Search is a prerequisite for successful performance in a broad range of tasks ranging from making decisions between consumer goods to memory retrieval. How does aging impact search processes in such disparate situations? Aging is associated with structural and neuromodulatory brain changes that underlie cognitive control processes, which in turn have been proposed as a domain‐general mechanism controlling search in external environments as well as memory. We review the aging literature to evaluate the cognitive control hypothesis that suggests that age‐related (...) change in cognitive control underlies age differences in both external and internal search. We also consider the limits of the cognitive control hypothesis and propose additional mechanisms such as changes in strategy use and affect that may be necessary to understand how aging affects search. (shrink)

Search is a prerequisite for successful performance in a broad range of tasks ranging from making decisions between consumer goods to memory retrieval. How does aging impact search processes in such disparate situations? Aging is associated with structural and neuromodulatory brain changes that underlie cognitive control processes, which in turn have been proposed as a domain-general mechanism controlling search in external environments as well as memory. We review the aging literature to evaluate the cognitive control hypothesis that suggests that age-related (...) change in cognitive control underlies age differences in both external and internal search. We also consider the limits of the cognitive control hypothesis and propose additional mechanisms such as changes in strategy use and affect that may be necessary to understand how aging affects search. (shrink)

The interpretation of functional imaging experiments is complicated by the pluripotency of brain regions. As there is a many-to-one mapping between cognitive functions and their neural substrates, region-based analyses of imaging data provide only weak support for cognitive theories. Price and Friston argue that we need a ‘cognitive ontology’ that abstractly categorizes the function of regions. I argue that abstract characterizations are unlikely to be cognitively interesting. I argue instead that we should attribute functions to regions in a context-sensitive manner. (...) I review recent meta-analyses that approach fMRI data in this light and argue that they have revisionary potential. (shrink)

In this commentary, I will argue that the componential and emergent views of cognitive control as defined by Cooper (2010) do not necessarily oppose each other, and I will try to make a case for their interdependence. First, I will use the construct of cognitive inhibition—one of the main componential control functions mentioned in the target articles—to illustrate my line of reasoning. Then, I will comment on how some of the target articles, each from a different perspective, bring arguments in (...) favor of this integrative view. (shrink)

In his review, Walter (2012) links conceptual perspectives on empathy with crucial results of neurocognitive and genetic studies and presents a descriptive neurocognitive model that identifies neuronal key structures and links them with both cognitive and affective empathy via a high and a low road. After discussion of this model, the remainder of this comment deals more generally with the possibilities and limitations of current neurocognitive models, considering ways to develop process models allowing specific quantitative predictions.

This paper motivates taking seriously the possibility that brains are basically protean: that they make use of neural structures in inventive, on-the-fly improvisations to suit circumstance and context. Accordingly, we should not always expect cognition to divide into functionally stable neural parts and pieces. We begin by reviewing recent work in cognitive ontology that highlights the inadequacy of traditional neuroscientific approaches when it comes to divining the function and structure of cognition. Cathy J. Price and Karl J. Friston, and Colin (...) Klein identify the limitations of relying on forward and reverse inferences to cast light on the relation between cognitive functions and neural structures. There is reason to prefer Klein’s approach to that of Price and Friston’s. But Klein’s approach is neurocentric - it assumes that we ought to look solely at neural contexts to fix cognitive ontology. Using recent work on mindreading as a case study, we motivate adopting a radically different approach to cognitive ontology. Promoting the Protean Brain Hypothesis, we posit the possibility that we may need to look beyond the brain when deciding which functions are being performed in acts of cognition and in understanding how the brain contributes to such acts by adapting to circumstance. (shrink)

The question of domain-specific versus domain-general processing is an ongoing source of inquiry surrounding cognitive control. Using a comparative evolutionary approach, Stout (2010) proposed two components of cognitive control: coordinating hierarchical action plans and social cognition. This article reports additional molecular and experimental evidence supporting a domain-general attentional process coordinating hierarchical action plans, with the earliest such control processing originating in the capacity of dynamic foraging behaviors—predating the vertebrate-invertebrate divergence (c. 700 million years ago). Further discussion addresses evidence required for (...) additional, domain-specific, cognitive control processes, noting that proposed social processes may simply provide emotionally valenced representational information to the above hierarchical process. (shrink)

