Abstract not found

We suggested recently that attention can be understood as inferring the level of uncertainty or precision during hierarchical perception. In this paper, we try to substantiate this claim using neuronal simulations of directed spatial attention and biased competition. These simulations assume that neuronal activity encodes a probabilistic representation of the world that optimizes free-energy in a Bayesian fashion. Because free-energy bounds surprise or the (negative) log-evidence for internal models of the world, this optimization can be regarded as evidence accumulation or (generalized) predictive coding. Crucially, both predictions about the state of the world generating sensory data and the precision of those data have to be optimized. Here, we show that if the precision depends on the states, one can explain many aspects of attention. We illustrate this in the context of the Posner paradigm, using the simulations to generate both psychophysical and electrophysiological responses. These simulated responses are consistent with attentional bias or gating, competition for attentional resources, attentional capture and associated speed-accuracy trade-offs. Furthermore, if we present both attended and nonattended stimuli simultaneously, biased competition for neuronal representation emerges as a principled and straightforward property of Bayes-optimal perception.

Theories of probabilistic cognition postulate that internal representations are made up of multiple simultaneously held hypotheses, each with its own probability of being correct (henceforth, “probability distributions”). However, subjects make discrete responses and report the phenomenal contents of their mind to be all-or-none states rather than graded probabilities. How can these 2 positions be reconciled? Selective attention tasks, such as those used to study crowding, the attentional blink, rapid serial visual, and so forth, were recast as probabilistic inference problems and used to assess how graded, probabilistic representations may produce discrete subjective states. The authors asked subjects to make multiple guesses per trial and used 2nd-order statistics to show that (a) visual selective attention operates in a graded fashion in time and space, selecting multiple targets to varying degrees on any given trial; and (b) responses are generated by a process of sampling from the probabilistic states that result from graded selection. The authors concluded that although people represent probability distributions, their discrete responses and conscious states are products of a process that samples from these probabilistic representations.