Intentional concepts in cognitive neuroscience

In this article, I develop an account of the use of intentional predicates in cognitive neuroscience explanations. As pointed out by Maxwell Bennett and Peter Hacker, intentional language abounds in neuroscience theories. According to Bennett and Hacker, the subpersonal use of intentional predicates results in conceptual confusion. I argue against this overly strong conclusion by evaluating the contested language use in light of its explanatory function. By employing conceptual resources from the contemporary philosophy of science, I show that although the use of intentional predicates in mechanistic explanations sometimes leads to explanatorily inert claims, intentional predicates can also successfully feature in mechanistic explanations as tools for the functional analysis of the explanandum phenomenon. Despite the similarities between my account and Daniel Dennett's intentional-stance approach, I argue that intentional stance should not be understood as a theory of subpersonal causal explanation, and therefore cannot be used to assess the explanatory role of intentional predicates in neuroscience. Finally, I outline a general strategy for answering the question of what kind of language can be employed in mechanistic explanations.     

From commonsense to science,and back: The use of cognitive concepts in neuroscience

Commonsense cognitive concepts (CCCs) are the concepts used in daily life to explain, predict and interpret behaviour. CCCs are also used to convey neuroscientific results, not only to wider audiences but also to the scientific inner circle. We show that translations from CCCs to brain activity, and from brain data to CCCs are made in implicit, loose and unsystematic ways. This results in hard to connect data as well as possibly unwarranted extrapolations. We argue that the cause of these problems is a covert adherence to a position known in philosophy of mind as ‘mental realism’. The most fruitful way forward to a clearer and more systematic employment of CCCs in cognitive neuroscience, we argue, is to explicitly adopt interpretivism as an alternative for mental realism. An interpretative stance will help to avoid conceptual confusion in cognitive science and implies caution when it comes to big conclusions about CCCs.     

Models and mechanisms in network neuroscience

This paper considers the way mathematical and computational models are used in network neuroscience to deliver mechanistic explanations. Two case studies are considered: Recent work on klinotaxis by Caenorhabditis elegans, and a long-standing research effort on the network basis of schizophrenia in humans. These case studies illustrate the various ways in which network, simulation, and dynamical models contribute to the aim of representing and understanding network mechanisms in the brain, and thus, of delivering mechanistic explanations. After outlining this mechanistic construal of network neuroscience, two concerns are addressed. In response to the concern that functional network models are nonexplanatory, it is argued that functional network models are in fact explanatory mechanism sketches. In response to the concern that models which emphasize a network’s organization over its composition do not explain mechanistically, it is argued that this emphasis is both appropriate and consistent with the principles of mechanistic explanation. What emerges is an improved understanding of the ways in which mathematical and computational models are deployed in network neuroscience, as well as an improved conception of mechanistic explanation in general.     

Towards truly human-level intelligence in artificial applications

Despite the fact that there are now a large number of successful bio-inspired applications in use in science and technology, we are still quite far removed from creating applications that display human-like intelligence. Putting together successful bio-inspired applications remains something of a black art; this is due to a lack of fundamental understanding of brain function. The causes for these problems were analysed in a ’Roadmap for Neuro-IT’ and were deemed to be sufficiently pressing to motivate one of five ’Grand Challenges’ in Neuro-IT: the ’Constructed Brain’. The challenge argued that one of the main bottlenecks to progress is that data taking and modelling in the neurosciences are being fractured across many research groups and communities; it makes proposals for addressing the issue. Similar observations, raised in two OECD workgroup papers have led to the formation of the International Neuroinformatics Coordinating Facility. As a consequence we can conclude that there is now a much higher awareness of the problems and that in the neurosciences the situation has improved dramatically. I will review recent initiatives to facilitate data management, modelling and simulation in the neurosciences. One problem remains unaddressed, however. The project-based funding of the brain sciences sets an upper limit to the complexity of brain models. Since the brain is truly complex, any individual project will fall short of capturing the brain’s complexity. The creation of a central infrastructure for the brain sciences is inescapable, but is unlikely to be realised soon. I will outline suggestions to handle the current situation.     

Some strangeness in the proportion,or how to stop worrying and learn to love the mechanistic forces of darkness

Understanding humans requires viewing them as mechanisms of some sort, since understanding anything requires seeing it as a mechanism. It is science’s job to reveal mechanisms. But science reveals much more than that: it also reveals enduring mystery—strangeness in the proportion. Concentrating just on the scientific side of Selinger’s and Engström’s call for a moratorium on cyborg discourse, I argue that this strangeness prevents cyborg discourse from diminishing us.     

