Updating of Implicit Adaptation Processes through Erroneous Numeric Feedback

Neurophysiological and neuroimaging work suggests that the cerebellum is critically involved in sensorimotor adaptation. Changes in cerebellar function alter behaviour when compensating for sensorimotor perturbations, as shown by non-invasive stimulation of the cerebellum and studies involving patients with cerebellar degeneration. It is known, however, that behavioural responses to sensorimotor perturbations reflect both explicit processes (such as volitional aiming to one side of a target to counteract a rotation of visual feedback) and implicit, error-driven updating of sensorimotor maps. The contribution of the cerebellum to these explicit and implicit processes remains unclear. Here, we examined the role of the cerebellum in sensorimotor adaptation to a 30° rotation of visual feedback of hand position during target-reaching, when the capacity to use explicit processes was manipulated by controlling movement preparation times. Explicit re-aiming was suppressed in one condition by requiring subjects to initiate their movements within 300ms of target presentation, and permitted in another condition by requiring subjects to wait approximately 1050ms after target presentation before movement initiation. Similar to previous work, applying anodal transcranial direct current stimulation (tDCS; 1.5mA) to the right cerebellum during adaptation resulted in faster compensation for errors imposed by the rotation. After exposure to the rotation, we evaluated implicit remapping in no-feedback trials after providing participants with explicit knowledge that the rotation had been removed. Crucially, movements were more adapted in these no-feedback trials following cerebellar anodal tDCS than after sham stimulation in both long and short preparation groups. Thus, cerebellar anodal tDCS increased implicit remapping during sensorimotor adaptation, irrespective of preparation time constraints. The results are consistent with the possibility that the cerebellum contributes to the formation of new visuomotor maps that correct perturbations in sensory feedback, even when explicit processes are suppressed during sensorimotor adaptation.

Sensorimotor learning is supported by at least two parallel systems: a strategic process that benefits from explicit knowledge and an implicit process that adapts subconsciously. How do these systems interact? Does one system’s contributions suppress the other, or do they operate independently? Here, we illustrate that during reaching, implicit and explicit systems both learn from visual target errors. This shared error leads to competition such that an increase in the explicit system’s response siphons away resources that are needed for implicit adaptation, thus reducing its learning. As a result, steady-state implicit learning can vary across experimental conditions, due to changes in strategy. Furthermore, strategies can mask changes in implicit learning properties, such as its error sensitivity. These ideas, however, become more complex in conditions where subjects adapt using multiple visual landmarks, a situation which introduces learning from sensory prediction errors in addition to target errors. These two types of implicit errors can oppose each other, leading to another type of competition. Thus, during sensorimotor adaptation, implicit and explicit learning systems compete for a common resource: error.

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.

