Levels of Analysis in Cognitive Science

Autonomist accounts of cognitive science suggest that cognitive model building and theory construction (can or should) proceed independently of findings in neuroscience. Common functionalist justifications of autonomy rely on there being relatively few constraints between neural structure and cognitive function (e.g., Weiskopf, 2011). In contrast, an integrative mechanistic perspective stresses the mutual constraining of structure and function (e.g., Piccinini & Craver, 2011; Povich, 2015). In this paper, I show how model-based cognitive neuroscience (MBCN) epitomizes the integrative mechanistic perspective and concentrates (...) the most revolutionary elements of the cognitive neuroscience revolution (Boone & Piccinini, 2016). I also show how the prominent subset account of functional realization supports the integrative mechanistic perspective I take on MBCN and use it to clarify the intralevel and interlevel components of integration. (shrink)

Rational analysis is an influential but contested account of how probabilistic modeling can be used to construct non-mechanistic but self-standing explanatory models of the mind. In this paper, I disentangle and assess several possible explanatory contributions which could be attributed to rational analysis. Although existing models suffer from evidential problems that question their explanatory power, I argue that rational analysis modeling can complement mechanistic theorizing by providing models of environmental affordances.

In her re-analysis of the evidence presented in Klein and Nichols (2012) to support their argument that patient R.B. temporarily lost possessory custody of consciously apprehended objects (in this case, objects that normally would be non-inferentially taken as episodic memory), Professor Roache concludes Klein and Nichols's claims are untenable. I argue that Professor Roache is incorrect in her re-interpretation, and that this is due, in part, to lack of sufficient familiarity with psychological theory on memory as well as clinical literature (...) on felt loss of ownership of one’s intentional objects. (shrink)

Philosophers of mind commonly draw a distinction between the personal level – the distinctive realm of conscious experience and reasoned deliberation – and the subpersonal level, the domain of mindless mechanism and brute cause and effect. Moreover, they tend to view cognitive science through the lens of this distinction. Facts about the personal level are given a priori, by introspection, or by common sense; the job of cognitive science is merely to investigate the mechanistic basis of these facts. I argue (...) that this view misrepresents the structure of cognitive-scientific enquiry. Taken at face value, cognitive science makes no commitment to the existence of a distinctive level at which persons or selves appear. Thus, in the age of cognitive science, we should not expect to find the self in an ontologically distinct realm. Instead, we should expect to locate it in cognitive-scientific models themselves. In closing, I indicate likely results of this approach. (shrink)

We discuss recent progress in the development of cognitive ontologies and summarize three challenges in the coordinated development and application of these resources. Challenge 1 is to adopt a standardized definition for cognitive processes. We describe three possibilities and recommend one that is consistent with the standard view in cognitive and biomedical sciences. Challenge 2 is harmonization. Gaps and conflicts in representation must be resolved so that these resources can be combined for mark-up and interpretation of multi-modal data. Finally, Challenge (...) 3 is to test the utility of these resources for large-scale annotation of data, search and query, and knowledge discovery and integration. As term definitions are tested and revised, harmonization should enable coordinated updates across ontologies. However, the true test of these definitions will be in their community-wide adoption which will test whether they support valid inferences about psychological and neuroscientific data. (shrink)

Is any unified theory of brain function possible? Following a line of thought dat- ing back to the early cybernetics (see, e.g., Cordeschi, 2002), Clark (in press) has proposed the action-oriented Hierarchical Predictive Coding (HPC) as the account to be pursued in the effort of gain- ing the “Grand Unified Theory of the Mind”—or “painting the big picture,” as Edelman (2012) put it. Such line of thought is indeed appealing, but to be effectively pursued it should be confronted with experimental (...) findings and explana- tory capabilities (Edelman, 2012). The point we are making in this note is that a brain with predictive capa- bilities is certainly necessary to endow the agent situated in the environment with forethought or foresight, a crucial issue to outline the unified account advocated by Clark. But the capacity for fore- thought is deeply entangled with the capacity for emotions and when emotions are brought into the game, cogni- tive functions become part of a large-scale functional brain network. However, for such complex networks a consistent view of hierarchical organization in large-scale functional networks has yet to emerge (Bressler and Menon, 2010), whilst heterarchical organization is likely to play a strategic role (Berntson et al., 2012). This raises the necessity of a multilevel approach that embraces causal relations across levels of explanation in either direc- tion (bottom–up or top–down), endorsing mutual calibration of constructs across levels (Berntson et al., 2012). Which, in turn, calls for a revised perspective on Marr’s levels of analysis framework (Marr, 1982). In the following we highlight some drawbacks of Clark’s proposal in address- ing the above issues. (shrink)

