Abstract

To engage in purposeful behavior, it is important to make plans, which organize subsequent actions. Most studies of planning involve “look-ahead” puzzle tasks that are unrelated to personal goals. We developed a task to assess autobiographical planning, which involves the formulation of personal plans in response to real-world goals, and examined autobiographical planning in 63 adults during fMRI scanning. Autobiographical planning was found to engage the default network, including medial-temporal lobe and midline structures, and executive control regions in lateral pFC and parietal cortex and caudate. To examine how specific qualitative features of autobiographical plans modulate neural activity, we performed parametric modulation analyses. Ratings of plan detail, novelty, temporal distance, ease of plan formulation, difficulty in goal completion, and confidence in goal accomplishment were used as covariates in six hierarchical linear regression models. This modeling procedure removed shared variance among the ratings, allowing us to determine the independent relationship between ratings of interest and trial-wise BOLD signal. We found that specific autobiographical planning, describing a detailed, achievable, and actionable planning process for attaining a clearly envisioned future, recruited both default and frontoparietal brain regions. In contrast, abstract autobiographical planning, plans that were constructed from more generalized semantic or affective representations of a less tangible and distant future, involved interactions among default, sensory perceptual, and limbic brain structures. Specific qualities of autobiographical plans are important predictors of default and frontoparietal control network engagement during plan formation and reflect the contribution of mnemonic and executive control processes to autobiographical planning.

INTRODUCTION

The ability to mentally represent the future, or prospection, is a broad concept that has been used to characterize a wide variety of future-oriented cognitions (e.g., Seligman, Railton, Baumeister, & Sripada, 2013; Gilbert & Wilson, 2007). Four modes of future thinking have been identified that encapsulate the bulk of research on prospective cognition: simulation, prediction, intention, and planning (Szpunar, Spreng, & Schacter, 2014). These modes of future thinking range from the initial conception of a possible future event to the process of attaining a goal. Planning involves the identification and sequencing of steps toward achieving a goal state. Autobiographical planning in particular involves the identification and organization of steps needed to arrive at a specific autobiographical future event or outcome. The process of autobiographical planning combines elements of autobiographical memory with goal-directed planning operations. Several studies have shown that autobiographical planning engages synchronized activity of medial-temporal lobe (MTL) memory structures as well as frontal executive regions (Gerlach, Spreng, Madore, & Schacter, 2014; Spreng & Schacter, 2012; Spreng, Stevens, Chamberlain, Gilmore, & Schacter, 2010). This work has emphasized the coordinated activation of large-scale brain systems, specifically, of the default and frontoparietal control networks.

The default network is a set of functionally connected brain regions engaged by self-generated thought and active across multiple functional domains including memory, future thinking, and social cognition (Andrews-Hanna, Smallwood, & Spreng, 2014; Spreng, Mar, & Kim, 2009; Buckner, Andrews-Hanna, & Schacter, 2008). The network includes the medial pFC; medial parietal cortex, including posterior cingulate cortex (PCC) and retrosplenial cortex (RSC); posterior inferior parietal lobule (IPL); MTLs; and lateral temporal cortex.

Default network activity has been implicated in future-oriented episodic simulation, which involves spatiotemporal unfolding of imagined events (Schacter et al., 2012; Schacter, Addis, & Buckner, 2008). Specific qualitative features of simulated events have been found to modulate brain activity. The richness and specificity of episodic detail during such simulations has been associated with increasing left (Addis & Schacter, 2008) and right (Addis, Cheng, Roberts, & Schacter, 2011) anterior hippocampal activation as well as left amygdala and right frontal polar regions (Addis & Schacter, 2008). Imagining more temporally distant future events results in greater hippocampal activity bilaterally (Addis & Schacter, 2008), and activation of medial pFC has also been observed while envisioning distal emotional events (D'Argembeau, Xue, Lu, Van der Linden, & Bechara, 2008). Simulation of events more proximal in time has also been associated with activation in default network brain regions (Tamir & Mitchell, 2011). Decreasing probability of an event occurring in the future has been linked with increasing right anterior hippocampal activity, controlling for effects of temporal distance, amount of detail, and emotionality (Weiler, Suchan, & Daum, 2010). Finally, optimism about future event occurrence has been shown to modulate ventromedial pFC (Sharot, Riccardi, Raio, & Phelps, 2007). Although much work has been done to elucidate these qualitative aspects of episodic simulation, it remains unclear what qualitative features of autobiographical plans may modulate neural activity.