This paper argues that the cognitive neuroscientific use of ordinary mental terms to report research results and draw implications can contribute to public confusion and misunderstanding regarding neuroscience results. This concern is raised at a time when cognitive neuroscientists are increasingly required by funding agencies to link their research to specific results of public benefit, and when neuroethicists have called for greater attention to public communication of neuroscience. The paper identifies an ethical dimension to the problem and presses for greater (...) sensitivity and responsibility among neuroscientists regarding their use of such terms. (shrink)

The central task of cognitive neuroscience to map cognitive capacities to neural mechanisms faces three interlocking conceptual problems that together frame the problem of cognitive ontology. First, they must establish which tasks elicit which cognitive capacities, and specifically when different tasks elicit the same capacity. To address this operationalization problem, scientists often assess whether the tasks engage the same neural mechanisms. But to determine whether mechanisms are of the same or different kinds, we need to solve the abstraction problem by (...) determining which mechanistic differences are and are not relevant, and also the boundary problem by distinguishing the mechanism from its background conditions. Solving these problems, in turn, requires understanding how cognitive capacities are elicited in tasks. These three problems, which have been noted and discussed elsewhere in the literature, together form a ‘cycle of kinds’ that frames the central problem-space of cognitive ontology. We describe this cycle to clarify the intellectual challenges facing the cognitive ontologist and to reveal the kind of iterative process by which ontological revision in cognitive neuroscience is likely to unfold. (shrink)

This article clarifies three principles that should guide the development of any cognitive ontology. First, that an adequate cognitive ontology depends essentially on an adequate task ontology; second, that the goal of developing a cognitive ontology is independent of the goal of finding neural implementations of the processes referred to in the ontology; and third, that cognitive ontologies are neutral regarding the metaphysical relationship between cognitive and neural processes.

The target article criticises reductionist programs in cognitive science for failing to take into account important explanatory features of the organism's physical embodiment and task environment. My aim in this commentary is to show how such features are increasingly being taken seriously by (some) researchers in cognitive neuroscience, who describe the functional activity of neural structures in terms that are context-sensitive rather than intrinsic. This approach can allow us to take seriously the concerns presented in Gallagher’s [2019] target article without (...) having to completely give up on neuroscientific explanations of human behaviour. (shrink)

In this commentary, I focus on the difference between processes and representations and how this distinction relates to the question of what is controlled. Despite some views that task switching is a prototypical control process, the analysis concludes that task switching depends on the task goal representation and that control processes are there to prevent goal representations from disintegrating. Over time, these processes become obsolete, leaving behind a representation that automatically controls task performance. The distinction between processes and representations relates (...) to practice effects and automaticity and sheds light on what is meant by the phrase “automatic control.”. (shrink)

We consider approaches to explanation within the cognitive sciences that begin with Marr's computational level or Marr's implementational level and argue that each is subject to fundamental limitations which impair their ability to provide adequate explanations of cognitive phenomena. For this reason, it is argued, explanation cannot proceed at either level without tight coupling to the algorithmic and representation level. Even at this level, however, we argue that additional constraints relating to the decomposition of the cognitive system into a set (...) of interacting subfunctions are required. Integrated cognitive architectures that permit abstract specification of the functions of components and that make contact with the neural level provide a powerful bridge for linking the algorithmic and representational level to both the computational level and the implementational level. (shrink)

Cognitive control is not only componential, but those components may interact in complicated ways in the service of cognitive control tasks. This complexity poses a challenge for developing an ontological description, because the mapping may not be direct between our task descriptions and true component differences reflected in indicators. To illustrate this point, I discuss two examples: (a) the relationship between adaptive gating and working memory and (b) the recent evidence for a control hierarchy. From these examples, I argue that (...) an ontological program must simultaneously seek to identify component processes and their interactions within a broader processing architecture. (shrink)

In this paper, I summarize an emerging debate in the cognitive sciences over the right taxonomy for understanding cognition – the right theory of and vocabulary for describing the structure of the mind – and the proper role of neuroscientific evidence in specifying this taxonomy. In part because the discussion clearly entails a deep reconsideration of the supposed autonomy of psychology from neuroscience, this is a debate in which philosophers should be interested, with which they should be familiar, and to (...) which they should contribute. Here, I outline some of the positions being advocated, and reflect on some of the possible implications of this work both for scientific and folk psychology. (shrink)

