What attention is. The priority structure account.

'Everyone knows what attention is' according to William James. Much work on attention in psychology and neuroscience cites this famous phrase only to quickly dismiss it. But James is right about this: 'attention' was not introduced into psychology and neuroscience as a theoretical concept. I argue that we should therefore study attention with broadly the same methodology that David Marr has applied to the study of perception. By focusing more on Marr's Computational Level of analysis, we arrive at a unified answer to the question of what attention is, what role it plays in the mind, and why organisms like us have that capacity. I propose a methodology for studying attention at Marr's Computational Level that optimizes in a three-dimensional space: it should capture core aspects of our first-person experience of attention, be explanatorily powerful in psychology and neuroscience, and fertile in an interdisciplinary context. I show how this methodology leads to what I call the priority structure account of attention. Attention is what organizes current information to make it more useful for the organism. We can identify it by four features. Attention, in this way, helps a cognitive system to integrate its informational state with its current motivational state. I describe how this account improves on alternatives and shows why attention is a useful concept in many disciplines and for connecting them. This article is categorized under: Philosophy > Psychological Capacities Psychology > Attention Philosophy > Foundations of Cognitive Science.

Spike-timing precision underlies the coding efficiency of auditory receptor neurons.

Sensory systems must translate incoming signals quickly and reliably so that an animal can act successfully in its environment. Even at the level of receptor neurons, however, functional aspects of the sensory encoding process are not yet fully understood. Specifically, this concerns the question how stimulus features and neural response characteristics lead to an efficient transmission of sensory information. To address this issue, we have recorded and analyzed spike trains from grasshopper auditory receptors, while systematically varying the stimulus statistics. The stimulus variations profoundly influenced the efficiency of neural encoding. This influence was largely attributable to the presence of specific stimulus features that triggered remarkably precise spikes whose trial-to-trial timing variability was as low as 0.15 ms--one order of magnitude shorter than typical stimulus time scales. Precise spikes decreased the noise entropy of the spike trains, thereby increasing the rate of information transmission. In contrast, the total spike train entropy, which quantifies the variety of different spike train patterns, hardly changed when stimulus conditions were altered, as long as the neural firing rate remained the same. This finding shows that stimulus distributions that were transmitted with high information rates did not invoke additional response patterns, but instead displayed exceptional temporal precision in their neural representation. The acoustic stimuli that led to the highest information rates and smallest spike-time jitter feature pronounced sound-pressure deflections lasting for 2-3 ms. These upstrokes are reminiscent of salient structures found in natural grasshopper communication signals, suggesting that precise spikes selectively encode particularly important aspects of the natural stimulus environment.

Honey bees navigate according to a map-like spatial memory.

By using harmonic radar, we report the complete flight paths of displaced bees. Test bees forage at a feeder or are recruited by a waggle dance indicating the feeder. The flights are recorded after the bees are captured when leaving the hive or the feeder and are released at an unexpected release site. A sequence of behavioral routines become apparent: (i) initial straight flights in which they fly the course that they were on when captured (foraging bees) or that they learned during dance communication (recruited bees); (ii) slow search flights with frequent changes of direction in which they attempt to "get their bearings"; and (iii) straight and rapid flights directed either to the hive or first to the feeding station and then to the hive. These straight homing flights start at locations all around the hive and at distances far out of the visual catchment area around the hive or the feeding station. Two essential criteria of a map-like spatial memory are met by these results: bees can set course at any arbitrary location in their familiar area, and they can choose between at least two goals. This finding suggests a rich, map-like organization of spatial memory in navigating honey bees.