There is an extensive body of literature investigating the neuropsychological and neurophysiological correlates of planning as a domain of executive functioning (see Owen, 1997, for a review). This work has typically employed laboratory-based, problem-solving paradigms that require individuals to formulate, sequence, and implement a series of steps toward attainment of a target goal state (cf. Tower of London [TOL] task; Owen, Doyon, Petrides, & Evans, 1996; Owen, Downes, Sahakian, Polkey, & Robbins, 1990), although efforts have been made to assess planning capacity in more ecologically valid settings (cf. Multiple Errands Test; Shallice & Burgess, 1991). Planning capacity has been associated with lateral pFC and parietal cortex activations (e.g., Spreng et al., 2010; Wagner, Koch, Reichenbach, Sauer, & Schlosser, 2006; van den Huevel et al., 2003; Owen et al., 1996) as well as subcortical structures, including associative striatum (e.g., Wunderlich, Dayan, & Dolan, 2012; Monchi, Ko, & Strafella, 2006; van den Huevel et al., 2003). Lateral pFC, the anterior extent of the IPL, dorsal anterior cingulate, and anterior insula comprise regions of an extended frontoparietal control system broadly involved in executive control (Niendam et al., 2012; Vincent, Kahn, Snyder, Raichle, & Buckner, 2008). In a recent study, regions of the dorsal attention network, including the FEFs and superior parietal cortex, coupled with a frontoparietal control network during performance of the TOL task (Spreng et al., 2010). Critically, during performance of an autobiographical planning analog of the TOL task in the same scanning session, network coupling shifted such that the frontoparietal control network was more closely coupled with regions of the default network. This finding suggests that generating plans for one's personal future requires engagement of both default network brain regions, to simulate personal future goal states, as well as the frontoparietal control network, to implement the control processes necessary to guide actions toward goal attainment.

Although the general network architecture supporting autobiographical planning is beginning to come into focus, the role of specific regions within these networks and the ways in which they are modulated by discrete planning features have yet to be determined. The aims of this study are to identify the distributed pattern of brain regions involved in autobiographical planning and to investigate how these are modulated by plan detail, novelty, temporal distance, ease of plan formulation, perceived difficulty in goal attainment, and confidence in plan completion. In light of the previous observations discussed above, we predicted that autobiographical planning would additionally engage both default and frontoparietal control network regions and further hypothesized that contributions of regions in both networks would be modulated by their qualitative features. Specifically, we predict that the modulation effect of qualitative features during episodic future event simulation, associated with default brain regions, would be replicated during autobiographical planning. Moreover, we suggest that greater specificity in planning processes will be associated with activity in the frontoparietal control brain regions as control processes are engaged to formulate a detailed path to a readily envisioned future. The results provide the first comprehensive assessment of how specific qualities of autobiographical plans are linked with engagement of default and frontoparietal control networks during plan formation.

METHODS

Participants

Sixty-three healthy young adults (Mage = 22.5 ± 2.6 years, range = 18–30 years; 40 women) consented to participate in this study approved by the Harvard institutional review board. This study is based on a novel analysis of previously published data (Spreng, Sepulcre, Turner, Stevens, & Schacter, 2013; Spreng & Schacter, 2012; Spreng et al., 2010).

Task

Only a brief description of the paradigm is provided here; for a full description, refer to Spreng et al. (2010). Autobiographical planning was assessed by a novel task that required participants to devise personal plans to meet specific goals. For example, “freedom from debt” constituted one of the goals in the autobiographical planning task. Participants viewed the goal and then saw two steps they could take toward achieving that goal (“good job” and “save money”) as well as an obstacle they needed to overcome to achieve the goal (“have fun”). Participants were instructed to integrate the steps and obstacles into a cohesive personal plan that would allow them to achieve the goal. Participants also performed a baseline counting task, which involved the sequential counting of vowels within random letter sequences. Performance on a TOL task was also scanned but not included in the current analysis. All stimuli were visually matched (see Spreng et al., 2010, for details).

Study A included 20 participants who generated 30 autobiographical plans (Spreng et al., 2010). Study B included 18 participants who generated 24 autobiographical plans (Spreng & Schacter, 2012). Study C included 25 participants who generated 20 autobiographical plans (Spreng, Gilmore, & Schacter, unpublished observations). All participants rated the extent of detail included in their plan immediately after each trial in the scanner. After the scan, participants were interviewed about their autobiographical plans. They rated each plan for novelty (i.e., how much the plan had been given prior consideration before participating in the study), ease of formulating the plan in the scanner, and foreseeable difficulty in accomplishing the goal. Ratings of confidence in achieving the goal were collected in Studies B and C. All characteristics of the autobiographical plans were rated on a Likert scale ranging from 1 to 4 (or 5, for detail in Study C). Because of some Likert scaling differences between studies and for ease of interpretation, ratings were subsequently rescaled from 1 to 100 before analysis. Participants also estimated the time to goal completion (number of days, months, and/or years). These values were calculated as a function of days from the present and log-transformed for subsequent analysis to correct for positive skew in the distribution (see Spreng & Levine, 2006; see Table 1).

