Performance of humans vs. exploration algorithms on the Tower of London Test

Harnessing Computational Complexity Theory to Model Human Decision-making and Cognition

A central aim of cognitive science is to understand the fundamental mechanisms that enable humans to navigate and make sense of complex environments. In this letter, we argue that computational complexity theory, a foundational framework for evaluating computational resource requirements, holds significant potential in addressing this challenge. As humans possess limited cognitive resources for processing vast amounts of information, understanding how humans perform complex cognitive tasks requires comprehending the underlying factors that drive information processing demands. Computational complexity theory provides a comprehensive theoretical framework to achieve this goal. By adopting this framework, we can gain new insights into how cognitive systems work and develop a more nuanced understanding of the relation between task complexity and human behavior. We provide empirical evidence supporting our argument and identify several open research questions and challenges in applying computational complexity theory to human decision-making and cognitive science at large.

A randomized wait-list controlled trial to investigate the role of cognitive mechanisms in parenting interventions on mothers with substance use disorder

Background

Maternal substance use disorder (SUD) represents a risk condition for quality of parenting and child development. The current literature highlights the need to identify interventions that effectively enhance the quality of parenting and to better understand which mechanisms are involved in the process of change. The present study protocol describes a randomized wait-list controlled trial that aims to examine (1) the efficacy of the Video-feedback Intervention to promote Positive Parenting and Sensitive Discipline (VIPP-SD) in improving the quality of parenting (i.e., sensitive parenting and sensitive discipline) in mothers with SUD, (2) whether the intervention affects parental cognitive mechanisms (i.e., attentional disengagement to infant negative emotions, inhibitory control confronted with children’s affective expression, and parental reflective functioning), and (3) whether changes in these processes act as mechanisms of change, mediating the effect of the VIPP-SD program on quality of parenting. Moreover, the study aims (4) to explore whether the VIPP-SD has an effect on parenting stress and (5) to compare mothers with SUD to low-risk mothers on the outcome measures.

Methods

The study will involve 40 mothers with SUD and 20 low-risk mothers of children aged between 14 months and 6 years old. Mothers in the SUD group will be randomly divided into two groups, one receiving the intervention (SUD experimental group) and one undergoing treatment as usual (SUD control group). All the mothers will be assessed pre-test and post-test. Quality of parenting will be assessed through observed parenting behaviors, whereas parental cognitive mechanisms will be assessed through neuropsychological tasks and self-report measures.

Discussion

The results of the study will reveal whether an intervention that has been proven effective in other at-risk samples is also effective in improving parenting behaviors in the context of SUD. The results will also provide insight into potential cognitive mechanisms involved in the process of change.

Trial registration

ISRCTN registry ISRCTN63070968. Registered on 25 June 2021. Retrospectively registered

Supplementary Information

The online version contains supplementary material available at 10.1186/s13063-022-06420-8.

On the assessment of procedural knowledge: From problem spaces to knowledge spaces

By generalizing and completing the work initiated by Stefanutti and Albert (2003, Journal of Universal Computer Science, 9, 1455), this article provides the mathematical foundations of a theoretical approach whose primary goal is to construct a bridge between problem solving, as initially conceived by Newell and Simon (1972, Human problem solving. Englewood Cliffs, NJ: Prentice-Hall.), and knowledge assessment (Doignon and Falmagne, 1985, International Journal of Man-Machine Studies, 23, 175; Doignon and Falmagne, 1999, Knowledge spaces. Berlin, Germany: Springer-Verlag.; Falmagne et al., 2013, Knowledge spaces: Applications in education. New York, NY: Springer-Verlag; Falmagne and Doignon, 2011, Learning spaces: Interdisciplinary applied mathematics. Berlin, Germany: Springer-Verlag.). It is shown that the collection of all possible knowledge states for a given problem space is a learning space. An algorithm for deriving a learning space from a problem space is illustrated. As an example, the algorithm is used to derive the learning space of a neuropsychological test whose problem space is well known: the Tower of London (TOL; Shallice, 1982, Philosophical Transactions of the Royal Society of London B: Biological Sciences, 298, 199). The derived learning space could then be used for adaptively assessing individual planning skills with the TOL.

Executive function deficits in persons with mild cognitive impairment: a study with a Tower of London task

This study assessed executive functions in persons with mild cognitive impairment (MCI) using the Tower of London (TOL). A second objective was to study the impact of three types of problem selected according to the presence or absence of a "trigger." A trigger (T) is an incitation to the participant, at the first move, to move a ball to its final position according to the model. A positive trigger (T+) is helpful, while a negative trigger (T-) creates an obstruction. Some problems have no trigger (N). This study includes 81 participants with MCI. After follow-up, one year later, two subgroups were distinguished: (a) 51 (63%) participants did not convert or decline (stable MCI); (b) 30 (37%) participants showed significant decline or progressed to dementia (decliner MCI). Persons with MCI were compared to an older adult group matched with respect to sex, age, and education. For the successes, there was a significant group difference between the three types of problem. The post hoc analysis showed that T+ took significantly less time than N or T-. There were significantly more successes for T+ than N, and these two types of problem had more success than T-. For "total number of moves," there was no significant difference between the groups. In post hoc analysis, T- involved more moves than N or T+. In qualitative analysis, T- MCI decliners produced significantly more rule breakings than the stable MCI and controls. A dysfunction in self-monitoring is a characteristic feature of persons with MCI.