A Response to Núñez et al.'s (2019) "What Happened to Cognitive Science?"

Núñez et al.'s (2019) negative assessment of the field of cognitive science derives from evaluation criteria that fail to reflect the true nature of the field. In reality, the field is thriving on both the research and educational fronts, and it shows great promise for the future.

Two Models of Moral Judgment.

This paper compares two theories and their two corresponding computational models of human moral judgment. In order to better address psychological realism and generality of theories of moral judgment, more detailed and more psychologically nuanced models are needed. In particular, a motivationally based theory of moral judgment (and its corresponding computational model) is developed in this paper that provides a more accurate account of human moral judgment than an existing emotion-reason conflict theory. Simulations based on the theory capture and explain a range of relevant human data. They account not only for the original data that were used to support the emotion-reason conflict theory, but also for a wider range of data and phenomena.

Getting it right generally, but not precisely: learning the relation between multiple inputs and outputs.

In real-world situations, people are often faced with the complex task of deciding which of many potential variables are affecting their own or others' behavior, as well as noting which specific aspects of behavior are being affected. Although it is common for professionals who encounter such conditions to claim that they acquire accurate and specific knowledge from their experience, it is unclear that such confidence is justified. Using a managerial task, we examined participants' ability to learn how various interventions affect various aspects of their employees' performance. The results of three experiments reveal that although participants appear to avoid prescribing an intervention that has a positive effect on a primary performance measure and a negative side effect on a secondary measure, when asked directly about the impact of the intervention, they respond by reducing their judgments of its positive impact. This was true regardless of whether participants indicated clear knowledge of its negative side effect (Experiment 3) or did not (Experiments 1 and 2). Thus, participants appear to be automatically integrating across the effects on different outcome measures.

Incubation, insight, and creative problem solving: a unified theory and a connectionist model.

This article proposes a unified framework for understanding creative problem solving, namely, the explicit-implicit interaction theory. This new theory of creative problem solving constitutes an attempt at providing a more unified explanation of relevant phenomena (in part by reinterpreting/integrating various fragmentary existing theories of incubation and insight). The explicit-implicit interaction theory relies mainly on 5 basic principles, namely, (a) the coexistence of and the difference between explicit and implicit knowledge, (b) the simultaneous involvement of implicit and explicit processes in most tasks, (c) the redundant representation of explicit and implicit knowledge, (d) the integration of the results of explicit and implicit processing, and (e) the iterative (and possibly bidirectional) processing. A computational implementation of the theory is developed based on the CLARION cognitive architecture and applied to the simulation of relevant human data. This work represents an initial step in the development of process-based theories of creativity encompassing incubation, insight, and various other related phenomena.

A motivationally-based simulation of performance degradation under pressure.

The CLARION cognitive architecture has been shown to be capable of simulating and explaining a wide range of psychological tasks and data. Currently, two theories exist to explain the psychological phenomenon of performance degradation under pressure: the distraction theory and the explicit-monitoring theory. However, neither provides a detailed mechanistic explanation of the exact processes involved. We propose such a detailed theory within the CLARION cognitive architecture that takes into account motivation and the interaction between explicit and implicit processes. We then use our theory to provide a plausible explanation of some existing data. The data are simulated using the theory within the CLARION cognitive architecture.

Capturing human data in a letter counting task: accessibility and action-centeredness in representing cognitive skills.

This work presents an integrated model of skill learning that takes into account both implicit and explicit processes and both action-centered and non-action-centered knowledge. The existing distinction between procedural and declarative knowledge unnecessarily confounds these two aspects (action-centeredness and accessibility), and can be made clearer through separating the two aspects. The model is used to simulate human data in a letter counting task. The work shows how the data in this task may be captured using either action-centered knowledge alone or both action-centered and non-action-centered knowledge, though the combined approach produced a better fit. The results demonstrate the difference between these approaches and provide a new perspective on skill learning.

Facilitative interactions of model- and experience-based processes: implications for type and flexibility of representation.

People are often taught using a combination of instruction and practice. In prior research, we have distinguished between model-based knowledge (i.e., acquired from explicit instruction) and experience-based knowledge (i.e., acquired from practice), and have argued that the issue of how these types of knowledge (and associated learning processes) interact has been largely neglected. Two experiments explore this issue using a dynamic control task. Results demonstrate the utility of providing model-based knowledge before practice with the task, but more importantly, suggest how this information improves learning. Results also show that learning in this manner can lead to "costs" such as slowed retrieval, and that this knowledge may not always transfer to new task situations as well as experientially acquired knowledge. Our findings also question the assumption that participants always acquire a highly specific "lookup" table representation while learning this task. We provide an alternate view and discuss the implications for theories of learning.

Hierarchical approaches to understanding consciousness.

There has been much discussion on what a scientific theory of consciousness would look like, and even whether such a theory is possible. Some common misunderstandings of the nature of theories (e.g., in the physical sciences) have confused the discussion of theories concerning consciousness. Theories in the physical sciences establish hierarchies of descriptions that relate high-level descriptions of macro-level phenomena to detailed-level descriptions at a micro level. Detailed descriptions are usually more accurate but information-dense and therefore often beyond human comprehensibility (unless limited to tiny segments of a macro-level phenomenon). High-level descriptions are usually much less information-dense but more approximate. The ability to map between levels of description, and in particular the understanding of when a shift from a higher-level to a more detailed description is needed to achieve a desired degree of accuracy, is fundamental to an effective theory in any field. The form of such a theory of consciousness is sketched, and the limitations of some alternative approaches described.

