Dissociable learning processes in comparative psychology

Comparative and cognitive psychologists interpret performance in different ways. Animal researchers invoke a dominant construct of associative learning. Human researchers acknowledge humans’ capacity for explicit-declarative cognition. This article offers a way to bridge a divide that defeats productive cross-talk. We show that animals often challenge the associative-learning construct, and that it does not work to try to stretch the associative-learning construct to encompass these performances. This approach thins and impoverishes that important construct. We describe an alternative approach that restrains the construct of associative learning by giving it a clear operational definition. We apply this approach in several comparative domains to show that different task variants change—in concert—the level of awareness, the declarative nature of knowledge, the dimensional breadth of knowledge, and the brain systems that organize learning. These changes reveal dissociable learning processes that a unitary associative construct cannot explain but a neural-systems framework can explain. These changes define the limit of associative learning and the threshold of explicit cognition. The neural-systems framework can broaden empirical horizons in comparative psychology. It can offer animal models of explicit cognition to cognitive researchers and neuroscientists. It can offer simple behavioral paradigms for exploring explicit cognition to developmental researchers. It can enliven the synergy between human and animal research, promising a productive future for both.     

One-back reinforcement dissociates implicit-procedural and explicit-declarative category learning

The debate over unitary/multiple category-learning utilities is reminiscent of debates about multiple memory systems and unitary/dual codes in knowledge representation. In categorization, researchers continue to seek paradigms to dissociate explicit learning processes (yielding verbalizable rules) from implicit learning processes (yielding stimulus–response associations that remain outside awareness). We introduce a new dissociation here. Participants learned matched category tasks with a multidimensional, information-integration solution or a one-dimensional, rule-based solution. They received reinforcement immediately (0-Back reinforcement) or after one intervening trial (1-Back reinforcement). Lagged reinforcement eliminated implicit, information-integration category learning but preserved explicit, rule-based learning. Moreover, information-integration learners facing lagged reinforcement spontaneously adopted explicit rule strategies that poorly suited their task. The results represent a strong process dissociation in categorization, broadening the range of empirical techniques for testing the multiple-process theoretical perspective. This and related methods that disable associative learning—fostering a transition to explicit-declarative cognition—could have broad utility in comparative, cognitive, and developmental science.     

A commentary on Rönnberg's distinction between perception and cognition

In a commentary on a note by Rönnberg (Rönnberg, J. On the distinction between perception and cognition. Scandinavian Journal of Psychology , 1990, 31 , 154–156), it is argued that one of Rönnberg's criteria for theoretically distinguishing perception from cognition, that of different biological purposes, is invalid. Congruent with a broad definition of perception and cognition which most researchers agree upon, Rönnberg's two other criteria essentially state that how internally represented information is processed distinguishes between perception and cognition. However, the more strict criterion of whether explicit retrieval of information from memory is necessary is too dependent on a particular, rather limited theoretical framework. A third, less theory-dependent criterion of whether the information available in the stimulus is sufficient to account for comprehension may be useful in research which investigates hypotheses about similarities between perception and cognition.     

自然事物认知偏好的初步实验研究

本研究从认知心理学角度采用实验方法探索自然科学学习的影响因素。发现：1自然事物的认知偏好中存在内隐学习现象。2．自然事物认知偏好的表现非常复杂：年龄的影响并不显著,经验的影响不可忽视;3．性别差异十分明显,男生比女生的自然事物认知偏好更为肯定更为积极,很可能社会性别角色是造成自然科学学习结果差异的重要原因;4．内隐组学习坦率好奇,倾向于把自然事物看得更熟悉更容易,具有更强的探究兴趣：外显组学生矜持审慎,倾向于把自然事物看得更陌生更难以理解,探究兴趣较弱。因此,科学教育要创设情境让学生潜移默化地学以致用;要帮助学生利用经验并从中获取乐趣、增长兴趣、树立志趣;要鼓励女孩子大胆挑战科学。     

Analyzing behavior implied by EWA learning: An emphasis on distinguishing reinforcement from belief learning

An important issue in the field of learning is to what extent one can distinguish between behavior resulting from either belief or reinforcement learning. Previous research suggests that it is difficult or even impossible to distinguish belief from reinforcement learning: belief and reinforcement models often fit the empirical data equally well. However, previous research has been confined to specific games in specific settings. In the present study we derive predictions for behavior in games using the EWA learning model (e.g., Camerer & Ho, 1999), a model that includes belief learning and a specific type of reinforcement learning as special cases. We conclude that belief and reinforcement learning can be distinguished, even in 2×2 games. Maximum differentiation in behavior resulting from either belief or reinforcement learning is obtained in games with pure Nash equilibria with negative payoffs and at least one other strategy combination with only positive payoffs. Our results help researchers to identify games in which belief and reinforcement learning can be discerned easily.     

