The Real Combination Problem : Panpsychism, Micro-Subjects, and Emergence

Panpsychism harbors an unresolved tension, the seriousness of which has yet to be fully appreciated. I capture this tension as a dilemma, and offer panpsychists advice on how to resolve it. The dilemma, briefly, is as follows. Panpsychists are committed to the perspicuous explanation of macro-mentality in terms of micro-mentality. But panpsychists take the micro-material realm to feature not just mental properties, but also micro-subjects to whom these properties belong. Yet it is impossible to explain the constitution of a macro-subject (like one of us) in terms of the assembly of micro-subjects, for, I show, subjects cannot combine. Therefore the panpsychist explanatory project is derailed by the insistence that the world’s ultimate material constituents (ultimates) are subjects of experience. The panpsychist faces a choice of abandoning her explanatory project, or recanting the claim that the ultimates are subjects. This is the dilemma. I argue that the latter option is to be preferred. This needn’t constitute a wholesale abandonment of panpsychism, however, since panpsychists can maintain that the ultimates possess phenomenal qualities, despite not being subjects of those qualities. This proposal requires us to make sense of phenomenal qualities existing independently of experiencing subjects, a challenge I tackle in the penultimate section. The position eventually reached is a form of neutral monism, so another way to express the overall argument is to say that, keeping true to their philosophical motivations, panpsychists should really be neutral monists.Peer reviewedFinal Accepted Versio

Evaluating Forecasting Methods

Ideally, forecasting methods should be evaluated in the situations for which they will be used. Underlying the evaluation procedure is the need to test methods against reasonable alternatives. Evaluation consists of four steps: testing assumptions, testing data and methods, replicating outputs, and assessing outputs. Most principles for testing forecasting methods are based on commonly accepted methodological procedures, such as to prespecify criteria or to obtain a large sample of forecast errors. However, forecasters often violate such principles, even in academic studies. Some principles might be surprising, such as do not use R-square, do not use Mean Square Error, and do not use the within-sample fit of the model to select the most accurate time-series model. A checklist of 32 principles is provided to help in systematically evaluating forecasting methods

Selecting Forecasting Methods

I examined six ways of selecting forecasting methods: Convenience, “what’s easy,” is inexpensive, but risky. Market popularity, “what others do,” sounds appealing but is unlikely to be of value because popularity and success may not be related and because it overlooks some methods. Structured judgment, “what experts advise,” which is to rate methods against prespecified criteria, is promising. Statistical criteria, “what should work,” are widely used and valuable, but risky if applied narrowly. Relative track records, “what has worked in this situation,” are expensive because they depend on conducting evaluation studies. Guidelines from prior research, “what works in this type of situation,” relies on published research and offers a low-cost, effective approach to selection. Using a systematic review of prior research, I developed a flow chart to guide forecasters in selecting among ten forecasting methods. Some key findings: Given enough data, quantitative methods are more accurate than judgmental methods. When large changes are expected, causal methods are more accurate than naive methods. Simple methods are preferable to complex methods; they are easier to understand, less expensive, and seldom less accurate. To select a judgmental method, determine whether there are large changes, frequent forecasts, conflicts among decision makers, and policy considerations. To select a quantitative method, consider the level of knowledge about relationships, the amount of change involved, the type of data, the need for policy analysis, and the extent of domain knowledge. When selection is difficult, combine forecasts from different methods

Investigation of attentional bias in obsessive compulsive disorder with and without depression in visual search

Copyright: © 2013 Morein-Zamir et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are creditedWhether Obsessive Compulsive Disorder (OCD) is associated with an increased attentional bias to emotive stimuli remains controversial. Additionally, it is unclear whether comorbid depression modulates abnormal emotional processing in OCD. This study examined attentional bias to OC-relevant scenes using a visual search task. Controls, non-depressed and depressed OCD patients searched for their personally selected positive images amongst their negative distractors, and vice versa. Whilst the OCD groups were slower than healthy individuals in rating the images, there were no group differences in the magnitude of negative bias to concern-related scenes. A second experiment employing a common set of images replicated the results on an additional sample of OCD patients. Although there was a larger bias to negative OC-related images without pre-exposure overall, no group differences in attentional bias were observed. However, OCD patients subsequently rated the images more slowly and more negatively, again suggesting post-attentional processing abnormalities. The results argue against a robust attentional bias in OCD patients, regardless of their depression status and speak to generalized difficulties disengaging from negative valence stimuli. Rather, post-attentional processing abnormalities may account for differences in emotional processing in OCD.Peer reviewedFinal Published versio

Differential effects of pre and post-payment on neurologists' response rates to a postal survey

