophrenia in healthy individuals. The study does not support the role of reinforcement learning as an intermediate phenotype in psychosis.

Dissecting the cognitive phenotype of post-stroke fatigue using computerized assessment and computational modeling of sustained attention

Post-stroke fatigue (PSF) is prevalent among stroke patients, but its mechanisms are poorly understood. Many patients with PSF experience cognitive difficulties, but studies aiming to identify cognitive correlates of PSF have been largely inconclusive. With the aim of characterizing the relationship between subjective fatigue and attentional function, we collected behavioral data using the attention network test (ANT) and self-reported fatigue scores using the fatigue severity scale (FSS) from 53 stroke patients. In order to evaluate the utility and added value of computational modeling for delineating specific underpinnings of response time (RT) distributions, we fitted a hierarchical drift diffusion model (hDDM) to the ANT data. Results revealed a relationship between fatigue and RT distributions. Specifically, there was a positive interaction between FSS score and elapsed time on RT. Group analyses suggested that patients without PSF increased speed during the course of the session, while patients with PSF did not. In line with the conventional analyses based on observed RT, the best fitting hDD model identified an interaction between elapsed time and fatigue on non-decision time, suggesting an increase in time needed for stimulus encoding and response execution rather than cognitive information processing and evidence accumulation. These novel results demonstrate the significance of considering the sustained nature of effort when defining the cognitive phenotype of PSF, intuitively indicating that the cognitive phenotype of fatigue entails an increased vulnerability to sustained effort, and suggest that the use of computational approaches offers a further characterization of specific processes underlying behavioral differences.

Visual hallucinations and illusions in Parkinson's disease: the role of ocular pathology

INTRODUCTION

Whether different mechanisms, particularly ocular pathology, could lead to the emergence of visual hallucinations (VH) (defined as false perceptions with no external stimulus) versus visual illusions (VI) (defined as a misperception of a real stimulus) in Parkinson's disease (PD) remains debated. We assessed retinal, clinical and structural brain characteristics depending on the presence of VH or VI in PD.

METHODS

In this case-control study, we compared retinal thickness using optical coherence tomography (OCT), between PD patients with: VI (PD-I; n = 26), VH (PD-H; n = 28), and without VI or VH (PD-C; n = 28), and assessed demographic data, disease severity, treatment, anatomical and functional visual complaints, cognitive and visuo-perceptive functions and MRI brain volumetry for each group of PD patients.

RESULTS

Parafoveal retina was thinner in PD-H compared to PD-C (p = 0.005) and PD-I (p = 0.009) but did not differ between PD-I and PD-C (p = 0.85). Multivariate analysis showed that 1/retinal parafoveal thinning and total brain gray matter atrophy were independently associated with the presence of VH compared to PD-I; 2/retinal parafoveal thickness, PD duration, sleep quality impairment and total brain gray matter volume were independent factors associated with the presence of VH compared to PD-C; 3/anterior ocular abnormalities were the only factor independently associated with the presence of illusions compared to PD-C.

CONCLUSION

These findings reinforce the hypothesis that there may be different mechanisms contributing to VH and VI in PD, suggesting that these two entities may also have a different prognosis rather than simply lying along a continuous spectrum.

REGISTRATION NUMBER

Clinicaltrials.gov number NCT01114321.

Subjective estimates of uncertainty during gambling and impulsivity after subthalamic deep brain stimulation for Parkinson's disease

Subthalamic deep brain stimulation (DBS) for Parkinson’s disease (PD) may modulate chronometric and instrumental aspects of choice behaviour, including motor inhibition, decisional slowing, and value sensitivity. However, it is not well known whether subthalamic DBS affects more complex aspects of decision-making, such as the influence of subjective estimates of uncertainty on choices. In this study, 38 participants with PD played a virtual casino prior to subthalamic DBS (whilst ‘on’ medication) and again, 3-months postoperatively (whilst ‘on’ stimulation). At the group level, there was a small but statistically significant decrease in impulsivity postoperatively, as quantified by the Barratt Impulsiveness Scale (BIS). The gambling behaviour of participants (bet increases, slot machine switches and double or nothing gambles) was associated with this self-reported measure of impulsivity. However, there was a large variance in outcome amongst participants, and we were interested in whether individual differences in subjective estimates of uncertainty (specifically, volatility) were related to differences in pre- and postoperative impulsivity. To examine these individual differences, we fit a computational model (the Hierarchical Gaussian Filter, HGF), to choices made during slot machine game play as well as a simpler reinforcement learning model based on the Rescorla-Wagner formalism. The HGF was superior in accounting for the behaviour of our participants, suggesting that participants incorporated beliefs about environmental uncertainty when updating their beliefs about gambling outcome and translating these beliefs into action. A specific aspect of subjective uncertainty, the participant’s estimate of the tendency of the slot machine’s winning probability to change (volatility), increased subsequent to DBS. Additionally, the decision temperature of the response model decreased post-operatively, implying greater stochasticity in the belief-to-choice mapping of participants. Model parameter estimates were significantly associated with impulsivity; specifically, increased uncertainty was related to increased postoperative impulsivity. Moreover, changes in these parameter estimates were significantly associated with the maximum post-operative change in impulsivity over a six month follow up period. Our findings suggest that impulsivity in PD patients may be influenced by subjective estimates of uncertainty (environmental volatility) and implicate a role for the subthalamic nucleus in the modulation of outcome certainty. Furthermore, our work outlines a possible approach to characterising those persons who become more impulsive after subthalamic DBS, an intervention in which non-motor outcomes can be highly variable.