Researchers in the cognitive sciences often seek neural correlates of psychological constructs. In this paper, I argue that even when these correlates are discovered, they do not always lead to reductive outcomes. To this end, I examine the psychological construct of a critical period and briefly describe research identifying its neural correlates. Although the critical period is correlated with certain neural mechanisms, this does not imply that there is a reductionist relationship between this psychological construct and its neural correlates. Instead, (...) this case study suggests that there may be many-to-many psychological-neural mappings, not just one-to-one or even one-to-many relations between psychological kinds and types of neural mechanisms. (shrink)

We discuss recent progress in the development of cognitive ontologies and summarize three challenges in the coordinated development and application of these resources. Challenge 1 is to adopt a standardized definition for cognitive processes. We describe three possibilities and recommend one that is consistent with the standard view in cognitive and biomedical sciences. Challenge 2 is harmonization. Gaps and conflicts in representation must be resolved so that these resources can be combined for mark-up and interpretation of multi-modal data. Finally, Challenge (...) 3 is to test the utility of these resources for large-scale annotation of data, search and query, and knowledge discovery and integration. As term definitions are tested and revised, harmonization should enable coordinated updates across ontologies. However, the true test of these definitions will be in their community-wide adoption which will test whether they support valid inferences about psychological and neuroscientific data. (shrink)

In fMRI research, the goal of correcting for multiple comparisons is to identify areas of activity that reflect true effects, and thus would be expected to replicate in future studies. Finding an appropriate balance between trying to minimize false positives (Type I error) while not being too stringent and omitting true effects (Type II error) can be challenging. Furthermore, the advantages and disadvantages of these types of errors may differ for different areas of study. In many areas of social neuroscience (...) that involve complex processes and considerable individual differences, such as the study of moral judgment, effects are typically smaller and statistical power weaker, leading to the suggestion that less stringent corrections that allow for more sensitivity may be beneficial, but also result in more false positives. Using moral judgment fMRI data, we evaluated four commonly used methods for multiple comparison correction implemented in SPM12 by examining which method produced the most precise overlap with results from a meta-analysis of relevant studies and with results from nonparametric permutation analyses. We found that voxel-wise thresholding with family-wise error correction based on Random Field Theory provides a more precise overlap (i.e., without omitting too few regions or encompassing too many additional regions) than either clusterwise thresholding, Bonferroni correction, or false discovery rate correction methods. (shrink)

A friend of my sent me the address where this paper has been posted by Adam Frank. Reading it, I realized that more than 90% of the main ideas of this paper (about the mind-brain problem, quantum mechanics (microparticles-wave relationship, Schrodinger equation, probabilities, “perceiving subject in physics”, the idea about consciousness and Nagel, etc. etc.) are UNBELIEVABLE similar to my ideas from my paper 2005 or my book 2008!!!

Many ideas from Georg Nortoff’s works (published one paper in 2010, mainly his book in 2011, other papers in 2012, 2103, 2014, especially those related to Kant’s philosophy and the notion of the “observer”, the mind-brain problem, default mode network, the self, the mental states and their “correspondence” to the brain) are surprisingly very similar to my ideas published in my article from 2002, 2005 and my book from 2008. In two papers from 2002 (also my paper from 2005 and (...) my book 2008), following Kant’s philosophy, I introduced the notion of the “observer” for the mind-brain problem. After 2010 (mainly his book 2011 and other papers after this book), Nortoff also uses Kant’s philosophy (even if his knowledge about Kant’s philosophy is very superficial!) and the notion of the “observer” for the mind-brain problem in a methodology very similar to my methodology. Moreover, instead of EDWs, Nortoff uses a kind of “transdisciplinary” view, quite close to parallelism – the closest approach to my EDWs! In his works until 2014, Northoff’s conclusion within the unicorn world was different than my conclusion. However, in his book 2014 (two volumes) using notions like “correlations” and even “correspondences” many times, his conclusion is very closed to my EDWs! This dramatic change of framework in 3 years is quite unbelievable!!! Incredible many ideas from this book are very similar to my ideas from 2005 and 2008! (shrink)

