Impulsivity and Active Inference

Mirza, M. Berk; Adams, Rick A.; Parr, Thomas; Friston, Karl J.

doi:10.1162/jocn_a_01352

Cited by 13 publications

(10 citation statements)

References 65 publications

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“…We have not attempted to distinguish the potential causes of aberrant exploratory behaviours in ASD and schizophrenia. Nevertheless, this MDP model of active inference has the potential to differentiate between abnormal behaviours with distinct causes 70 .…”

Section: Discussionmentioning

confidence: 99%

Introducing a Bayesian model of selective attention based on active inference

Mirza

Adams

Friston

et al. 2019

Sci Rep

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Information gathering comprises actions whose (sensory) consequences resolve uncertainty (i.e., are salient). In other words, actions that solicit salient information cause the greatest shift in beliefs (i.e., information gain) about the causes of our sensations. However, not all information is relevant to the task at hand: this is especially the case in complex, naturalistic scenes. This paper introduces a formal model of selective attention based on active inference and contextual epistemic foraging. We consider a visual search task with a special emphasis on goal-directed and task-relevant exploration. In this scheme, attention modulates the expected fidelity (precision) of the mapping between observations and hidden states in a state-dependent or context-sensitive manner. This ensures task-irrelevant observations have little expected information gain, and so the agent – driven to reduce expected surprise (i.e., uncertainty) – does not actively seek them out. Instead, it selectively samples task-relevant observations, which inform (task-relevant) hidden states. We further show, through simulations, that the atypical exploratory behaviours in conditions such as autism and anxiety may be due to a failure to appropriately modulate sensory precision in a context-specific way.

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Section: Discussionmentioning

confidence: 99%

Introducing a Bayesian model of selective attention based on active inference

Mirza

Adams

Friston

et al. 2019

Sci Rep

Self Cite

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“…Other fronto-striatal disorders. Recent simulation work has shown that transition uncertainty is also one potential cause of impulsive behavior, specifically delay discounting (Mirza, Adams, Parr, & Friston, 2019). Impulsivity is one of the core features of attention deficit hyperactivity disorder (ADHD), which also exhibits partial epidemiological and neurobiological overlap with OCD (Brem, Grünblatt, Drechsler, Riederer, & Walitza, 2014).…”

Section: Impairments In Bayesian Inference In Other Disordersmentioning

confidence: 99%

Searching for an anchor in an unpredictable world: A computational model of obsessive compulsive disorder.

Fradkin

Adams

Parr

et al. 2020

Psychological Review

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In this article, we develop a computational model of obsessive compulsive disorder (OCD). We propose that OCD is characterized by a difficulty in relying on past events to predict the consequences of patients' own actions and the unfolding of possible events. Clinically, this corresponds both to patients' difficulty in trusting their own actions (and therefore repeating them), and to their common preoccupation with unlikely chains of events. Critically, we develop this idea on the basis of the well-developed framework of the Bayesian brain, where this impairment is formalized as excessive uncertainty regarding state transitions. We illustrate the validity of this idea using quantitative simulations and use these to form specific empirical predictions. These predictions are evaluated in relation to existing evidence, and are used to delineate directions for future research. We show how seemingly unrelated findings and phenomena in OCD can be explained by the model including: a persistent experience that actions were not adequately performed and a tendency to repeat actions; excessive information gathering (i.e. checking); indecisiveness and pathological doubt; overreliance on habits at the expense of goal-directed behavior; and over-responsiveness to sensory stimuli, thoughts, and feedback. We discuss the relationship and interaction between our model and other prominent models of OCD, including models focusing on harm-avoidance, not-just-right experiences, or impairments in goal-directed behavior. Finally, we outline potential clinical implications and suggest lines for future research.

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“…The second aspect of the computational perspective that is especially relevant to the present manuscript has to do with the need for the (both phylogenetic and ontogenetic) calibration of several high-level prior expectations about the general structure of the environment. In the context of this paper, the most relevant class of such prior expectations that must be learned early in development has been variably referred to as beliefs about "transition precision" or "volatility" (Lawson et al 2017;Mirza et al 2019). In brief, these parameters reflect how predictable the regularities within the environment are over a given temporal scale.…”

Section: Computational Neurosciencementioning

confidence: 99%