Transfer of Cognitive Skills in Developmental Tasks

The main question we try to answer in this paper is whetherstage-like progression in cognitive development can beexplained by transfer of cognitive skill among tasks. Wefocus on the following question: To what extent does trainingon one task improve the performance on another task? Thetasks are Piaget’s (1959) Balance Scale Task and NumberConservation Task, and a task that we will call the Une-Sentence Task, which is taken from Karmiloff-Smith's (1979)experiment on the acquisition of determiners in French. Were-implemented already existing models within theframework of the PRIMs cognitive architecture (Taatgen,2013). Each task was subdivided in certain stages related tothe complexity of the problem-solving strategies. We showthat mastery of a certain stage of a problem becomes easier ifa higher stage of another task is mastered first

Modelling individual variability in cognitive development

Investigating variability in reasoning tasks can provide insights into key issues in the study of cognitive development. These include the mechanisms that underlie developmental transitions, and the distinction between individual differences and developmental disorders. We explored the mechanistic basis of variability in two connectionist models of cognitive development, a model of the Piagetian balance scale task (McClelland, 1989) and a model of the Piagetian conservation task (Shultz, 1998). For the balance scale task, we began with a simple feed-forward connectionist model and training patterns based on McClelland (1989). We investigated computational parameters, problem encodings, and training environments that contributed to variability in development, both across groups and within individuals. We report on the parameters that affect the complexity of reasoning and the nature of ‘rule’ transitions exhibited by networks learning to reason about balance scale problems. For the conservation task, we took the task structure and problem encoding of Shultz (1998) as our base model. We examined the computational parameters, problem encodings, and training environments that contributed to variability in development, in particular examining the parameters that affected the emergence of abstraction. We relate the findings to existing cognitive theories on the causes of individual differences in development

Braitenberg Vehicles as Developmental Neurosimulation

The connection between brain and behavior is a longstanding issue in the areas of behavioral science, artificial intelligence, and neurobiology. Particularly in artificial intelligence research, behavior is generated by a black box approximating the brain. As is standard among models of artificial and biological neural networks, an analogue of the fully mature brain is presented as a blank slate. This model generates outputs and behaviors from a priori associations, yet this does not consider the realities of biological development and developmental learning. Our purpose is to model the development of an artificial organism that exhibits complex behaviors. We will introduce our approach, which is to use Braitenberg Vehicles (BVs) to model the development of an artificial nervous system. The resulting developmental BVs will generate behaviors that range from stimulus responses to group behavior that resembles collective motion. Next, we will situate this work in the domain of artificial brain networks. Then we will focus on broader themes such as embodied cognition, feedback, and emergence. Our perspective will then be exemplified by three software instantiations that demonstrate how a BV-genetic algorithm hybrid model, multisensory Hebbian learning model, and multi-agent approaches can be used to approach BV development. We introduce use cases such as optimized spatial cognition (vehicle-genetic algorithm hybrid model), hinges connecting behavioral and neural models (multisensory Hebbian learning model), and cumulative classification (multi-agent approaches). In conclusion, we will revisit concepts related to our approach and how they might guide future development.Comment: 32 pages, 8 figures, 2 table

Five-Year-Olds' Systematic Errors in Second-Order False Belief Tasks Are Due to First-Order Theory of Mind Strategy Selection:A Computational Modeling Study

The focus of studies on second-order false belief reasoning generally was on investigating the roles of executive functions and language with correlational studies. Different from those studies, we focus on the question how 5-year-olds select and revise reasoning strategies in second-order false belief tasks by constructing two computational cognitive models of this process: an instance-based learning model and a reinforcement learning model. Unlike the reinforcement learning model, the instance-based learning model predicted that children who fail second-order false belief tasks would give answers based on first-order theory of mind (ToM) reasoning as opposed to zero-order reasoning. This prediction was confirmed with an empirical study that we conducted with 72 5- to 6-year-old children. The results showed that 17% of the answers were correct and 83% of the answers were wrong. In line with our prediction, 65% of the wrong answers were based on a first-order ToM strategy, while only 29% of them were based on a zero-order strategy (the remaining 6% of subjects did not provide any answer). Based on our instance-based learning model, we propose that when children get feedback "Wrong," they explicitly revise their strategy to a higher level instead of implicitly selecting one of the available ToM strategies. Moreover, we predict that children's failures are due to lack of experience and that with exposure to second-order false belief reasoning, children can revise their wrong first-order reasoning strategy to a correct second-order reasoning strategy

