Attention as an effect not a cause

Abstract

Attention is commonly thought to be important for managing the limited resources available in sensory areas of neocortex. Here we present an alternative view that attention arises as a byproduct of circuits centered on the basal ganglia involved in value-based decision-making. The central idea is that decision-making depends on properly estimating the current state of the animal and its environment, and that the weighted inputs to the currently prevailing estimate give rise to the filter-like properties of attention. After outlining this new framework, we describe findings from physiology, anatomy, computational and clinical work that support this point of view. We conclude that the brain mechanisms responsible for attention employ a conserved circuit motif that predates the emergence of the neocortex.

LIP activity in the interstimulus interval of a change detection task biases the behavioral response

“…This hypothesis suggests that covert visuospatial attention has thus evolved to refine the priority map for the guidance of eye movements. This conclusion dovetails nicely with a recent review that suggested that attention arises as a functional consequence of the networks involved in value-based decision making (Krauzlis et al 2014). In our case, the decision is where to look, and the network includes LIP, the frontal eye field, and the superior colliculus.…”

Attention modeled as information in learning multisensory integration

“…Our model fits in well with the view recently expressed by Krauzlis et al [32] that attention may be not so much a mechanism causing certain behavioral and neural phenomena, but an effect emerging from the need for effective information processing. Krauzlis et al [32] argue that for example the SC is involved in regulating spatial attention behaviorally, but neural activity related to selective attention in visual cortex remains after collicular deactivation.…”

Bayesian accounts of covert selective attention: A tutorial review

“…Alternatively, we may look outwardly to the environment and ask why these behavioural effects occurred. This cause/effect distinction first highlighted by James, is rarely discussed directly, but more recent examinations show that it is crucial to address (and hopefully resolve or reconcile) these different approaches (James, 1890;Johnston & Dark, 1986;Fernandez-Duque & Johnson, 2002;Anderson, 2011;Krauzlis, Bollimunta, Arcizet, & Wang, 2014). The causal approach, which could be mapped onto the algorithm or implementation levels of analysis of Marr (1982), proceeds broadly as follows: a) observe some behavioural effects, b) infer the existence of a mechanism which caused those effects, c) refine the proposed mechanism as more data are observed over time.…”