Dataset Information

One step ahead: The perceived kinematics of others' actions are biased toward expected goals.

ABSTRACT: Action observation is often conceptualized in a bottom-up manner, where sensory information activates conceptual (or motor) representations. In contrast, here we show that expectations about an actor's goal have a top-down predictive effect on action perception, biasing it toward these goals. In 3 experiments, participants observed hands reach for or withdraw from objects and judged whether a probe stimulus corresponded to the hand's final position. Before action onset, participants generated action expectations on the basis of either object types (safe or painful, Experiments 1 and 2) or abstract color cues (Experiment 3). Participants more readily mistook probes displaced in a predicted position (relative to unpredicted positions) for the hand's final position, and this predictive bias was larger when the movement and expectation were aligned. These effects were evident for low-level movement and high-level goal expectancies. Expectations bias action observation toward the predicted goals. These results challenge current bottom-up views and support recent predictive models of action observation.

SUBMITTER: Hudson M

PROVIDER: S-EPMC4694084 | biostudies-other | 2016 Jan

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

One step ahead: The perceived kinematics of others' actions are biased toward expected goals.

Hudson Matthew M Nicholson Toby T Simpson William A WA Ellis Rob R Bach Patric P

Journal of experimental psychology. General 20151123 1

Action observation is often conceptualized in a bottom-up manner, where sensory information activates conceptual (or motor) representations. In contrast, here we show that expectations about an actor's goal have a top-down predictive effect on action perception, biasing it toward these goals. In 3 experiments, participants observed hands reach for or withdraw from objects and judged whether a probe stimulus corresponded to the hand's final position. Before action onset, participants generated ac ...[more]

PMID: 26595838

Similar Datasets

Project description:Background and objectiveDifferentiating tropical infections are difficult due to its homogenous nature of clinical and laboratorial presentations among them. Sophisticated differential tests and prediction tools are better ways to tackle this issue. Here, we aimed to develop a clinician assisted decision making tool to differentiate the common tropical infections.MethodologyA cross sectional study through 9 item self-administered questionnaire were performed to understand the need of developing a decision making tool and its parameters. The most significant differential parameters among the identified infections were measured through a retrospective study and decision tree was developed. Based on the parameters identified, a multinomial logistic regression model and a machine learning model were developed which could better differentiate the infection.ResultsA total of 40 physicians involved in the management of tropical infections were included for need analysis. Dengue, malaria, leptospirosis and scrub typhus were the common tropical infections in our settings. Sodium, total bilirubin, albumin, lymphocytes and platelets were the laboratory parameters; and abdominal pain, arthralgia, myalgia and urine output were the clinical presentation identified as better predictors. In multinomial logistic regression analysis with dengue as a reference revealed a predictability of 60.7%, 62.5% and 66% for dengue, malaria and leptospirosis, respectively, whereas, scrub typhus showed only 38% of predictability. The multi classification machine learning model observed to have an overall predictability of 55-60%, whereas a binary classification machine learning algorithms showed an average of 79-84% for one vs other and 69-88% for one vs one disease category.ConclusionThis is a first of its kind study where both statistical and machine learning approaches were explored simultaneously for differentiating tropical infections. Machine learning techniques in healthcare sectors will aid in early detection and better patient care.

Dataset Information

One step ahead: The perceived kinematics of others' actions are biased toward expected goals.

Publications

One step ahead: The perceived kinematics of others' actions are biased toward expected goals.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets