SUMMARY / RELATED TOPICS

Predictive coding

Predictive coding is a theory of brain function in which the brain is generating and updating a mental model of the environment. The model is used to generate predictions of sensory input that are compared to actual sensory input; this comparison results in prediction errors that are used to update and revise the mental model. Theoretical ancestors to predictive coding date back as early as 1860 with Helmholtz's concept of unconscious inference. Unconscious inference refers to the idea that the human brain fills in visual information to make sense of a scene. For example, if something is smaller than another object in the visual field, the brain uses that information as a cue of depth, such that the perceiver experiences depth; the understanding of perception as the interaction between sensory stimuli and conceptual knowledge continued to be established by Jerome Bruner who, starting in the 1940s, studied the ways in which needs and expectations influence perception, research that came to be known as'New Look' psychology.

In 1981, McClelland and Rumelhart in their seminal paper examined the interaction between processing features which form letters, which in turn form words. While the features suggest the presence of a word, they found that when letters were situated in the context of a word, people were able to identify them faster than when they were situated in a non-word without semantic context. McClelland and Rumelhart's parallel processing model describes perception as the meeting of top-down and bottom-up elements. In the late 1990s, the idea of top-down and bottom-up processing was translated into a computational model of vision by Rao and Ballard, their paper demonstrated that there could be a generative model of a scene, which would receive feedback via error signals, which would subsequently lead to updating the prediction. The computational model was able to replicate well-established receptive field effects, as well as less understood extra-classical receptive field effects such as end-stopping.

Today, the fields of computer science and cognitive science incorporate these same concepts to create the multilayer generative models that underlie machine learning and neural nets. Most of the research literature in the field has been about sensory perception vision, more conceptualized. However, the predictive coding framework could be applied to different neural systems. Taking the sensory system as an example, the brain solves the intractable problem of modelling distal causes of sensory input through a version of Bayesian inference, it does this by modelling predictions of lower-level sensory inputs via backward connections from higher levels in a cortical hierarchy. Constrained by the statistical regularities of the outside world, the brain encodes top-down generative models at various temporal and spatial scales in order to predict and suppress sensory inputs rising up from lower levels. A comparison between predictions and sensory input yields a difference measure which, if it is sufficiently large beyond the levels of expected statistical noise, will cause the generative model to update so that it better predicts sensory input in the future.

If, the model predicts driving sensory signals, activity at higher levels cancels out activity at lower levels, the posterior probability of the model is increased. Thus, predictive coding inverts the conventional view of perception as a bottom-up process, suggesting that it is constrained by prior predictions, where signals from the external world only shape perception to the extent that they are propagated up the cortical hierarchy in the form of prediction error. Expectations about the precision of incoming sensory input are crucial for minimizing prediction error in that the expected precision of a given prediction error can inform confidence in that error, which influences the extent to which the error is weighted in updating predictions. Given that the world we live in is loaded with statistical noise, precision expectations must be represented as part of the brain's generative models, they should be able to flexibly adapt to changing contexts. For instance, the expected precision of visual prediction errors varies between dawn and dusk, such that greater conditional confidence is assigned to errors in broad daylight than errors in prediction at nightfall.

It has been proposed that such weighting of prediction errors in proportion to their estimated precision is, in essence and that the process of devoting attention may be neurobiologically accomplished by ascending reticular activating systems optimizing the “gain” of prediction error units. The same principle of prediction error minimization has been used to provide an account of behavior in which motor actions are not commands but descending proprioceptive predictions. In this scheme of active inference, classical reflex arcs are coordinated so as to selectively sample sensory input in ways that better fulfill predictions, thereby minimizing proprioceptive prediction errors. Indeed, Adams et al. review evidence suggesting that this view of hierarchical predictive coding in the motor system provides a principled and neurally plausible framework for explaining the agranular organization of the motor cortex. This

Titan Stadium (Cal State Fullerton)

Titan Stadium is a 10,000-capacity multi-purpose stadium on the campus of California State University, Fullerton in Fullerton, California. Scheduled to open in time for the 1991 football season, delays caused the opening date of Titan Stadium to be pushed back until 1992. Despite being planned as the home stadium for the Cal State Fullerton Titans football program, the delays in stadium construction put in question the possibility of the team taking the field. Budget cuts and strict NCAA regulations signaled the end of the football program in 1992 after playing one season at the stadium. Since Titan Stadium was designed to host the football team, it is one of the most lavish soccer stadiums in Southern California. Titan Stadium has 2,000 chairback seats and 2,500 bleachers seats with backrests on the western side of the stadium. In addition, there are concrete steps on the opposite side; the pitch features an underground drainage system that allows it to be perfectly flat. The main press box seats over 50 people and features 10 separate booths used for broadcasting, etc.

The stadium is home to the CSUF Titans men's soccer and CSUF Titans women's soccer teams. The Cal State Fullerton Titans football team played at the stadium in 1992. Titan Stadium was one of the hosts of the Los Angeles Salsa, a former professional soccer team in the now defunct American Professional Soccer League between 1993 and 1994; the Los Angeles Galaxy used the field as their home ground during their run to the 2001 Lamar Hunt U. S. Open Cup. In addition to professional soccer, NCAA Tournament games were hosted in 1994, 1996, 1998, 2005; the United States men's national soccer team has used Titan Stadium to host international soccer matches as well. In addition to being a soccer stadium, Titan Stadium has been featured in television programs and commercials due to its proximity to Los Angeles; the UCLA Bruins have used the stadium as a practice field for their football program, once again bringing the sport to the CSUF Titans campus. The Los Angeles Blues soccer team of USL Pro played their home games at Titan Stadium.

California United, a North American Soccer League club, are the new tenants of Titan Stadium. List of soccer stadiums in the United States