Previous research suggests individual task rule representations are activated during task preparation along with a higher-level goal representation that integrates and coordinates those rules during task performance. We hypothesized that the order of these two processes would reverse between novel and practiced task preparation, with novel preparation involving activation of individual rules before their integration by a higher-level task representation, and practiced preparation involving cued recall of a higher-level task representation that then activates individual rules from memory. Accordingly, we predicted a reversal of information flow between DLPFC (a lower-level control region) and aPFC (a higher-level control region) in the transition from novel to practiced task preparation. This prediction was confirmed by an fMRI double dissociation of aPFC and DLPFC across novel and practiced task preparation () and further supported by a reversal in MEG directed connectivity between aPFC and DLPFC ().
The present study investigated two ways in which the human brain is rapidly reconfigured for task performance. Importantly, novel task preparation can be characterized in terms of RITL, a powerful form of learning largely unique to humans. We show here that humans can rapidly (i.e., within 5–10 s) learn complex novel tasks and perform accurately with no practice (91% on first trials). Other animals typically use slower forms of learning such as operant conditioning, which can take days or months (especially for abstract or complex tasks). Some nonhuman primates can perform RITL using imitation, but imitation is typically ineffective for abstract and complex tasks like those used here given the many examples necessary to specify the appropriate task rules. Compatible with the present study's neural localization of this human ability, aPFC and DLPFC are among a select number of regions that grew substantially since our common ancestor with chimpanzees (Semendeferi et al., 2001
; Avants et al., 2006
), suggesting that development of these (and related) regions first allowed RITL in early humans. These regions likely provide humans with the enhanced WM integration and subgoaling skills (Braver and Bongiolatti, 2002
; De Pisapia et al., 2007
) necessary for rapidly integrating instructions into coherent task sets during RITL.
Previous studies have suggested that aPFC specifies task sets in DLPFC and more posterior regions during practiced task preparation (Sakai and Passingham, 2003
). However, these studies did not demonstrate the direction of information flow between these regions, leaving open the possibility that posterior regions actually drive activity in anterior regions during task preparation. Given that these studies all used practiced tasks and given that practiced task preparation is likely a top-down process involving retrieval of task sets from LTM (Mayr and Kliegl, 2003
), we expected (and demonstrated) that aPFC indeed drives activity in DLPFC during practiced task preparation (aPFC to DLPFC). This is in sharp contrast to novel task preparation, which likely involves a bottom-up series of transformations from instruction stimuli to rule representations to an integrated task representation. Compatible with a shift from bottom-up to top-down processing, we show here that DLPFC originally drives aPFC activity (DLPFC to aPFC) during RITL, but the direction of information flow reverses with practice (as task sets become familiar enough to be retrieved from memory).
This reversal of information flow is compatible with—indeed, it was predicted based on—theories of an anterior-to-posterior hierarchy in PFC (Koechlin et al., 2003
; Badre, 2008
). The present set of results suggests the need to consolidate seemingly opposing theories of the hierarchical organization of PFC. Rather than supporting just a representational PFC hierarchy [involving increasing relational complexity (Bunge et al., 2005
; De Pisapia et al., 2007
) and conceptual abstraction (Badre and D'Esposito, 2007
; Christoff et al., 2009
)] or a control PFC hierarchy [involving actions/procedures in a temporal or goal hierarchy (Koechlin et al., 1999
; Fuster, 2001
; Botvinick, 2008
)], the data support each one in turn. The aPFC-to-DLPFC information flow is most compatible with a control PFC hierarchy in which higher-level regions influence lower-level regions to control/coordinate their activities. In contrast, the DLPFC-to-aPFC information flow is most compatible with a representational PFC hierarchy in which lower-level regions feed information upward to be integrated into higher-level representations. However, it may be that the PFC hierarchy functions by representing higher-order relationships at each hierarchical stage, with the activation of these representations controlling lower-level stages via feedback connections. According to this interpretation, the DLPFC-toaPFC activation during novel task preparation forms a higher-level task set representation in aPFC (which can be later retrieved from LTM during practiced task preparation) that controls/coordinates the rule representations in DLPFC for subsequent task performance. Given that there are likely other plausible interpretations of the present results, however, further research is necessary to verify this interpretation.
