Page | 48 Friston’s account is one of information theoretic free energy but what might the motivation be to reduce free energy, or prediction error, in the brain? One response might be as follows: processing information is costly in the
computational system and this cost increases when there is a large amount of information that requires sophisticated processing. Minimizing prediction error is in the interest of the living organism because it reduces the computational cost of processing sensory input. Computational cost is reduced when prediction error is minimized because what is transmitted is the discrepancy between the predicted and input signal. If the prediction is good, the prediction error is small and less taxing to process. Having good representations of the world and efficient responses to the environment is important to a living organism and is affected by prediction error which needs to be minimized (Clark 2013: 187). The result of unsuccessful minimization of free energy is ineffective mechanisms for action and perception (Friston & Stephan 2007: 428 - 429). If an organism cannot behave appropriately in its environment or fails to respond to threats in an efficient and cost effective manner, the organism will not survive. Survival of the individual animal depends on behaviour that allows the animal to reproduce, feed and protect itself in an efficient and cost-effective manner. According to Friston and Clark, this is successfully realized through the minimization of free energy or prediction error. Minimizing free energy, according to Friston and Clark, is “...a necessary, if not sufficient, characteristic of evolutionary successful systems.” (Friston & Stephan 2007: 428)
Page | 49 Sterelny offer evolutionary accounts of cognition and action that provide insight into why particular actions are selected at particular times. They propose that actions are selected because it enables an animal to deal with cognitive
complexity and enables the animal to be more efficient in its responses to the environment. Sterelny proposes that there are distinct levels of complexity in cognition, and offers numerous examples of the architecture and mechanisms involved in simple action and response mechanisms. His project, however, lacks an account of the architecture and mechanisms involved in more complex cognitive systems, such as decoupled representation. In other words, Sterelny’s account does not offer a solution to the question, how are actions selected? In response to the absence of an account of cognitive architecture, I propose the predictive processing account, recently examined by Clark in a Behavioural Brain Sciences target article. The predictive processing account offers a general
account of cognition and offers insight into the mechanisms involved in cognition and action. Clark does not overtly consider the processes and mechanisms involved in action selection but his account makes three claims about the nature of cognition. First, the predictive processing account proposes that cognition is bidirectional in nature which means that cognition is a constant interaction between top-down information and bottom-up stimuli. Perception, Clark claims, occurs through the encounter between bottom-up input and top-down
predictions; this process of interaction between bottom-up and top-down information occurs at various levels in cognition. This is the second property of cognition put forth by the predictive processing account, cognition is hierarchical in nature. Finally, the account proposes that the processing of information is Bayesian in nature; this concerns the selection of predictions in higher levels of cognition about the causes of stimuli. There is good reason to support these claims regarding the architecture and nature of cognition given experiments such as binocular rivalry which involve an experiment where the bottom-up incoming stimuli is constant but the top-down predictions alternate. Clark’s
Page | 50 account provides insight that can be used to develop a theory of cognition about both action selection and the mechanisms and processes involved in cognition
The predictive processing account of cognition also makes an important claim about the function of cognition. Friston and Clark propose that the function of cognition is to minimize free energy; in the neural domain, free energy is reported by prediction error. It is the main function of the cognitive system to reduce prediction error, which is the discrepancy that arises as a result of the interaction between bottom-up input and top-down predictions; prediction error reports free energy in the brain. Friston proposes that living organisms have a biological imperative to minimize free energy; he also claims that adaptive fitness and negative free energy are considered by some to be the same thing (Friston et al. 2012: 2). Friston goes on to say that if a biological system (a living organism) fails to minimize free energy, it will encounter interactions with the environment that may lead to its demise (Friston & Stephan 2007: 434). Negative free energy may simply be understood as the state in which an agent’s
expectations are met or when there is a lack of surprisal. It is therefore the biological imperative of living organisms to reduce prediction error and minimize free energy because this increases adaptive fitness. Prediction error
minimization can take place in two ways. First, the prediction error can be fed backward to hypotheses in higher levels which are then updated to decrease prediction error in the future. This follows the same logic as a Kalman filter that take into account different variables when forming predictions about unknown stimuli (Friston 2002; Grush 2004; Rao & Ballard 1999). The second way in which prediction error can be minimized is through action. Living organisms act on the environment to reduce prediction error by moving through time and space in ways that fulfil its expectations. From this follows that perception and action are driven by predictions and expectations because the main function of perception
Page | 51 and action is to fulfil expectations. In the next few paragraphs, I discuss an
objection to the claim made by the predictive processing account and, thereafter, examine the responses by Karl Friston and Andy Clark to this objection known as the dark room problem.