Table 1. 

Mean Within-subject Correlations among the Ratings and Descriptive Statistics

DetailNoveltyTemporal DistanceEase of FormulationDifficulty in Goal AttainmentConfidence in Completion
Detail –      
Novelty −.28 (−.32)** –     
Temporal distance −.02 (.21) .05 (.25) –    
Ease of formulation .24 (.33)** −.35 (.34)** .04 (.25) –   
Difficulty in goal attainment −.13 (.26)** .12 (.33)* −.04 (.26) −.57 (.44)** –  
Confidence in completion .02 (.22) .03 (.24) −.40 (.25)** .00 (.26) .03 (.22) – 
M 65 31 4m 10d 68 42 79 
SD 14 12 10d 15 15 10 
DetailNoveltyTemporal DistanceEase of FormulationDifficulty in Goal AttainmentConfidence in Completion
Detail –      
Novelty −.28 (−.32)** –     
Temporal distance −.02 (.21) .05 (.25) –    
Ease of formulation .24 (.33)** −.35 (.34)** .04 (.25) –   
Difficulty in goal attainment −.13 (.26)** .12 (.33)* −.04 (.26) −.57 (.44)** –  
Confidence in completion .02 (.22) .03 (.24) −.40 (.25)** .00 (.26) .03 (.22) – 
M 65 31 4m 10d 68 42 79 
SD 14 12 10d 15 15 10 

SDs of the within-subject correlations are in parentheses. Ratings are presented on a scale of 1–100. m = months; d = days.

*p < .01.

**p < .001.

To examine the association among the behavioral ratings, trial-wise within-subject correlations were computed. To determine which associations were significant, the within-subject correlations were submitted to a Fisher's r-to-z transform and tested by a simple t test (test criteria = 0, no correlation). Mean z scores were then converted back to r values for interpretation purposes (Table 1).

Neuroimaging

Neuroimages were acquired on a Siemens Trio 3-T scanner (Erlangen, Germany) with a 12- or 32-channel head coil. BOLD functional scans were acquired with a T2*-weighted EPI pulse sequence (repetition time = 2500 msec, echo time = 30 msec, 3 × 3 × 3 mm voxels). Details of the scanning parameters for Study A (Spreng et al., 2010) and Study B (Spreng & Schacter, 2012) can be found in the original published reports. For Study C, anatomical scans and five 10-min-and-15-sec BOLD functional scans were acquired with the same imaging parameters as Spreng and Schacter (2012). All fMRI data were subjected to standard preprocessing steps, including slice timing and motion correction, atlas registration to the Montreal Neurological Institute template, and spatial smoothing with a 6-mm Gaussian kernel at FWHM, as detailed in Spreng et al. (2010).

In this study, neuroimaging data were analyzed with SPM8. First, brain activity during autobiographical planning was examined relative to counting. We generated a general linear model for each participant, modeling cognitive events with the canonical hemodynamic response function, its temporal derivative, and its dispersion derivative; mean and linear drift for each functional run; and the six motion parameters. Cognitive tasks were composed of autobiographical planning, counting, and the TOL. Study C modulated autobiographical load by including three or six items to integrate into the plan. The present analysis merged across these trials, and load was included as a covariate of no interest. The t contrast image for autobiographical planning > counting was used in a second-level random effects analysis, which included study and head coil type as second-level regressors. This whole-brain contrast was corrected for multiple comparisons using the false discovery rate (FDR) significance threshold of p < .05 and a required cluster size of k > 20.

Although there are significant associations among the qualitative autobiographical plan ratings (see the Behavior section under Results), statistically independent relationships between these ratings and brain activity during planning were determined. To examine unique brain activity associated with each of the ratings, six hierarchical linear models were built, such that associations with the rating of interest were orthogonalized with respect to the other ratings. This modeling procedure removed the shared variance among the different ratings, allowing us to determine the independent relationship between the rating of interest and the BOLD signal during autobiographical planning. To assess how brain activity during autobiographical planning was modulated according to the ratings, we generated a hierarchical linear model for each participant, modeling the cognitive tasks with the canonical hemodynamic response function, its temporal derivative, and its dispersion derivative; mean and linear drift for each functional run; and the six motion parameters as well as the parametric regressors. Participants' in-scan rating for detail and postscan ratings of novelty, confidence, temporal distance, ease of formulating the plan in the scanner, and difficulty in accomplishing the goal in life were included as parametric regressors in six distinct models. Four participants were excluded because of collinearity among the ratings. The resulting parametric t contrast images for the ratings were then used in a second-level random effects analyses, which included study and scanner type as second-level regressors. To examine how neural activity was modulated as a function of the parametric regressors, we performed a simple t test, masked by the autobiographical planning > counting contrast image and, again, a significance threshold of p < .05, FDR-corrected, and k > 20. For a priori investigation into the temporal distance effect on hippocampal modulation, the cluster extent criteria were relaxed to k > 10 (cf. Addis, Wong, & Schacter, 2007).