In addition to a hierarchical account of PFC, the present results are also compatible with theories suggesting DLPFC controls activity in posterior regions in preparation for task performance (Miller and Cohen, 2001
; Yeung et al., 2006
). Most posterior regions were coactive with DLPFC (rather than aPFC), suggesting that DLPFC encodes rule representations in WM and actively maintains them via interactions with these regions. Some of the posterior regions included portions of auditory, visual, motor, and somatosensory cortices, which is compatible with recent evidence that even when considered abstractly (i.e., with words and in imagined situations), semantic rules activate the same brain areas that would be involved in processing that information if it were directly experienced (e.g., visual cortex for color rules, auditory cortex for sound rules, SMA for motor rules, etc.) (Goldberg et al., 2006
). Other posterior regions that coactivate with DLPFC have been implicated in logic rule representation [in addition to DLPFC itself (Miller et al., 2002
)], including pTL (Bunge et al., 2003
; Donohue et al., 2005
), PMC (Wallis and Miller, 2003
), and PPC (Stoet and Snyder, 2004
). Note that ventrolateral PFC (VLPFC) was also involved in novel task encoding, compatible with evidence that it is involved in LTM retrieval of individual rules (as opposed to entire task sets) (Donohue et al., 2005
). This set of results, in conjunction with previous findings, suggests that individual rule representations in posterior regions are prepared for task performance via interactions with lateral PFC.
We have emphasized task preparation during first trials, although this process might be better characterized as initial task execution. Importantly, it has been suggested that initial task execution involves preparatory processes that require the presence of task stimuli to complete (Rubinstein et al., 2001
; Monsell, 2003
). Supporting this view, we found that first trials were slower than second trials (while second and third trials showed no differences), suggesting an additional preparatory process might be present during first trials only. Also supporting this conclusion, we found with MEG that the DLPFC-to-aPFC influence during novel task encoding remained during first trials (suggesting a continuation of preparatory processes) but not second trials. However, if these first trial effects were caused by some process other than task preparation, the present results would need to be reinterpreted in terms of task preparation versus task execution (rather than two preparatory processes). This would entail a shift from DLPFC during task preparation to aPFC during task implementation for novel tasks and from aPFC during task preparation to DLPFC during task implementation for practiced tasks, suggesting a shift in the location of ongoing task control with practice. Further research is necessary to assess the extent to which first trial effects that differentiate between novel and practiced tasks are specific to task execution rather than task preparation.
Although the hemispheric lateralization effect in PFC was useful for accurate MEG source localization, it was not expected a priori. It is possible that right DLPFC was involved because of the need to represent sensory semantic categories [a process associated with right-lateralized function (Seger et al., 2000
)] in the particular task paradigm used here. Left aPFC may have been involved because of the need to perform WM integration during novel task preparation, as WM integration has been better associated with left than right aPFC (De Pisapia et al., 2007
). This suggests that the hemispheric effect in aPFC would replicate in a different experimental context, while a different set of task rules would possibly involve left DLPFC instead of (or in addition to) right DLPFC. This conclusion is also supported by findings indicating that the same aPFC region interacts with different posterior PFC regions depending on the particular task being prepared for (Sakai and Passingham, 2003
Implicit in task preparation is the need to resolve interference from previously used task sets. Some of the preparatory brain activity certainly reflected this interference resolution process. Importantly, however, the results indicate that the level of interference did not differ between novel and practiced tasks, suggesting that the observed results do not reflect interference-related processes. Evidence for this comes from two sources. First, assuming there was additional interference for one condition (and assuming PFC resolves that interference), then that condition should always involve greater PFC activity than the other; yet, we observed a reversal in which both conditions involved substantial PFC activity. Second, the task switching costs [which partially reflect task interference (Wylie and Allport, 2000
)] were virtually identical (within 1 ms) for novel and practiced tasks, strongly suggesting that any interference from previous tasks was equated across conditions.
It was perhaps surprising that there was relatively little performance benefit from practice (just 2% accuracy, 23 ms RT). One possible explanation is that excessive time to prepare (5–10 s) and respond (average 1295 ms) allowed a lazy preparation strategy that reduced differences between conditions. Alternatively, the human brain may be highly effective at transferring rules to new task contexts during RITL (despite potentially massive rule interference) to the point where performance is little affected by novel rule combinations. Singley and Anderson (1989
; chapter 3) demonstrated this ability using complex text editing tasks, showing that transfer of rules practiced in one task improves performance of related novel tasks beyond any performance decrement from interference. This benefit of practiced rule transfer may explain subjects' minimal rule interference as well as their high performance on novel tasks here.
We have shown a reversal in processing order and information flow between aPFC and DLPFC that suggests a fundamental difference between novel and practiced task preparation processes. Novel task preparation likely involves activation of individual rules in posterior regions and DLPFC before those rules are integrated into a unified task set by aPFC for coordinated task performance. In contrast, practiced task preparation likely involves retrieval of a higher-level task set representation from LTM that is loaded into aPFC for reconfiguration of DLPFC and posterior regions representing individual rules. This shift in dynamics between novel and practiced task preparation illustrates that there are two ways in which humans are able to rapidly reconfigure their minds via instruction. Importantly, the novel task preparation process begins to explain how we are able to rapidly learn a virtually infinite variety of possible tasks (i.e., RITL), allowing our species to efficiently adapt to the many unique situations and new technologies of an ever-changing world.