Logistic regression models can be combined with single-neuron recording to predict likely SOZs in epilepsy patients being evaluated for resective surgery, providing an automated source of clinically useful information.

It remains unclear how single neurons in the human brain represent whole-object visual stimuli. While recordings in both human and nonhuman primates have shown distributed representations of objects (many neurons encoding multiple objects), recordings of single neurons in the human medial temporal lobe, taken as subjects' discriminated objects during multiple presentations, have shown gnostic representations (single neurons encoding one object). Because some studies suggest that repeated viewing may enhance neural selectivity for objects, we had human subjects discriminate objects in a single, more naturalistic viewing session. We found that, across 432 well isolated neurons recorded in the hippocampus and amygdala, the average fraction of objects encoded was 26%. We also found that more neurons encoded several objects versus only one object in the hippocampus (28 vs 18%, p Ͻ 0.001) and in the amygdala (30 vs 19%, p Ͻ 0.001). Thus, during realistic viewing experiences, typical neurons in the human medial temporal lobe code for a considerable range of objects, across multiple semantic categories.

No abstract

The present study used 1/f noise to examine how spatial, physical, and timing constraints affect planning and control processes in aiming. Participants moved objects of different masses to different distances at preferred speed (Experiment 1) and as quickly as possible (Experiment 2). Power spectral density, standardized dispersion, rescaled range, and an autoregressive fractionally integrated moving average (ARFIMA) model selection procedure were used to quantify 1/f noise. Measures from all four analyses were in reasonable agreement, with more ARFIMA (long-range) models selected at peak velocity in Experiment 1 and fewer selected at peak velocity in Experiment 2. There also was a nonsignificant trend where, at preferred speed, of those participants who showed 1/f noise, more tended to show 1/f noise at peak velocity, when planning and control would overlap most. This trend disappeared for fast movements, where planning and control would have less time to overlap. Summing short-range processes at different timescales can produce 1/f-like noise. As planning is a slower-moving process and control faster, present results suggest that, with enough time for both planning and control, 1/f noise in aiming may arise from a similar summation of processes. Potential limitations of time series length in the present task are discussed.

Perceiving the weight of an object in the hand is common to many real-world and laboratory activities. Despite its ubiquity, though, it is not a simple process. When we grasp an object to judge its weight, we sense its physical properties (e.g., its mass), process this information to form a percept of weight, and then make a decision about how to transform this internal percept into an outward report of heaviness. 1 These three subprocesses-sensory, perceptual, and decisional-combine to guide our reports of perceived heaviness. These subprocesses also provide three opportunities for features other than mass (e.g., size) to influence perceived heaviness. Disentangling such effects (i.e., determining how each feature influences each subprocess) can be difficult because each can produce equivalent effects on perceptual reports of heaviness. However, contemporary psychophysical techniques, based on signal detection theory, offer a way to distinguish the effects of sensory, perceptual, and decisional subprocesses for multidimensional stimuli. In the present experiments, such multidimensional signal detection techniques were applied in order to determine the influence of length, diameter, and mass on haptically perceived heaviness with and without vision. Sensory, Perceptual, and Decisional Subprocesses in Weight PerceptionThe process of generating a perceptual report of heaviness can be divided into three subprocesses (Amazeen, 1999;Oberle & Amazeen, 2003). The first is the sensory subprocess, in which the observer detects the physical property or properties relevant to perceiving weight. The second is the perceptual subprocess, in which there is a percept or other psychological or neural code associated with the object's weight. The third is the decisional subprocess, in which the observer uses a criterion or some other rule to generate a perceived heaviness response based on the experienced percept of weight. The fact that multiple processes intervene between stimulus and response presents a major challenge to psychophysical research; namely, a perceptual report is not just a measure of one's percept but represents a combination of, or interaction among, stimulus, percept, and decision. Complicating matters even further is the fact that multiple processes may produce mathematically equivalent distributions of responses (e.g., Cohen, 2003). Psychophysical techniques based on signal detection theory (Green & Swets, 1966) can be useful in making such distinctions among subprocesses. Figure 1 illustrates these effects in the context of unidimensional signal detection theory. The participant's report of heaviness (in this case, heavier or lighter) is a function of the physical variable mass, the location of the percept along the dimension of perceived weight (measured by d ), and the decision criterion (C ) used.Weight perception presents additional challenges, though, which necessitate the use of methodological and analytical techniques that go beyond unidimensional signal detection theory. The main challenge is that t...