This commentary takes a closer look at how constructive models of subjective perception," referred to by Collerton et al. (sect. 2), might contribute to the Perception and Attention Deficit (PAD) model. It focuses oil the neuronal mechanisms that could mediate hallucinations, or false inference - in particular, the role of cholinergic systems in encoding uncertainty in the context of hierarchical Bayesian models of perceptual inference Friston 20021); Yu & Dayan 2002)

Friston, KJ

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Hallucinations and perceptual inferenceKarl J. FristonDepartment of Imaging Neuroscience, Institute of Neurology, London WC1N3BG, United Kingdom. k.friston@fil.ion.ucl.ac.ukhttp://www.fil.ion.ucl.ac.ukAbstract: This commentary takes a closer look at how “constructive mod-els of subjective perception,” referred to by Collerton et al. (sect. 2), mightcontribute to the Perception and Attention Deficit (PAD) model. It fo-cuses on the neuronal mechanisms that could mediate hallucinations, orfalse inference – in particular, the role of cholinergic systems in encodinguncertainty in the context of hierarchical Bayesian models of perceptualinference (Friston 2002b; Yu & Dayan 2002).Collerton et al. provide a compelling synthesis implicating cholin-ergic dysfunction in the aetiology of recurrent complex visual hal-lucinations (RCVH). Furthermore, they observe “that both sen-sory release and top-down activation are necessary, but neither initself is sufficient to cause high rates of RCVH” (sect. 6.3, para. 3).This fits very comfortably with models of perceptual inferencebased on hierarchical Bayes, in which cholinergic mechanismsmay balance bottom-up sensory evidence and top-down priors byencoding their relative uncertainty or precision. In short, cholin-ergic dysfunction may result in a failure to properly integrate sen-sory information and prior expectations. In what follows, I try toexplain how this might happen.Perceptual inference is the same as statistical inference andCommentary/Collerton et al.: A novel Perception and Attention Deficit model for recurrent complex visual hallucination764 BEHAVIORAL AND BRAIN SCIENCES (2005) 28:6rests on the probability density of the causes of sensory informa-tion (i.e., the conditional probability). In classical inference, using,say, t-tests, inference is based on two things: (1) an estimate of theeffect and (2) the standard error or uncertainty about that esti-mate. The t-statistic is simply the ratio of these two quantities. Thebasic idea here is that hallucinations can be regarded as false in-ference that arises not because of impaired estimation (i.e., sen-sation) but a failure to encode the uncertainty. In the t-test exam-ple, this might mean the standard error was always too small,leading to false inference based on pathologically large t-values.How might this happen in the brain?Current thinking in computational neuroscience and machinelearning points to hierarchical Bayes as the best candidate for un-derstanding perception. I have introduced the notion of empiricalBayes in this context: empirical Bayes using the conditional inde-pendence  among  hierarchical  levels  to  form  empirical  priorsbased on the sensory data. This means (almost paradoxically) thatcortical hierarchies can construct their own priors, where eachlevel of the hierarchy is subject to constraints or priors from thelevel above (top-down effects) when accounting for sensory evi-dence from below (bottom-up effects). There are many issues thatattend this theoretical perspective (see Friston 2002b for review).Here I focus on the putative role of cholinergic neurotransmissionin the genesis of hallucinations.Mathematically, neuronal dynamics and synaptic efficacy areconsidered to minimise something called the free energy(F, a con-cept from statistical physics). The quantities that minimise thefree energy are the conditional density q(n) of the causes n of sen-sory input (e.g., a high-level representation of a face) and somehyperparameters l encoding the uncertainty or noise. These twoquantities correspond loosely to the numerator and denominatorof the t-statistic above and are updated in two iterated steps: theE-step and the M-step. This is known as expectation maximisationin statistics.E q(n)   mq in FM l   ml in FFor a hierarchical model, the E- and M-steps for the i-th level canbe implemented with the following descent scheme, for any gen-erative or constructive causal model ni   gi(ni+1) under Gaussianassumptions:This can be implemented in a simple neuronal architecture of thesort shown in Figure 1. Here the conditional density is repre-sented in terms of its average or expectation n ˆi and covariance  i,i.e., q(ni)   N(n ˆi,  i) wherewhich  is  an  implicit  function  of  the  hyperparameters.  In  thisscheme, the quantities n ˆiand prediction error  icorrespond to theactivity of two neuronal subpopulations, whereas the hyperparam-eters li are encoded by the synaptic efficacy of lateral connec-tions.1Note that this scheme converges when n ˆicannot further re-duce prediction error and   i/ niT  i   0. In Friston (2002b) Idiscuss the potential role of cholinergic neurotransmission in me-diating the M-step. A related theme, using a different perspective,is discussed in Yu and Dayan (2002). What would happen if thehyperparameters were encoded improperly with cholinergic dys-function?A failure to optimise the hyperparameters will produce an in-appropriate balance between sensory and prior influences on theconditional  expectation  of  what  caused  any  sensation.  