Disturbed time experience during and after psychosis

Disturbances in time experience have been argued to play a significant, if not causative role in the clinical presentation of schizophrenia. Phenomenological considerations suggest a fragmented or dis-articulated time experience causing both primary symptoms such as hallucinations, delusions, and self-disorders, as well as an intersubjective desynchronization. We employed content analysis on material collected from patients diagnosed with schizophrenia using the Time Questionnaire to generate hypotheses on possible disturbances of time experience in schizophrenia. As a key result we find evidence for the distinction between acute psychotic and post-psychotic syndromes. Acute psychosis is predominantly a disturbance of the passage of time, whereas the remission from psychosis is primarily defined by changes in the experience of the explicit structure of time integrating past, present, and future. We discuss our findings with regards to previous insights and observations on time experience and time perception. We suggest our findings hold significance for the diagnostic and therapeutic understanding of schizophrenia as well as for future integrative research on time experience in general.

Increased weighting on prior knowledge in Lewy body-associated visual hallucinations

Hallucinations are a common and distressing feature of many psychiatric and neurodegenerative conditions. In Lewy body disease, visual hallucinations are a defining feature, associated with worse outcomes; yet their mechanisms remain unclear and treatment options are limited. Here, we show that hallucinations in Lewy body disease are associated with altered integration of top-down predictions with incoming sensory evidence, specifically with an increased relative weighting of prior knowledge. We tested 37 individuals with Lewy body disease, 17 habitual hallucinators and 20 without hallucinations, and 20 age-matched healthy individuals. We employed an image-based learning paradigm to test whether people with Lewy body disease and visual hallucinations show higher dependence on prior knowledge. We used two-tone images that are difficult to disambiguate without any prior information but generate a strong percept when information is provided. We measured discrimination sensitivity before and after this information was provided. We observed that in people with Lewy body disease who experience hallucinations, there was greater improvement in discrimination sensitivity after information was provided, compared to non-hallucinators and controls. This suggests that people with Lewy body disease and hallucinations place higher relative weighting on prior knowledge than those who do not hallucinate. Importantly, increased severity of visual hallucinations was associated with an increased effect of prior knowledge. Together these findings suggest that visual hallucinations in Lewy body disease are linked to a shift towards top-down influences on perception and away from sensory evidence, perhaps due to an increase in sensory noise. This provides important mechanistic insights to how hallucinations develop in Lewy body disease, with potential for revealing new therapeutic targets.

Changes in structural network topology correlate with severity of hallucinatory behavior in Parkinson's disease

Inefficient integration between bottom-up visual input and higher order visual processing regions is implicated in visual hallucinations in Parkinson's disease (PD). Here, we investigated white matter contributions to this perceptual imbalance hypothesis. Twenty-nine PD patients were assessed for hallucinatory behavior. Hallucination severity was correlated to connectivity strength of the network using the network-based statistic approach. The results showed that hallucination severity was associated with reduced connectivity within a subnetwork that included the majority of the diverse club. This network showed overall greater between-module scores compared with nodes not associated with hallucination severity. Reduced between-module connectivity in the lateral occipital cortex, insula, and pars orbitalis and decreased within-module connectivity in the prefrontal, somatosensory, and primary visual cortices were associated with hallucination severity. Conversely, hallucination severity was associated with increased between- and within-module connectivity in the orbitofrontal and temporal cortex, as well as regions comprising the dorsal attentional and default mode network. These results suggest that hallucination severity is associated with marked alterations in structural network topology with changes in participation along the perceptual hierarchy. This may result in the inefficient transfer of information that gives rise to hallucinations in PD.

