Psychologism is defined as “the doctrine that the laws of mathematics and logic can be reduced to or depend on the laws governing thinking” (Moran & Cohen, 2012 266). And for Husserl, the laws of logic include the laws of meaning: “logic evidently is the science of meanings as such [Wissenschaft von Bedeutungen als solchen]” (Husserl ( 1975 ) 98/2001 225). I argue that, since it is sufficient for a theory to be psychologistic if the empiricistic theory of abstraction is (...) employed, it follows that neural networks are psychologistic insofar as they use this theory of abstraction, which I demonstrate is the case (Husserl ( 1975 ) 191/2001 120). It’s sufficient for psychologism because, according to Husserl, the theory in question reduces one’s phenomenological ability to intend types (or universals) to one’s past history of intending tokens (or particulars), usually amalgamated in some fashion (classically via associations; recently via autoencoders) (ibid; Kelleher, 2019 ). Similarly dynamical systems theory entails psychologism. For dynamical systems theory ties content to the temporal evolution of a system, which, according to Husserl, violates the fact that intentionality toward validities and objectivities does not pertain to "particular temporal experience[s]" (Husserl ( 1975 ) 194/2001 121). It follows that neither the species (neural networks), nor the genus (dynamical systems), can avoid psychologism and intend objects "in specie" (ibid). After critiquing these two approaches, I proceed to give an account based on the essentialist school of cognitive psychology of how we may intend objects "in specie" while avoiding the empiricistic theory of abstraction (Keil, 1989, Carey, 2009, Marcus & Davis, 2019 ). Such an account preserves the type-token distinction without psychologistic reduction to the temporal evolution of a dynamical system (Hinzen, 2006 ). This opens the way toward a truly unifying account of Husserlian phenomenology in league with cognitive science that avoids Yoshimi's ( 2016 ) and neurophenomenology's psychologistic foundation (herein demonstrated) and builds upon Sokolowski's ( 2003 ) syntactic account of Husserlian phenomenology. (shrink)

Going from cognitive theory to neural data to ACT‐R models, the authors relate brain activity in a lexical ambiguity priming task to brain processes that resolve ambiguity in word meanings. These detailed data were tested and found compatible to the results of an ACT‐R computational model of reinforcement learning (RL). The model confirms and extends the behavioral findings to provide a RL account of individual differences in lexical ambiguity resolution.

Findings suggest a positive impact of bilingualism on cognition, including the later onset of dementia. However, it is not clear to what extent these effects are influenced by variations in attentional control demands in response to specific task requirements. In this study, 20 bilingual and 20 monolingual older adults performed a task-switching task under explicit task-cuing vs. memory-based switching conditions. In the cued condition, task switches occurred in random order and a visual cue signaled the next task to be performed. (...) In the memory-based condition, the task alternated after every second trial in a predictable sequence without presenting a cue. The performance of bilinguals did not vary across experimental conditions, whereas monolinguals experienced a pronounced increase in response latencies and error rates in the cued condition. Both groups produced similar switch costs and mixing costs, but bilinguals produced them with lower response latencies. The cognitive benefits of bilingualism seem not to apply to executive functions per se but to affect specific cognitive processes that involve task-relevant context processing. The present results suggest that lifelong bilingualism could promote in older adults a flexible adjustment to environmental cues, but only with increased task demands. However, due to the small sample size, the results should be interpreted with caution. (shrink)

The Role of Dorsal Premotor Cortex in Resolving Abstract Motor Rules provides alternative hypotheses about the cognitive functions affected by the application of repetitive transcranial magnetic stimulation. Their model simulated the effect of stimulation of the left dorsal premotor cortex right as participants provide a Models were used to demonstrate that the increased variability in observed response times can result from interference in replanning during the process of responding to the uninstructed stimulus.

Lopes ( 2021 ) has argued against my use of neural networks and dynamical systems theory in neurophenomenology. Responding to his argument provides an opportunity to articulate a pluralist approach to neurophenomenology, according to which multiple theoretical frameworks—symbolic, dynamical systems, connectionist, etc.—can be used to study consciousness and its relationship to neural activity. Each type of analysis is best suited to specific phenomena, but they are mutually compatible and can inform and constrain one another in non-trivial ways. I use historical (...) and conceptual arguments to elaborate on this type of pluralism as it applies to cognitive science, phenomenology, and neurophenomenology. (shrink)

Several attempts have been made previously to provide a biological grounding for cognitive architectures by relating their components to the computations of specific brain circuits. Often, the architecture's action selection system is identified with the basal ganglia. However, this identification overlooks one of the most important features of the basal ganglia—the existence of a direct and an indirect pathway that compete against each other. This characteristic has important consequences in decision-making tasks, which are brought to light by Parkinson's disease as (...) well as genetic differences in dopamine receptors. This paper shows that a standard model of action selection in a cognitive architecture cannot replicate any of these findings, details an alternative solution that reconciles action selection in the architecture with the physiology of the basal ganglia, and extends the domain of application of cognitive architectures. The implication of this solution for other architectures and existing models are discussed. (shrink)

The ability to reason and problem‐solve in novel situations, as measured by the Raven's Advanced Progressive Matrices (RAPM), is highly predictive of both cognitive task performance and real‐world outcomes. Here we provide evidence that RAPM performance depends on the ability to reallocate attention in response to self‐generated feedback about progress. We propose that such an ability is underpinned by the basal ganglia nuclei, which are critically tied to both reward processing and cognitive control. This hypothesis was implemented in a neurocomputational (...) model of the RAPM task, which was used to derive novel predictions at the behavioral and neural levels. These predictions were then verified in one neuroimaging and two behavioral experiments. Furthermore, an effective connectivity analysis of the neuroimaging data confirmed a role for the basal ganglia in modulating attention. Taken together, these results suggest that individual differences in a neural circuit related to reward processing underpin human fluid reasoning abilities. (shrink)

We discuss the evolutionary implications of connections drawn between the authors' learned “secondary modules” and the habit-formation system that appears to be ubiquitous among vertebrates. Prior to any subsequent coevolution with social learning, we suggest that aspects of general intelligence likely arose in tandem with mechanisms of adaptive motor control that rely on basal ganglia circuitry.