Remote control and children's understanding of robots

Children use goal-directed motion to classify agents as living things from early in infancy. In the current study, we asked whether preschoolers are flexible in their application of this criterion by introducing them to robots that engaged in goal-directed motion. In one case the robot appeared to move fully autonomously, and in the other case it was controlled by a remote. We found that 4- and 5-year-olds attributed fewer living thing properties to the robot after seeing it controlled by a remote, suggesting that they are flexible in their application of the goal-directed motion criterion in the face of conflicting evidence of living thing status. Children can flexibly incorporate internal causes for an agent’s behavior to enrich their understanding of novel agents.     

Contributions of Functional Neuroimaging to the Study of Social Cognition

ABSTRACT— Increasingly, researchers have been applying the methods of cognitive neuroscience—especially functional neuroimaging—to address questions about how humans make inferences about the mental states of others. At the same time, a number of critics have warned against the use of these new techniques by suggesting that functional neuroimaging has been unable to provide novel insights into the nature of social cognition. Addressing these critiques, this article briefly describes some of the ways in which functional neuroimaging has indeed redirected the study of the social mind, reviewing not only the novel data these techniques have provided but also the ways in which cognitive neuroscience has prompted researchers to consider entirely new questions about the organization of human social cognition. Such questions include whether or not there are cognitive processes dedicated for social thought; what the constituent parts of our social-cognitive system might be; how social cognition overlaps with other mental processes in previously unanticipated ways; and whether social cognition might play a privileged role in the human cognitive repertoire.     

A moratorium on cyborgs: Computation,cognition, and commerce

By examining the contingent alliance that has emerged between the computational theory of mind and cyborg theory, we discern some questionable ways in which the literalization of technological metaphors and the over-extension of the “computational” have functioned, not only to influence conceptions of cognition, but also by becoming normative perspectives on how minds and bodies should be transformed, such that they can capitalize on technology’s capacity to enhance cognition and thus amend our sense of what it is to be “human”. We consider “a moratorium on cyborg discourse” as a way of focusing the conceptual and social–political problems posed by this alliance.     

Social cognition and social functioning in patients with amnestic mild cognitive impairment or Alzheimer’s dementia

The aim of the present study was to examine social cognition and social functioning in a group of amnestic mild cognitive impairment (aMCI) and Alzheimer’s dementia (AD) patients. Thirty one people with aMCI, 29 individuals with AD, and 45 healthy older adults participated in the study. Facial expressions of happiness, anger, fear, disgust, and surprise presented in different intensities had to be labelled. Mentalizing was assessed using first-order belief theory of mind (ToM) stories and everyday social functioning by the Inventory of Interpersonal Situations (IIS), completed by an informant. aMCI patients were impaired in recognizing the emotions anger, disgust, and fear, while AD patients were impaired in recognizing the emotions anger, disgust, and surprise. More importantly, no significant differences between aMCI and AD patients were found on overall emotion recognition. Both the aMCI and AD patients were impaired on the ToM task, but no differences between the aMCI and AD patients were found. On everyday social functioning, only the AD patients showed impairments. No associations between the IIS and ToM were found, but the IIS and emotion perception were significantly correlated. Regression analysis taking all potentially confounding variables into account showed that only mood, but not the social-cognitive task performance or any other cognitive variable, predicted social functioning. aMCI and AD patients demonstrated impairments in mentalizing and facial emotion perception, and showed decrements in everyday social functioning. Informing caregivers about these deficits may help them to understand deficits in social cognition that may be present already in the MCI stage of Alzheimer’s disease.     

Neural Computation and the Computational Theory of Cognition

We begin by distinguishing computationalism from a number of other theses that are sometimes conflated with it. We also distinguish between several important kinds of computation: computation in a generic sense, digital computation, and analog computation. Then, we defend a weak version of computationalism—neural processes are computations in the generic sense. After that, we reject on empirical grounds the common assimilation of neural computation to either analog or digital computation, concluding that neural computation is sui generis. Analog computation requires continuous signals; digital computation requires strings of digits. But current neuroscientific evidence indicates that typical neural signals, such as spike trains, are graded like continuous signals but are constituted by discrete functional elements (spikes); thus, typical neural signals are neither continuous signals nor strings of digits. It follows that neural computation is sui generis. Finally, we highlight three important consequences of a proper understanding of neural computation for the theory of cognition. First, understanding neural computation requires a specially designed mathematical theory (or theories) rather than the mathematical theories of analog or digital computation. Second, several popular views about neural computation turn out to be incorrect. Third, computational theories of cognition that rely on non‐neural notions of computation ought to be replaced or reinterpreted in terms of neural computation.     