The interaction of implicit learning, explicit hypothesis testing learning and implicit-to-explicit knowledge extraction.

To further explore the interaction between the implicit and explicit learning processes in skill acquisition (which have been tackled before, e.g. in [Sun, R., Merrill, E., & Peterson, T. (2001). From implicit skill to explicit knowledge: A bottom-up model of skill learning. Cognitive Science, 25(2), 203-244; Sun, R., Slusarz, P., & Terry, C. (2005). The interaction of the explicit and the implicit in skill learning: A dual-process approach. Psychological Review, 112(1), 159-192]), this paper explores details of the interaction of different learning modes: implicit learning, explicit hypothesis testing learning, and implicit-to-explicit knowledge extraction. Contrary to the common tendency in the literature to study each type of learning in isolation, this paper highlights the interaction among them and various effects of the interaction on learning, including the synergy effect. This work advocates an integrated model of skill learning that takes into account both implicit and explicit learning processes; moreover, it also uniquely embodies a bottom-up (implicit-to-explicit) learning approach in addition to other types of learning. The paper shows that this model accounts for various effects in the human behavioural data from the psychological experiments with the process control task, in addition to accounting for other data in other psychological experiments (which has been reported elsewhere). The paper shows that to account for these effects, implicit learning, bottom-up implicit-to-explicit extraction and explicit hypothesis testing learning are all needed.

Developing rich and quickly accessed knowledge of an artificial grammar.

In contrast to prior research, our results demonstrate that it is possible to acquire rich, highly accurate, and quickly accessed knowledge of an artificial grammar. Across two experiments, we trained participants by using a string-edit task and highlighting relatively low-level (letters), medium-level (chunks), or high-level (structural; i.e., grammar diagram) information to increase the efficiency of grammar acquisition. In both experiments, participants who had structural information available during training generated more highly accurate strings during a cued generation test than did those in other conditions, with equivalent speed. Experiment 2 revealed that structural information enhanced acquisition only when relevant features were highlighted during the task using animation. We suggest that two critical components for producing enhanced performance from provided model-based knowledge involve (1) using the model to acquire experience-based knowledge, rather than using a representation of the model to generate responses, and (2) receiving that knowledge precisely when it is needed during training.

Learning to cooperate in solving the traveling salesman problem.

A cooperative team of agents may perform many tasks better than single agents. The question is how cooperation among self-interested agents should be achieved. It is important that, while we encourage cooperation among agents in a team, we maintain autonomy of individual agents as much as possible, so as to maintain flexibility and generality. This paper presents an approach based on bidding utilizing reinforcement values acquired through reinforcement learning. We tested and analyzed this approach and demonstrated that a team indeed performed better than the best single agent as well as the average of single agents.

The interaction of the explicit and the implicit in skill learning: a dual-process approach.

This article explicates the interaction between implicit and explicit processes in skill learning, in contrast to the tendency of researchers to study each type in isolation. It highlights various effects of the interaction on learning (including synergy effects). The authors argue for an integrated model of skill learning that takes into account both implicit and explicit processes. Moreover, they argue for a bottom-up approach (first learning implicit knowledge and then explicit knowledge) in the integrated model. A variety of qualitative data can be accounted for by the approach. A computational model, CLARION, is then used to simulate a range of quantitative data. The results demonstrate the plausibility of the model, which provides a new perspective on skill learning.

Effects of model-based and memory-based processing on speed and accuracy of grammar string generation.

Learners are able to use 2 different types of knowledge to perform a skill. One type is a conscious mental model, and the other is based on memories of instances. The authors conducted 3 experiments that manipulated training conditions designed to affect the availability of 1 or both types of knowledge about an artificial grammar. Participants were tested for both speed and accuracy of their ability to generate letter sequences. Results indicate that model-based training leads to slow accurate responding. Memory-based training leads to fast, less accurate responding and highest achievement when perfect accuracy was not required. Evidence supports participants' preference for using the memory-based mode when exposed to both types of training. Finally, the accuracy contributed by model-based training declined over a retention interval.

Criteria for an effective theory of consciousness and some preliminary attempts.

In the physical sciences a rigorous theory is a hierarchy of descriptions in which causal relationships between many general types of entity at a phenomenological level can be derived from causal relationships between smaller numbers of simpler entities at more detailed levels. The hierarchy of descriptions resembles the modular hierarchy created in electronic systems in order to be able to modify a complex functionality without excessive side effects. Such a hierarchy would make it possible to establish a rigorous scientific theory of consciousness. The causal relationships implicit in definitions of access consciousness and phenomenal consciousness are made explicit, and the corresponding causal relationships at the more detailed levels of perception, memory, and skill learning described. Extension of these causal relationships to physiological and neural levels is discussed. The general capability of a range of current consciousness models to support a modular hierarchy which could generate these causal relationships is reviewed, and the specific capabilities of two models with good general capabilities are compared in some detail.

Learning, action and consciousness: a hybrid approach toward modelling consciousness.

This paper is an attempt at understanding the issue of consciousness through investigating its functional role, especially in learning, and through devising hybrid neural network models that (in a qualitative manner) approximate characteristics of human consciousness. In doing so, the paper examines explicit and implicit learning in a variety of psychological experiments and delineates the conscious/unconscious distinction in terms of the two types of learning and their respective products. The distinctions are captured in a two-level action-based model CLARION. Some fundamental theoretical issues are also clarified with the help of the model. Comparisons with existing models of consciousness are made to accentuate the present approach.