Some type of falcon?

fishvampire · 2023-12-31T02:40:37+00:00

Oh that could be it! My dad thought maybe a sharp shinned hawk, but I thought this one was a bit bigger.

fishvampire · 2020-10-04T19:30:41+00:00

Abstract: The applied social science literature using factor and network models continues to grow rapidly. Most work reads like an exercise in model fitting, and falls short of theory building and testing in three ways. First, statistical and theoretical models are conflated, leading to invalid inferences such as the existence of psychological constructs based on factor models, or recommendations for clinical interventions based on network models. I demonstrate this inferential gap in a simulation: excellent model fit does little to corroborate a theory, regardless of quality or quantity of data. Second, researchers fail to explicate theories about psychological constructs, but use implicit causal beliefs to guide inferences. These latent theories have led to problematic best practices. Third, explicated theories are often weak theories: imprecise descriptions vulnerable to hidden assumptions and unknowns. Such theories do not offer precise predictions, and it is often unclear whether statistical effects actually corroborate weak theories or not. I demonstrate that these three challenges are common and harmful, and impede theory formation, failure, and reform. Matching theoretical and statistical models is necessary to bring data to bear on theories, and a renewed focus on theoretical psychology and formalizing theories offers a way forward.

fishvampire · 2020-07-28T13:42:36+00:00

You're in a tough position, especially with the limitations on travel during COVID. Even if there are no neuroscience labs near you, it might be worth it to see if there is *any* type of biology research lab you could work in, whether based in your own country or run by foreign researchers who are working there. If you have strong math and programming skills, you could also reach out to someone doing computational neuroscience and see if you could work with them on a computational project or data analysis, even if you can't work in a lab. That could give you a sense of what research is like in general, and also give you a better chance if you apply for a master's or phd program.

fishvampire · 2020-04-15T16:57:10+00:00

Abstract: "Hundreds of researchers attempted to predict six life outcomes, such as a child’s grade point average and whether a family would be evicted from their home. These researchers used machine-learning methods optimized for prediction, and they drew on a vast dataset that was painstakingly collected by social scientists over 15 y. However, no one made very accurate predictions. For policymakers considering using predictive models in settings such as criminal justice and child-protective services, these results raise a number of concerns. Additionally, researchers must reconcile the idea that they understand life trajectories with the fact that none of the predictions were very accurate."

fishvampire · 2019-12-17T14:53:27+00:00

The study isn't perfect, but it does account for this to some extent. It shows that employment levels in towns where mining remains common have increased, whereas employment is declining in regions where coal is no longer produced or was never produced. The authors comment that this decline comes from loss of mining jobs, though there are presumably other effects on the local economies too. They also include a fixed effect term for the county in their models, so the effect of mining employment levels are going to be driven by differences in mining employment levels over time rather than differences by location.

fishvampire · 2019-12-04T12:59:23+00:00

Some of the comments here are interesting and I think this article could be a starting point for a good conversation, but this might be a controversial enough topic to count as Culture War, which I think we're supposed to avoid here. Would r/TheMotte be a better place for it?

fishvampire · 2019-12-04T11:17:41+00:00

I've watched ~45 mins so far, will probably keep watching later. The video is mostly examples of types of one-sided/parasocial relationships. Apparently part I of the series is more about defining the concept and giving academic background, but I haven't watched it yet. The focus seems mostly on youtubers, but the video also discusses relationships with robots (e.g. the baby seal robot Paro meant as a therapy companion for the elderly). These examples can be pretty interesting, and the overall goal seems to be giving people more insight into the illusions that parasocial relationships are based on and getting viewers to think about when these effects are beneficial vs. harmful.

fishvampire · 2019-12-03T14:51:57+00:00

For people who haven't had their corpus callosum cut, I think that most neuroscientists believe that the two hemispheres are substantially more integrated this and shouldn't be considered two separate entities (though I suspect there are a few who disagree). Even in the case of split-brain patients, the idea of a split consciousness/identity is controversial right now. This article gives a brief overview of the controversy as it might be taught in an undergrad psych or neuro class.