LOT,CTM, and the Elephant in the Room

According to the language of thought (LOT) approach and the related computational theory of mind (CTM), thinking is the processing of symbols in an inner mental language that is distinct from any public language. Herein, I explore a deep problem at the heart of the LOT/CTM program—it has yet to provide a plausible conception of a mental symbol.     

Is a modular cognitive architecture compatible with the direct perception of mental states?

The Direct Social Perception Hypothesis maintains that we can perceive other people’s psychological states. Furthermore, it claims that doing so does not require any cognitive process that is simulative or theory-like, putting it in sharp contrast with mainstream accounts of social cognition. This paper contrasts the DSPH against the modular account of mindreading as proposed by Peter Carruthers and H. Clark Barrett. It maintains that the modularity view can respond to the challenges levelled by the DSPH, and that the positions are not as distinct as they originally appear. Finally, the paper discusses the role of non-folk psychological state concepts in our perceptions of other people.     

A Computational Evaluation of Sentence Processing Deficits in Aphasia

Individuals with agrammatic Broca's aphasia experience difficulty when processing reversible non‐canonical sentences. Different accounts have been proposed to explain this phenomenon. The Trace Deletion account (Grodzinsky, 1995, 2000, 2006) attributes this deficit to an impairment in syntactic representations, whereas others (e.g., Caplan, Waters, Dede, Michaud, & Reddy, 2007; Haarmann, Just, & Carpenter, 1997) propose that the underlying structural representations are unimpaired, but sentence comprehension is affected by processing deficits, such as slow lexical activation, reduction in memory resources, slowed processing and/or intermittent deficiency, among others. We test the claims of two processing accounts, slowed processing and intermittent deficiency, and two versions of the Trace Deletion Hypothesis (TDH), in a computational framework for sentence processing (Lewis & Vasishth, 2005) implemented in ACT‐R (Anderson, Byrne, Douglass, Lebiere, & Qin, 2004). The assumption of slowed processing is operationalized as slow procedural memory, so that each processing action is performed slower than normal, and intermittent deficiency as extra noise in the procedural memory, so that the parsing steps are more noisy than normal. We operationalize the TDH as an absence of trace information in the parse tree. To test the predictions of the models implementing these theories, we use the data from a German sentence—picture matching study reported in Hanne, Sekerina, Vasishth, Burchert, and De Bleser (2011). The data consist of offline (sentence‐picture matching accuracies and response times) and online (eye fixation proportions) measures. From among the models considered, the model assuming that both slowed processing and intermittent deficiency are present emerges as the best model of sentence processing difficulty in aphasia. The modeling of individual differences suggests that, if we assume that patients have both slowed processing and intermittent deficiency, they have them in differing degrees.     

Fodor’s riddle of abduction

How can abductive reasoning be physical, feasible, and reliable? This is Fodor’s riddle of abduction, and its apparent intractability is the cause of Fodor’s recent pessimism regarding the prospects for cognitive science. I argue that this riddle can be solved if we augment the computational theory of mind to allow for non-computational mental processes, such as those posited by classical associationists and contemporary connectionists. The resulting hybrid theory appeals to computational mechanisms to explain the semantic coherence of inference and associative mechanisms to explain the efficient retrieval of relevant information from memory. The interaction of these mechanisms explains how abduction can be physical, feasible, and reliable.     

Did she mean to do it? Acquiring a folk theory of intentionality

The ability to both identify and explain others’ intentional acts is fundamental for successful social interaction. In two cross-sectional studies, we investigated 3- to 9-year-olds’ (n = 148) understanding of the folk concept of intentionality, using three types of intentionality measures. The relationship between this type of reasoning and false belief and interpretive mind understanding was also examined. Judgment of the appropriateness of an explanation was based on adult responses (n = 20). Overall, the results indicated that the ability to both identify and appropriately explain a range of intentional acts does not fully emerge until 7 years of age or later. The pattern of explanations revealed the gradual development of a folk concept of intentionality. Preschool- and early school-age children focused on the protagonists’ desires and actions, whereas 8- and 9-year-olds and adults were more likely to reference the protagonists’ awareness and skills.     