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. (shrink)

Conceptual review as task analysis method Meta-analysis is a crucial research tool in cognitive neuroscience. For meta-analysis to succeed, it is important that studies that are grouped together investigate the same cognitive process and that studies that investigate different cognitive processes are grouped apart. After all, comparing apples and oranges makes no sense. Studies’ comparability depends on the cognitive tasks employed. Yet current meta-analyses, especially when automated (e.g. Neurosynth, BrainMap), select and group studies based on cognitive labels (e.g. ‘working memory’, (...) ‘self-reflection’). Unfortunately, labels are often applied inconsistently to tasks: different tasks may receive the same cognitive label, leading to a comparison of apples and oranges during meta-analysis; and the same task may receive different labels, leading to meta-analyses that fail to include all apples, so to speak. I propose conceptual review as a method for overcoming this problem. A conceptual review analyzes the conceptual implications of task choices made in a cognitive neuroscience subfield. It applies philosophical ways of uncovering and analyzing implicit assumptions to the methodological choices neuroscientists make. I explicate how this works and discuss several ways in which conceptual review would benefit cognitive neuroscience. Conceptual review could be combined with neuro-informatics to improve the quality of automated versions of meta-analysis and thereby provide an important contribution to progress in cognitive neuroscience. (shrink)

Cognitive neuroscience aims to map mental processes onto brain function, which begs the question of what “mental processes” exist and how they relate to the tasks that are used to manipulate and measure them. This topic has been addressed informally in prior work, but we propose that cumulative progress in cognitive neuroscience requires a more systematic approach to representing the mental entities that are being mapped to brain function and the tasks used to manipulate and measure mental processes. We describe (...) a new open collaborative project that aims to provide a knowledge base for cognitive neuroscience, called the Cognitive Atlas, and outline how this project has the potential to drive novel discoveries about both mind and brain. (shrink)

This chapter analyses the prospects of using neuroimaging methods, in particular functional magnetic resonance imaging (fMRI), for philosophical purposes. To do so, it will use two case studies from the field of emotion research: Greene et al. (2001) used fMRI to uncover the mental processes underlying moral intuitions, while Lindquist et al. (2012) used fMRI to inform the debate around the nature of a specific mental process, namely, emotion. These studies illustrate two main approaches in cognitive neuroscience: Reverse inference and (...) ontology testing, respectively. With regards to Greene et al.’s study, the use of Neurosynth (Yarkoni 2011) will show that the available formulations of reverse inference, although viable a priori, seem to be of limited use in practice. On the other hand, the discussion of Lindquist et al.’s study will present the so far neglected potential of ontology-testing approaches to inform philosophical questions. (shrink)

Neuroimaging technology is the most widely used tool to study human cognition. While originally a promising tool for mapping the content of cognitive theories onto the structures of the brain, recently developed tools for the analysis, handling and sharing of data have changed the theoretical landscape of cognitive neuroscience. Even with these advancements philosophical analyses of evidence in neuroimaging remain skeptical of the promise of neuroimaging technology. These views often treat the analysis techniques used to make sense of data produced (...) in a neuroimaging experiment as one, attributing the inferential limitations of analysis pipelines to the technology as a whole. Situated against the neuroscientists own critical assessment of their methods and the limitations of those methods, this skepticism appears based on a misunderstanding of the role data analysis techniques play in neuroimaging research. My project picks up here, examining how data analysis techniques, such as pattern classification analysis, are used to assess the evidential value of neuroimaging data. The project takes the form of three papers. In the first I identify the use of multiple data analysis techniques as an important aspect of the data interpretation process that is overlooked by critics. In the second I develop an account of inferences in neuroimaging research that is sensitive to this use of data analysis techniques, arguing that interpreting neuroimaging data is a process of isolating and explaining a variety of data patterns. In the third I argue that the development and uptake of new techniques for analyzing data must be accompanied by changes in research practices and standards of evidence if they are to promote knowledge generation. My approach to this work is both traditionally philosophical, insofar as it involves reading and analyzing the work of philosophers and neuroscientists, and embedded insofar as most of the research was conducted while engaged in attending lab meetings and participating in the work of those scientists whose work is the object of my research. (shrink)