Dataset Information

Re-imaging the intentional stance.

ABSTRACT: The commonly used paradigm to investigate Dennet's 'intentional stance' compares neural activation when participants compete with a human versus a computer. This paradigm confounds whether the opponent is natural or artificial and whether it is intentional or an automaton. This functional magnetic resonance imaging study is, to our knowledge, the first to investigate the intentional stance by orthogonally varying perceptions of the opponents' intentionality (responding actively or passively according to a script) and embodiment (human or a computer). The mere perception of the opponent (whether human or computer) as intentional activated the mentalizing network: the temporoparietal junction (TPJ) bilaterally, right temporal pole, anterior paracingulate cortex (aPCC) and the precuneus. Interacting with humans versus computers induced activations in a more circumscribed right lateralized subnetwork within the mentalizing network, consisting of the TPJ and the aPCC, possibly reflective of the tendency to spontaneously attribute intentionality to humans. The interaction between intentionality (active versus passive) and opponent (human versus computer) recruited the left frontal pole, possibly in response to violations of the default intentional stance towards humans and computers. Employing an orthogonal design is important to adequately capture Dennett's conception of the intentional stance as a mentalizing strategy that can apply equally well to humans and other intentional agents.

SUBMITTER: Abu-Akel AM

PROVIDER: S-EPMC7211435 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Re-imaging the intentional stance.

Abu-Akel Ahmad M AM Apperly Ian A IA Wood Stephen J SJ Hansen Peter C PC

Proceedings. Biological sciences 20200415 1925

The commonly used paradigm to investigate Dennet's 'intentional stance' compares neural activation when participants compete with a human versus a computer. This paradigm confounds whether the opponent is natural or artificial and whether it is intentional or an automaton. This functional magnetic resonance imaging study is, to our knowledge, the first to investigate the intentional stance by orthogonally varying perceptions of the opponents' intentionality (responding actively or passively acco ...[more]

PMID: 32290800

Similar Datasets

Project description:PurposeThe dataset contains annotations for lung nodules collected by the Lung Imaging Data Consortium and Image Database Resource Initiative (LIDC) stored as standard DICOM objects. The annotations accompany a collection of computed tomography (CT) scans for over 1000 subjects annotated by multiple expert readers, and correspond to "nodules ≥ 3 mm", defined as any lesion considered to be a nodule with greatest in-plane dimension in the range 3-30 mm regardless of presumed histology. The present dataset aims to simplify reuse of the data with the readily available tools, and is targeted towards researchers interested in the analysis of lung CT images.Acquisition and validation methodsOpen source tools were utilized to parse the project-specific XML representation of LIDC-IDRI annotations and save the result as standard DICOM objects. Validation procedures focused on establishing compliance of the resulting objects with the standard, consistency of the data between the DICOM and project-specific representation, and evaluating interoperability with the existing tools.Data format and usage notesThe dataset utilizes DICOM Segmentation objects for storing annotations of the lung nodules, and DICOM Structured Reporting objects for communicating qualitative evaluations (nine attributes) and quantitative measurements (three attributes) associated with the nodules. The total of 875 subjects contain 6859 nodule annotations. Clustering of the neighboring annotations resulted in 2651 distinct nodules. The data are available in TCIA at https://doi.org/10.7937/TCIA.2018.h7umfurq.Potential applicationsThe standardized dataset maintains the content of the original contribution of the LIDC-IDRI consortium, and should be helpful in developing automated tools for characterization of lung lesions and image phenotyping. In addition to those properties, the representation of the present dataset makes it more FAIR (Findable, Accessible, Interoperable, Reusable) for the research community, and enables its integration with other standardized data collections.

Project description:Saccadic eye movements enable us to rapidly direct our high-resolution fovea onto relevant parts of the visual world. However, while we can intentionally select a location as a saccade target, the wider visual scene also influences our executed movements. In the presence of multiple objects, eye movements may be "captured" to the location of a distractor object, or be biased toward the intermediate position between objects (the "global effect"). Here we examined how the relative strengths of the global effect and visual object capture changed with saccade latency, the separation between visual items and stimulus contrast. Importantly, while many previous studies have omitted giving observers explicit instructions, we instructed participants to either saccade to a specified target object or to the midpoint between two stimuli. This allowed us to examine how their explicit movement goal influenced the likelihood that their saccades terminated at either the target, distractor, or intermediate locations. Using a probabilistic mixture model, we found evidence that both visual object capture and the global effect co-occurred at short latencies and declined as latency increased. As object separation increased, capture came to dominate the landing positions of fast saccades, with reduced global effect. Using the mixture model fits, we dissociated the proportion of unavoidably captured saccades to each location from those intentionally directed to the task goal. From this we could extract the time course of competition between automatic capture and intentional targeting. We show that task instructions substantially altered the distribution of saccade landing points, even at the shortest latencies.NEW & NOTEWORTHY When making an eye movement to a target location, the presence of a nearby distractor can cause the saccade to unintentionally terminate at the distractor itself or the average position in between stimuli. With probabilistic mixture models, we quantified how both unavoidable capture and goal-directed targeting were influenced by changing the task and the target-distractor separation. Using this novel technique, we could extract the time course over which automatic and intentional processes compete for control of saccades.

Dataset Information

Re-imaging the intentional stance.

Publications

Re-imaging the intentional stance.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets