A dual role for prediction error in associative learning

Confronted with a rich sensory environment, the brain must learn statistical regularities across sensory domains to construct causal models of the world. Here, we used functional magnetic resonance imaging and dynamic causal modeling (DCM) to furnish neurophysiological evidence that statistical associations are learnt, even when task-irrelevant. Subjects performed an audio-visual target-detection task while being exposed to distractor stimuli. Unknown to them, auditory distractors predicted the presence or absence of subsequent visual distractors. We modeled incidental learning of these associations using a Rescorla--Wagner (RW) model. Activity in primary visual cortex and putamen reflected learning-dependent surprise: these areas responded progressively more to unpredicted, and progressively less to predicted visual stimuli. Critically, this prediction-error response was observed even when the absence of a visual stimulus was surprising. We investigated the underlying mechanism by embedding the RW model into a DCM to show that auditory to visual connectivity changed significantly over time as a function of prediction error. Thus, consistent with predictive coding models of perception, associative learning is mediated by prediction-error dependent changes in connectivity. These results posit a dual role for prediction-error in encoding surprise and driving associative plasticity

Evidence for surprise minimization over value maximization in choice behavior

Classical economic models are predicated on the idea that the ultimate aim of choice is to maximize utility or reward. In contrast, an alternative perspective highlights the fact that adaptive behavior requires agents' to model their environment and minimize surprise about the states they frequent. We propose that choice behavior can be more accurately accounted for by surprise minimization compared to reward or utility maximization alone. Minimizing surprise makes a prediction at variance with expected utility models; namely, that in addition to attaining valuable states, agents attempt to maximize the entropy over outcomes and thus 'keep their options open'. We tested this prediction using a simple binary choice paradigm and show that human decision-making is better explained by surprise minimization compared to utility maximization. Furthermore, we replicated this entropy-seeking behavior in a control task with no explicit utilities. These findings highlight a limitation of purely economic motivations in explaining choice behavior and instead emphasize the importance of belief-based motivations

CMR findings in patients with hypertrophic cardiomyopathy and atrial fibrillation

<p>Abstract</p> <p>Objectives</p> <p>We sought to evaluate the relation between atrial fibrillation (AF) and the extent of myocardial scarring together with left ventricular (LV) and atrial parameters assessed by late gadolinium-enhancement (LGE) cardiovascular magnetic resonance (CMR) in patients with hypertrophic cardiomyopathy (HCM).</p> <p>Background</p> <p>AF is the most common arrhythmia in HCM. Myocardial scarring is also identified frequently in HCM. However, the impact of myocardial scarring assessed by LGE CMR on the presence of AF has not been evaluated yet.</p> <p>Methods</p> <p>87 HCM patients underwent LGE CMR, echocardiography and regular ECG recordings. LV function, volumes, myocardial thickness, left atrial (LA) volume and the extent of LGE, were assessed using CMR and correlated to AF. Additionally, the presence of diastolic dysfunction and mitral regurgitation were obtained by echocardiography and also correlated to AF.</p> <p>Results</p> <p>Episodes of AF were documented in 37 patients (42%). Indexed LV volumes and mass were comparable between HCM patients with and without AF. However, indexed LA volume was significantly higher in HCM patients with AF than in HCM patients without AF (68 ± 24 ml·m^-2versus 46 ± 18 ml·m^-2, p = 0.0002, respectively). The mean extent of LGE was higher in HCM patients with AF than those without AF (12.4 ± 14.5% versus 6.0 ± 8.6%, p = 0.02). When adjusting for age, gender and LV mass, LGE and indexed LA volume significantly correlated to AF (r = 0.34, p = 0.02 and r = 0.42, p < 0.001 respectively). By echocardiographic examination, LV diastolic dysfunction was evident in 35 (40%) patients. Mitral regurgitation greater than II was observed in 12 patients (14%). Multivariate analysis demonstrated that LA volume and presence of diastolic dysfunction were the only independent determinant of AF in HCM patients (p = 0.006, p = 0.01 respectively). Receiver operating characteristic curve analysis indicated good predictive performance of LA volume and LGE (AUC = 0.74 and 0.64 respectively) with respect to AF.</p> <p>Conclusion</p> <p>HCM patients with AF display significantly more LGE than HCM patients without AF. However, the extent of LGE is inferior to the LA size for predicting AF prevalence. LA dilation is the strongest determinant of AF in HCM patients, and is related to the extent of LGE in the LV, irrespective of LV mass.</p

Optimal inference with suboptimal models:Addiction and active Bayesian inference

When casting behaviour as active (Bayesian) inference, optimal inference is defined with respect to an agent's beliefs - based on its generative model of the world. This contrasts with normative accounts of choice behaviour, in which optimal actions are considered in relation to the true structure of the environment - as opposed to the agent's beliefs about worldly states (or the task). This distinction shifts an understanding of suboptimal or pathological behaviour away from aberrant inference as such, to understanding the prior beliefs of a subject that cause them to behave less 'optimally' than our prior beliefs suggest they should behave. Put simply, suboptimal or pathological behaviour does not speak against understanding behaviour in terms of (Bayes optimal) inference, but rather calls for a more refined understanding of the subject's generative model upon which their (optimal) Bayesian inference is based. Here, we discuss this fundamental distinction and its implications for understanding optimality, bounded rationality and pathological (choice) behaviour. We illustrate our argument using addictive choice behaviour in a recently described 'limited offer' task. Our simulations of pathological choices and addictive behaviour also generate some clear hypotheses, which we hope to pursue in ongoing empirical work