Parameters, Predictions, and Evidence in Computational Modeling: A Statistical View Informed by ACT-R

Model validation in computational cognitive psychology often relies on methods drawn from the testing of theories in experimental physics. However, applications of these methods to computational models in typical cognitive experiments can hide multiple, plausible sources of variation arising from human participants and from stochastic cognitive theories, encouraging a "model fixed, data variable" paradigm that makes it difficult to interpret model predictions and to account for individual differences. This article proposes a likelihood-based, "data fixed, model variable" paradigm in which models are treated as stochastic processes in experiments with participant-to-participant variation that can be applied to a broad range of mechanistic cognitive architectures. This article discusses the implementation and implications of this view in model validation, with a concrete focus on a simple class of ACT-R models of cognition. This article is not intended as a recipe for broad application of these preliminary, proof-of-concept methods, but as a framework for communication between statisticians searching for interesting problems in the cognitive modeling sphere, and cognitive modelers interested in generalizing from deterministic to stochastic model validation, in the face of random variation in human experimental data.     

The tractable cognition thesis

The recognition that human minds/brains are finite systems with limited resources for computation has led some researchers to advance the Tractable Cognition thesis : Human cognitive capacities are constrained by computational tractability. This thesis, if true, serves cognitive psychology by constraining the space of computational-level theories of cognition. To utilize this constraint, a precise and workable definition of "computational tractability" is needed. Following computer science tradition, many cognitive scientists and psychologists define computational tractability as polynomial-time computability, leading to the P-Cognition thesis . This article explains how and why the P-Cognition thesis may be overly restrictive, risking the exclusion of veridical computational-level theories from scientific investigation. An argument is made to replace the P-Cognition thesis by the FPT-Cognition thesis as an alternative formalization of the Tractable Cognition thesis (here, FPT stands for fixed-parameter tractable). Possible objections to the Tractable Cognition thesis, and its proposed formalization, are discussed, and existing misconceptions are clarified.     

Exposure to behavioral regularities in everyday life predicts infants’ acquisition of mental state vocabulary

There are two broad views of children's theory of mind. The mentalist view is that it emerges in infancy and is possibly innate. The minimalist view is that it emerges more gradually in childhood and is heavily dependent on learning. According to minimalism, children initially understand behaviors rather than mental states, and they are assisted in doing so by recognizing repeating patterns in behavior. The regularities in behavior allow them to predict future behaviors, succeed on theory-of-mind tasks, acquire mental state words, and eventually, understand the mental states underlying behavior. The present study provided the first clear evidence for the plausibility of this view by fitting head cameras to 54 infants aged 6 to 25 months, and recording their view of the world in their daily lives. At 6 and 12 months, infants viewed an average of 146.5 repeated behaviors per hour, a rate consistent with approximately 560,000 repetitions in their first year, and with repetitions correlating with children's acquisition of mental state words, even after controlling for their general vocabulary and a range of variables indexing social interaction. We also recorded infants’ view of people searching or searching for and retrieving objects. These were 92 times less common and did not correlate with mental state vocabulary. Overall, the findings indicate that repeated behaviors provide a rich source of information for children that would readily allow them to recognize patterns in behavior and help them acquire mental state words, providing the first clear evidence for this claim of minimalism.

Research Highlights

• Six- to 25-month-olds wore head cameras to record home life from infants’ point-of-view and help adjudicate between nativist and minimalist views of theory-of-mind (ToM).
• Nativists say ToM is too early developing to enable learning, whereas minimalists say infants learn to predict behaviors from behavior patterns in environment.
• Consistent with minimalism, infants had an incredibly rich exposure (146.5/h, >560,000 in first year) to repeated behaviors (e.g., drinking from a cup repeatedly).
• Consistent with minimalism, more repeated behaviors correlated with infants’ mental state vocabulary, even after controlling for gender, age, searches witnessed and non-mental state vocabulary.

     

关于儿童特质理解的心理理论研究

特质理解是心理理论研究中的一个重要研究领域,它对于社会能力的促进和发展具有重要的意义,章概述了国外该研究领域的主要研究成果及研究动态,包括心理理论与特质、儿童特质的起源信念研究、儿童特质词使用与特质理解发展、特质推理与特质理解发展研究,在此基础上,分析了该领域研究中存在的问题并对该领域未来的发展趋势作了展望。