Eric Hochstein

Some argue that the term “explanation” in science is ambiguous, referring to at least three distinct concepts: a communicative concept, a representational concept, and an ontic concept. Each is defined in a different way with its own sets of norms and goals, and each of which can apply in contexts where the others do not. In this paper, I argue that such a view is false. Instead, I propose that a scientific explanation is a complex entity that can always be analyzed along a communicative dimension, a representational dimension, and an ontic dimension. But all three are always present within scientific explanations. I highlight what such an account looks like, and the potential problems it faces (namely that a single explanation can appear to have incompatible sets of norms and goals that govern it). I propose a solution to this problem and demonstrate how this account can help to dissolve current disputes in philosophy of science regarding debates between epistemic and ontic accounts of mechanistic explanations in the life sciences.

It is traditionally thought that integration in cognitive science requires combining different perspectives, elements, and insights into an integrated model or theory of the target phenomenon. In this paper I argue that this type of integration is frequently not possible in cognitive science due to our reliance on using different idealizing and simplifying assumptions in our models and theories. Despite this, I argue that we can still have integration in cognitive science and attain all the benefits that integrated models would provide, without the need for their construction. Models which make incompatible idealizing assumptions about the target phenomenon can still be integrated by understanding how to draw coherent and compatible inferences across them. I discuss how this is possible, and demonstrate how this supports a different kind of integration. This sense of integration allows us to use collections of contradictory models to develop a consistent, comprehensive and non-contradictory understanding of a single unified phenomenon without the need for a single integrated model or theory.

Anil Gupta’s Conscious Experience: A Logical Inquiry provides an impressive and novel account of rational justification based on conscious experience which is used as a foundation for a new theory of empiricism. In this critical notice, I argue that Gupta’s project is fascinating, but is often hampered by a lack of sufficient philosophical justification and clarity regarding some essential features of his project, as well as a lack of engagement with relevant scientific domains that would directly bear on it, such as computational neuroscience, cognitive neuroscience, and cognitive psychology. This limits the sort of logical inquiry available to him in problematic ways.

Talk of “levels” can be found throughout the sciences, from “levels of abstraction”, to “levels of organization”, to “levels of analysis”. This has led to substantial disagreement regarding the ontology of levels, and whether the various senses of levels each have genuine value and utility to scientific practice. In this paper, I propose a unified framework for thinking about levels in science which ties together the various ways in which levels are invoked in science, and which can overcome the problems that different senses of levels have faced. I argue that levels can best be understood as choices scientists make regarding what sort of information to foreground in their models and theories, and what sort of information to push into the background. To change levels is to change the foregrounding and backgrounding of information for different representational and pragmatic purposes.

Anil Gupta’s Conscious Experience: A Logical Inquiry provides an impressive and novel account of rational justification based on conscious experience which is used as a foundation for a new theory of empiricism. In this critical notice, I argue that Gupta’s project is fascinating, but is often hampered by a lack of sufficient philosophical justification and clarity regarding some essential features of his project, as well as a lack of engagement with relevant scientific domains that would directly bear on it, such as computational neuroscience, cognitive neuroscience, and cognitive psychology. This limits the sort of logical inquiry available to him in problematic ways.

This paper explores the current obstacles that a cognitive theory of humor faces. More specifically, I argue that the nebulous and ill-defined nature of humor makes it difficult to tell what counts as clear instances of, and deficits in, the phenomenon.Without getting clear on this, we cannot identify the underlying cognitive mechanisms responsible for humor. Moreover, being too quick to draw generalizations regarding the ubiquity of humor, or its uniqueness to humans, without substantially clarifying the phenomenon and its occurrences is not only unwise but can actually be a detriment to our study of humor. As such, these sorts of claims must be resisted. I conclude the paper by pointing the way forward to addressing these obstacles.

There is a long-standing debate in the philosophy of mind and philosophy of science regarding how best to interpret the relationship between neuroscience and psychology. It has traditionally been argued that either the two domains will evolve and change over time until they converge on a single unified account of human behaviour, or else that they will continue to work in isolation given that they identify properties and states that exist autonomously from one another. In this paper, I argue that progress in psychology and neuroscience is contingent on the fact that both of these positions are false. Contra the convergence position, I argue that the theories of psychology and the theories of neuroscience are scientifically valuable as representational tools precisely because they cannot be integrated into a single account. However, contra the autonomy position, I propose that the theories of psychology and neuroscience are deeply dependent on one another for further refinement and improvement. In this respect, there is an irreconcilable codependence between psychology and neuroscience that is necessary for both domains to improve and progress. The two domains are forever linked while simultaneously being unable to integrate.

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. 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. 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.

