PDF Archive

Easily share your PDF documents with your contacts, on the Web and Social Networks.

Share a file Manage my documents Convert Recover PDF Search Help Contact



Probability and Cognition.pdf


Preview of PDF document probability-and-cognition.pdf

Page 12316

Text preview


The purpose of this paper is to briefly summarize theories regarding the role of
probability in human cognition. Theories discussed are generative model-based
approaches such as hierarchical predictive coding and Bayesianism; the
heuristics and biases program; the frequentist hypothesis from evolutionary
psychology; and statistical learning.

Generative Model-Based Approaches
Generative models statistically simulate observable data based on probability
functions. In the context of cognition, the generative model-based approach
entails there being a mental framework in which the observable data of the world
are assumed to be generated by causally structured processes (Perfors &
Tenenbaum, 2011; Clark, 2013). According to Goodman and Tenenbaum (2013),
“The generative approach to cognition posits that some mental representations
are more like [scientific] theories in this way: they capture general descriptions of
how the world works.” Thus generative model-based theories may help explain
cognitive processes in which inductive inference is necessary for dealing with
uncertainty, and in which an internal model or representation of how the world
works aids in rapid processing of incoming data.
Predictive Coding
In mammalian perceptual systems, information flows bi-directionally between
hierarchically organized populations of neurons. For instance, in the human
visual system, incoming sensory data are first transduced at the retinas, after
which sensory information flows further into the brain up a hierarchy: from the
retinas to the lateral geniculate nucleus (LGN), then to the primary visual cortex
(V1), the secondary visual cortex, association areas, and other cortical areas.
However, for undetermined reasons, much of the flow of information is feedback;
it is estimated that 80% of the input to the LGN comes not from the retinas but
from V1 and other areas of the brain, such as the thalamus and the brain stem
(Bear et al., 2016). Prior to this discovery it was widely assumed that the visual
system was only feedforward.
The question of what purpose it serves for brain systems to have so much
downward-flowing information is addressed by predictive coding theories. Such
theories posit that perceptual systems are structured as hierarchically organized
sets of generative models with increasingly general models at higher levels
(Winkler & Czigler, 2012). Theories vary in scope, with some focusing on only
sensory perception, and others proposing a unifying framework that includes all
cognitive functions.
The hierarchical predictive processing hypothesis—also hierarchical predictive
coding (Rao and Ballard, 1999), prediction error minimization (Hohwy, 2013), or
action-oriented predictive processing (Clark, 2013)—says that at each level of a
hierarchical brain system, predictions of what the incoming sensory data are
most likely to be are encoded by populations of neurons. Prediction signals are