Vanja Subotić

Deep learning (DL) is a statistical technique for pattern classification through which AI researchers train artificial neural networks containing multiple layers that process massive amounts of data. I present a three-level account of explanation that can be reasonably expected from DL models in cognitive neuroscience and that illustrates the explanatory dynamics within a future-biased research program (Feest Philosophy of Science 84:1165–1176, 2017 ; Doerig et al. Nature Reviews: Neuroscience 24:431–450, 2023 ). By relying on the mechanistic framework (Craver Explaining the brain: Mechanisms and the mosaic unity of neuroscience. Clarendon, 2007 ; Stinson Eppuor si muove: Doing history and philosophy of Science with Peter Machamer. Springer, 2017, The Routledge Handbook of the computational mind. Routledge, 2018 ), I develop an account that corresponds to the stages of mechanism discovery, i.e., our shifting epistemic position and epistemic goals, and propose a representative model for each level. Generic, theoretical DL models at _Level 1_ address the general features of a cognitive phenomenon through exploration and provide us with how-possibly explanations. On the other hand, DL models at _Level 2_ either identify the interaction between the features or represent the epistemic stage when the researcher is still unsure if the modeled features are crucial or arbitrary. These models should provide us with how-plausibly explanations. Finally, specific DL models of specific brain areas, i.e., ersatz models filled with relevant cognitive and neuroscientific details, are at _Level 3_. At this level, a researcher can advance how-actually explanations of cognitive phenomena. The main strength of this account is that it elucidates both _global explanatory dynamics_ and _local explanatory dynamics_ (cf. Dresow Synthese 199:10441–10474, 2021 ). The former occurs when the transition between levels happens in accordance with the process of obtaining more details about a particular cognitive phenomenon through multiple DL models. The latter, meanwhile, involves cases in which the transition between levels takes place within a single DL model by elucidating internal mechanisms (e.g., using Explainable AI techniques for rendering models more transparent).

The topic of this dissertation is the nature of linguistic competence, the capacity to understand and produce sentences of natural language. I defend the empiricist account of linguistic competence embedded in the connectionist cognitive science. This strand of cognitive science has been opposed to the traditional symbolic cognitive science, coupled with transformational-generative grammar, which was committed to nativism due to the view that human cognition, including language capacity, should be construed in terms of symbolic representations and hardwired rules. Similarly, linguistic competence in this framework was regarded as being innate, rule-governed, domain-specific, and fundamentally different from performance, i.e., idiosyncrasies and factors governing linguistic behavior. I analyze state-of-the-art connectionist, deep learning models of natural language processing, most notably large language models, to see what they can tell us about linguistic competence. Deep learning is a statistical technique for the classification of patterns through which artificial intelligence researchers train artificial neural networks containing multiple layers that crunch a gargantuan amount of textual and/or visual data. I argue that these models suggest that linguistic competence should be construed as stochastic, pattern-based, and stemming from domain-general mechanisms. Moreover, I distinguish syntactic from semantic competence, and I show for each the ramifications of the endorsement of a connectionist research program as opposed to the traditional symbolic cognitive science and transformational-generative grammar. I provide a unifying front, consisting of usage-based theories, a construction grammar approach, and an embodied approach to cognition to show that the more multimodal and diverse models are in terms of architectural features and training data, the stronger the case is for the connectionist linguistic competence. I also propose to discard the competence vs. performance distinction as theoretically inferior so that a novel and integrative account of linguistic competence originating in connectionism and empiricism that I propose and defend in the dissertation could be put forward in scientific and philosophical literature.

Recently, Charles Goldhaber (2019) has argued that Tyler Burge’s (2005, 2010, 2011) arguments against disjunctivism in the philosophy of perception fail when juxtaposed with the literature in perceptual psychology. In addition, Goldhaber traces Burge’s motives for dismissing disjunctivism: his underlying theoretical assumptions vis-à-vis human rationality virtually force him to maintain that there is a genuine inconsistency between disjunctivism and perceptual psychology. While Goldhaber aims to defend epistemological disjunctivism à la John McDowell, my concern will be the other target of Burge’s attack, namely John Campbell’s (2002a, 2002b, 2011a) relationism. I will reexamine the Burge/Campbell debate concerning the role of perceptual psychology in theorizing about the nature of perception and the status of perceptual beliefs so that I can support Goldhaber’s stance that Burge’s plan to put the kibosh on disjunctivism backfires in the end. Finally, by using the challenge of cognitive penetrability, I show how Burge’s argumentation strategy can be turned against him.

Recently, I have proposed an extension of the framework of the ethics of collegiality (Berber & Subotić, forthcoming). By incorporating an anti-individual perspective and the notion of epistemic competence, this framework can reveal the epistemic virtue/vice relativism, which, in turn, charts the tension between being a good colleague and an efficient, loyal employee. In this paper, however, I want to sketch how the ethics of collegiality could be applied to practical domains, such as the historical accountability and atonement of corporations that participated in the anti-Semitic policies of the Third Reich and contributed to the Holocaust by using slave or forced labor. New studies suggest that corporations ought to engage in deeper historical reflection and ethical dialogue between Shoah survivors and top managers to address the issue of industrial compliance (Federman 2021), whereas most of the work on this topic traditionally focused on the issue of reparations litigation (Kelly 2016, Neuborne 2003). Through the notions of collective institutional epistemic vice and institutional ethos (Fricker 2021), the upshot is to assess whether it is feasible for corporations to be genuinely repentant regarding their role in the Holocaust thanks to the ethics of collegiality instead of merely offering compensation. I will argue that instead of emphasizing ethical leadership and the top-down approach to the (re-)implementation of values in corporate conduct, the spotlights should be on the bottom-up approach grounded in collegial solidarity among all employees.