Solving the Credit Assignment Problem: The interaction of Explicit and Implicit learning with Internal and External State Information Wai-Tat Fu (wfu@cmu.edu) Human Factors Division and Beckman Institute University of Illinois at Urbana-Champaign 1 Airport Road, Savoy, IL 61874, USA John R. Anderson (ja+@cmu.edu) Department of Psychology Carnegie Mellon University 5000 Forbes Avenue Pittsburgh, PA 15213 USA recognition of external state information (signs on the walls). Indeed, many have argued that real-world skills often involve the interplay between cognition (internal), perception, and action (external) that the understanding of these interactive skills requires careful study of how internal (memory) and external information (cues in the environment) are processed in the learning processes (Ballard, 1997; Fu & Gray, 2000; 2004; Gray & Fu, 2004; Larkin, 1989; Gray, Sims, Fu, & Schoelles, in press). The navigation problem above is an example of one of the most difficult situations in skill learning: when the learner has to perform a sequence of actions but only gets feedback on their success at the end of the sequence (e.g., when the destination is reached). This creates a credit-assignment problem, in which the learner has to assign credits to earlier actions that are responsible for eventual success. When actions are interdependent, either memory of previous actions or recognition of the correct problem state in the external environment is required to properly assign credits to the appropriate actions. In this article, we present results from an experiment in which we study how people learn to solve the credit-assignment problem in a simple but challenging example of such a situation. Our focus is on the recent proposal that humans exhibit two distinct learning processes and we apply it to learning of action sequences with delayed feedback: an explicit process (with awareness) that requires memory for actions and outcomes, and an implicit process (without awareness) that does not require such memory. We will first review research in some related areas that informed the design of our experiment. Abstract In most problem-solving activities, feedback is received at the end of an action sequence. This creates a credit-assignment problem where the learner must associate the feedback with earlier actions, and the interdependencies of actions require the learner to either remember past choices of actions (internal state information) or rely on external cues in the environment (external state information) to select the right actions. We investigated the nature of explicit and implicit learning processes in the credit-assignment problem using a probabilistic sequential choice task with and without external state information. We found that when explicit memory encoding was dominant, subjects were faster to select the better option in their first choices than in the last choices; when implicit reinforcement learning was dominant subjects were faster to select the better option in their last choices than in their first choices. However, implicit reinforcement learning was only successful when distinct external state information was available. The results suggest the nature of learning in credit assignment: an explicit memory encoding process that keeps track of internal state information and a reinforcement-learning process that uses state information to propagate reinforcement backwards to previous choices. However, the implicit reinforcement learning process is effective only when the valences can be attributed to the appropriate states in the system – either internally generated states in the cognitive system or externally presented stimuli in the environment. Introduction Consider a person navigating in a large office building. The person has to decide when to turn left or right at various hallway intersections. The sequence of decisions is interdependent – e.g., turning left at a particular hallway intersection will affect the decisions at the next intersections. The person may therefore need to keep track of previous actions to inform what actions to take in the future. In reality, memory of previous actions (internal state information) may not be necessary as people can explicitly seek information in the environment (external state information) to know where one is located or which direction to go to reach a destination (Fu & Gray, 2006). Learning to navigate is therefore likely to involve both the retention of internal state information (memory) and the Explicit and Implicit Learning Probability Learning and Classification There have been numerous studies on the learning of the probabilistic relationship between choices and their consequences. The simplest situation is the probability- learning experiment in which subjects guess which of the alternatives occurs and then receives feedback on their guesses (e.g., Estes, 1964). One robust finding is that subjects often “probability match”; that is, they will choose a particular alternative with the same probability that it is reinforced (e.g., Friedman et al., 1964). This leads many to propose that probability matching is the result of an implicit

Implicit motor recalibration allows us to flexibly move in novel and changing environments. Conventionally, implicit recalibration is thought to be driven by errors in predicting the sensory outcome of movement (i.e., sensory prediction errors). However, recent studies have shown that implicit recalibration is also influenced by errors in achieving the movement goal (i.e., task errors). Exactly how sensory prediction errors and task errors interact to drive implicit recalibration and, in particular, whether task errors alone might be sufficient to drive implicit recalibration remain unknown. To test this, we induced task errors in the absence of sensory prediction errors by displacing the target mid-movement. We found that task errors alone failed to induce implicit recalibration. In additional experiments, we simultaneously varied the size of sensory prediction errors and task errors. We found that implicit recalibration driven by sensory prediction errors could be continuously modulated by task errors, revealing an unappreciated dependency between these two sources of error. Moreover, implicit recalibration was attenuated when the target was simply flickered in its original location, even though this manipulation did not affect task error – an effect likely attributed to attention being directed away from the feedback cursor. Taken as a whole, the results were accounted for by a computational model in which sensory prediction errors and task errors, modulated by attention, interact to determine the extent of implicit recalibration.

