Abstract
Cognitive systems exhibit astounding prediction capabilities that allow them to reap rewards from regularities in their environment. How do organisms predict environmental input and how well do they do it? As a prerequisite to answering that question, we first address the limits on prediction strategy inference, given a series of inputs and predictions from an observer. We study the special case of Bayesian observers, allowing for a probability that the observer randomly ignores data when building her model. We demonstrate that an observer’s prediction model can be correctly inferred for binary stimuli generated from a finite-order Markov model. However, we can not necessarily infer the model’s parameter values unless we have access to several “clones” of the observer. As stimuli become increasingly complicated, correct inference requires exponentially more data points, computational power, and computational time. These factors place a practical limit on how well we are able to infer an observer’s prediction strategy in an experimental or observational setting.
Highlights
Over the last 30 years, brains have been increasingly viewed as prediction machines.Organisms are bombarded by information, which they heavily compress and use to predict both their environment and the consequences of their actions in their environment
We focus on the general problem of inferring stochastic processes, which can be represented as a sequence learning task for the purpose of experimentation with real-world observers
The predictive brain is the dominant framework in cognitive science today, viewing humans or other animals as prediction machines
Summary
Over the last 30 years, brains have been increasingly viewed as prediction machines. Organisms are bombarded by information, which they heavily compress and use to predict both their environment and the consequences of their actions in their environment. Bayesian models are a useful theoretical tool because they define what the optimal ideal observer would infer in a given problem domain and allow experimentalists to compare human performance to that ideal. Humans have been shown to perform close to optimal on a variety of cognitive tasks, ranging from motor control [14], visual perception [15], motion illusions [16], pattern segmentation [17], categorization [18], word learning [19], causal inference [20], mental simulation [21], to symbolic reasoning [22]. We focus on the general problem of inferring stochastic processes, which can be represented as a sequence learning task for the purpose of experimentation with real-world observers. Perhaps surprisingly, that it is difficult in a standard sequence learning experiment to uncover a single observer’s prediction strategy
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