RESULTS

Behavior

On average, participants generated moderately detailed plans. As anticipated, many of the plans were personally meaningful and had been thought of previously, as indicated by low novelty ratings. Participants did not experience difficulty formulating their plans in the scanner. They felt that their plans were modestly challenging and achievable. On average, participants predicted that they would accomplish their goals in a little more than 4 months; however, there was substantial variability across goals, ranging from 1 day to 50 years (see Table 1 for means and standard deviations).

We observed significant associations among the ratings for autobiographical plans (see Table 1 for all means and standard deviations of correlations among ratings). Plans made with greater detail were associated with lower novelty (t = −6.74, p < .001), greater ease in formulating the plan in the scanner (t = 5.59, p < .001), and less difficulty to accomplish in the world (t = −3.85, p < .001). The less novel the plan, the easier it was to formulate in the scanner (t = −8.13, p < .001) and the less difficult it was perceived to accomplish in the world (t = 2.67, p < .01). Ease in formulating plans in the scanner was associated with decreased difficultly in accomplishing those plans in the world (t = −10.88, p < .001). Temporally distant goals were associated with lower confidence in completion (t = −10.50, p < .001). No other correlations reliably differed from zero across participants.

Brain

Whole-brain Results

In examining the neural correlates of autobiographical planning, we first determined whole-brain activity relative to a counting baseline task. The results of this whole-brain contrast (Figure 1) were consistent with the previous reports that utilized a multivariate method, partial least squares, to contrast task conditions (Spreng et al., 2010, 2013; Spreng & Schacter, 2012). Autobiographical planning, relative to counting, engaged a number of regions associated with cognitive control in the left hemisphere, including rostral and caudal aspects of middle frontal gyrus, the anterior extent of the IPL, dorsal anterior cingulate, and the anterior insula. Autobiographical planning also robustly engaged the default network bilaterally, including medial pFC, superior and inferior frontal gyri, posterior IPL, lateral temporal cortex, PCC, RSC, and the MTLs, including the amygdala and hippocampus. Additional activity was also observed in the medial occipital cortex, ventral temporal cortex, and posterior insula.

Figure 1. 

Brain regions associated with autobiographical planning relative to counting. Results images are FDR-corrected, p < .05, k > 20, and displayed on an inflated surface map (population average landmark surface: PALS-B12) using CARET software (Van Essen, 2005).

Figure 1. 

Brain regions associated with autobiographical planning relative to counting. Results images are FDR-corrected, p < .05, k > 20, and displayed on an inflated surface map (population average landmark surface: PALS-B12) using CARET software (Van Essen, 2005).

Modulation by Qualitative Plan Features

Next, the relationship between the ratings and brain activity during autobiographical planning was determined. Positive and negative modulation was examined for plan detail, novelty, temporal distance, ease of formulating the plan in the scanner, difficulty in accomplishing the goal, and confidence in completing the goal. The results of each hierarchical linear regression analysis are presented in turn.

Detail

Modulation of activity in a number of brain structures was observed for autobiographical plan detail (see Figure 2A and Table 2). On the lateral surfaces, increasing detail was associated with greater activity in the lateral temporal cortex, inferior parietal cortex, and inferior frontal and middle frontal gyrus. On the medial surface, increasing detail was associated with greater activity in the RSC, PCC, and medial pFC. Highly detailed plans also increased activity in the thalamus, caudate, and hippocampus. Lower levels of plan detail were associated with increasing right temporal pole as well as bilateral cuneus and lingual gyrus activity.

Figure 2. 

Modulated autobiographical planning activity 1. (A) Detail. More detailed autobiographical plans were associated with default and frontoparietal brain network activity in cortex and subcortically in hippocampus and caudate, depicted in warm colors. Cool colors depict low detailed plans. (B) Novelty. More novel autobiographical plans were associated with default and frontoparietal brain network activity in cortex and subcortically in hippocampus and caudate. No regions were associated with low novelty. (C) Temporal distance. Modulation of activity for temporally proximal goals was associated with greater activity in warm colors. Distant goals were associated with right anterior hippocampal activity (circled) in cool colors. Result images are FDR-corrected, p < .05, k > 20, and displayed on an inflated surface map (population average landmark surface: PALS-B12) using CARET software (Van Essen, 2005) or displayed in the volume image to depict subcortical structures.

