Abstract
We use the hierarchical nature of Bayesian perceptual inference to explain a fundamental aspect of the temporality of experience, namely the phenomenology of temporal flow. The explanation says that the sense of temporal flow in conscious perception stems from probabilistic inference that the present cannot be trusted. The account begins by describing hierarchical inference under the notion of prediction error minimization, and exemplifies distrust of the present within bistable visual perception and action initiation. Distrust of the present is then discussed in relation to previous research on temporal phenomenology. Finally, we discuss how there may be individual differences in the experience of temporal flow, in particular along the autism spectrum. The resulting view is that the sense of temporal flow in conscious perception results from an internal, inferential process.
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Notes
Recently, there has been a focus on the role of counterfactual processing in predictive processing, which has been used to explain the emergence of action guiding salience maps (Friston et al. 2012a), and phenomenological presence (Seth 2014), as well we aspects of social perception (Palmer et al. 2015a, b). This differs from the type of mechanism discussed here, which is not counterfactual in the same strict sense of off-line representation of subjunctive outcomes. A hierarchical system will distrust the present whether it can engage in counterfactual representations or not, though counterfactually supported salience maps will enhance the selection of new hypotheses.
The notion of distrusting the present is consistent with formal developments in dynamical systems theory. The state dependent dynamics of self-organising and autopoietic systems, systems like the brain, determine the transitions between states. These transitions, driven by dynamical instabilities, of the sort seen in chaotic itinerancy (Breakspear 2001), provoke not only an exquisite sensitivity to differences but importantly, a tendency to switch between states, seeking better explanations of the causes of its sensory inputs. The resulting self-organised criticality is not an imposed feature of the system but is a direct consequence of consideration of a hierarchical Bayesian prediction error minimization framework which is itself immersed in a causally deep and complex environment (Friston et al. 2012b; Friston et al. 2014). When an environment features hierarchically deep causes of sensory input, where complex and chaotic interactions furnish dynamic instabilities, the best approach to survive in such an environment is, inevitably and in line with the Good Regulator theorem (Conant and Ashby 1970), to adopt the best model of that environment (Friston 2013).
There is a complimentary approach to the notion of expecting change, expressed in terms of salience and reward. Irrespective of the presence or absence of expected external change, an organism may deplete resources in a current state (or attractor). This reduces the salience of the current state, since it now holds little subjective relevance or reward. This relates to the exploitation-exploration distinction, which has also been discussed from the approximate Bayesian inference (or free energy) perspective (e.g., Friston et al. 2015). In the prediction error minimization framework, reward is the absence of prediction error, so subjective relevance (or reward) is just being in states with low prediction error. Essentially, the agent believes it will occupy such states (this is core to the free energy principle). So if the agent has depleted resources it has minimized prediction error. Now the question arises what the agent should do next. It is in fact in a high reward/low prediction error state, so it could be expected to do nothing, and yet agents always tend to move to a new state when resources are depleted. This can only be because the agent believes that the world is a changing place such that its own state will be threatened by doing nothing, and here ‘threat’ means simply that prediction error will be expected to increase. Hence it moves to a new state. (This in effect rehearses a version of the ‘dark room problem’ which is central to appreciating active inference under the free energy principle (Friston et al. 2012c). Even if the world is (contrary to fact) not changing at the moment, the agent believes it is or will be changing soon. Most basically, agents who deplete resources and stay put will experience increase of interoceptive prediction error (relative to expected set states). We thank a reviewer for raising this issue about action and reward, which we thus believe can be incorporated into our account.
More formally, Grush’s proposals for how to augment the classic Kalman filter pushes the account into territory that is occupied by contemporary literature on predictive processing, in particular relating to the free energy principle. Here, generalized filtering is adopted rather than vanilla Kalman filters, or Kalman-Bucy filters, which allows addressing issues concerning linear, quadratic cost functions, and discrete estimates rather than trajectories (Friston et al. 2010).
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Hohwy, J., Paton, B. & Palmer, C. Distrusting the present. Phenom Cogn Sci 15, 315–335 (2016). https://doi.org/10.1007/s11097-015-9439-6
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DOI: https://doi.org/10.1007/s11097-015-9439-6