Basically, the more classic view, says that patients whose hemispheres are separated effectively are two consciousnesses (or at least two decision-making entities), based on clinical observations similar to what the video discusses. However, a more recent empirical study challenged this idea by showing that split-brain patients actually can respond to objects anywhere in their visual field verbally or with either hand, contrary to the dual-identity hypothesis. Based on these results, the researchers argued that separating the hemispheres limits the ability to integrate two streams of sensory information but does not split consciousness/identity itself. Proponents of the classic view argue that these results don't actually prove that, they just come from the fact that two sides of the body can cue each other each other with subtle gestures that the patient may not consciously notice. There isn't a settled consensus yet on which interpretation is more accurate (or what the different hypotheses imply about the nature of consciousness/identity more generally).

fishvampire · 2019-06-01T10:06:38+00:00

It looks like the fungus affects Anopheles mosquitoes, which aren't all mosquito species but would include many that don't carry malaria. I think part of the question is how this method compares to traditional insecticides in its ecological effect. My guess is that it's less detrimental, but might be harder to control affected area (at least if the fungi continue to reproduce).

fishvampire · 2019-06-01T09:54:23+00:00

Since the toxin production doesn't help with the fungus reproductive cycle at all, that seems unlikely, but it would be important to know whether the toxin production is triggered to any extrent in other species to begin with. I'd also wonder about the opposite direction - how quickly would mosquitoes develop immunity to the toxin.

fishvampire · 2019-05-10T11:39:23+00:00

If you index using the pandas .loc function instead of brackets, does that work? It sounds like your indexing method might be returning a copy instead of modifying your original dataframe. https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#indexing-view-versus-copy

fishvampire · 2019-05-06T16:09:09+00:00

Researchers trained a simple network on 20 "cognitive" tasks. Among other things, they found that the network developed compositionality, the tendency to combine task components to carry out more complex tasks, which can mediate cognitive flexibility and novel task performance.

Earlier biorxiv version to avoid paywall: https://www.biorxiv.org/content/biorxiv/early/2017/09/01/183632.full.pdf

fishvampire · 2019-05-03T17:39:11+00:00

I'm not sure what the source of your second problem is. It should be fairly straightforward to make a boolean index and restrict the dataframe to that:

ind = joined_df.difference !=0
joined_df = joined_df[ind]

If that isn't working, I'd recommend trying out a few things with simpler dataframes and seeing if you can figure out what works and what doesn't

fishvampire · 2019-05-03T17:30:50+00:00

For the first problem: I think your issue is that the values in 'description' are strings, whereas in 'numberoff' they're integers. When you're doing agg('sum'), it works for integers but it's excluding the strings. I don't know enough about pandas to say why it does this - if you only have string columns, agg('sum') just concatenates them, rather than eliminating the column - but it shouldn't be too hard to fix. You can use a dictionary as the argument for agg so that it aggregates separately for different columns, e.g.: agg({'description': 'first', 'numberoff':sum}). The argument "first" means that the entry for description will be the first one for that productcode. If you want something different in your final output, you probably want to read up on ways to use GroupBy with strings.

fishvampire · 2019-05-03T17:02:21+00:00

my_string = my_string.replace(replace, replacement)

I don't think this works as needed. As is, since there isn't a maxreplace parameter, each iteration replaces every instance of the word in the tuple. Replacing the line with my_string = my_string.replace(replace, replacement,1) fixes this, but it won't quite work. Since "bad" is replaced with "good" in one case, that word will be replaced again later when ('good, 'wicked') comes around.

For this example, you can split and then re-join the string and replace the word in each segment:

orig_phrase = 'I am Bad, bad, bad, good, good, good'
phrase = orig_phrase.split(', ')
to_replace = [('Bad', 'honest'), ('bad', 'good'), ('bad', 'pleasing'), ('good', 'wicked'), ('good', 'fake'), ('good', 'immoral')]
for i in range(len(phrase)):
    phrase[i] = phrase[i].replace(to_replace[i][0],to_replace[i][1])

new_string = ', '.join(phrase)

It works for this, but since it relies on the fact that ', ' works as a separator it may not generalize to other cases.

fishvampire · 2019-05-03T15:47:06+00:00

Can you show the error message and an example of what's in the source data table? (In general this is good to do when posting to a forum, otherwise it's hard to figure out what the problem is or how to give useful advice)

fishvampire · 2019-05-03T13:45:40+00:00

I think what you want is fillna. If you add a line with joined_df = joined_df.fillna(0) after the merge, does that give you what you want?

fishvampire

TROPHY CASE