Figure 2. 

Modulated autobiographical planning activity 1. (A) Detail. More detailed autobiographical plans were associated with default and frontoparietal brain network activity in cortex and subcortically in hippocampus and caudate, depicted in warm colors. Cool colors depict low detailed plans. (B) Novelty. More novel autobiographical plans were associated with default and frontoparietal brain network activity in cortex and subcortically in hippocampus and caudate. No regions were associated with low novelty. (C) Temporal distance. Modulation of activity for temporally proximal goals was associated with greater activity in warm colors. Distant goals were associated with right anterior hippocampal activity (circled) in cool colors. Result images are FDR-corrected, p < .05, k > 20, and displayed on an inflated surface map (population average landmark surface: PALS-B12) using CARET software (Van Essen, 2005) or displayed in the volume image to depict subcortical structures.

Table 2. 

Detail

Regionxyztk
Positive Modulation 
Middle frontal gyrus −34 52 4.38 409 
TPJ/angular gyrus −46 −68 24 4.18 1016 
Anterior medial pFC −16 52 4.10 382 
Ventromedial pFC −2 36 −14 3.96 384 
Caudate −2 −2 3.93 290 
Thalamus −2 −6 3.82 82 
Temporal pole 48 −4 −24 3.81 115 
Angular gyrus 46 −72 34 3.68 221 
Hippocampus −22 −16 −20 3.62 534 
RSC 16 −42 3.61 635 
Hippocampus 20 −16 −20 3.53 236 
Inferior frontal gyrus −42 10 22 3.48 355 
Medial superior pFC −6 60 3.46 78 
Supramarginal gyrus −34 −30 50 3.32 22 
Middle frontal gyrus −22 40 22 3.30 126 
Posterior insula −36 −26 22 3.22 49 
Pons −6 −24 −26 3.07 20 
PCC −2 −46 38 2.98 168 
Anterior medial pFC 12 56 2.96 52 
Medial pFC 10 48 30 2.94 57 
Posterior STS 42 −42 2.93 116 
Superior frontal gyrus −18 20 54 2.79 213 
Inferior frontal gyrus −48 28 2.76 131 
Caudate 10 14 2.75 32 
STS −60 −16 −6 2.68 82 
Middle temporal gyrus 64 −40 2.60 26 
Middle temporal gyrus −60 −12 −24 2.57 27 
STS −44 −36 2.55 31 
Medial pFC −2 34 24 2.54 31 
Inferior frontal gyrus 60 18 2.53 20 
Superior temporal gyrus 66 −38 12 2.52 31 
Precentral gyrus 32 −22 60 2.49 32 
Middle temporal gyrus 62 −18 −18 2.44 27 
 
Negative Modulation 
Temporal pole 36 16 −22 3.18 27 
Cuneus 20 −76 20 2.90 56 
Cuneus −14 −78 16 2.86 36 
Lingual gyrus 18 −88 2.72 96 
Lingual gyrus −12 −76 −2 2.32 36 
Regionxyztk
Positive Modulation 
Middle frontal gyrus −34 52 4.38 409 
TPJ/angular gyrus −46 −68 24 4.18 1016 
Anterior medial pFC −16 52 4.10 382 
Ventromedial pFC −2 36 −14 3.96 384 
Caudate −2 −2 3.93 290 
Thalamus −2 −6 3.82 82 
Temporal pole 48 −4 −24 3.81 115 
Angular gyrus 46 −72 34 3.68 221 
Hippocampus −22 −16 −20 3.62 534 
RSC 16 −42 3.61 635 
Hippocampus 20 −16 −20 3.53 236 
Inferior frontal gyrus −42 10 22 3.48 355 
Medial superior pFC −6 60 3.46 78 
Supramarginal gyrus −34 −30 50 3.32 22 
Middle frontal gyrus −22 40 22 3.30 126 
Posterior insula −36 −26 22 3.22 49 
Pons −6 −24 −26 3.07 20 
PCC −2 −46 38 2.98 168 
Anterior medial pFC 12 56 2.96 52 
Medial pFC 10 48 30 2.94 57 
Posterior STS 42 −42 2.93 116 
Superior frontal gyrus −18 20 54 2.79 213 
Inferior frontal gyrus −48 28 2.76 131 
Caudate 10 14 2.75 32 
STS −60 −16 −6 2.68 82 
Middle temporal gyrus 64 −40 2.60 26 
Middle temporal gyrus −60 −12 −24 2.57 27 
STS −44 −36 2.55 31 
Medial pFC −2 34 24 2.54 31 
Inferior frontal gyrus 60 18 2.53 20 
Superior temporal gyrus 66 −38 12 2.52 31 
Precentral gyrus 32 −22 60 2.49 32 
Middle temporal gyrus 62 −18 −18 2.44 27 
 
Negative Modulation 
Temporal pole 36 16 −22 3.18 27 
Cuneus 20 −76 20 2.90 56 
Cuneus −14 −78 16 2.86 36 
Lingual gyrus 18 −88 2.72 96 
Lingual gyrus −12 −76 −2 2.32 36 

R = right; L = left.