In this article, I argue that the use of scientific models that attribute intentional content to complex systems bears a striking similarity to the way in which statistical descriptions are used. To demonstrate this, I compare and contrast an intentional model with a statistical model, and argue that key similarities between the two give us compelling reasons to consider both as a type of phenomenological model. I then demonstrate how intentional descriptions play an important role in scientific methodology as a type of phenomenal model, and argue that this makes them as essential as any other model of this type

In this article, I argue that intentional psychology (i.e. the interpretation of human behaviour in terms of intentional states and propositional attitudes) plays an essential role in the sciences of the mind. However, this role is not one of identifying scientifically respectable states of the world. Rather, I argue that intentional psychology acts as a type of phenomenological model, as opposed to a mechanistic one. I demonstrate that, like other phenomenological models in science, intentional psychology is a methodological tool with its own benefits and insights that complements our mechanistic understanding of systems. As a result, intentional psychology's distinctive scientific benefit is its ability to model systems in unique, non-mechanistic, ways. This allows us to generate predictions that we cannot otherwise generate using the mechanistic models of neuroscience and cognitive psychology necessary for various scientific tasks.

There is a long-standing debate in the philosophy of mind and philosophy of science regarding how best to interpret the relationship between neuroscience and psychology. It has traditionally been argued that either the two domains will evolve and change over time until they converge on a single unified account of human behaviour, or else that they will continue to work in isolation given that they identify properties and states that exist autonomously from one another (due to the multiple-realizability of psychological states). In this paper, I argue that progress in psychology and neuroscience is contingent on the fact that both of these positions are false. Contra the convergence position, I argue that the theories of psychology and the theories of neuroscience are scientifically valuable as representational tools precisely because they cannot be integrated into a single account. However, contra the autonomy position, I propose that the theories of psychology and neuroscience are deeply dependent on one another for further refinement and improvement. In this respect, there is an irreconcilable codependence between psychology and neuroscience that is necessary for both domains to improve and progress. The two domains are forever linked while simultaneously being unable to integrate.

There have been recent disagreements in the philosophy of neuroscience regarding which sorts of scientific models provide mechanistic explanations, and which do not. These disagreements often hinge on two commonly adopted, but conflicting, ways of understanding mechanistic explanations: what I call the “representation-as” account, and the “representation-of” account. In this paper, I argue that neither account does justice to neuroscientific practice. In their place, I offer a new alternative that can defuse some of these disagreements. I argue that individual models do not provide mechanistic explanations by themselves. Instead, individual models are always used to complement a huge body of background information and pre-existing models about the target system. With this in mind, I argue that mechanistic explanations are distributed across sets of different, and sometimes contradictory, scientific models. Each of these models contributes limited, but essential, information to the same mechanistic explanation, but none can be considered a mechanistic explanation in isolation of the others

In this dissertation, I examine the role that intentional descriptions play in our scientific study of the mind. Behavioural scientists often use intentional language in their characterization of cognitive systems, making reference to “beliefs”, “representations”, or “states of information”. What is the scientific value gained from employing such intentional terminology? I begin the dissertation by contrasting intentional descriptions with mechanistic descriptions, as these are the descriptions most commonly used to provide explanations in the behavioural sciences. I then examine the way that intentional descriptions are employed in various scientific contexts. I conclude that while mechanistic descriptions characterize the underlying structure of systems, intentional descriptions allow us to generate predictions of systems while remaining agnostic as to their mechanistic underpinnings. Having established this, I then argue that intentional descriptions share much in common with statistical models in the way they characterize systems. Given these similarities, I theorize that intentional descriptions are employed within scientific practice as a particular type of phenomenological model. Phenomenological models are used to study, characterize, and predict the phenomena produced by mechanistic systems without describing their underlying structure. I demonstrate why such models are integral to our scientific discovery, and understanding, of the mechanisms that make up the brain. With my account on the table, I then look back at previous accounts of intentional language that philosophers have offered in the past. I highlight insights that each brought to our understanding of intentional language, and point out where each ultimately goes astray. I conclude the dissertation by examining the ontological implications of my theory. I demonstrate that my account is compatible with versions of both realism, and anti-realism, regarding the existence of intentional states.

It has commonly been argued that certain types of mental descriptions, specifically those characterized in terms of propositional attitudes, are part of a folk psychological understanding of the mind. Recently, however, it has also been argued that this is the case even when such descriptions are employed as part of scientific theories in domains like social psychology and comparative psychology. In this paper, I argue that there is no plausible way to understand the distinction between folk and scientific psychology that can support such claims. Moreover, these sorts of claims can have adverse consequences for the neuroscientific study of the brain by downplaying the value of many psychological theories that provide information neuroscientists need in order to build and test neurological models

A common view in the philosophy of mind and philosophy of psychology is that there is an ideally correct way of categorizing the structures and operations of the mind, and that the goal of neuroscience and psychology is to find this correct categorizational scheme. Categories which cannot find a place within this correct framework ought to be eliminated from scientific practice. In this paper, I argue that this general idea runs counter to productive scientific practices. Such a view ignores the plurality of aims and goals that neuroscientists and psychologists have in studying mental phenomena, and the necessity of employing distinct classificatory frameworks to achieve them.