The recency effect: Implicit learning with explicit retrieval?

The recency effect in free recall features prominently in 1960s' theorizing about short-term memory, but has since been largely ignored. We argue that this stems from a preoccupation with the role of recency in the concept of primary memory and the neglect of its role in a broader working-memory framework. It is suggested that the recency effect reflects the application of an explicit retrieval strategy to the residue of implicit learning within a range of cognitive systems. When retrieved implicitly, the same residue is assumed to form the basis of priming effects. The various criteria for implicit learning described by Tulving and Schacter (1990) are successfully applied to the recency effect, and a retrieval process is outlined that can account for both long- and short-term recency effects. It is suggested that a framework combining recency, priming, and implicit learning provides a basis for understanding one of the most important features of cognition and memory, namely, that of maintaining orientation in time and place.     

Implicit learning as an ability

The ability to automatically and implicitly detect complex and noisy regularities in the environment is a fundamental aspect of human cognition. Despite considerable interest in implicit processes, few researchers have conceptualized implicit learning as an ability with meaningful individual differences. Instead, various researchers (e.g., Reber, 1993; Stanovich, 2009) have suggested that individual differences in implicit learning are minimal relative to individual differences in explicit learning. In the current study of English 16–17 year old students, we investigated the association of individual differences in implicit learning with a variety of cognitive and personality variables. Consistent with prior research and theorizing, implicit learning, as measured by a probabilistic sequence learning task, was more weakly related to psychometric intelligence than was explicit associative learning, and was unrelated to working memory. Structural equation modeling revealed that implicit learning was independently related to two components of psychometric intelligence: verbal analogical reasoning and processing speed. Implicit learning was also independently related to academic performance on two foreign language exams (French, German). Further, implicit learning was significantly associated with aspects of self-reported personality, including intuition, Openness to Experience, and impulsivity. We discuss the implications of implicit learning as an ability for dual-process theories of cognition, intelligence, personality, skill learning, complex cognition, and language acquisition.     

Curiosity-driven recommendation strategy for adaptive learning via deep reinforcement learning

The design of recommendation strategies in the adaptive learning systems focuses on utilizing currently available information to provide learners with individual-specific learning instructions. As a critical motivate for human behaviours, curiosity is essentially the drive to explore knowledge and seek information. In a psychologically inspired view, we propose a curiosity-driven recommendation policy within the reinforcement learning framework, allowing for an efficient and enjoyable personalized learning path. Specifically, a curiosity reward from a well-designed predictive model is generated to model one's familiarity with the knowledge space. Given such curiosity rewards, we apply the actor–critic method to approximate the policy directly through neural networks. Numerical analyses with a large continuous knowledge state space and concrete learning scenarios are provided to further demonstrate the efficiency of the proposed method.     

Optimistic biases in observational learning of value

Action-outcome contingencies can be learnt either by active trial-and-error, or vicariously, by observing the outcomes of actions performed by others. The extant literature is ambiguous as to which of these modes of learning is more effective, as controlled comparisons of operant and observational learning are rare. Here, we contrasted human operant and observational value learning, assessing implicit and explicit measures of learning from positive and negative reinforcement. Compared to direct operant learning, we show observational learning is associated with an optimistic over-valuation of low-value options, a pattern apparent both in participants’ choice preferences and their explicit post-hoc estimates of value. Learning of higher value options showed no such bias. We suggest that such a bias can be explained as a tendency for optimistic underestimation of the chance of experiencing negative events, an optimism repressed when information is gathered through direct operant learning.     

The impact of functional analysis methodology on treatment choice for self-injurious and aggressive behavior.