Novelty

Many brain regions increased in activity as a function of higher novelty or of the extent to which the plan had been given prior consideration before participating in the study (Figure 2B and Table 3). Greater novelty was associated with increasing bilateral activity in the lateral and ventral temporal cortex, inferior parietal cortex, left lateral pFC, and dorsomedial pFC. Higher novelty was also associated with greater activity in the precuneus and right PCC as well as bilateral MTLs and caudate. No regions of the brain showed more activity for decreasing levels of novelty.

Table 3. 

Novelty

Regionxyztk
Positive Modulation 
Parahippocampus 38 −28 −20 4.42 369 
Hippocampus 30 −18 −20 3.53 — 
Caudate 14 12 4.20 39 
Inferior frontal gyrus −54 30 4.13 154 
Ventral temporal lobe −38 −10 −36 4.10 810 
Hippocampus −18 −10 −26 3.09 — 
Middle frontal gyrus −32 26 30 3.90 219 
Anterior superior frontal gyrus −8 64 28 3.86 162 
Fusiform gyrus −54 −46 −12 3.84 278 
Parahippocampus −42 −32 −16 3.81 162 
Middle temporal gyrus −60 −44 3.73 231 
Caudate body −16 10 3.60 56 
Parahippocampal gyrus 16 −40 3.34 20 
IPL −48 −48 26 3.25 370 
Middle frontal gyrus −36 40 3.21 221 
Middle temporal gyrus 66 −44 2.99 87 
PCC −36 40 2.97 259 
Anterior superior frontal gyrus −20 54 30 2.94 180 
Inferior frontal gyrus −42 36 −12 2.91 41 
Medial frontal gyrus 12 50 34 2.80 25 
Superior frontal gyrus −8 24 52 2.72 160 
Angular gyrus 56 −56 38 2.71 162 
Temporal pole 30 14 −30 2.67 24 
Temporal pole −48 −2 −26 2.64 59 
Posterior hippocampus −10 −36 2.62 52 
Superior frontal gyrus 20 24 48 2.55 70 
PCC −12 −42 34 2.51 20 
Precuneus −68 46 2.35 23 
 
Negative Modulation 
None 
Regionxyztk
Positive Modulation 
Parahippocampus 38 −28 −20 4.42 369 
Hippocampus 30 −18 −20 3.53 — 
Caudate 14 12 4.20 39 
Inferior frontal gyrus −54 30 4.13 154 
Ventral temporal lobe −38 −10 −36 4.10 810 
Hippocampus −18 −10 −26 3.09 — 
Middle frontal gyrus −32 26 30 3.90 219 
Anterior superior frontal gyrus −8 64 28 3.86 162 
Fusiform gyrus −54 −46 −12 3.84 278 
Parahippocampus −42 −32 −16 3.81 162 
Middle temporal gyrus −60 −44 3.73 231 
Caudate body −16 10 3.60 56 
Parahippocampal gyrus 16 −40 3.34 20 
IPL −48 −48 26 3.25 370 
Middle frontal gyrus −36 40 3.21 221 
Middle temporal gyrus 66 −44 2.99 87 
PCC −36 40 2.97 259 
Anterior superior frontal gyrus −20 54 30 2.94 180 
Inferior frontal gyrus −42 36 −12 2.91 41 
Medial frontal gyrus 12 50 34 2.80 25 
Superior frontal gyrus −8 24 52 2.72 160 
Angular gyrus 56 −56 38 2.71 162 
Temporal pole 30 14 −30 2.67 24 
Temporal pole −48 −2 −26 2.64 59 
Posterior hippocampus −10 −36 2.62 52 
Superior frontal gyrus 20 24 48 2.55 70 
PCC −12 −42 34 2.51 20 
Precuneus −68 46 2.35 23 
 
Negative Modulation 
None 
Temporal distance

Goals that were situated nearer in time demonstrated an extended pattern of increasing activity in the medial pFC, RSC, temporal pole, lateral temporal cortex, and angular gyrus as well as the left parahippocampus and left hippocampus. Proximal temporal distance also modulated aspects of the posterior lateral pFC and dorsal ACC as well as the cuneus and intracalcarine cortex. Planning for goals that were temporally more distant, in contrast, engaged the right hippocampus (see Figure 2C and Table 4).

