Nina Poth

Common-sense learning and reasoning is a landmark of human-like intelligence. While classical-AI expert systems could convince at it in only narrow domains, contemporary deep-neural network models surprise with rapid performance improvements across many domains. At the same time, Bayesian Intuitive Theories have been influential in cognitive science as formal accounts of rational learning and reasoning. This paper targets Bayesian Intuitive Theories insofar as they rely on inferential-role semantics for conceptual content, to critically evaluate the promises and limits of this influential approach at mediating a theory of human-like common sense. I argue that both insights from deep-learning models and from Bayesian intuitive theories (insofar as they rely on inferential-role semantics for conceptual content) are insufficient to capture what seems to be centrally important in human-like common-sense learning and reasoning: Not just its internal consistency, but also its inherent relationship to the outside world. To address this challenge, I propose a situated revision of Bayesian Intuitive Theories that preserves the epistemic standards of rational reasoning while grounding domain-structured semantic content in ecological perception. By focusing on the role of semantic structure in embodied intervention, this proposal shows why common-sense reasoning inherently relates to the outside world. Consequently, machine-learning models mediate a theory of human-like common-sense only if the inferences that they implement satisfy both semantic and (inter)active constraints.

The rise of artificial intelligence (AI) raises the question of whether we should introduce a new category of representations, next to mental and scientific representations. We argue that AI ‘representations’, in particular of deep neural networks, differ significantly from the mental and scientific representations central to the philosophy of (cognitive) science. These systems lack essential aspects, such as semantic content, the ability to misrepresent, and a clear use condition guiding behavior; it is often unclear why and what they represent. They also lack the capacity to form or identify misrepresentations which makes it impossible to assess their accuracy. Furthermore, AI systems do not satisfy a use condition in the same way as mental and scientific representations. We conclude that, while AI systems can, under certain conditions, be useful tools for scientific discovery, their internal states should not be mistaken for mental and scientific representations.

While recent cognitive science research shows a renewed interest in understanding intelligence, there is still little consensus on what constitutes intelligent behaviour and how it should be assessed. Here we propose a refined approach to biological intelligence as accurate prediction, according to which intelligent behaviour should be understood as adaptive control driven by the minimisation of uncertainty in dynamic environments with limited information. Central to this view is the concept of accuracy, which we argue is key to determining the success of predictions. We identify tensions in applying this framework to contemporary artificial systems such as large-language models, which, despite their impressive capacities for abstract prediction, show deficits in terms of context-sensitive knowledge transfer.

Carey’s (2009) account of bootstrapping in developmental psychology has been criticized out of a lack of theoretical precision and because of its alleged circularity (Rips et al. 2013, Cognition 128 (3): 320–330; Fodor 2010, Times Literary Supplement, 7–8; Rey 2014, Mind & Language 29 (2): 109–132). In this paper, we respond to these criticisms by connecting the debate on bootstrapping with recent accounts of conceptual creativity in philosophy of science. Specifically, we build on Nersessian’s (2010) hybrid-models-based theory of scientific conceptual change to develop a refined model of bootstrapping. The key explanatory feature of this model, which we will call hybrid bootstrapping, is the iterated hybridization of different representational domains. We show how hybrid bootstrapping can answer two major critiques that have been leveled against Carey’s account: the circularity challenge and the specification challenge.

Tenenbaum and Griffiths (Behavioral and Brain Sciences 24(4):629–640, 2001) have proposed that their Bayesian model of generalisation unifies Shepard’s (Science 237(4820): 1317–1323, 1987) and Tversky’s (Psychological Review 84(4): 327–352, 1977) similarity-based explanations of two distinct patterns of generalisation behaviours by reconciling them under a single coherent task analysis. I argue that this proposal needs refinement: instead of unifying the heterogeneous notion of psychological similarity, the Bayesian approach unifies generalisation by rendering the distinct patterns of behaviours informationally relevant. I suggest that generalisation as a Bayesian inference should be seen as a complement to, instead of a replacement of, similarity-based explanations. Furthermore, I show that the unificatory powers of the Bayesian model of generalisation can contribute to the selection of one of these models of psychological similarity.

The notions of psychological similarity and probabilistic learning are key posits in cognitive, computational, and developmental psychology and in machine learning. However, their explanatory relationship is rarely made explicit within and across these research fields. This opinionated review critically evaluates how these notions can mutually inform each other within computational cognitive science. Using probabilistic models of concept learning as a case study, I argue that two notions of psychological similarity offer important normative constraints to guide modelers’ interpretations of representational primitives. In particular, the two notions furnish probabilistic models of cognition with meaningful interpretations of what the associated subjective probabilities in the model represent and how they attach to experiences from which the agent learns. Similarity representations thereby provide probabilistic models with cognitive, as opposed to purely mathematical, content.

The feature-matching approach pioneered by Amos Tversky remains a groundwork for psychological models of similarity and categorization but is rarely explicitly justified considering recent advances in thinking about cognition. While psychologists often view similarity as an unproblematic foundational concept that explains generalization and conceptual thought, long-standing philosophical problems challenging this assumption suggest that similarity derives from processes of higher-level cognition, including inference and conceptual thought. This paper addresses three specific challenges to Tversky’s approach: (i) the feature-selection problem, (ii) the problem of cognitive implausibility, and (iii) the problem of unprincipled tweaking. It subsequently supports key insights from Tversky’s account based on recent developments in Bayesian modeling of cognition. A novel computational view of similarity as inference is proposed that addresses each challenge by considering the contrast class as constitutive of similarity and selecting for highly informative features. In so doing, this view illustrates the ongoing promise of the feature-matching approach in explaining perception, generalization and conceptual thought by grounding them in principles of probabilistic inference.