Self-injurious behavior (SIB) and aggression have been the concern of researchers because of the serious impact these behaviors have on individuals' lives. Despite the plethora of research on the treatment of SIB and aggressive behavior, the reported findings have been inconsistent regarding the effectiveness of reinforcement-based versus punishment-based procedures. We conducted a literature review to determine whether a trend could be detected in researchers' selection of reinforcement-based procedures versus punishment-based procedures, particularly since the introduction of functional analysis to behavioral assessment. The data are consistent with predictions made in the past regarding the potential impact of functional analysis methodology. Specifically, the findings indicate that, once maintaining variables for problem behavior are identified, experimenters tend to choose reinforcement-based procedures rather than punishment-based procedures as treatment for both SIB and aggressive behavior. Results indicated an increased interest in studies on the treatment of SIB and aggressive behavior, particularly since 1988.     

Distributed learning: Educating and assessing extended cognitive systems

Extended and distributed cognition theories argue that human cognitive systems sometimes include non-biological objects. On these views, the physical supervenience base of cognitive systems is thus not the biological brain or even the embodied organism, but an organism-plus-artifacts. In this paper, we provide a novel account of the implications of these views for learning, education, and assessment. We start by conceptualizing how we learn to assemble extended cognitive systems by internalizing cultural norms and practices. Having a better grip on how extended cognitive systems are assembled, we focus on the question: If our cognition extends, how should we educate and assess such extended cognitive systems? We suggest various ways to minimize possible negative effects of extending one’s cognition and to efficiently find and organize (online) information by adopting a virtue epistemology approach. Educational and assessment implications are foregrounded, particularly in the case of Danish students’ use of the internet during exams.     

身体活动的双系统理论:一种强化学习的视角

以理性决策为基础的锻炼行为理论被认为是理解身体活动的主导体系, 它提供了与身体活动相关的认知构念作为有价值的信息。基于社会生态模型设计的行为干预措施, 因表现出了更好的效果而备受关注。近期研究表明, 积极的运动认知和当前体育环境都没能很好地促进个人锻炼习惯的养成, 因此有必要探索新的理论体系来阐明个人锻炼习惯的形成机制。解释身体活动的最新体系是双系统理论, 由于其考虑了身体活动的无意识和快乐决定因素, 有望提供一个更广泛的动机视角。一方面, 多个有代表性的身体活动双系统模型, 从简单的自发路径, 到情境线索与锻炼习惯, 再到突出自动情感评价作用的复杂概念模型, 阐明了系统1的构建, 结合锻炼行为理论所关注的系统2, 为模型的构建提供了依据。另一方面, 通过对双系统的竞争、协调和层级控制原则的分析, 为模型的控制提供了建议。经典的强化学习框架解释了双系统模型的构建与控制原则:在模型的构建方面, 无模型与基于模型的强化学习分别表示系统1和系统2。在模型的控制方面, Dyna协作架构与分层强化学习, 为身体活动可能是一种相互协作、分层执行的复杂行动组合提供了合理解释。最后提出强化学习视角下锻炼者-体育环境的互动模式, 试图从一个全新的角度探讨锻炼行为。     

Emotivational psychology: How distinct emotions facilitate fundamental motives

Fundamental motivational systems and distinct emotions have both been suggested to be critically involved in the orchestration of adaptive responses to recurrent challenges in humans' evolutionary history. Research on motivation has, however, proceeded largely independently from research on emotions. Here, we contend that distinct emotions are what motivate behavior, and that these emotions may have evolved in tandem with fundamental motivational systems because they play a critical role in the functioning of those systems. Specifically, once a threat or opportunity has been identified, a distinct emotion is elicited, automatically galvanizing and guiding physiological, cognitive, and behavioral responses toward an adaptive outcome; this entire process occurs via a motivational system. We map six characteristic distinct emotions to six fundamental motivational systems and review evidence supporting each hypothesized link. In doing so, we propose a novel framework for understanding human motivation and the corresponding emergence of distinct emotions.     

Optimal Hierarchical Learning Path Design With Reinforcement Learning

E-learning systems are capable of providing more adaptive and efficient learning experiences for learners than traditional classroom settings. A key component of such systems is the learning policy. The learning policy is an algorithm that designs the learning paths or rather it selects learning materials for learners based on information such as the learners’ current progresses and skills, learning material contents. In this article, the authors address the problem of finding the optimal learning policy. To this end, a model for learners’ hierarchical skills in the E-learning system is first developed. Based on the hierarchical skill model and the classical cognitive diagnosis model, a framework to model various mastery levels related to hierarchical skills is further developed. The optimal learning path in consideration of the hierarchical structure of skills is found by applying a model-free reinforcement learning method, which does not require any assumption about learners’ learning transition processes. The effectiveness of the proposed framework is demonstrated via simulation studies.     

