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A New Look at Infant Problem-Solving: Using DeepLabCut to Investigate Exploratory Problem-Solving Approaches

ABSTRACT: When confronted with novel problems, problem-solvers must decide whether to copy a modeled solution or to explore their own unique solutions. While past work has established that infants can learn to solve problems both through their own exploration and through imitation, little work has explored the factors that influence which of these approaches infants select to solve a given problem. Moreover, past work has treated imitation and exploration as qualitatively distinct, although these two possibilities may exist along a continuum. Here, we apply a program novel to developmental psychology (DeepLabCut) to archival data (Lucca et al., 2020) to investigate the influence of the effort and success of an adult’s modeled solution, and infants’ firsthand experience with failure, on infants’ imitative versus exploratory problem-solving approaches. Our results reveal that tendencies toward exploration are relatively immune to the information from the adult model, but that exploration generally increased in response to firsthand experience with failure. In addition, we found that increases in maximum force and decreases in trying time were associated with greater exploration, and that exploration subsequently predicted problem-solving success on a new iteration of the task. Thus, our results demonstrate that infants increase exploration in response to failure and that exploration may operate in a larger motivational framework with force, trying time, and expectations of task success.

SUBMITTER: Solby H

PROVIDER: S-EPMC8606407 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:In this paper we argue that a synthesis of findings across the various sub-areas of research in complex problem solving and consequently progress in theory building is hampered by an insufficient differentiation of complexity and difficulty. In the proposed framework of person, task, and situation (PTS), complexity is conceptualized as a quality that is determined by the cognitive demands that the characteristics of the task and the situation impose. Difficulty represents the quantifiable level of a person's success in dealing with such demands. We use the well-documented "semantic effect" as an exemplar for testing some of the conceptual assumptions derived from the PTS framework. We demonstrate how a differentiation between complexity and difficulty can help take beyond a potentially too narrowly defined psychometric perspective and subsequently gain a better understanding of the cognitive mechanisms behind this effect. In an empirical study a total of 240 university students were randomly allocated to one of four conditions. The four conditions resulted from contrasting the semanticity level of the variable labels used in the CPS system (high vs. low) and two instruction conditions for how to explore the CPS system's causal structure (starting with the assumption that all relationships between variables existed vs. starting with the assumption that none of the relationships existed). The variation in the instruction aimed at inducing knowledge acquisition processes of either (1) systematic elimination of presumptions, or (2) systematic compilation of a mental representation of the causal structure underpinning the system. Results indicate that (a) it is more complex to adopt a "blank slate" perspective under high semanticity as it requires processes of inhibiting prior assumptions, and (b) it seems more difficult to employ a systematic heuristic when testing against presumptions. In combination, situational characteristics, such as the semanticity of variable labels, have the potential to trigger qualitatively different tasks. Failing to differentiate between 'task' and 'situation' as independent sources of complexity and treating complexity and difficulty synonymously threaten the validity of performance scores obtained in CPS research.

Project description:BackgroundIn recognition of the interconnected nature of complex challenges such as COVID-19, a collaborative, multisectoral, and transdisciplinary approach, referred to as One Health, has been employed to address sustainable development and strengthen global health security. Although significant investments have been made to build global health capacity, characterization of the One Health is absent from the literature.Methods and findingsWe collected and analyzed perspectives from students, graduates, workers, and employers in One Health through a multinational online survey across health disciplines and sectors. Respondents were recruited through professional networks. A total of 828 respondents from 66 countries participated, representing governmental and academic institutions and students, among others; 57% were female, and 56% had completed professional health degrees. Interpersonal communication, communication with non-scientific audiences, and the ability to work in transdisciplinary teams were valued in the workplace and were considered essential competencies to build an interdisciplinary health workforce. Employers indicated difficulty recruiting workers, while workers indicated limited availability of positions. Employers identified limited funding and ill-defined career pathways as prominent challenges for retaining One Health workers.ConclusionsSuccessful One Health workers use interpersonal skills and scientific knowledge to address complex health challenges. Aligning the definition of One Health will likely improve the matching of job seekers and employers. Encouraging the employment of the One Health approach for a diverse range of positions, even if they do not explicitly include "One Health" in the job title, and clarifying the expectations, roles and responsibilities within a transdisciplinary team will lead to building a stronger workforce. As One Health has evolved to address food insecurity, emerging diseases, and antimicrobial resistance, it holds promise for supporting an interdisciplinary global health workforce that can make substantial progress on Sustainable Development Goals and improve global health security for all.

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A New Look at Infant Problem-Solving: Using DeepLabCut to Investigate Exploratory Problem-Solving Approaches

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