The effect of negative bias in information processing in persons with depression is widely discussed in the literature. This perception bias is viewed as a possible mechanisms of the overall emotional decrease in patients with depression. However, most studies in this area were conducted using explicit processing of emotionogenic information. The results were ambiguous and sometimes showed the effects of positive bias in healthy respondents. In this study, an experimental design with both explicit and implicit processing of emotional stimuli was implemented. The face images of negative and neutral valence were shown in groups of healthy respondents and patients with depression. Among 106 participants there were 57 patients with moderate depression. The participants were presented with images of faces with neutral or angry expressions (Lundqvist et al., 1998) and instructed to count their number based on emotional valence in the explicit condition, or gender in the implicit condition (De Lyssnyder et al., 2012). A comparison of processing time by group, valence and task, showed complementary effects in tasks with explicit process- ing. The patients with depression took longer to process negative stimuli, and the healthy participants spent more time processing neutral stimuli. In the implicit processing condition, the healthy respondents did not display any significant differences in processing times for negative and neutral images, while the patients were still fixating on negative images. However, after statistically controlling the age-related effects, these differences were reduced to a strong fixation on neutral faces during explicit processing in the non-depressed group. The results suggest a possible preventative mechanism — preferential processing of positive information — providing protection from depressive symptoms. This view offers an alternative explanation to possible causes of depression onset in contrast with a pathogenic mechanism of fixation on negative information in patients with depression.

The effect of an acute bout of exercise on implicit sensorimotor adaptation.

Where does fear originate in the brain? A coordinate-based meta-analysis of explicit and implicit fear processing

Implicit and explicit processing of emotional facial expressions in Parkinson’s disease

Previous studies have shown that neutral observers are able to identify kinship in strangers by matching photographs of children with their parents. We asked whether this ability depended on implicit and/or explicit cognitive processes. Fifty unrelated male observers viewed triads of photographs (one woman in her early 20's and two older women) and had to select which of the two older women was the mother, and rate their confidence in their decision. Observers identified 62.5% of mother-daughter pairs correctly (p < .001). Signal detection analyses showed that confidence was related to accuracy (d' = .28) and observers could report the cues they utilized. However, those who failed to show a relationship between confidence and accuracy (d' ≤ 0) still performed significantly above chance, and both confidence and d' decreased over trials whereas accuracy did not. Results show that neutral observers spontaneously used both explicit and implicit cognitive processes in the task. Recognition of kinship by neutral observers may be a task which allows the interplay between explicit and implicit cognition for a system relevant to ancestral social environments to be observed in the laboratory.

Influence of attention mechanisms on cerebellar and basal ganglia activity during vocal emotion decoding.

Explicit and implicit cognitive processes of the public towards people who stutter

The prefrontal cortex is differentially involved in implicit and explicit facial emotion processing: An fNIRS study

Neuroimaging studies have identified a number of cortical areas involved in the executive control of conscious actions. The areas most frequently implicated are prefrontal and cingulate cortices. Evidence suggests that both of these areas may be essential for executive control of willed action. Prefrontal cortex, however, may be responsible for the initial processing. Executive control is usually discussed with reference to willed actions and is assumed to regulate complex cognitive responses. Although many implicit processes involve complex responses, it is not known whether these actions are also controlled by executive processes. Significantly, some implicit tasks like those involving motor sequence learning and cross-modality priming activate the same areas of prefrontal cortex that are implicated in the executive control of willed actions. It is, however, not clear whether a single executive process controls both implicit and explicit processes, or the implicit processes are regulated by a separate set of executive control having distinct neuroanatomical location and processing properties.

The goal of this research is to explore implicit and explicit processes in shaping an individual’s characteristic behavioral patterns, that is, personality. The questions addressed are how psychological processes may be separated into implicit and explicit types, and how such a separation figures into personality. In particular, it focuses on the role of instinct and intuition (two kinds of mostly implicit processes) in determining personality. This paper argues that personality may be fundamentally based on instincts resulting from basic human motivation, along with related processes, within a comprehensive cognitive architecture. This approach is implemented as a computational model. Various tests and simulations show that this model captures major personality traits and accounts for empirical data. The work shows how a cognitive architecture with the implicit–explicit distinction may capture instinct, intuition, and personality.