Biology subjects

Price, M. H.

Publications and source records attributed to Price, M. H..

5 recordsLinked to original sources

bioRxiv2020

End-to-end Bayesian analysis of 14C dates reveals new insights into lowland Maya demography

ABSTRACTArchaeologists and demographers increasingly employ aggregations of published radiocarbon (14C) dates as demographic proxies summarizing changes in human activity in past societies. Presently, summed probability densities (SPDs) of calibrated radiocarbon dates are the dominant method of using 14C dates to reconstruct demographic trends. Unfortunately, SPDs are incapable of converging on their true generating distributions even as the number of observations gets large. To overcome this problem, we propose a more principled alternative that combines finite mixture models and Bayesian inference to identify the generating distribution of a set of radiocarbon dates. Numerical simulations and an assessment of the statistical identifiability of our method demonstrate that it correctly converges on the generating distribution. We apply this novel end-to-end Bayesian approach to reconstruct prehistoric Maya demographic growth using a recently compiled Mesoamerican radiocarbon database. Our results show that the Maya Lowlands experienced a century of rapid growth rates (1%) during the Late Classic, followed by a rapid decrease in population during the Terminal Classic, and a subsequent more-modest resurgence in population during the Postclassic. Additionally, a detailed population reconstruction of the important political center of Tikal verifies that slow population growth between the Preclassic and Early Classic gave pace to rapid growth starting around AD 500 and peaking at the beginning of the eight century. Our proposed method verifies previous reconstructions based on settlement patterns and ceramics, but with far more precise time-resolution and characterization of uncertainty than has been possible.Competing Interest StatementThe authors have declared no competing interest.View Full Text

ecology↗

bioRxiv2019

Repetition Attenuates the Influence of Recency on Recognition Memory: Behavioral and Electrophysiological Evidence

Studies of recognition memory often demonstrate a recency effect on behavioral performance, whereby response times (RTs) are faster for stimuli that were previously presented recently as opposed to more remotely in the past. This relationship between performance and presentation lag has been taken to reflect that memories are accessed by serially searching backwards in time, such that RT indicates the self-terminating moment of such a process. Here, we investigated the conditions under which this serial search gives way to more efficient means of retrieving memories. Event-related potentials (ERPs) were recorded during a continuous recognition task in which subjects made binary old/new judgments to stimuli that were each presented up to four times across a range of lags. Stimulus repetition and shorter presentation lag both gave rise to speeded RTs, consistent with previous findings, and we novelly extend these effects to a robust latency measure of the left parietal ERP effect associated with retrieval success. Importantly, the relationship between repetition and recency was further elucidated, such that repetition attenuated lag-related differences that were initially present in both the behavioral and neural latency data. These findings are consistent with the idea that a serial search through recent memory can quickly be abandoned in favor of relying on more efficient time-independent cognitive processes or neural signals.

neuroscience↗

bioRxiv2017

Failure to reactivate salient episodic information during indirect and direct tests of memory retrieval

Several fMRI and EEG studies have demonstrated that successful episodic retrieval is accompanied by the reactivation of cortical regions that were active during encoding. These findings are consistent with influential models of episodic memory that posit that conscious retrieval (recollection) relies on hippocampally-mediated cortical reinstatement. Evidence of reactivation corresponding to episodic information that is beyond conscious awareness at the time of memory retrieval, however, is limited. A recent exception is from an EEG study by Wimber, Maa{beta}, Staudigl, Richardson-Klavehn, and Hanslmayr (2012) in which words were encoded in the context of highly salient visual flicker entrainment and then presented at retrieval in the absence of any flicker. In that study, coherent (phase-locked) neural activity was observed at the corresponding entrained frequencies during retrieval, consistent with the notion that encoding representations were reactivated. Given the important implications of unconscious reactivation to past findings and the modeling literature, the current study set out to provide a direct replication of the previous study. Additionally, an attempt was made to extend such findings to intentional retrieval by acquiring EEG while subjects were explicitly asked to make memory judgments about the flicker frequency from encoding. Throughout a comprehensive set of analyses, the current study consistently failed to demonstrate evidence for unconscious reactivation, and instead provided support that test items were indistinguishable according to their prior encoding context. The findings thus establish an important boundary condition for the involvement of cortical reinstatement in episodic memory.

neuroscience↗

bioRxiv2017

A general goodness-of-fit test for survival analysis

Existing goodness-of-fit tests for survival data are either exclusively graphical in nature or only test specific model assumptions, such as the proportional hazards assumption. We describe a flexible, parameter-free goodness-of-fit test that provides a simple numerical assessment of a models suitability regardless of the structure of the underlying model. Intuitively, the goodness-of-fit test utilizes the fact that for a good model early event occurrence is predicted to be just as likely as late event occurrence, whereas a bad model has a bias towards early or late events. Formally, the goodness-of-fit test is based on a novel generalized Martingale residual which we call the martingale survival residual. The martingale survival residual has a uniform probability density function defined on the interval -0.5 to +0.5 if censoring is either absent or accounted for as one outcome in a competing hazards framework. For a good model, the set of calculated residuals is statistically indistinguishable from the uniform distribution, which is tested using the Kolmogorov-Smirnov statistic.

epidemiology↗

bioRxiv2016

Hierarchical Evolutionary Preferences Explain Discrepancies in Expected Utility Theory

The standard axiomatic theory of rationality posits that agents order preferences according to the average utilities associated with different choices. Expected Utility Theory has repeatedly failed as a predictive theory of choice behavior, as reflected in a growing literature in behavioral economics. Evolutionary theorists have suggested that seemingly irrational behaviors in contemporary contexts may have once served important functions, but there has been little attempt to formalize the relationship between evolutionary fitness and choice behavior. Biological agents should optimize fitness, but fitness itself is not a reasonable value function for decision-making since its time-scale exceeds the lifespan of the decision-maker. Consequently, organisms use proximate motivational systems that work on appropriate time-scales and are amenable to feedback and learning. We develop an evolutionary principal-agent model in which individuals maximize a set of proximal choice variables (age-specific demographic rates), the interests of which are aligned with fitness. The solution to our model yields pessimistic probability weightings compatible with the Rank-Dependent Expected Utility family of choice models. The pessimistic probability weighting characteristic of these models emerges naturally in an evolutionary framework because of extreme intolerance to zeros in multiplicative growth processes.

evolutionary biology↗