<p>Abstract</p> <p>Background</p> <p>Monetary incentives are an effective way of increasing response rates to surveys, though they are generally less effective in physicians, and are more effective when the incentive is paid up-front rather than when made conditional on completion.</p> <p>Methods</p> <p>In this study we examine the effectiveness of pre- and post-completion incentives on the response rates of all the neurologists in the UK to a survey about conversion disorder, using a cluster randomised controlled design. A postal survey was sent to all practicing consultant neurologists, in two rounds, including either a book token, the promise of a book token, or nothing at all.</p> <p>Results</p> <p>Three hundred and fifty-one of 591 eligible neurologists completed the survey, for a response rate of 59%. While the post-completion incentive exerted no discernible influence on response rates, a pre-completion incentive did, with an odds-ratio of 2.1 (95% confidence interval 1.5 - 3.0).</p> <p>Conclusions</p> <p>We conclude that neurologists, in the UK at least, may be influenced to respond to a postal survey by a pre-payment incentive but are unaffected by a promised reward.</p

Fire History from Life-History: Determining the Fire Regime that a Plant Community Is Adapted Using Life-Histories

Wildfire is a fundamental disturbance process in many ecological communities, and is critical in maintaining the structure of some plant communities. In the past century, changes in global land use practices have led to changes in fire regimes that have radically altered the composition of many plant communities. As the severe biodiversity impacts of inappropriate fire management regimes are recognized, attempts are being made to manage fires within a more ‘natural’ regime. In this aim, the focus has typically been on determining the fire regime to which the community has adapted. Here we take a subtly different approach and focus on the probability of a patch being burnt. We hypothesize that competing sympatric taxa from different plant functional groups are able to coexist due to the stochasticity of the fire regime, which creates opportunities in both time and space that are exploited differentially by each group. We exploit this situation to find the fire probability at which three sympatric grasses, from different functional groups, are able to co-exist. We do this by parameterizing a spatio-temporal simulation model with the life-history strategies of the three species and then search for the fire frequency and scale at which they are able to coexist when in competition. The simulation gives a clear result that these species only coexist across a very narrow range of fire probabilities centred at 0.2. Conversely, fire scale was found only to be important at very large scales. Our work demonstrates the efficacy of using competing sympatric species with different regeneration niches to determine the probability of fire in any given patch. Estimating this probability allows us to construct an expected historical distribution of fire return intervals for the community; a critical resource for managing fire-driven biodiversity in the face of a growing carbon economy and ongoing climate change

"They all work...when you stick to them" : a qualitative investigation of dieting, weight loss, and physical exercise, in obese individuals

BackgroundTo explore the extent to which people living with obesity have attempted to lose weight, their attitudes towards dieting, physical exercise and weight loss solutions, why their weight loss attempts have failed, and their opinions about what would be most beneficial to them in their struggle with their weight.MethodQualitative study, using open-ended interviews, of 76 people living with obesity in Victoria, Australia in 2006/7. Individuals with a BMI of 30 or over were recruited using articles in local newspapers, convenience sampling, and at a later stage purposive sampling techniques to diversify the sample. Data analysis was conducted by hand using a constant, comparative method to develop and test analytical categories. Data were interpreted both within team meetings and through providing research participants the chance to comment on the study findings.ResultsWhilst participants repeatedly turned to commercial diets in their weight loss attempts, few had used, or were motivated to participate in physical activity. Friends or family members had introduced most individuals to weight loss techniques. Those who took part in interventions with members of their social network were more likely to report feeling accepted and supported. Participants blamed themselves for being unable to maintain their weight loss or \u27stick\u27 to diets. Whilst diets did not result in sustained weight loss, two thirds of participants felt that dieting was an effective way to lose weight.ConclusionIndividuals with obesity receive numerous instructions about what to do to address their weight, but very few are given appropriate long term guidance or support with which to follow through those instructions. Understanding the positive role of social networks may be particularly important in engaging individuals in physical activity. Public health approaches to obesity must engage and consult with those currently living with obesity, if patterns of social change are to occur

Simulations of events for the LUX-ZEPLIN (LZ) dark matter experiment

The LUX-ZEPLIN dark matter search aims to achieve a sensitivity to the WIMP-nucleon spin-independent cross-section down to (1–2)×10−12 pb at a WIMP mass of 40 GeV/c2. This paper describes the simulations framework that, along with radioactivity measurements, was used to support this projection, and also to provide mock data for validating reconstruction and analysis software. Of particular note are the event generators, which allow us to model the background radiation, and the detector response physics used in the production of raw signals, which can be converted into digitized waveforms similar to data from the operational detector. Inclusion of the detector response allows us to process simulated data using the same analysis routines as developed to process the experimental data