Table 4. 

Temporal Distance

Regionxyztk
Positive Modulation (Distal) 
Hippocampus 26 −10 −14 2.33 15 
 
Negative Modulation (Proximal) 
Ventromedial pFC −4 24 −10 3.73 391 
RSC −12 −44 3.71 818 
Temporal pole 50 14 −22 3.54 48 
Posterior STS 48 −42 3.36 149 
Inferior frontal gyrus 54 30 −4 3.37 45 
Middle frontal gyrus −36 42 3.36 265 
Superior frontal gyrus 16 24 48 3.26 37 
Superior frontal gyrus 16 38 38 2.99 56 
Inferior frontal gyrus −60 14 26 2.99 24 
Anterior medial pFC 14 60 2.96 38 
Dorsomedial pFC −8 36 46 2.95 74 
Temporal pole 42 20 −34 2.85 24 
Parahippocampus −28 −36 −12 2.82 35 
Dorsal ACC −2 18 42 2.74 96 
Temporal pole −50 12 −28 2.72 25 
Cuneus 14 −70 22 2.72 302 
Superior frontal gyrus −18 34 40 2.71 75 
Angular gyrus −50 −54 28 2.69 35 
Cuneus 16 −90 16 2.68 21 
Middle temporal gyrus 68 −32 2.60 35 
RSC 18 −48 10 2.51 72 
Superior temporal gyrus −62 −44 16 2.50 26 
Hippocampus −24 −18 −22 2.45 23 
Medial pFC −10 38 30 2.41 32 
Intracalcarine cortex 22 −68 2.37 29 
Superior temporal gyrus −62 −28 2.30 21 
Medial pFC 46 36 2.20 31 
Medial pFC 54 14 2.18 23 
Regionxyztk
Positive Modulation (Distal) 
Hippocampus 26 −10 −14 2.33 15 
 
Negative Modulation (Proximal) 
Ventromedial pFC −4 24 −10 3.73 391 
RSC −12 −44 3.71 818 
Temporal pole 50 14 −22 3.54 48 
Posterior STS 48 −42 3.36 149 
Inferior frontal gyrus 54 30 −4 3.37 45 
Middle frontal gyrus −36 42 3.36 265 
Superior frontal gyrus 16 24 48 3.26 37 
Superior frontal gyrus 16 38 38 2.99 56 
Inferior frontal gyrus −60 14 26 2.99 24 
Anterior medial pFC 14 60 2.96 38 
Dorsomedial pFC −8 36 46 2.95 74 
Temporal pole 42 20 −34 2.85 24 
Parahippocampus −28 −36 −12 2.82 35 
Dorsal ACC −2 18 42 2.74 96 
Temporal pole −50 12 −28 2.72 25 
Cuneus 14 −70 22 2.72 302 
Superior frontal gyrus −18 34 40 2.71 75 
Angular gyrus −50 −54 28 2.69 35 
Cuneus 16 −90 16 2.68 21 
Middle temporal gyrus 68 −32 2.60 35 
RSC 18 −48 10 2.51 72 
Superior temporal gyrus −62 −44 16 2.50 26 
Hippocampus −24 −18 −22 2.45 23 
Medial pFC −10 38 30 2.41 32 
Intracalcarine cortex 22 −68 2.37 29 
Superior temporal gyrus −62 −28 2.30 21 
Medial pFC 46 36 2.20 31 
Medial pFC 54 14 2.18 23 
Ease of formulating the plan in the scanner

The process of planning in the scanner revealed differential activity by perceived difficulty. Plans that were easier to form showed greater activity in the temporal poles bilaterally as well as left-lateralized activity in the inferior temporal gyrus, fusiform, and anterior lateral pFC (BA 9). Plans that were more difficult to form in the scanner, in contrast, revealed more activity in the right fusiform and cuneus, in addition to the left anterior insula (see Table 5).

Table 5. 

