Daniel Weiskopf

A ‘Radical Simulationist’ account of how folk psychology functions has been developed by Robert Gordon. I argue that Radical Simulationism is false. In its simplest form it is not sufficient to explain our attribution of mental states to subjects whose desires and preferences differ from our own. Modifying the theory to capture these attributions invariably generates innumerable other false attributions. Further, the theory predicts that deficits in mentalizing ought to co-occur with certain deficits in imagining perceptually-based scenarios. I present evidence suggesting that this prediction is false, and outline further possible empirical tests of the theory.

There are good reasons to think natural languages are compositional. But compound nominals (CNs) are largely productive constructions that have proven highly recalcitrant to compositional semantic analysis. I evaluate existing proposals to treat CNs compositionally and argue that they are unsuccessful. I then articulate an alternative proposal according to which CNs contain covert indexicals. Features of the context allow a variety of relations to be expressed using CNs, but this variety is not expressed in the lexicon or the semantic rules of the language. This proposal accounts for the diversity of contents CNs can be used to express while preserving compositionality. Finally, I defend this proposal against some recent anti-contextualist arguments.

The view that special science properties are multiply realizable has been attacked in recent years by Shapiro, Bechtel and Mundale, Polger, and others. Focusing on psychological and neuroscientific properties, I argue that these attacks are unsuccessful. By drawing on interspecies physiological comparisons I show that diverse physical mechanisms can converge on common functional properties at multiple levels. This is illustrated with examples from the psychophysics and neuroscience of early vision. This convergence is compatible with the existence of general constraints on the evolution of cognitive systems, and does not involve any ad hoc typing of coarse-grained higher level properties. The mechanisms that realize these common higher level properties are really distinct by the criteria laid down by critics of multiple readability. Finally, I present an account of how such functional properties might constitute special science kinds by playing a central explanatory role in a range of cognitive models. Behavioral science kinds in particular are the functionally defined constituents picked out by our most successful models of the multilevel systems and mechanisms that explain cognitive capacities

Neuroscience constrains psychology, but stating these constraints with precision is not simple. Here I consider whether mechanistic analysis provides a useful way to integrate models of cognitive and neural structure. Recent evidence suggests that cognitive systems map onto overlapping, distributed networks of brain regions. These highly entangled networks often depart from stereotypical mechanistic behaviors. While this casts doubt on the prospects for classical mechanistic integration of psychology and neuroscience, I argue that it does not impugn a realistic interpretation of either type of model. Cognitive and neural models may depict different, but equally real, causal structures within the mind/brain.