Electrocortical components of anticipation and consumption in a monetary incentive delay task

In order to improve our understanding of the components that reflect functionally important processes during reward anticipation and consumption, we used principle components analyses (PCA) to separate and quantify averaged ERP data obtained from each stage of a modified monetary incentive delay (MID) task. Although a small number of recent ERP studies have reported that reward and loss cues potentiate ERPs during anticipation, action preparation, and consummatory stages of reward processing, these findings are inconsistent due to temporal and spatial overlap between the relevant electrophysiological components. Our results show three components following cue presentation are sensitive to incentive cues (N1, P3a, P3b). In contrast to previous research, reward‐related enhancement occurred only in the P3b, with earlier components more sensitive to break‐even and loss cues. During feedback anticipation, we observed a lateralized centroparietal negativity that was sensitive to response hand but not cue type. We also show that use of PCA on ERPs reflecting reward consumption successfully separates the reward positivity from the independently modulated feedback‐P3. Last, we observe for the first time a new reward consumption component: a late negativity distributed over the left frontal pole. This component appears to be sensitive to response hand, especially in the context of monetary gain. These results illustrate that the time course and sensitivities of electrophysiological activity that follows incentive cues do not follow a simple heuristic in which reward incentive cues produce enhanced activity at all stages and substages

NEURODYNAMICS OF CATEGORY LEARNING: TOWARDS UNDERSTANDING THE CREATION OF MEANING IN THE BRAIN

Category learning, the formation and use of categories (equivalence classes of meaning), is an elemental function of cognition. We report our approach to study the physiological mechanisms underlying category learning using high-density multi-channel recordings of electrocorticograms in rodents. These data suggest the coexistence of separate coding principles for representing physical stimulus attributes ("stimulus representation") and subjectively relevant information (meaning) about stimuli, respectively. The implications of these findings for the construction of interactive cortical sensory neuroprostheses are discussed

Probing neural mechanisms underlying auditory stream segregation in humans by transcranial direct current stimulation (tDCS)

One hypothesis concerning the neural underpinnings of auditory streaming states that frequency tuning of tonotopically organized neurons in primary auditory fields in combination with physiological forward suppression is necessary for the separation of representations of high-frequency A and low-frequency B tones. The extent of spatial overlap between the tonotopic activations of A and B tones is thought to underlie the perceptual organization of streaming sequences into one coherent or two separate streams. The present study attempts to interfere with these mechanisms by transcranial direct current stimulation (tDCS) and to probe behavioral outcomes reflecting the perception of ABAB streaming sequences. We hypothesized that tDCS by modulating cortical excitability causes a change in the separateness of the representations of A and B tones, which leads to a change in the proportions of one-stream and two-stream percepts. To test this, 22 subjects were presented with ambiguous ABAB sequences of three different frequency separations (∆F) and had to decide on their current percept after receiving sham, anodal, or cathodal tDCS over the left auditory cortex. We could confirm our hypothesis at the most ambiguous ∆F condition of 6 semitones. For anodal compared with sham and cathodal stimulation, we found a significant decrease in the proportion of two-stream perception and an increase in the proportion of one-stream perception. The results demonstrate the feasibility of using tDCS to probe mechanisms underlying auditory streaming through the use of various behavioral measures. Moreover, this approach allows one to probe the functions of auditory regions and their interactions with other processing stages

NEURODYNAMICS OF CATEGORY LEARNING: TOWARDS UNDERSTANDING THE CREATION OF MEANING IN THE BRAIN

Category learning, the formation and use of categories (equivalence classes of meaning), is an elemental function of cognition. We report our approach to study the physiological mechanisms underlying category learning using high-density multi-channel recordings of electrocorticograms in rodents. These data suggest the coexistence of separate coding principles for representing physical stimulus attributes ("stimulus representation") and subjectively relevant information (meaning) about stimuli, respectively. The implications of these findings for the construction of interactive cortical sensory neuroprostheses are discussed.Cortical dynamics, category learning, neuroprostheses

NEURODYNAMICS OF CATEGORY LEARNING: TOWARDS UNDERSTANDING THE CREATION OF MEANING IN THE BRAIN

Category learning, the formation and use of categories (equivalence classes of meaning), is an elemental function of cognition. We report our approach to study the physiological mechanisms underlying category learning using high-density multi-channel recordings of electrocorticograms in rodents. These data suggest the coexistence of separate coding principles for representing physical stimulus attributes ("stimulus representation") and subjectively relevant information (meaning) about stimuli, respectively. The implications of these findings for the construction of interactive cortical sensory neuroprostheses are discussed