The aim of this paper is to show that the nascent field of ethics of collegiality may considerably benefit from a symbiosis with virtue and vice epistemology. We start by bringing the epistemic virtue and vice perspective to the table by showing that competence, deemed as an essential characteristic of a good colleague (Betzler & Löschke 2021), should be construed broadly to encompass epistemic competence. By endorsing the anti-individualistic stance in epistemology as well as context-specificity of epistemic traits, we show how the individual vice of a colleague can have a positive epistemic outcome for the team while individual virtue can be damaging if all team members share it, thereby contributing to the negative epistemic outcome. We argue that the evaluation and analysis of collegial relationships should be done through one’s contribution to the team dynamics: without this collectivist perspective, the ethics of collegiality cannot aspire to become encompassing normative theory.

The aim of this paper is to discuss monastic paideia in the context of providing shelter for ASD individuals in the High Middle Ages. Firstly, we will canvas the historical and conceptual shift from Ancient Greek paideitic ideas to their Christian counterparts. Then, by drawing on the recent literature in the history of medicine that traces the signs and symptoms of autism spectrum disorder (ASD) in Hildegard of Bingen, a German abbess in the 12th century, we will turn to her invented language Lingua Ignota as a source of both her diagnosis and a manner of dealing with her disability. This, in turn, could be further advanced by identifying monasteries as therapeutic loci for neuroatypical individuals who had trouble finding their place within the arid and somewhat monochromatic medieval society.

Three decades ago, William Ramsey, Steven Stich & Joseph Garon put forward an argument in favor of the following conditional: if connectionist models that implement parallelly distributed processing represent faithfully human cognitive processing, eliminativism about propositional attitudes is true. The corollary of their argument (if it proves to be sound) is that there is no place for folk psychology in contemporary cognitive science. This understanding of connectionism as a hypothesis about cognitive architecture compatible with eliminativism is also endorsed by Paul Churchland, a radical opponent of folk psychology and a prominent supporter of eliminative materialism. I aim to examine whether current connectionist models based on long-short term memory (LSTM) neural networks can back up these arguments in favor of eliminativism. Nonetheless, I will rather put my faith in the eliminativism of the limited domain. This position amounts to the claim that even though connectionist cognitive science has no need whatsoever for folk psychology qua theory, this does not entail the illegitimacy of folk psychology per se in other scientific domains, most notably in humanities, but only on condition that one regards folk psychology as mere heuristics.

Cognitive scientists used to deem reasoning either as a higher cognitive process based on the manipulation of abstract rules or as a higher cognitive process that is stochastic rather than involving abstract rules. I maintain that these different perspectives are closely intertwined with a theoretical and methodological endorsement of either cognitivism or connectionism. Cognitivism and connectionism represent two prevailing and opposed paradigms in cognitive science. I aim to extoll the virtues of connectionist models of enthymematic reasoning by following means: via the phenomenon of creative enthymeme, viz. the inference where one cannot even articulate the missing premise, I introduce a connectionist mechanism of pattern recognition as underlying expertise; via Gestalt switch or Gestalt click, I demonstrate how differences in pattern recognition of an expert and a novice can be construed as qualitatively different, and not merely a matter of faster reasoning.

This paper aims to offer a new view of the role of connectionist models in the study of human cognition through the conceptualization of the history of connectionism – from the simplest perceptrons to convolutional neural nets based on deep learning techniques, as well as through the interpretation of criticism coming from symbolic cognitive science. Namely, the connectionist approach in cognitive science was the target of sharp criticism from the symbolists, which on several occasions caused its marginalization and almost complete abandonment of its assumptions in the study of cognition. Criticisms have mostly pointed to its explanatory inadequacy as a theory of cognition or to its biological implausibility as a theory of implementation, and critics often focused on specific shortcomings of some connectionist models and argued that they apply to connectionism in general. In this paper, we want to show that both types of critique are based on the assumption that the only valid explanations in cognitive science are instances of homuncular functionalism and that by removing this assumption and by adopting an alternative methodology – exploratory mechanistic strategy, we can reject most objections to connectionism as irrelevant, explain the progress of connectionist models despite their shortcomings and sketch the trajectory of their future development. By adopting mechanistic explanations and by criticizing functionalism, we will reject the objections of explanatory inadequacy, by characterizing connectionist models as generic rather than concrete mechanisms, we will reject the objections of biological implausibility, and by attributing the exploratory character to connectionist models we will show that practice of generalizing current to general failures of connectionism is unjustified.

The epistemic attitudes of scientists, such as epistemic tolerance and authoritarianism, play important roles in the discourse about rivaling theories. Epistemic tolerance stands for the mental attitude of an epistemic agent, e.g., a scientist, who is open to opposing views, while epistemic authoritarianism represents the tendency to uncritically accept views of authorities. Another relevant epistemic factor when it comes to the epistemic decisions of scientists is the skepticism towards the scientific method. However, the question is whether these epistemic attitudes are influenced by their sociopolitical counterparts, such as the researcher’s degree of conservatism. To empirically investigate the interplay between epistemic and sociopolitical attitudes of scientists, we conducted a survey with researchers (N = 655) across different disciplines. We propose scales for measuring epistemic tolerance and epistemic authoritarianism, as well as a scale for detecting the participants' readiness to question the scientific method. Furthermore, we investigate the relationship between epistemic tolerance and epistemic authoritarianism on the one hand, and career stage and sociopolitical views on the other hand. Interestingly, our study found only small correlations between the participants' degree of conservatism and their epistemic attitudes. This suggests that political views, against common argumentation, actually do not play an important role in one’s scientific decisions. Moreover, social scientists scored higher on the epistemic tolerance and lower on the epistemic authoritarianism scale than natural scientists. Finally, the results indicate that natural scientists question the scientific method less than social scientists.