Explanatory anti-psychologism overturned by lay and scientific case classifications

Waskan, Jonathan; Harmon, Ian; Horne, Zachary; Spino, Joseph; Clevenger, John

doi:10.1007/s11229-013-0304-2

Cited by 24 publications

(20 citation statements)

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“…After reading, participants completed a distractor task, which involved reading and answering questions about a distractor story. This was an approximately 1000-word selection from a fictional article on gamma ray bursts (Waskan, Harmon, Horne, Spino, & Clevenger, 2014). Importantly, this distractor story did not include the word ''knew'' or any near synonyms.…”

Section: Materials and Proceduresmentioning

confidence: 99%

A Bayesian framework for knowledge attribution: Evidence from semantic integration

et al. 2015

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a b s t r a c tWe propose a Bayesian framework for the attribution of knowledge, and apply this framework to generate novel predictions about knowledge attribution for different types of ''Gettier cases'', in which an agent is led to a justified true belief yet has made erroneous assumptions. We tested these predictions using a paradigm based on semantic integration. We coded the frequencies with which participants falsely recalled the word ''thought'' as ''knew'' (or a near synonym), yielding an implicit measure of conceptual activation. Our experiments confirmed the predictions of our Bayesian account of knowledge attribution across three experiments. We found that Gettier cases due to counterfeit objects were not treated as knowledge (Experiment 1), but those due to intentionally-replaced evidence were (Experiment 2). Our findings are not well explained by an alternative account focused only on luck, because accidentally-replaced evidence activated the knowledge concept more strongly than did similar false belief cases (Experiment 3). We observed a consistent pattern of results across a number of different vignettes that varied the quality and type of evidence available to agents, the relative stakes involved, and surface details of content. Accordingly, the present findings establish basic phenomena surrounding people's knowledge attributions in Gettier cases, and provide explanations of these phenomena within a Bayesian framework.

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Section: Materials and Proceduresmentioning

confidence: 99%

A Bayesian framework for knowledge attribution: Evidence from semantic integration

et al. 2015

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“…(1) Successfully conveying understanding about the target phenomenon, or making it intelligible, to an audience or inquirer (Achinstein 1983;Braverman et al 2012Waskan et al, 2014.…”

Section: Introductionmentioning

confidence: 99%

“…Traditionally, the explanatory power of a theory or model in science has been thought to relate to its ability to help us satisfy certain kinds of scientific goals. While there are disagreements regarding which goals in particular ought to be considered essential for explanation, a list of frequently defended scientific goals include:(1) Successfully conveying understanding about the target phenomenon, or making it intelligible, to an audience or inquirer (Achinstein 1983;Braverman et al 2012Waskan et al, 2014.(2) Determining when a given phenomenon is expected to occur, and under what conditions (Hempel & Oppenheim 1948;Hempel 1965;Chemero & Silberstein 2008;Rice 2015). (5) Providing information sufficient to control, manipulate, and reproduce the target phenomenon (Woodward 2000(Woodward , 2003Eliasmith 2010) It is the attainment of these scientific goals that are thought to imbue scientific models with explanatory power.…”

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“…For example, some psychological studies have shown that scientists are unlikely to consider a model or theory explanatory unless it provides understanding (Braverman et al 2012Waskan et al, 2014. Others have shown that both scientists and laypeople tend to view a theory or model as explanatory when it subsumes phenomena under general principles or patterns which can be used for making future predictions (Lombrozo and Carey 2006).…”

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“…In such a case, the claim that the model provides a good scientific explanation loses much of its force. In fact, recent studies have shown that practicing scientists are unlikely to consider such models explanatory if they are unable to provide understanding (Braverman et al 2012;Waskan et al 2014aWaskan et al , 2014bWaskan et al 2014). In a set of experiments conducted by Waskan et al (2014),…”

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Why one model is never enough: a defense of explanatory holism

Hochstein

2017

Biol Philos

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Abstract:Traditionally, a scientific model is thought to provide a good scientific explanation to the extent that it satisfies certain scientific goals that are thought to be constitutive of explanation (e.g. generating understanding, identifying mechanisms, making predictions, identifying high-level patterns, allowing us to control and manipulate phenomena). Problems arise when we realize that individual scientific models cannot simultaneously satisfy all the scientific goals typically associated with explanation. A given model's ability to satisfy some goals must always come at the expense of satisfying others. This has resulted in philosophical disputes regarding which of these goals are in fact necessary for explanation, and as such which types of models can and cannot provide explanations (e.g. dynamical models, optimality models, topological models, etc). Explanatory monists argue that one goal will be explanatory in all contexts, while explanatory pluralists argue that the goal will vary based on pragmatic considerations. In this paper, I argue that such debates are misguided, and that both monists and pluralists are incorrect. Instead of any goal being given explanatory priority over others in a given context, the different goals are all deeply dependent on one another for their explanatory power. Any model that sacrifices some explanatory goals to attain others will always necessarily undermine its own explanatory power in the process. And so when forced to choose between individual scientific models, there can be no explanatory victors. Given that no model can satisfy all the goals typically associated with explanation, no one model in isolation can provide a good scientific explanation. Instead we must appeal to collections of models. Collections of models provide an explanation when they satisfy the web of interconnected goals that justify the explanatory power of one another.What kind of information must a scientific model convey in order to provide a good scientific explanation? This question has been at the heart of many recent debates within philosophy of science (see, for example: Batterman 2002;Craver 2006;Potochnik 2007Potochnik , 2010Weber 2008;Huneman 2010;Kaplan & Craver 2011;Lange 2013;Chirimuuta 2014;Rice 2015;Povich 2016). Traditionally, the explanatory power of a theory or model in science has been thought to relate to its ability to help us satisfy certain kinds of scientific goals. While there are disagreements regarding which goals in particular ought to be considered essential for explanation, a list of frequently defended scientific goals include:(1) Successfully conveying understanding about the target phenomenon, or making it intelligible, to an audience or inquirer (Achinstein 1983;Braverman et al. 2012Waskan et al, 2014.(2) Determining when a given phenomenon is expected to occur, and under what conditions (Hempel & Oppenheim 1948;Hempel 1965;Chemero & Silberstein 2008;Rice 2015). (5) Providing information sufficient to control, manipulate, and reproduce the target phenomenon (...

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Experimental Philosophy of Science

Machery¹

2016

A Companion to Experimental Philosophy

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Explanatory anti-psychologism overturned by lay and scientific case classifications

Cited by 24 publications

References 23 publications

A Bayesian framework for knowledge attribution: Evidence from semantic integration

A Bayesian framework for knowledge attribution: Evidence from semantic integration

Why one model is never enough: a defense of explanatory holism

Experimental Philosophy of Science

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