A Brain-Based Account of the Development of Rule Use In Childhood (original) (raw)
The Journal of Neuroscience, 2017
Humans use rules to organize their actions to achieve specific goals. While simple rules that link a sensory stimulus to one response may suffice in some situations, often the application of multiple, hierarchically-organized rules is required. Recent theories suggest that progressively higher level rules are encoded along an anterior-to-posterior gradient within PFC. While some work supports the existence of such a functional gradient, other studies argue for a lesser degree of specialization within PFC. We used fMRI to investigate whether rules at different hierarchical levels are represented at distinct locations in the brain or encoded by a single system. Thirty-seven male and female participants represented and applied hierarchical rule sets containing one lower-level stimulus-response rule and one higher-level selection rule. We used multivariate pattern analysis to directly investigate the representation of rules at each hierarchical level in absence of information about rules from other levels or other task-related information, thus providing a clear identification of low- and high-level rule representations. We could decode low- and high-level rules from local patterns of brain activity within a wide frontoparietal network. However, no significant difference existed between regions encoding representations of rules from both levels, except for precentral gyrus that represented only low-level rule information. Our findings show that the brain represents conditional rules irrespective of their level in the explored hierarchy, and thus that the human control system did not organize task representation according to this dimension. Our paradigm represents a promising approach to identify critical principles that shape this control system.
A connectionist single mechanism account of rule-like behavior in infancy
Proceedings of the …, 2000
One of the most controversial issues in cognitive science pertains to whether rules are necessary to explain complex behavior. Nowhere has the debate over rules been more heated than within the field of language acquisition. Most researchers agree on the need for statistical learning mechanisms in language acquisition, but disagree on whether rule-learning components are also needed. Marcus, Vijayan, Rao, & Vishton (1999) have provided evidence of rule-like behavior which they claim can only be explained by a dual- ...
Effects of age, reminders, and task difficulty on young children's rule-switching flexibility
Cognitive Development, 2004
To test preschoolers’ ability to flexibly switch between abstract rules differing in difficulty, ninty-three 3-, 4-, and 5-year-olds were instructed to switch from an (easier) shape-sorting to a (harder) function-sorting rule, or vice versa. Children learned one rule, sorted four test sets, then learned the other rule, and sorted four more sets. In a control condition, seventy-two 3–5-year-old children learned one rule and were re-trained on that rule before the second test block. Half of each group received metacognitive reminders to “think about” the current rule before each test trial. The shape rule was easier: many 3-year-olds failed to follow the function rule, confirming findings of Deák et al. (2002). Switching rules did not reduce overall rule-following. However, reminders facilitated rule-following when rules were switched, but not when a rule was repeated (i.e., control condition). Reminders actually reduced rule-following by control children who got the easier (shape) rule. The results show (1) 4-year-olds readily switch between abstract rules, even if the second rule requires ignoring obvious, conflicting perceptual information (i.e., shape); (2) some rule-switching tasks do not impose performance costs on children, and (3) children’s rule-following consistency and flexibility depend on the nature of available social support.
Abstract Rule Learning: The Differential Effects of Lesions in Frontal Cortex
Cerebral Cortex, 2013
Learning progressively more abstract stimulus-response mappings requires progressively more anterior regions of the lateral frontal cortex. Using an individual differences approach, we studied subjects with frontal lesions performing a hierarchical reinforcement-learning task to investigate how frontal cortex contributes to abstract rule learning. We predicted that subjects with lesions of the left pre-premotor (pre-PMd) cortex, a region implicated in abstract rule learning, would demonstrate impaired acquisition of second-order, as opposed to first-order, rules. We found that 4 subjects with such lesions did indeed demonstrate a second-order rule-learning impairment, but that these subjects nonetheless performed better than subjects with other frontal lesions in a second-order rule condition. This finding resulted from both their restricted exploration of the feature space and the task structure of this condition, for which they identified partially representative first-order rules. Significantly, across all subjects, suboptimal but above-chance performance in this condition correlated with increasing disconnection of left pre-PMd from the putative functional hierarchy, defined by reduced functional connectivity between left pre-PMd and adjacent nodes. These findings support the theory that activity within lateral frontal cortex shapes the search for relevant stimulus-response mappings, while emphasizing that the behavioral correlate of impairments depends critically on task structure.
2006
Abstract The ability to use abstract rules or principles allows behavior to generalize from specific circumstances. We have previously shown that such rules are encoded in the lateral prefrontal cortex (PFC) and premotor cortex (PMC). Here, we extend these investigations to two other areas directly connected with the PFC and the PMC, the inferior temporal cortex (ITC) and the dorsal striatum (STR). Monkeys were trained to use two abstract rules:“same” or “different”.
Initial neural representation of individual rules after first-time instruction
2019
By following explicit instructions humans can instantaneously get the hang of tasks they have never performed before. Here, we used a specially calibrated multivariate analysis technique to uncover the elusive representational states following newly instructed arbitrary behavioural rules such as 'for coffee, press red button', while transitioning from 'knowing what to do' to 'actually doing it'. Subtle variation in distributed neural activity patterns reflected rule-specific representations within the ventrolateral prefrontal cortex (VLPFC), confined to instructed stimulus-response learning in contrast to incidental learning involving the same stimuli and responses. VLPFC representations were established right after first-time instruction and remained stable across early implementation trials. More and more fluent application of novel rule representations was channelled through increasing cooperation between VLPFC and anterior striatum. These findings inform representational theories on how the prefrontal cortex supports behavioural flexibility by enabling ad-hoc coding of novel task rules without recourse to familiar sub-routines .
A neurocomputational model of automaticity and maintenance of abstract rules
2009 International Joint Conference on Neural Networks, 2009
Rule-guided behavior is essential in quickly adapting to one's ever-changing environment. In particular, learned rules can quickly be used in new contexts or applied to new stimuli (which confers an advantage over restricting learning to perceptual-motor associations). Here, we propose a new neurocomputational model of automaticity in ruleguided behavior. The proposed model assumes two parallel neural pathways corresponding to "naïve" and "expert" rule use. The development of automaticity is characterized by a transfer of control of rule-guided behavior from a pathway mediated by the prefrontal cortex to a direct parietalpremotor pathway. The model includes differential equations that describe voltage changes in the relevant brain areas and difference equations that describe the Hebbian learning. A simulation shows that the model accounts for some critical single-cell recording data from several key brain areas as well as some important behavioral results.
Cerebral Cortex, 2009
Hannes Ruge and Uta Wolfensteller have contributed equally to this work. The present functional magnetic resonance imaging study investigated the instruction-based learning of novel arbitrary stimulus-response mappings in order to understand the brain mechanisms that enable successful behavioral rule implementation in the absence of trial-and-error learning. We developed a novel task design that allowed the examination of rapidly evolving brain activation dynamics starting from an explicit instruction phase and further across a short behavioral practice phase. As a first key result, the study revealed that different sets of brain regions displayed either decreasing or increasing activation profiles already across the first few practice trials, suggesting an impressively rapid redistribution of labor throughout the brain. Furthermore, behavioral performance improvement across practice was tightly coupled with brain activation during the practice phase (caudate nucleus), the instruction phase (lateral midprefrontal cortex), or both (lateral premotor cortex bordering prefrontal cortex). Together, the present results provide first important insights into the brain systems involved in the rapid transfer of control from initially abstract rule representations induced by explicit instructions toward pragmatic representations enabling the fluent behavioral implementation.