Agent Detection

Agent detection bias, also known as illusory agency detection, social cognition hypertrophy, or overactive agent detection device (HADD), is the tendency to mistakenly believe that phenomena can be explained in terms of a conscious agent. Agent discovery is the tendency of animals, including humans, to assume the deliberate intervention of an intelligent or intelligent agent in situations that may or may not include it. Gray and Wegner also argued that the capture of an agent is likely “the basis for human belief in God,” but “mere over-attribution of an agent cannot fully explain belief in God …” because the human ability to form a theory of reason and that they call the “existential theory of mind”, it is also necessary to “give us the basic cognitive ability to comprehend God. Moreover, measures of agent perception and intentionality were not associated with individual differences in supernatural beliefs, although they were associated with negativity of prejudice. [Sources: 1, 2, 6]

The evolutionary and neurocognitive considerations discussed here indicate that human behavioral biases are encoded in the pre-motor mechanism, re-identifying an object through observation gaps, and overcoming it needs to be constructed, also as part of the pre-motor system, similar to replacing the agent with a mechanism Mapping structure. As the generator of action. If this model is correct, ignoring agent bias to construct a mechanical interpretation of observed events requires structural mapping reasoning to be implemented by the pre-exercise action planning system, which uses mechanisms instead of agents as the cause of non-movement changes. Observable in the contextual attributes or characteristics of the object. The plasticity of MNS at its input allows agent bias, allowing observable non-biological movements to be mapped to represent first-person actions and their typical accompanying intentions, thereby representing inanimate non-agents as agents (Catmur et al., 2007); Hey, 2010, 2012). [Sources: 3]

For example, probable Type 1 candidates include the processes of implicit perceptual learning of the agent’s repetitive properties, perceptual recognition of the kinematics and biomechanics of the agent’s movements, face recognition and sounds that signal the presence of a certain agent, and attention distortion. in relation to agents and persons, as well as the activation of basic emotional reactions to the agent. In the area of ​​agent identification, several studies show that contextual information about the social roles of target people can influence the tracking of people’s identities (Allen and Gabbert 2013), sometimes conveyed by gossip (Anderson et al. It is expected that a tracking mechanism based on basic human recognition is not will be reliable for tracking target agents from a set of indistinguishable agents. [Sources: 0]

Understanding that HADD is integral to human nature is part of the skeptics’ fundamental knowledge base. Bruce Hood, author of Supersense, traces in his book the psychological research that documented and described human tendency to think about objects differently from agents. In most cases, an important function of perceptual-based tracking mechanisms is to track a number of characteristics that can be used to identify a target agent. [Sources: 0, 4]

We can perceive freedom of action in non-living beings if they act as if they were agents. If all unobservable causal processes are agents, their role in re-identifying an object over time makes the identity of the object itself an agency dependency. A wide variety of perception and recognition processes for identifying an agent can fall into the category of type 1 processes. [Sources: 0, 3, 4]

When HADD is activated, we tend to see a hidden agent working behind the scenes, forcing events to unfold the way they do, and perhaps even deliberately hiding their tracks. So, at a fundamental level, our brains process agents differently from objects from the moment we see them. Research has also shown that HADD is more likely to fire when the stimulus is ambiguous, so this is generally our default assumption: an object is an agent until we are sure it is just an object. [Sources: 4]

This may also explain why we can react emotionally to characters when watching cartoons as if they are real: they are not alive, but we treat them as agents. We inject essence into agents-a unique vitality, even if they are still children. [Sources: 4]

Another reason the internal model does not have to be perfect is because humans have limited cognitive energy. The main claim of PP is that the human mind is a self-learning Bayesian prediction engine. Like all minds, the human mind must be able to quickly enter the environment to survive and develop. [Sources: 5]

Consequently, the human mind cannot stop too often to check if its internal pattern matches well the input it receives from the environment. While other cognitive models also argue that input is filtered by top-down processes, PP radicalizes this idea. Activities that consume a lot of cognitive energy tend to negatively affect cognitive performance. While efficiency prevents the mind from updating its model too often, internal models can also, among other things, be affected by an unrepresentative dataset. [Sources: 5]

While the predictive mind will shift towards greater precision, performance requirements imply that the subject will fail and not have to do things right. While both are important, the second contributes the most to the experience. [Sources: 5]


— Slimane Zouggari

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