Joint-action coordination in transferring objects

Here we report a study of joint-action coordination in transferring objects. Fourteen dyads were asked to repeatedly reposition a cylinder in a shared workspace without using dialogue. Variations in task constraints concerned the size of the two target regions in which the cylinder had to be (re)positioned and the size and weight of the transferred cylinder. Movements of the wrist, index finger and thumb of both actors were recorded by means of a 3D motion-tracking system. Data analyses focused on the interpersonal transfer of lifting-height and movement-speed variations. Whereas the analyses of variance did not reveal any interpersonal transfer effects targeted data comparisons demonstrated that the actor who fetched the cylinder from where the other actor had put it was systematically less surprised by cylinder-weight changes than the actor who was first confronted with such changes. In addition, a moderate, accuracy-constraint independent adaptation to each other’s movement speed was found. The current findings suggest that motor resonance plays only a moderate role in collaborative motor control and confirm the independency between sensorimotor and cognitive processing of action-related information

Losing ourselves:Active inference, depersonalization, and meditation

This is the final version. Available on open access from Frontiers Media via the DOI in this recordDisruptions in the ordinary sense of selfhood underpin both pathological and “enlightened” states of consciousness. People suffering from depersonalization can experience the loss of a sense of self as devastating, often accompanied by intense feelings of alienation, fear, and hopelessness. However, for meditative contemplatives from various traditions, “selfless” experiences are highly sought after, being associated with enduring peace and joy. Little is understood about how these contrasting dysphoric and euphoric experiences should be conceptualized. In this paper, we propose a unified account of these selfless experiences within the active inference framework. Building on our recent active inference research, we propose an account of the experiences of selfhood as emerging from a temporally deep generative model. We go on to develop a view of the self as playing a central role in structuring ordinary experience by “tuning” agents to the counterfactually rich possibilities for action. Finally, we explore how depersonalization may result from an inferred loss of allostatic control and contrast this phenomenology with selfless experiences reported by meditation practitioners. We will show how, by beginning with a conception of self-modeling within an active inference framework, we have available to us a new way of conceptualizing the striking experiential similarities and important differences between these selfless experiences within a unifying theoretical framework. We will explore the implications for understanding and treating dissociative disorders, as well as elucidate both the therapeutic potential, and possible dangers, of meditation.European Union Horizon 202

Comprehensive review:Computational modelling of Schizophrenia

Computational modelling has been used to address: (1) the variety of symptoms observed in schizophrenia using abstract models of behavior (e.g. Bayesian models - top-down descriptive models of psychopathology); (2) the causes of these symptoms using biologically realistic models involving abnormal neuromodulation and/or receptor imbalance (e.g. connectionist and neural networks - bottom-up realistic models of neural processes). These different levels of analysis have been used to answer different questions (i.e. understanding behavioral vs. neurobiological anomalies) about the nature of the disorder. As such, these computational studies have mostly supported diverging hypotheses of schizophrenia's pathophysiology, resulting in a literature that is not always expanding coherently. Some of these hypotheses are however ripe for revision using novel empirical evidence.Here we present a review that first synthesizes the literature of computational modelling for schizophrenia and psychotic symptoms into categories supporting the dopamine, glutamate, GABA, dysconnection and Bayesian inference hypotheses respectively. Secondly, we compare model predictions against the accumulated empirical evidence and finally we identify specific hypotheses that have been left relatively under-investigated

Motor imagery and action observation: cognitive tools for rehabilitation

Rehabilitation, for a large part may be seen as a learning process where old skills have to be re-acquired and new ones have to be learned on the basis of practice. Active exercising creates a flow of sensory (afferent) information. It is known that motor recovery and motor learning have many aspects in common. Both are largely based on response-produced sensory information. In the present article it is asked whether active physical exercise is always necessary for creating this sensory flow. Numerous studies have indicated that motor imagery may result in the same plastic changes in the motor system as actual physical practice. Motor imagery is the mental execution of a movement without any overt movement or without any peripheral (muscle) activation. It has been shown that motor imagery leads to the activation of the same brain areas as actual movement. The present article discusses the role that motor imagery may play in neurological rehabilitation. Furthermore, it will be discussed to what extent the observation of a movement performed by another subject may play a similar role in learning. It is concluded that, although the clinical evidence is still meager, the use of motor imagery in neurological rehabilitation may be defended on theoretical grounds and on the basis of the results of experimental studies with healthy subjects

Observing the Observer (I): Meta-Bayesian Models of Learning and Decision-Making

In this paper, we present a generic approach that can be used to infer how subjects make optimal decisions under uncertainty. This approach induces a distinction between a subject's perceptual model, which underlies the representation of a hidden "state of affairs" and a response model, which predicts the ensuing behavioural (or neurophysiological) responses to those inputs. We start with the premise that subjects continuously update a probabilistic representation of the causes of their sensory inputs to optimise their behaviour. In addition, subjects have preferences or goals that guide decisions about actions given the above uncertain representation of these hidden causes or state of affairs. From a Bayesian decision theoretic perspective, uncertain representations are so-called "posterior" beliefs, which are influenced by subjective "prior" beliefs. Preferences and goals are encoded through a "loss" (or "utility") function, which measures the cost incurred by making any admissible decision for any given (hidden) state of affair. By assuming that subjects make optimal decisions on the basis of updated (posterior) beliefs and utility (loss) functions, one can evaluate the likelihood of observed behaviour. Critically, this enables one to "observe the observer", i.e. identify (context-or subject-dependent) prior beliefs and utility-functions using psychophysical or neurophysiological measures. In this paper, we describe the main theoretical components of this meta-Bayesian approach (i.e. a Bayesian treatment of Bayesian decision theoretic predictions). In a companion paper ('Observing the observer (II): deciding when to decide'), we describe a concrete implementation of it and demonstrate its utility by applying it to simulated and real reaction time data from an associative learning task