Introduction: the neuroscientific turn in political science The observation that brains and political orders are interdependent is almost trivial. Obviously, political orders require brain processes in order to emerge and to remain in place, as these processes enable action and cognition. Conversely, every since Aristotle coined man as “by nature a political animal” (Aristotle, Pol.: 1252a 3; cf. Eth. Nic.: 1097b 11), this also suggests that the political engagements of this animal has likely consequences for its natural development, including the (...) development of its psychological functions. Given these mutual interdependencies, it is remarkable that only since the 1960s, the more general domain of ‘biopolitics’ has attracted attention though first particularly in the form of behavioral politics (Alford and Hibbing). Since then biopolitics has gained in interest, so much so that different subdomains can be identified. Indeed, a 2008 review of the field of biopolitics identified five ‘headings’ of it: “(1) the case for a ‘more biologically oriented political science’, (2) ‘biologically related’ public policy issues, (3) physiological measures of political attitudes and behaviour, (4) the influence of physiological factors on actual political behaviour, and (5) the manner in which our species’ evolutionary history has left homo sapiens genetically endowed with certain social and political behavioural tendencies” (Somit and Peterson 43). Striking is how the relation between biology and politics is taken here in a rather unidirectional way, emphasizing particularly the decisive power of biology upon politics. The reverse relation is not mentioned specifically, reflecting the field of biopolitics, perhaps until quite recently. This absence of studies of political influences on our biology may have to do with the difficulty in investigating such influences. Empirical studies in biopolitics have two foci, broadly speaking: genetics and the brain, both of which have turned out to be complex and dynamic phenomena (Alford and Hibbing). Yet the studies of genetics and brain processes have made much progress in the last few decades, thanks to the development of research instruments - like fMRI brain scanners and TMS brain stimulation instruments - and of computational tools for data analysis and the simulation of explanatory models. For the field of biopolitics it is particularly relevant that within cognitive neuroscience the study of social and political issues has witnessed an increasing interest of researchers even more recently. Indeed, aware of the enabling and mediating role of the brain regarding those issues, a truly ‘neuroscientific turn’ can be observed in the social sciences, testified for example by the emergence of the field of ‘neuropolitics’ (Connolly; Vandervalk). Developing a systematic neuropolitics or biopolitics in general is a difficult challenge because of the wealth of causal influences on and interdependencies between biological, brain, cognitive and socio-political factors. Taking a somewhat more abstract perspective, this paper focuses on the process of emerging complexity in adaptive systems, enabling those to conduct ever more complex processes. Yet, parallel to that development can be observed that such systems, or organisms, are also capable in reducing the complexity of the information they are to process. Once they’re capable of developing and adjusting such compressed and complex representations of information, those systems or organisms can handle more information faster and more efficient and adaptive, yielding important benefits to the organism in navigating its environment (Halford, Wilson and Phillips). Before focusing on the role of narrative as a cognitive strategy for such a reduction of informational complexity, I will discuss the development of stable structures and increasing complexity in dynamic systems. Such a more general perspective prepares our discussion of the structures of both narratives and politics and in doing so contributes to the explanation of their interaction. (shrink)

In The Innocent Eye, Nico Orlandi argues that vision is not a cognitive process. In particular, she argues that forming subject-level visual representations that are available for reasoning should not itself be understood as a process of inference. This comes to the claim that vision (properly so-called) is a process that produces representations but is not best understood as a process that uses representations.

We overview the main historical and technological elements characterising the rise, the fall and the recent renaissance of the cognitive approaches to Artificial Intelligence and provide some insights and suggestions about the future directions and challenges that, in our opinion, this discipline needs to face in the next years.

The new paradigm in the psychology of reasoning draws on Bayesian formal frameworks, and some advocates of the new paradigm think of these formal frameworks as providing a computational-level theory of rational human inference. I argue that Bayesian theories should not be seen as providing a computational-level theory of rational human inference, where by “Bayesian theories” I mean theories that claim that all rational credal states are probabilistically coherent and that rational adjustments of degrees of belief in the light of (...) new evidence must be in accordance with some sort of conditionalization. The problems with the view I am criticizing can best be seen when we look at chains of inferences, rather than single-step inferences. Chains of inferences have been neglected almost entirely within the new paradigm. (shrink)

One question of the bounds of cognition is that of which things have it. A scientifically relevant debate on this question must explain the persistent and selective use of psychological predicates to report findings throughout biology: for example, that neurons prefer, fruit flies and plants decide, and bacteria communicate linguistically. This paper argues that these claims should enjoy default literal interpretation. An epistemic consequence is that these findings can contribute directly to understanding the nature of psychological capacities.

John Bickle recently proposed a metascientific reading of certain neuroscientific practices related to phenomena traditionally studied by psychology. According to this view, the dominant explanatory proceeding in neuroscience is to apply empirical reductions to explain psychological phenomena in cellular and molecular terms, relegating the role of psychology to a mere heuristic, without explanatory power. Thus, according to the strength of the conclusions reached by this method, it puts in check the value of psychological functional explanations. In this work, we seek (...) to reformulate some of these challenging proposals in philosophy of neuroscience. To this end, the main weaknesses of Bickle's proposal will be shown. Also, here will put into perspective some (reductionist, eliminativists?) features of ruthless reductionism, showing that this model maybe represent only a reading of the pragmatic use of certain methods in neuroscience, rather than a statement of principles generalizable to all cognitive science. (shrink)

In Rahul Banerjee and Bikas K. Chakrabarti (eds.), Progress in Brain Research, 168: 215-246. Amsterdam: Elsevier. Electronic offprint available upon request.