Hallucinations and Strong Priors

Hallucinations, perceptions in the absence of objectively identifiable stimuli, illustrate the constructive nature of perception. Here, we highlight the role of prior beliefs as a critical elicitor of hallucinations. Recent empirical work from independent laboratories shows strong, overly precise priors can engender hallucinations in healthy subjects and that individuals who hallucinate in the real world are more susceptible to these laboratory phenomena. We consider these observations in light of work demonstrating apparently weak, or imprecise, priors in psychosis. Appreciating the interactions within and between hierarchies of inference can reconcile this apparent disconnect. Data from neural networks, human behavior, and neuroimaging support this contention. This work underlines the continuum from normal to aberrant perception, encouraging a more empathic approach to clinical hallucinations.

Is There a Problem in the Laboratory?

Problem-solving research in the field of psychology has been closely linked to laboratory investigations throughout its development. However, there is a questionable conceptual assumption underlying this commitment to the laboratory, namely the assumption that one can reduce all problem-solving behavior to a cognitive mechanism. Upon validating this assumption from a phenomenological standpoint, doubts about its foundations emerge. For when we consider the experiential conditions that characterize a problematic situation, we come to determine several phenomenal aspects that are not taken into account in this approach. A phenomenologically revised notion of the problem therefore demands a modification of the scope of the empirical research. First, this paper investigates the configuration of the laboratory as an arena of experience based on Lewin's field theory. This investigation indicates instructions as a key component of the laboratory. Second, a phenomenological description proposes a novel understanding of the problem. In this part it is shown that it is wrong to presume that problematic situations can be evoked arbitrarily by instructions. Finally, further contemplations help outlining the empirical requirements for exhaustive research. They call for novel paradigms in empirical psychology, such as live streaming, which are more faithful to the phenomenology of problems.

Attention or salience?

While attention is widely recognised as central to perception, the term is often used to mean very different things. Prominent theories of attention - notably the premotor theory - relate it to planned or executed eye movements. This contrasts with the notion of attention as a gain control process that weights the information carried by different sensory channels. We draw upon recent advances in theoretical neurobiology to argue for a distinction between attentional gain mechanisms and salience attribution. The former depends upon estimating the precision of sensory data, while the latter is a consequence of the need to actively engage with the sensorium. Having established this distinction, we consider the intimate relationship between attention and salience.

Precision and False Perceptual Inference

Accurate perceptual inference fundamentally depends upon accurate beliefs about the reliability of sensory data. In this paper, we describe a Bayes optimal and biologically plausible scheme that refines these beliefs through a gradient descent on variational free energy. To illustrate this, we simulate belief updating during visual foraging and show that changes in estimated sensory precision (i.e., confidence in visual data) are highly sensitive to prior beliefs about the contents of a visual scene. In brief, confident prior beliefs induce an increase in estimated precision when consistent with sensory evidence, but a decrease when they conflict. Prior beliefs held with low confidence are rapidly updated to posterior beliefs, determined by sensory data. These induce much smaller changes in beliefs about sensory precision. We argue that pathologies of scene construction may be due to abnormal priors, and show that these can induce a reduction in estimated sensory precision. Having previously associated this precision with cholinergic signaling, we note that several neurodegenerative conditions are associated with visual disturbances and cholinergic deficits; notably, the synucleinopathies. On relating the message passing in our model to the functional anatomy of the ventral visual stream, we find that simulated neuronal loss in temporal lobe regions induces confident, inaccurate, empirical prior beliefs at lower levels in the visual hierarchy. This provides a plausible, if speculative, computational mechanism for the loss of cholinergic signaling and the visual disturbances associated with temporal lobe Lewy body pathology. This may be seen as an illustration of the sorts of hypotheses that may be expressed within this computational framework.

Computational Neuropsychology and Bayesian Inference

Computational theories of brain function have become very influential in neuroscience. They have facilitated the growth of formal approaches to disease, particularly in psychiatric research. In this paper, we provide a narrative review of the body of computational research addressing neuropsychological syndromes, and focus on those that employ Bayesian frameworks. Bayesian approaches to understanding brain function formulate perception and action as inferential processes. These inferences combine ‘prior’ beliefs with a generative (predictive) model to explain the causes of sensations. Under this view, neuropsychological deficits can be thought of as false inferences that arise due to aberrant prior beliefs (that are poor fits to the real world). This draws upon the notion of a Bayes optimal pathology – optimal inference with suboptimal priors – and provides a means for computational phenotyping. In principle, any given neuropsychological disorder could be characterized by the set of prior beliefs that would make a patient’s behavior appear Bayes optimal. We start with an overview of some key theoretical constructs and use these to motivate a form of computational neuropsychology that relates anatomical structures in the brain to the computations they perform. Throughout, we draw upon computational accounts of neuropsychological syndromes. These are selected to emphasize the key features of a Bayesian approach, and the possible types of pathological prior that may be present. They range from visual neglect through hallucinations to autism. Through these illustrative examples, we review the use of Bayesian approaches to understand the link between biology and computation that is at the heart of neuropsychology.