Several philosophers and psychologists have characterized belief in conspiracy theories as a product of irrational reasoning. Proponents of conspiracy theories apparently resist revising their beliefs given disconfirming evidence and tend to believe in more than one conspiracy, even when the relevant beliefs are mutually inconsistent. In this paper, we bring leading views on conspiracy theoretic beliefs closer together by exploring their rationality under a probabilistic framework. We question the claim that the irrationality of conspiracy theoretic beliefs stems from an inadequate response to disconfirming evidence and internal incoherence. Drawing analogies to Lakatosian research programs, we argue that maintaining a core conspiracy belief can be Bayes-rational when it is embedded in a network of auxiliary beliefs, which can be revised to protect the more central belief from disconfirmation. We propose that the (ir)rationality associated with conspiracy belief lies not in a flawed updating method, but in their failure to converge toward well-confirmed, stable belief networks in the long run. This approach not only reconciles previously disjointed views, but also points toward more specific hypotheses explaining why some agents may be prone to adopting beliefs in conspiracy theories.

Proponents of the predictive processing (PP) framework often claim that one of the framework’s significant virtues is its unificatory power. What is supposedly unified are predictive processes in the mind, and these are explained in virtue of a common prediction error-minimisation (PEM) schema. In this paper, I argue against the claim that PP currently converges towards a unified explanation of cognitive processes. Although the notion of PEM systematically relates a set of posits such as ‘efficiency’ and ‘hierarchical coding’ into a unified conceptual schema, neither the frameworks’ algorithmic specifications nor its hypotheses about their implementations in the brain are clearly unified. I propose a novel way to understand the fruitfulness of the research program in light of a set of research heuristics that are partly shared with those common to Bayesian reverse engineering. An interesting consequence of this proposal is that pluralism is at least as important as unification to promote the positive development of the predictive mind.

Tenenbaum and Griffiths (2001) have proposed that their Bayesian model of generalisation unifies Shepard’s (1987) and Tversky’s (1977) similarity-based explanations of two distinct patterns of generalisation behaviours by reconciling them under a single coherent task analysis. I argue that this proposal needs refinement: instead of unifying the heterogeneous notion of psychological similarity, the Bayesian approach unifies generalisation by rendering the distinct patterns of behaviours informationally relevant. I suggest that generalisation as a Bayesian inference should be seen as a complement to, instead of a replacement of, similarity-based explanations. Furthermore, I show that the unificatory powers of the Bayesian model of generalisation can contribute to the selection of one of these models of psychological similarity.

Several philosophers and psychologists have characterized belief in conspiracy theories as a product of irrational reasoning. Proponents of conspiracy theories apparently resist revising their beliefs given disconfirming evidence and tend to believe in more than one conspiracy, even when the relevant beliefs are mutually inconsistent. In this paper, we bring leading views on conspiracy theoretic beliefs closer together by exploring their rationality under a probabilistic framework. We question the claim that the irrationality of conspiracy theoretic beliefs stems from an inadequate response to disconfirming evidence and internal incoherence. Drawing analogies to Lakatosian research programs, we argue that maintaining a core conspiracy belief can be Bayes-rational when it is embedded in a network of auxiliary beliefs, which can be revised to protect the more central belief from disconfirmation. We propose that the irrationality associated with conspiracy belief lies not in a flawed updating method, but in a failure to converge toward wellconfirmed, stable belief networks in the long run. This approach not only reconciles previously disjointed views, but also points toward more specific descriptions of why agents may be prone to adopting beliefs in conspiracy theories.

Shepard’s (1987) universal law of generalisation (ULG) illustrates that an invariant gradient of generalisation across species and across stimuli conditions can be obtained by mapping the probability of a generalisation response onto the representations of similarity between individual stimuli. Tenenbaum and Griffiths (2001) Bayesian account of generalisation expands ULG towards generalisation from multiple examples. Though the Bayesian model starts from Shepard’s account it refrains from any commitment to the notion of psychological similarity to explain categorisation. This chapter presents the conceptual spaces theory (Gärdenfors 2000, 2014) as a mediator between Shepard’s and Tenenbaum & Griffiths’ conflicting views on the role of psychological similarity for a successful model of categorisation. It suggests that the conceptual spaces theory can help to improve the Bayesian model while finding an explanatory role for psychological similarity.

Children acquire complex concepts like DOG earlier than simple concepts like BROWN, even though our best neuroscientific theories suggest that learning the former is harder than learning the latter and, thus, should take more time (Werning 2010). This is the Complex- First Paradox. We present a novel solution to the Complex-First Paradox. Our solution builds on a generalization of Xu and Tenenbaum’s (2007) Bayesian model of word learning. By focusing on a rational theory of concept learning, we show that it is easier to infer the meaning of complex concepts than that of simple concepts.

Philosophical orthodoxy holds that pains are mental states, taking this to reflect the ordinary conception of pain. Despite this, evidence is mounting that English speakers do not tend to conceptualize pains in this way; rather, they tend to treat pains as being bodily states. We hypothesize that this is driven by two primary factors—the phenomenology of feeling pains and the surface grammar of pain reports. There is reason to expect that neither of these factors is culturally specific, however, and thus reason to expect that the empirical findings for English speakers will generalize to other cultures and other languages. In this article we begin to test this hypothesis, reporting the results of two cross-cultural studies comparing judgments about the location of referred pains between two groups—Americans and South Koreans—that we might otherwise expect to differ in how they understand pains. In line with our predictions, we find that both groups tend to conceive of pains as bodily states.