In this paper, I claim that the personal/subpersonal distinction is first and foremost a distinction between two kinds of psychological theory or explanation: it is only in this form that we can understand why the distinction was first introduced, and how it continues to earn its keep. I go on to examine the different ontological commitments that might lead us from the primary distinction between personal and subpersonal explanations to a derivative distinction between personal and subpersonal states. I argue that (...) on one of the most common metaphysical interpretations of the explanatory distinction, talk of a distinction between personal and subpersonal states simply makes no sense. When people insist on applying the personal/subpersonal terminology to psychological states, I allow that they are often making a genuine distinction, but one that it is best understood in terms of Stich's (1978) distinction between doxastic and subdoxastic states. I end the paper by considering some other common misinterpretations of the personal/subpersonal distinction, such as those involving consciousness, normativity, or autonomy. (shrink)

(June 2013) “The mind-body problem in cognitive neuroscience”, Philosophia Scientiae 17/2, Gabriel Vacariu and Mihai Vacariu (eds.): 1. William Bechtel (Philosophy, Center for Chronobiology, and Interdisciplinary Program in Cognitive Science University of California, San Diego) “The endogenously active brain: the need for an alternative cognitive architecture” 2. Rolls T. Edmund (Oxford Centre for Computational Neuroscience, Oxford, UK) “On the relation between the mind and the brain: a neuroscience perspective” 3. Cees van Leeuwen (University of Leuven, Belgium; Riken Brain Science Institute, (...) Japan) “Brain and mind” 4. Kari Theurer (Trinity College) and John Bickle (Philosophy, Mississippi State University) “What’s old is new again: Kemeny-Oppenheim reduction at work in current molecular neuroscience” 5. Bernard Andrieu (Staps Université de Lorraine) “Sentir son cerveau? Les dispositifs neuro-expérientiels en 1er personne” 6. Corey Maley and Gualtiero Piccinini (Philosophy, University of Missouri – St. Louis) “Get the latest upgrade: Functionalism 6.3.1” 7. Paula Droege (Philosophy, Pennsylvania State University) “Memory and consciousness” 8. Gabriel Vacariu and Mihai Vacariu (Philosophy, University of Bucharest) “Troubles with cognitive neuroscience”. (shrink)

Many cognitive scientists, having discovered that some computational-level characterization f of a cognitive capacity φ is intractable, invoke heuristics as algorithmic-level explanations of how cognizers compute f. We argue that such explanations are actually dysfunctional, and rebut five possible objections. We then propose computational-level theory revision as a principled and workable alternative.

The cognitive neurosciences are based on the idea that the level of neurons or neural networks constitutes a privileged level of analysis for the explanation of mental phenomena. This paper brings to mind several arguments to the effect that this presumption is ill-conceived and unwarranted in light of what is currently understood about the physical principles underlying mental achievements. It then scrutinizes the question why such conceptions are nevertheless currently prevailing in many areas of psychology. The paper argues that corresponding (...) conceptions are rooted in four different aspects of our common-sense conception of mental phenomena and their explanation, which are illegitimately transferred to scientific enquiry. These four aspects pertain to the notion of explanation, to conceptions about which mental phenomena are singled out for enquiry, to an inductivist epistemology, and, in the wake of behavioristic conceptions, to a bias favoring investigations of input–output relations at the expense of enquiries into internal principles. To the extent that the cognitive neurosciences methodologically adhere to these tacit assumptions, they are prone to turn into a largely a-theoretical and data - driven endeavor while at the same time enhancing the prospects for receiving widespread public appreciation of their empirical findings. (shrink)

As much as assumptions about mechanisms and mechanistic explanation have deeply affected psychology, they have received disproportionately little analysis in philosophy. After a historical survey of the influences of mechanistic approaches to explanation of psychological phenomena, we specify the nature of mechanisms and mechanistic explanation. Contrary to some treatments of mechanistic explanation, we maintain that explanation is an epistemic activity that involves representing and reasoning about mechanisms. We discuss the manner in which mechanistic approaches serve to bridge levels rather than (...) reduce them, as well as the different ways in which mechanisms are discovered. Finally, we offer a more detailed example of an important psychological phenomenon for which mechanistic explanation has provided the main source of scientific understanding. (shrink)