Reasoning about self and others

Dit proefschrift gaat over hoe mensen redeneren over andermans denken - hun kennis, gedachten en intenties. Het redeneren over andermans denken is noodzakelijk in sociale interacties, om het gedrag van anderen te begrijpen.In tegenstelling tot andere studies, die impliceren dat ‘sociaal redeneren’ complex is en gelimiteerd door cognitieve capaciteiten, laten wij zien dat mensen beter kunnen worden in deze vaardigheid. Cognitieve limieten verdwijnen door simpele ingrepen, zoals stapsgewijs trainen, visuele ondersteuning en het stellen van vragen. Ons onderzoek laat tevens zien dat suboptimale uitkomsten in sociale interacties soms te wijten zijn aan het gebruik van verkeerde strategieën. Simpele ingrepen kunnen ook hier uitkomst zijn: door mensen te helpen betere strategieën te ontdekken en toepassen.De belangrijkste bevinding van dit proefschrift is dat mensen sociale interacties daadwerkelijk interpreteren in termen van andermans denken, ook al is een niet-mentale interpretatie die formeel equivalent is, mogelijk. Zo wordt een rationale computer opponent niet hetzelfde behandeld als een equivalent mechaniek, ook al is de uitkomst in beide situaties hetzelfde. Bovendien is het spelen van een spel vanuit het perspectief van een ander moeilijker dan het spelen van datzelfde spel vanuit het eigen perspectief. Met andere woorden, sociaal redeneren gaat echt over andermans denken. Als zodanig, is het een unieke cognitieve vaardigheid.The topic of this dissertation is how people reason about the minds of others, their beliefs, desires, and intentions. Such reasoning is required in social interactions when we are trying to understand other people’s behavior. Whereas previous research seems to imply that ‘social reasoning’ is complex and limited by cognitive resources, we show that it is susceptible to improvement. Our research shows that cognitive limitations can be alleviated by relatively simple measures, such as stepwise instruction, visual cues, and interactive prompts. Furthermore, additional findings seem to hint at the possibility that suboptimal performance might not be due to limited cognitive capacity, but due to suboptimal strategies instead. The previously mentioned measures might be beneficial here as well: Help people discover and apply better strategies when reasoning about the minds of others. The most important finding of this dissertation is that people do not interpret social interactions as formal or logical problems without considering mental states, such as beliefs, desires, and intentions. For example, a rational computer opponent in a game is still considered differently than an equivalent mechanical device, even if the outcome is the same in both situations. Moreover, playing a game from someone else’s perspective is more complicated than playing the same game oneself. In other words, social reasoning really is about the minds of others. As such, it is a unique cognitive skill

Cognitive modelling of attentional networks: efficiencies, interactions, impairments and development

According to the attention network theory, attention is viewed as an organ system comprising specialised networks that carry out functions of alerting, orienting and executive control. The Attention Network Test (ANT) is a simple and popular experiment that measures the efficiencies and interactions of these three subcomponents of attention in a single task, and has been used for adults, children and attention deficit patients. In this thesis, cognitive modelling is used as a research tool to simulate the performance of subjects on the ANT, as well as variations of the ANT using ACT-R 6.0 cognitive architecture. All models are validated against human data using various goodness-of-fit criteria at multiple measures of the latency, accuracy and efficiency of the three networks. Once the simulation of healthy human performance on the ANT is established, modifications inspired by psychology literature are made to simulate the performance on ANT by children and patients affected with Alzheimer‘s disease (AD) and mild traumatic brain injury (mTBI). The implementation of networks, their interactions and impairments in the models are shown to be theoretically grounded. Based on the simulation results and the understanding gained through model processes, a number of novel predictions are made, behaviour of the networks and a few discrepancies in human data are explained. The model predicts that in the case of Alzheimer‘s disease, the orienting network may be impaired and cueing may have a positive effect on conflict resolution. Also, in the case of mTBI, it was predicted that the validity effect may be impaired only in the earlier weeks after the injury. For children, a possible relationship between processing speed and mechanism of inhibitory control is predicted. It is posited that there is not always a 'global clock' that controls processing speed and further different processes may be running with different processing times