Improving Accuracy and Temporal Resolution of Learning Curve Estimation for within- and across-Session Analysis

Estimation of learning curves is ubiquitously based on proportions of correct responses within moving trial windows. Thereby, it is tacitly assumed that learning performance is constant within the moving windows, which, however, is often not the case. In the present study we demonstrate that violations of this assumption lead to systematic errors in the analysis of learning curves, and we explored the dependency of these errors on window size, different statistical models, and learning phase. To reduce these errors in the analysis of single-subject data as well as on the population level, we propose adequate statistical methods for the estimation of learning curves and the construction of confidence intervals, trial by trial. Applied to data from an avoidance learning experiment with rodents, these methods revealed performance changes occurring at multiple time scales within and across training sessions which were otherwise obscured in the conventional analysis. Our work shows that the proper assessment of the behavioral dynamics of learning at high temporal resolution can shed new light on specific learning processes, and, thus, allows to refine existing learning concepts. It further disambiguates the interpretation of neurophysiological signal changes recorded during training in relation to learning

Improving Accuracy and Temporal Resolution of Learning Curve Estimation for within- and across-Session Analysis

Estimation of learning curves is ubiquitously based on proportions of correct responses within moving trial windows. Thereby, it is tacitly assumed that learning performance is constant within the moving windows, which, however, is often not the case. In the present study we demonstrate that violations of this assumption lead to systematic errors in the analysis of learning curves, and we explored the dependency of these errors on window size, different statistical models, and learning phase. To reduce these errors in the analysis of single-subject data as well as on the population level, we propose adequate statistical methods for the estimation of learning curves and the construction of confidence intervals, trial by trial. Applied to data from an avoidance learning experiment with rodents, these methods revealed performance changes occurring at multiple time scales within and across training sessions which were otherwise obscured in the conventional analysis. Our work shows that the proper assessment of the behavioral dynamics of learning at high temporal resolution can shed new light on specific learning processes, and, thus, allows to refine existing learning concepts. It further disambiguates the interpretation of neurophysiological signal changes recorded during training in relation to learning

Improving accuracy and temporal resolution of learning curve estimation for within- and across-session analysis

Estimation of learning curves is ubiquitously based on proportions of correct responses within moving trial windows. In this approach, it is tacitly assumed that learning performance is constant within the moving windows, which, however, is often not the case. In the present study we demonstrate that violations of this assumption lead to systematic errors in the analysis of learning curves, and we explored the dependency of these errors on window size, different statistical models, and learning phase. To reduce these errors for single subjects as well as on the population level, we propose adequate statistical methods for the estimation of learning curves and the construction of confidence intervals, trial by trial. Applied to data from a shuttle-box avoidance experiment with Mongolian gerbils, our approach revealed performance changes occurring at multiple temporal scales within and across training sessions which were otherwise obscured in the conventional analysis. The proper assessment of the behavioral dynamics of learning at a high temporal resolution clarified and extended current descriptions of the process of avoidance learning. It further disambiguated the interpretation of neurophysiological signal changes recorded during training in relation to learning

Non-invasive recording of high-frequency signals from the human spinal cord

Throughout the somatosensory system, neuronal ensembles generate high-frequency signals in the range of several hundred Hertz in response to sensory input. High-frequency signals have been related to neuronal spiking, and could thus help clarify the functional architecture of sensory processing. Recording high-frequency signals from subcortical regions, however, has been limited to clinical pathology whose treatment allows for invasive recordings. Here, we demonstrate the feasibility to record 200-1200 Hz signals from the human spinal cord non-invasively, and in healthy individuals. Using standard electroencephalography equipment in a cervical electrode montage, we observed high-frequency signals between 200 and 1200 Hz in a time window between 8 and 16 ms after electric median nerve stimulation (n = 15). These signals overlapped in latency, and, partly, in frequency, with signals obtained via invasive, epidural recordings from the spinal cord in a patient with neuropathic pain. Importantly, the observed high-frequency signals were dissociable from classic spinal evoked responses. A spatial filter that optimized the signal-to-noise ratio of high-frequency signals led to submaximal amplitudes of the evoked response, and vice versa, ruling out the possibility that high-frequency signals are merely a spectral representation of the evoked response. Furthermore, we observed spontaneous fluctuations in the amplitude of high-frequency signals over time, in the absence of any concurrent, systematic change to the evoked response. High-frequency, "spike-like" signals from the human spinal cord thus carry information that is complementary to the evoked response. The possibility to assess these signals non-invasively provides a novel window onto the neurophysiology of the human spinal cord, both in a context of top-down control over perception, as well as in pathology