David Colaço

When detecting scientific phenomena, researchers often aim to separate datasets into signal and noise. This signal-noise distinction captures that not all data one collects is relevant to this phenomenon. However, what happens when scientists “mistakenly” think that they have found a phenomenon of interest? In this paper, I review the case of perytons, where terrestrial waveforms, initially of interest to radio telescope researchers, were unmasked as emanating from a break room microwave oven. This case exemplifies that the signal-noise distinction can be read in two different ways: that of data relevant to a phenomenon of interest and data irrelevant to it, and that of data with structure and data lacking it. I articulate these readings, discuss how they differently matter to detecting phenomena in datasets, and address how they complicate philosophical appeals to the signal-noise distinction. Further, I discuss whether this case counts as a mistake.

Sometimes we try to explain one thing but end up explaining something else. In this paper, I address when this is a problem, which I call the “Problem of Explaining Shifting Targets” (PEST). In PEST, the explanatory target shifts to another, where the original and shifted targets are manifestly different. Nonetheless, the explaining is treated as for the original target or one equivalent to it, as the agent does not recognize the change. Through the analysis of three candidate cases of PEST, I address the conditions under which it occurs. Further, I address three factors—agents are interested in the target they start with, which then shifts, but they do not recognize it—that capture why PEST should not be all that surprising. PEST is thus an error that makes salient that we must reflect on the dynamic relations between explanation and its target. Because it is subtle, PEST is insidious. It involves appeals to good evidence, and there may be no clear marker that someone has erred. Likewise, it is consequential. Shifts can wreak havoc on a research program or lead to people defending targets that they should not.

This chapter reviews the idea of the memory trace, capturing appeals to it over time and across different intellectual traditions. Though this idea has a long history and remains popular amongst philosophers and scientists, there is both debate over what these traces are and what explanatory need, if any, they serve. I address three approaches to the idea of the memory trace: as a metaphor, as an explanatory posit, and as an empirical target. I discuss how these approaches can complement one another. I also address two outstanding questions investigations of these approaches reveal. These outstanding questions are (1) what we aim to explain with traces, and (2) when appeal to traces might be counterproductive.

The human brain makes up just 2% of body mass but consumes closer to 20% of the body’s energy. Nonetheless, it is significantly more energy-efficient than most modern computers. Although these facts are well-known, models of cognitive capacities rarely account for metabolic factors. In this paper, we argue that metabolic considerations should be integrated into cognitive models. We distinguish two uses of metabolic considerations in modeling. First, metabolic considerations can be used to evaluate models. Evaluative metabolic considerations function as explanatory constraints. Metabolism limits which types of computation are possible in biological brains. Further, it structures and guides the flow of information in neural systems. Second, metabolic considerations can be used to generate new models. They provide: a starting point for inquiry into the relation between brain structure and information processing, a proof-of-concept that metabolic knowledge is relevant to cognitive modeling, and potential explanations of how a particular type of computation is implemented. Evaluative metabolic considerations allow researchers to prune and partition the space of possible models for a given cognitive capacity or neural system, while generative considerations populate that space with new models. Our account suggests cognitive models should be consistent with the brain’s metabolic limits, and modelers should assess how their models fit within these bounds. Our account offers fresh insights into the role of metabolism for cognitive models of mental effort, philosophical views of multiple realization and medium independence, and the comparison of biological and artificial computational systems.

The advent of novel technologies has bolstered the idea of extended memory, where memory processes extend beyond the human body or brain. However, investigations of extended memory, and extended cognition more generally, do not address how biological componentry might be externalized from the body and into artifacts. In the paper, I introduce and explore a case where human engrams are externalized into exograms, stored via organic data memory technologies, and reimplanted later. This case involves the same medium of memory storage in internal and external systems, which I call a _biological memory extension_. Further, it creates a concern about a blur between where agent ends and artifact begins, highlighting a potentially inconsistent consideration of the boundary between the two. These concerns reflect the need for us to answer a fundamental question: for extended memory, must the artifact merely extend beyond our bodies, or must it extend beyond our biology?

How should we assess systems whose mnemonic status is contested? There are, for instance, debates over whether immune memory is “really” memory, or akin to memory as ordinarily attributed to human cognition. In this paper, I challenge two arguments often given by detractors in this debate. The first is that the system does not exhibit errors exemplified in human memory. The second is that it can be described and explained in causal terms alone. I argue that our limited knowledge of the causal basis of immunological phenomena and human memory undercuts our ability to conclude that one is merely causal while the other is not. By consequence, it is unclear why we should reject that the immune system exhibits these errors. With my challenges, I question whether we are consistent in assessing alleged mnemonic systems. Though we have schematic causal accounts of both systems, we have differing expectations of what we can conclude about them. Yet, it is unclear what might warrant appeals to these expectations.

