The study of decision making spans such varied fields as neuroscience, psychology, economics, statistics, political science, and computer science. Despite this diversity of applications, most decisions share common elements including deliberation and commitment. Here we evaluate recent progress in understanding how these basic elements of decision formation are implemented in the brain. We focus on simple decisions that can be studied in the laboratory but emphasize general principles likely to extend to other settings.

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The Neural Basis of Decision Making

Noise — random disturbances of signals — poses a fundamental problem for information processing and affects all aspects of nervous-system function. However, the nature, amount and impact of noise in the nervous system have only recently been addressed in a quantitative manner. Experimental and computational methods have shown that multiple noise sources contribute to cellular and behavioural trial-to-trial variability. We review the sources of noise in the nervous system, from the molecular to the behavioural level, and show how noise contributes to trial-to-trial variability. We highlight how noise affects neuronal networks and the principles the nervous system applies to counter detrimental effects of noise, and briefly discuss noise's potential benefits.

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Noise in the nervous system.

In this review, we consider the potential functional role of beta-band oscillations, which at present is not yet well understood. We discuss evidence from recent studies on top-down mechanisms involved in cognitive processing, on the motor system and on the pathophysiology of movement disorders that suggest a unifying hypothesis: beta-band activity seems related to the maintenance of the current sensorimotor or cognitive state. We hypothesize that beta oscillations and/or coupling in the beta-band are expressed more strongly if the maintenance of the status quo is intended or predicted, than if a change is expected. Moreover, we suggest that pathological enhancement of beta-band activity is likely to result in an abnormal persistence of the status quo and a deterioration of flexible behavioural and cognitive control.

Beta-band oscillations--signalling the status quo?

We recorded the activity of single neurons in the posterior parietal cortex (area LIP) of two rhesus monkeys while they discriminated the direction of motion in random-dot visual stimuli. The visual task was similar to a motion discrimination task that has been used in previous investigations of motion-sensitive regions of the extrastriate cortex. The monkeys were trained to decide whether the direction of motion was toward one of two choice targets that appeared on either side of the random-dot stimulus. At the end of the trial, the monkeys reported their direction judgment by making an eye movement to the appropriate target. We studied neurons in LIP that exhibited spatially selective persistent activity during delayed saccadic eye movement tasks. These neurons are thought to carry high-level signals appropriate for identifying salient visual targets and for guiding saccadic eye movements. We arranged the motion discrimination task so that one of the choice targets was in the LIP neuron's response field (RF) while the other target was positioned well away from the RF. During motion viewing, neurons in LIP altered their firing rate in a manner that predicted the saccadic eye movement that the monkey would make at the end of the trial. The activity thus predicted the monkey's judgment of motion direction. This predictive activity began early in the motion-viewing period and became increasingly reliable as the monkey viewed the random-dot motion. The neural activity predicted the monkey's direction judgment on both easy and difficult trials (strong and weak motion), whether or not the judgment was correct. In addition, the timing and magnitude of the response was affected by the strength of the motion signal in the stimulus. When the direction of motion was toward the RF, stronger motion led to larger neural responses earlier in the motion-viewing period. When motion was away from the RF, stronger motion led to greater suppression of ongoing activity. Thus the activity of single neurons in area LIP reflects both the direction of an impending gaze shift and the quality of the sensory information that instructs such a response. The time course of the neural response suggests that LIP accumulates sensory signals relevant to the selection of a target for an eye movement.

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Neural Basis of a Perceptual Decision in the Parietal Cortex (Area LIP) of the Rhesus Monkey

How the brain encodes information in population activity, and how it combines and manipulates that activity as it carries out computations, are questions that lie at the heart of systems neuroscience. During the past decade, with the advent of multi-electrode recording and improved theoretical models, these questions have begun to yield answers. However, a complete understanding of neuronal variability, and, in particular, how it affects population codes, is missing. This is because variability in the brain is typically correlated, and although the exact effects of these correlations are not known, it is known that they can be large. Here, we review studies that address the interaction between neuronal noise and population codes, and discuss their implications for population coding in general.