This  isshown schematically in Figure 2. Here, we assume the deficit pro-duces hyperparameters that fail to encode uncertainty in the pri-ors. This means too much weight is afforded to the prior expecta-tion from supraordinate cortical levels, and false inference ensues.Collerton et al. discuss a similar notion from the point of view ofa “failure to select the correct proto-object in the PAD model”=∂∂∂∂++− ∑ξνξνλ iTiiii iI () 1 2 1E∂∂=−∂∂=−∂∂−∂∂=−−ˆˆννξνξξνξξiiiTiiiTiiitF 11ν ννλ ξλλξλξii i i iiiiTigtF−−∂∂=−∂∂=−∂∂−+ (ˆ ) 1M ( () 1 1 + − λiCommentary/Collerton et al.: A novel Perception and Attention Deficit model for recurrent complex visual hallucinationBEHAVIORAL AND BRAIN SCIENCES (2005) 28:6 765Figure 1 (Friston). The top panel is a schematic showing twoneuronal subpopulations representing the conditional expectationof sensory causes for a single cortical level and the influences theyare subject to. The bottom panel shows the implicit probabilitydensities encoded by these neuronal activities and synaptic effica-cies after convergence. Note that the uncertainty or width of thesedensities is determined by the hyperparameters. The conditionaldensity, upon which inference is based, is drawn in a solid line.Figure 2 (Friston). A schematic showing one way in which hal-lucinations could occur. In this example, the hyperparameter en-coding prior uncertainty has been made too small li r l ˜i, result-ing in overconfidence in the priors and a false or hallucinatoryconditional expectation.(sect. 7.4.2, para. 2) when cholinergic inhibition leads to incorrect“pattern matching.”The mechanistic understanding afforded by this computationalapproach can usefully account for many observations made byCollerton et al. For example, “Either impaired attention [i.e., priorexpectations] or impaired sensory activation [i.e., evidence] alonewill rarely produce hallucinations” (sect. 7.3, para. 1). It is their re-lationship that defines a hallucination. In this sense, the integra-tion, through the conditional density, is the key mechanism in per-ception  and  this  integration  may  depend  on  the  integrity  ofcholinergic mechanisms. The false learning associated with moreenduring changes mediated by the M-step may improperly pairsensory contexts with high-level representations leading to “thesame image being triggered again and may account for the repeti-tion of specific images” (sect. 7.5.1, last para.). In empirical Bayesthe priors are driven by prediction errors from the level below (seeFig. 1). In the absence of sensory input, priors are not induced.This may account for what the target article describes as “an oth-erwise puzzling feature of hallucinations – that they disappear oneye closure or on complete visual loss” (sect. 7.5.2, para. 2).In terms of clinical neuroscience, there are remarkable overlapsbetween the PAD model for hallucinations and the disconnectionhypothesis for schizophrenia, a disorder associated with halluci-nations. In terms of functional anatomy, Collerton et al. note that“Object-based attention depends primarily on the function of lat-eral frontal cortex, and object perception depends primarily onthe ventral visual stream” (sect. 7.3, point 3). They later cite evi-dence from functional imaging of patients who are prone to hal-lucinations. In fact, the disconnection hypothesis was based onearly observations of abnormal coupling between left dorsolateralprefrontal cortex and posterior temporal regions, as measuredwith positron emission tomography in schizophrenics (see Friston1998 for review).The disconnection hypothesis posits abnormal functional inte-gration (at the synaptic level) as the primary pathophysiologicalmechanism in schizophrenia. The premise is that synaptic plastic-ity  is  regulated  abnormally  during  emotional  and  perceptuallearning. The abnormal regulation probably involves dopaminer-gic dysfunction in emotional learning or operant conditioning (i.e.,the formation of stimulus-response links) and cholinergic dys-function in perceptual learning (i.e., the formation of stimulus-stimulus associations). Exactly the same neurotransmitters are im-plicated by Collerton et al. in RCVH: “pharmacological data so faravailable indicate a primary role for cholinergic and secondary rolefor dopaminergic dysfunction in the aetiology of RCVH” (sect.3.2, last para.). However, they later note “that dopamine receptorsare not prevalent in visual processing areas (whereas muscariniccholinergic receptors are)” (sect. 7.4.2, para. 2). This is consistentwith the conclusion of a recent editorial on disconnection and cog-nitive dysmetria in schizophrenia: “In short, normal interactionsbetween dopamine and the cellular or synaptic mechanisms re-sponsible  for  plasticity  are  essential  for  emotional  learning,whereas the interaction between cholinergic neurotransmissionand associative plasticity is important for perceptual learning”(Friston 2005). Although Collerton et al. state, “Eye disease andschizophrenia pose greater challenges to our model” (sect. 7.6.2.2,para. 5), there are encouraging and important points of contact be-tween  the  PAD  model  and  theoretical  treatments  of  cerebralpathology in schizophrenia.ACKNOWLEDGMENTThe Wellcome Trust funded this work.NOTE1. In this summary I have assumed that the parameters of the genera-tive model of how sensory inputs are caused have already been learned (inthe M-step). These parameters are encoded by the synaptic efficacy of for-ward and backward connections linking levels.Commentary/Collerton et al.: A novel Perception and Attention Deficit model for recurrent complex visual hallucination766 BEHAVIORAL AND BRAIN SCIENCES (2005) 28:6

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