Event-learning and robust policy heuristics

In this paper we introduce a novel reinforcement learning algorithm called event-learning. The algorithm uses events, ordered pairs of two consecutive states. We define event-value function and we derive learning rules. Combining our method with a well-known robust control method, the SDS algorithm, we introduce Robust Policy Heuristics (RPH). It is shown that RPH, a fast-adapting non-Markovian policy, is particularly useful for coarse models of the environment and could be useful for some partially observed systems. RPH may be of help in alleviating the ‘curse of dimensionality’ problem. Event-learning and RPH can be used to separate time scales of learning of value functions and adaptation. We argue that the definition of modules is straightforward for event-learning and event-learning makes planning feasible in the RL framework. Computer simulations of a rotational inverted pendulum with coarse discretization are shown to demonstrate the principle.     

工作记忆成分与儿童算术认知

根据Baddeley和Hitch(1974)提出的工作记忆多成分模型,研究者对工作记忆的各个成分与儿童算术认知之间的关系进行了广泛的探索。同语音环和视空间模板相比,中央执行在儿童算术认知加工中的作用显得更为关键。目前,探讨各种中央执行功能与儿童算术认知的关系,以及由此来解释算术学习困难儿童的认知成因是这个领域研究者最为关注的焦点。     

The social foundation of executive function

In this study, we propose that infant social cognition may ‘bootstrap' the successive development of domain‐general cognition in line with the cultural intelligence hypothesis. Using a longitudinal design, 6‐month‐old infants (N = 118) were assessed on two basic social cognitive tasks targeting the abilities to share attention with others and understanding other peoples' actions. At 10 months, we measured the quality of the child's social learning environment, indexed by parent's abilities to provide scaffolding behaviors during a problem‐solving task. Eight months later, the children were followed up with a cognitive test‐battery, including tasks of inhibitory control and working memory. Our results showed that better infant social action understanding interacted with better parental scaffolding skills in predicting simple inhibitory control in toddlerhood. This suggests that infants' who are better at understanding other's actions are also better equipped to make the most of existing social learning opportunities, which in turn may benefit future non‐social cognitive outcomes.     

Pretense,Counterfactuals, and Bayesian Causal Models: Why What Is Not Real Really Matters

Young children spend a large portion of their time pretending about non‐real situations. Why? We answer this question by using the framework of Bayesian causal models to argue that pretending and counterfactual reasoning engage the same component cognitive abilities: disengaging with current reality, making inferences about an alternative representation of reality, and keeping this representation separate from reality. In turn, according to causal models accounts, counterfactual reasoning is a crucial tool that children need to plan for the future and learn about the world. Both planning with causal models and learning about them require the ability to create false premises and generate conclusions from these premises. We argue that pretending allows children to practice these important cognitive skills. We also consider the prevalence of unrealistic scenarios in children's play and explain how they can be useful in learning, despite appearances to the contrary.     

心智化社会认知观的演变及发展——来自潜心智化的思考

社会认知的机制一直存在心智化与具身认知观点的争论。前者认为社会认知是对心理状态的推测,而后者则认为社会认知是具身实践活动。虽然,心智化研究者认为具身认知有关灵长类动物和婴儿的社会认知的解释可以兼容于内隐心智化,但内隐心智化在解释社会互动时仍然存在间接性的问题。近期,潜心智化理论旨在挑战上述立场中有关社会认知先天论的预设,该理论将个体的潜心智化视为内隐心智化的替代,通过检验与分析相关研究的构想效度,强调社会认知是从非社会性的一般认知功能中衍生而来的,这种立场在社会认知的领域特殊性与非社会认知的领域一般性之间建立起了纽带,从而挑战了心智化与具身认知的争论。未来研究应设计更为严谨的心智化研究方法并对其进行方法学验证,借助先进的技术手段,尝试在神经科学层面探索一般认知功能与社会认知的关系。