Date of Degree


Document Type


Degree Name





Jesse Prinz

Committee Members

Barbara Montero

David Papineau

Subject Categories

Philosophy of Mind


Intentionality, Psychosemantics, Information Semantics, Content, Representation


When we think, we typically think ‘about’ something, a peculiar property of mental states often called ‘intentionality’. My dissertation is a collection of papers addressing key questions about the nature of intentionality. These questions demand answers because intentionality is poorly understood, yet fundamental to the way we talk and think about the mind in both folk and scientific contexts. The role of intentionality in the theory of mind is, in fact, so pronounced that it is regularly proposed as a candidate positive criterion of mentality, a so-called ‘mark of the mental’. While it is unclear whether intentionality does in fact provide a satisfactory criterion of mentality, is it clear that a theory of intentionality will help us resolve some significant issues faced by the theory of mind.

First, the theory of mind is in the midst of something of a crisis of uncertainty as long-run dogmas are being rapidly overturned. It is increasingly clear, for instance, that our own mentality is not exhausted by states of which we are conscious, which injects doubt into the value of self-report and introspection – two tools that have been historically used a great deal in the study of the mind. Likewise, it is generally accepted now that animals have minds, rejecting an anthropocentric doctrine that has perhaps protected the theory of mind from ambiguity of scope. This ambiguity is pushed further by the rise of increasingly competent artificial intelligences, coupled by increasing attention to the not-quite-so-strange-today-as-yesterday possibility that plant life may possess some form of mind after all. A satisfactory theory of intentionality would help us to reorient the study of the mind in the face of this rising uncertainty, and so may help us find a way forward as we move beyond past misconceptions.

Second, we face challenges unifying the insights and approaches of the many disciplines that study the mind. Research ranging across psychology, neuroscience, computer science, anthropology, sociology, linguistics, and a number of other disciplines – of course including philosophy – seem at times to share interest in the mind, and each bring to the understanding of the mind something of value. However, cross-disciplinary integration is complex and, if not done in a disciplined way, it is easy to create a great deal of confusion without generating anything of value. Clarifying the nature of intentionality will help us bring the methods and findings of these various fields into concert with one another and will go a long way to helping us build a successful cross-disciplinary understanding of the mind.

Finally, we are in need of a firm foundation for our theory of mind. That our theory of mind may rest upon a vague and potentially unsound notion is itself dissatisfying and gives us good reason to be skeptical of any proposed theory of mind until its foundations are shown to be sound. This is especially pronounced if we are committed, as I think we ought to be, to a naturalistic theory of mind – broadly, one which explains the mind in non-mental terms that are in some general sense amenable to scientific inquiry. A naturalistic theory of mind seems to demand a naturalistic theory of intentionality – either replacing it or explaining it in naturalistic terms – but this is easier said than done. Providing a solid understanding of intentionality will help us ground the study of mental phenomena – at least of the intentional sort – and ensure that our project does not rest upon an empty or invalid notion.

This dissertation aims to address – in some part – these problems over the course of five chapters. The first chapter is a broad review of naturalistic models of intentionality in terms of their strengths and weaknesses for the sake of developing a general picture of what a successful theory of intentionality might look like. I focus on work in naturalistic psychosemantics – which aims to provide a naturalistic solution to the central problem of intentionality – the question of what gives intentional states content, both in general and in particular. I introduce four problems that need to be resolved by any successful naturalistic theory of intentionality. A successful theory must (1) bound intentional states and content determining facts to distinguish them from other states and facts, (2) partition the space of intentional states and content determining facts to distinguish them from one another, (3) provide a function that maps between intentional states and their contents appropriately, and (4) provide guidance for how we should use and understand intentional concepts in folk and scientific contexts.

In chapter two, I argue against psychosemantic phenomenalism – the view that phenomenal properties play a critical role in psychosemantics, an inseparatist position which has been gaining momentum in recent years. First, I introduce the general thesis of psychosemantic phenomenalism – that phenomenal properties underlie important elements of intentionality. Next, I respond to one of the major arguments in favor of psychosemantic phenomenalism – that non-phenomenal psychosemantics is unacceptably indeterminate – and argue that phenomenal properties provide no path to determinacy and that the kinds of indeterminacy the phenomenalist takes to threaten non-phenomenal psychosemantics are ultimately benign. Then, I argue against the other major argument in favor of psychosemantic phenomenalism – that phenomenal properties are needed to meaningfully demarcate intentional states – and argue again that neither is the phenomenalist solution successful nor has it identified a real problem. Finally, I argue that psychosemantic phenomenalism is unlikely in-principle to succeed due to the risk of introducing a new interaction problem.

In chapter three, I argue in favor of dynamicism with respect to mental content in virtue of the importance of temporal factors in the structure of structured representations. First, I lay out the basic framework of dynamicism, as well as its commitment to the importance of real time in understanding of cognitive phenomena. Next, I argue that several key properties of contentful mental states can only be explained if we account for real time as a parameter, and thus dynamicism is supported with respect to mental content. Then, I show that real time is implicated in psychosemantics at the timescale of both occurrent processing and the development of semantic systems. Finally, I review some consequences this commitment to dynamicism may have for our theory of mind more broadly and so highlight some areas for future research.

In chapter four, I develop the infodynamic model of intentional content. Infodynamics is a syncretic combination of tools from various frameworks which is uniquely well suited to account for psychosemantic content. First, I describe the frameworks that infodynamics employs to create its account. Second, I lay out the formal and conceptual structure of infodynamics and demonstrate the results of this structure by applying it to a simple case. Third, I compare infodynamics to near-neighbors in the psychosemantic literature to demonstrate the advantages it confers. Finally, I review some remaining questions and avenues for future research in developing infodynamics and its consequences.

In chapter five, I show that infodynamic psychosemantics is compatible with the predictive coding framework of the mind. First, I review the predictive mind hypothesis - which takes the mind to be a kind of prediction machine - in context of the predictive coding tools used to formulate this hypothesis. Next, I review empirical and computational evidence in favor of the predictive mind hypothesis. Then, I show that infodynamics is compatible with predictive coding, and so may serve as a theory of content for the predictive mind hypothesis. Finally, I lay out several promising areas for future research combining infodynamics and predictive coding.