Metacognition in monkeys during an oculomotor task.

This study investigated whether rhesus monkeys show evidence of metacognition in a reduced, visual oculomotor task that is particularly suitable for use in fMRI and electrophysiology. The 2-stage task involved punctate visual stimulation and saccadic eye movement responses. In each trial, monkeys made a decision and then made a bet. To earn maximum reward, they had to monitor their decision and use that information to bet advantageously. Two monkeys learned to base their bets on their decisions within a few weeks. We implemented an operational definition of metacognitive behavior that relied on trial-by-trial analyses and signal detection theory. Both monkeys exhibited metacognition according to these quantitative criteria. Neither external visual cues nor potential reaction time cues explained the betting behavior; the animals seemed to rely exclusively on internal traces of their decisions. We documented the learning process of one monkey. During a 10-session transition phase, betting switched from random to a decision-based strategy. The results reinforce previous findings of metacognitive ability in monkeys and may facilitate the neurophysiological investigation of metacognitive functions.

Reversible inactivation of macaque frontal eye field.

The macaque frontal eye field (FEF) is involved in the generation of saccadic eye movements and fixations. To better understand the role of the FEF, we reversibly inactivated a portion of it while a monkey made saccades and fixations in response to visual stimuli. Lidocaine was infused into a FEF and neural inactivation was monitored with a nearby microelectrode. We used two saccadic tasks. In the delay task, a target was presented and then extinguished, but the monkey was not allowed to make a saccade to its location until a cue to move was given. In the step task, the monkey was allowed to look at a target as soon as it appeared. During FEF inactivation, monkeys were severely impaired at making saccades to locations of extinguished contralateral targets in the delay task. They were similarly impaired at making saccades to locations of contralateral targets in the step task if the target was flashed for < or =100 ms, such that it was gone before the saccade was initiated. Deficits included increases in saccadic latency, increases in saccadic error, and increases in the frequency of trials in which a saccade was not made. We varied the initial fixation location and found that the impairment specifically affected contraversive saccades rather than affecting all saccades made into head-centered contralateral space. Monkeys were impaired only slightly at making saccades to contralateral targets in the step task if the target duration was 1000 ms, such that the target was present during the saccade: latency increased, but increases in saccadic error were mild and increases in the frequency of trials in which a saccade was not made were insignificant. During FEF inactivation there usually was a direct correlation between the latency and the error of saccades made in response to contralateral targets. In the delay task, FEF inactivation increased the frequency of making premature saccades to ipsilateral targets. FEF inactivation had inconsistent and mild effects on saccadic peak velocity. FEF inactivation caused impairments in the ability to fixate lights steadily in contralateral space. FEF inactivation always caused an ipsiversive deviation of the eyes in darkness. In summary, our results suggest that the FEF plays major roles in (1) generating contraversive saccades to locations of extinguished or flashed targets, (2) maintaining contralateral fixations, and (3) suppressing inappropriate ipsiversive saccades.

Advances in Dynamic Modeling and Computation for Count Data

A class of multi-process models is developed for collections of time indexed count data. Autocorrelation in counts is achieved with dynamic models for the natural parameter of the binomial distribution. In addition to modeling binomial time series, the framework includes dynamic models for multinomial and Poisson time series. Markov chain Monte Carlo (MCMC) and Po ́lya-Gamma data augmentation (Polson et al., 2013) are critical for fitting multi-process models of counts. To facilitate computation when the counts are high, a Gaussian approximation to the P ́olya- Gamma random variable is developed.

Three applied analyses are presented to explore the utility and versatility of the framework. The first analysis develops a model for complex dynamic behavior of themes in collections of text documents. Documents are modeled as a “bag of words”, and the multinomial distribution is used to characterize uncertainty in the vocabulary terms appearing in each document. State-space models for the natural parameters of the multinomial distribution induce autocorrelation in themes and their proportional representation in the corpus over time.

The second analysis develops a dynamic mixed membership model for Poisson counts. The model is applied to a collection of time series which record neuron level firing patterns in rhesus monkeys. The monkey is exposed to two sounds simultaneously, and Gaussian processes are used to smoothly model the time-varying rate at which the neuron’s firing pattern fluctuates between features associated with each sound in isolation.

The third analysis presents a switching dynamic generalized linear model for the time-varying home run totals of professional baseball players. The model endows each player with an age specific latent natural ability class and a performance enhancing drug (PED) use indicator. As players age, they randomly transition through a sequence of ability classes in a manner consistent with traditional aging patterns. When the performance of the player significantly deviates from the expected aging pattern, he is identified as a player whose performance is consistent with PED use.

All three models provide a mechanism for sharing information across related series locally in time. The models are fit with variations on the P ́olya-Gamma Gibbs sampler, MCMC convergence diagnostics are developed, and reproducible inference is emphasized throughout the dissertation.

Wear and its effects on dental topography measures in howling monkeys (Alouatta palliata).

OBJECTIVES: Three dental topography measurements: Dirichlet Normal Energy (DNE), Relief Index (RFI), and Orientation Patch Count Rotated (OPCR) are examined for their interaction with measures of wear, within and between upper and lower molars in Alouatta palliata. Potential inferences of the "dental sculpting" phenomenon are explored. MATERIALS AND METHODS: Fifteen occluding pairs of howling monkey first molars (15 upper, 15 lower) opportunistically collected from La Pacifica, Costa Rica, were selected to sample wear stages ranging from unworn to heavily worn as measured by the Dentine Exposure Ratio (DER). DNE, RFI, and OPCR were measured from three-dimensional surface reconstructions (PLY files) derived from high-resolution CT scans. Relationships among the variables were tested with regression analyses. RESULTS: Upper molars have more cutting edges, exhibiting significantly higher DNE, but have significantly lower RFI values. However, the relationships among the measures are concordant across both sets of molars. DER and EDJL are curvilinearly related. DER is positively correlated with DNE, negatively correlated with RFI, and uncorrelated with OPCR. EDJL is not correlated with DNE, or RFI, but is positively correlated with OPCR among lower molars only. DISCUSSION: The relationships among these metrics suggest that howling monkey teeth adaptively engage macrowear. DNE increases with wear in this sample presumably improving food breakdown. RFI is initially high but declines with wear, suggesting that the initially high RFI safeguards against dental senescence. OPCR values in howling monkey teeth do not show a clear relationship with wear changes.