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Neural correlations, population coding and computation

The ability to discriminate between two sequential stimuli requires evaluation of current sensory information in reference to stored information Where and how does this evaluation occur? We trained monkeys to compare two mechanical vibrations applied sequentially to the fingertips and to report which of the two had the higher frequency We recorded single neurons in secondary somatosensory cortex (S2) while the monkeys performed the task During the first stimulus period, the firing rate of S2 neurons encoded the stimulus frequency During the second stimulus period, however, some S2 neurons did not merely encode the stimulus frequency The responses of these neurons were a function of both the remembered (first) and current (second) stimulus Moreover, a few hundred milliseconds after the presentation of the second stimulus, these responses were correlated with the monkey's decision This suggests that some S2 neurons may combine past and present sensory information for decision-making

/pdf/neuronal-correlates-of-decision-making-in-secondary-3bvkjl3e7e.pdf

Neuronal correlates of decision-making in secondary somatosensory cortex

We trained monkeys to compare the frequencies of two mechanical vibrations applied sequentially to the tip of a finger and to report which of the two stimuli had the higher frequency. This task requires remembering the first frequency during the delay period between the two stimuli. Recordings were made from neurons in the inferior convexity of the prefrontal cortex (PFC) while the monkeys performed the task. We report neurons that fire persistently during the delay period, with a firing rate that is a monotonic function of the frequency of the first stimulus. Separately from, and in addition to, their correlation with the first stimulus, the delay period firing rates of these neurons were correlated with the behavior of the monkey, in a manner consistent with their interpretation as the neural substrate of working memory during the task. Most neurons had firing rates that varied systematically with time during the delay period. We suggest that this time-dependent activity may encode time itself and may be an intrinsic part of active memory maintenance mechanisms.

Timing and Neural Encoding of Somatosensory Parametric Working Memory in Macaque Prefrontal Cortex

http://www.math.pitt.edu/~bard/bardware/classes/jc/jc_deanne.pdf

Neuronal Correlates of a Perceptual Decision in Ventral Premotor Cortex

The flutter sensation is felt when mechanical vibrations between 5 and 50 Hz are applied to the skin. Neurons with rapidly adapting properties in the somatosensory system of primates are driven very effectively by periodic flutter stimuli; their evoked spike trains typically have a periodic structure with highly regular time differences between spikes. A long-standing conjecture is that, such periodic structure may underlie a subject's capacity to discriminate the frequencies of periodic vibrotactile stimuli and that, in primary somatosensory areas, stimulus frequency is encoded by the regular time intervals between evoked spikes, not by the mean rate at which these are fired. We examined this hypothesis by analyzing extracellular recordings from primary (S1) and secondary (S2) somatosensory cortices of awake monkeys performing a frequency discrimination task. We quantified stimulus-driven modulations in firing rate and in spike train periodicity, seeking to determine their relevance for frequency discrimination. We found that periodicity was extremely high in S1 but almost absent in S2. We also found that periodicity was enhanced when the stimuli were relevant for behavior. However, periodicity did not covary with psychophysical performance in single trials. On the other hand, rate modulations were similar in both areas, and with periodic and aperiodic stimuli, they were enhanced when stimuli were important for behavior, and were significantly correlated with psychophysical performance in single trials. Thus, the exquisitely timed, stimulus-driven spikes of primary somatosensory neurons may or may not contribute to the neural code for flutter frequency, but firing rate seems to be an important component of it.

https://www.jneurosci.org/content/jneuro/20/14/5503.full.pdf

Antonio Zainos

Papers

Neuronal correlates of decision-making in secondary somatosensory cortex

Timing and Neural Encoding of Somatosensory Parametric Working Memory in Macaque Prefrontal Cortex

Neuronal Correlates of a Perceptual Decision in Ventral Premotor Cortex

Periodicity and Firing Rate As Candidate Neural Codes for the Frequency of Vibrotactile Stimuli

Sensing without Touching: Psychophysical Performance Based on Cortical Microstimulation