Scientists aim to remediate artifacts in their experimental datasets. However, the remediation of one artifact can result in another. Why might this happen, and what does this consequence tell us about how we should account for artifacts and their control? In this paper, I explore a case in functional neuroimaging where remediation appears to have caused this problem. I argue that remediation amounts to a change to an experimental arrangement. These changes need not be surgical, and the arrangement need not satisfy the criterion of causal modularity. Thus, remediation can affect more than just the factor responsible for the artifact. However, if researchers can determine the consequences of their remediation, they can make adjustments that control for the present artifact as well as for previously controlled ones. Current philosophical accounts of artifacts and the factors responsible for them cannot adequately address this issue, as they do not account for what is needed for artifact remediation (and specifically correction). I support my argument by paralleling it with ongoing concerns regarding the transparency of complex computational systems, as near future remediation across the experimental life sciences will likely make greater use of AI tools to correct for artifacts.

The study of the neural basis of memory has advanced over the past decade. A key contributor to this memory “renaissance” has been new tools. On its face, this matches what might be described as a neuroscientific revolution stemming from the development of tools, where this revolution is largely independent of theory. In this paper, we challenge this tool revolution account by focusing on a problem that arises in applying it to this “renaissance”: it is centered around memory, but the tools were not developed for solving problems in memory science. To resolve this problem, we introduce an account that distinguishes tool development and tool uptake, and we argue that while theoretical considerations may not inform development, they do inform uptake. Acknowledging the distance between these stages of tool use draws our attention to the questions of why and how tool uptake occurs in the domains that it does.

This chapter sheds light on a response to experimental philosophy that has not yet received enough attention: the reflection defense. According to proponents of this defense, judgments about philosophical cases are relevant only when they are the product of careful, nuanced, and conceptually rigorous reflection. The chapter argues that the reflection defense is misguided: Five studies (N>1800) are presented, showing that people make the same judgments when they are primed to engage in careful reflection as they do in the conditions standardly used by experimental philosophers.

Philosophers and scientists propose the idea that plants are cognitive, which has been met with criticisms. These criticisms focus on the fact that plants do not possess the properties traditionally associated with cognition. By contrast, several proponents introduce novel ways to conceptualize cognition. How should we make sense of this debate? In this paper, I argue that the plant cognition debate is not about whether plants meet a set of well-delineated and agreed-upon criteria according to which they count as cognitive. Rather, many proponents are hypothesizing about cognition. They construe COGNITION not as an expression of what cognition is, but rather as a conjecture about what cognition might be. These conjectures orient research that can uncover novel similarities amongst the phenomena to which these concepts extend. In defending this view, I argue that investigating plant cognition is valuable, even if the results of these investigations lead us to reject the claim that plants are cognitive.

Scientists often respond to failures to replicate by citing differences between the experimental components of an original study and those of its attempted replication. In this paper, we investigate these purported mismatch explanations. We assess a body of failures to replicate in neuroscience studies on spinal cord injury. We argue that a defensible mismatch explanation is one where (1) a mismatch of components is a difference maker for a mismatch of outcomes, and (2) the components are relevantly different in the follow-up study, given the scope of the original study. With this account, we argue that not all differences between studies are meaningful, even if they are difference makers. As our examples show, focusing only on these differences results in disregarding the representativeness of the original experiment’s components and the scope of its outcomes, undercutting other epistemic aims, such as translation, in the process.

My dissertation is on scientific phenomena, their characterization, and their role in scientific inquiry. I focus on three questions. First, what do characterizations of scientific phenomena represent? To answer this, I investigate what it means to characterize a phenomenon, as opposed to describing the results of individual studies. Second, how do researchers develop these characterizations? This question relates to the logic of discovery: I examine how researchers use existing theories and methods to explore systems, search for phenomena, and develop representations of them. Third, how do researchers evaluate these characterizations? This question relates to the logic of justification: I investigate how empirical findings serve as defeasible evidence for the characterizations of phenomena, and in light of what evidence we should accept, suspend judgment about, or reject them.

While many neuroscientists and philosophers share an interest in integrating neuroscience, both camps suggest that this integration is challenging. Why is this the case? In this paper, I account for why tools, or the materials and technologies that researchers use to study brain structure and activity, are obstructions to integrating neuroscience. The constraints of tools and their productivity create neuroscience practices that can be in tension with methodological, data, and explanatory integration. My account explains why integration in neuroscience likely will not occur unless new tools are developed. At the same time, my account explains why new tools can facilitate local integration between neuroscience subfields for the same reasons why they can obstruct it.

This paper accounts for broad definitions of memory, which extend to paradigmatic memory phenomena, like episodic memory in humans, and phenomena in worms and sea snails. These definitions may seem too broad, suggesting that they extend to phenomena that don’t count as memory or illustrate that memory is not a natural kind. However, these responses fail to consider a definition as a hypothesis. As opposed to construing definitions as expressing memory’s properties, a definition as a hypothesis is the basis to test inferences about phenomena. A definition as a hypothesis is valuable when the “kinding” of phenomena is ongoing.

In this paper, I investigate how researchers evaluate their characterizations of scientific phenomena. Characterizing phenomena is an important – albeit often overlooked – aspect of scientific research, as phenomena are targets of explanation and theorization. As a result, there is a lacuna in the literature regarding how researchers determine whether their characterization of a target phenomenon is appropriate for their aims. This issue has become apparent for accounts of scientific explanation that take phenomena to be explananda. In particular, philosophers who endorse mechanistic explanation suggest that the discovery of the mechanisms that explain a phenomenon can lead to its recharacterization. However, they fail to make clear how these explanations provide warrant for recharacterizing their explananda phenomena. Drawing from cases of neurobiological research on potentiation phenomena, I argue that attempting to explain a phenomenon may provide reason to suspend judgment about its characterization, but this cannot provide warrant to recharacterize it if researchers cannot infer a phenomenon’s characteristics from this explanation. To explicate this, I go beyond explanation – mechanistic or otherwise – to analyze why and how researchers change their epistemic commitments in light of new evidence.

In this article, we analyse the evidential value of the corpus of experimental philosophy. While experimental philosophers claim that their studies provide insight into philosophical problems, some philosophers and psychologists have expressed concerns that the findings from these studies lack evidential value. Barriers to evidential value include selection bias and p-hacking. To find out whether the significant findings in x-phi papers result from selection bias or p-hacking, we applied a p-curve analysis to a corpus of 365 x-phi chapters and articles. Our results suggest that this corpus has evidential value, although there are hints of p-hacking in a few parts of the x-phi corpus.

In epistemology, fake-barn thought experiments are often taken to be intuitively clear cases in which a justified true belief does not qualify as knowledge. We report a study designed to determine whether non-philosophers share this intuition. The data suggest that while participants are less inclined to attribute knowledge in fake-barn cases than in unproblematic cases of knowledge, they nonetheless do attribute knowledge to protagonists in fake-barn cases. Moreover, the intuition that fake-barn cases do count as knowledge is negatively correlated with age; older participants are less likely than younger participants to attribute knowledge in fake-barn cases. We also found that increasing the number of defeaters (fakes) does not decrease the inclination to attribute knowledge.

To explain their role in discovery and contrast them with theory-driven research, philosophers of science have characterized exploratory experiments in terms of what they lack: namely, that they lack direction from what have been called “local theories” of the target system or object under investigation. I argue that this is incorrect: it’s not whether or not there is direction from a local theory that matters, but instead how such a theory is used to direct an experiment that matters. Appealing to contemporary exploratory experiments that involve the use of experimental techniques—specifically, examples where scientists explore the interaction of neural activity and human behavior by magnetically stimulating brains—I argue that local theories of a target system can inform auxiliary hypotheses in exploratory experiments, which direct these experiments. These examples illustrate how local theories can direct the exploration of target systems where researchers do not aim to evaluate these theories.

In this paper, I investigate the nature of empirical findings that provide evidence for the characterization of a scientific phenomenon, and the defeasible nature of this evidence. To do so, I explore an exemplary instance of the rejection of a characterization of a scientific phenomenon: memory transfer. I examine the reason why the characterization of memory transfer was rejected, and analyze how this rejection tied to researchers’ failures to resolve experimental issues relating to replication and confounds. I criticize the presentation of the case by Harry Collins and Trevor Pinch, who claim that no sufficient reason was provided to abandon research on memory transfer. I argue that skeptics about memory transfer adopted what I call a defeater strategy, in which researchers exploit the defeasibility of the evidence for a characterization of a phenomenon.