Ease of Formulating Plan

Regionxyztk
Positive Modulation 
Temporal pole 44 −4 −32 3.08 77 
Temporal pole −54 −24 2.75 20 
Fusiform gyrus −28 −32 −24 2.74 21 
Inferior temporal gyrus −52 −14 −28 2.68 54 
Anterior superior frontal gyrus −8 64 30 2.64 22 
Temporal pole −44 −30 2.61 77 
 
Negative Modulation 
Occipital fusiform gyrus 22 −78 −10 2.55 64 
Anterior insula −38 22 2.24 29 
Cuneus 16 −66 14 2.21 30 
Regionxyztk
Positive Modulation 
Temporal pole 44 −4 −32 3.08 77 
Temporal pole −54 −24 2.75 20 
Fusiform gyrus −28 −32 −24 2.74 21 
Inferior temporal gyrus −52 −14 −28 2.68 54 
Anterior superior frontal gyrus −8 64 30 2.64 22 
Temporal pole −44 −30 2.61 77 
 
Negative Modulation 
Occipital fusiform gyrus 22 −78 −10 2.55 64 
Anterior insula −38 22 2.24 29 
Cuneus 16 −66 14 2.21 30 
Difficulty accomplishing the goal

Plans that were foreseen to be more difficult to accomplish were associated with increasing activity in the left amygdala, OFC, and ventral temporal cortex as well as the right parahippocampal cortex. Plans that were foreseen to be easier to accomplish were associated with increasing activity in the precuneus, posterior STS, left anterior insula, and occipital cortex (see Figure 3A and Table 6).

Figure 3. 

Modulated autobiographical planning activity 2. (A) Perceived difficulty in fulfilling the goal. High difficulty is depicted in warm colors, including the amygdala and OFC (both circled); low difficulty is depicted in cool colors. Left shows the ventral view of the brain's surface. (B) Confidence in fulfilling the goal. High confidence is depicted in warm colors; low confidence in goal fulfillment is depicted in cool colors, including the hippocampus (circled). Left shows a posterior view of the right hemisphere. Result images are FDR-corrected, p < .05, k > 20, and displayed on an inflated surface map (population average landmark surface: PALS-B12) using CARET software (Van Essen, 2005) or displayed in the volume image to depict subcortical structures.

Figure 3. 

Modulated autobiographical planning activity 2. (A) Perceived difficulty in fulfilling the goal. High difficulty is depicted in warm colors, including the amygdala and OFC (both circled); low difficulty is depicted in cool colors. Left shows the ventral view of the brain's surface. (B) Confidence in fulfilling the goal. High confidence is depicted in warm colors; low confidence in goal fulfillment is depicted in cool colors, including the hippocampus (circled). Left shows a posterior view of the right hemisphere. Result images are FDR-corrected, p < .05, k > 20, and displayed on an inflated surface map (population average landmark surface: PALS-B12) using CARET software (Van Essen, 2005) or displayed in the volume image to depict subcortical structures.

Table 6. 

Difficulty Accomplishing Goal

Regionxyztk
Positive Modulation 
Amygdala −28 −2 −20 3.09 25 
OFC −32 36 −12 2.98 30 
Ventral temporal cortex −48 −18 −28 2.59 23 
Parahippocampus 20 −26 −20 2.43 22 
 
Negative Modulation 
TPJ −58 −54 12 3.27 53 
Ventral occipital cortex 32 −70 −16 3.00 117 
Anterior insula −32 20 −2 2.75 97 
Precuneus −6 −46 44 2.58 50 
Lingual gyrus 16 −68 2.53 25 
Cuneus −10 −84 32 2.52 29 
Precuneus −44 38 2.50 21 
Middle temporal gyrus 56 −22 −12 2.49 21 
Lateral parietal 42 −64 42 2.42 62 
Regionxyztk
Positive Modulation 
Amygdala −28 −2 −20 3.09 25 
OFC −32 36 −12 2.98 30 
Ventral temporal cortex −48 −18 −28 2.59 23 
Parahippocampus 20 −26 −20 2.43 22 
 
Negative Modulation 
TPJ −58 −54 12 3.27 53 
Ventral occipital cortex 32 −70 −16 3.00 117 
Anterior insula −32 20 −2 2.75 97 
Precuneus −6 −46 44 2.58 50 
Lingual gyrus 16 −68 2.53 25 
Cuneus −10 −84 32 2.52 29 
Precuneus −44 38 2.50 21 
Middle temporal gyrus 56 −22 −12 2.49 21 
Lateral parietal 42 −64 42 2.42 62 
Confidence in completion

Plans for which participants had high confidence in eventually fulfilling resulted in greater activity in the inferior temporal gyrus, superior lateral occipital cortex, and PCC. Plans for which participants had lower confidence in fulfilling led to greater activity in the right hippocampus, angular gyrus, and RSC. Lower confidence plans were also associated with greater ventral occipital and temporal cortex activity as well as activity in the inferior frontal gyrus (see Figure 3B and Table 7).

Table 7. 

Confidence

Regionxyztk
Positive Modulation 
Inferior temporal gyrus −42 −10 −36 3.25 31 
Superior lateral occipital cortex 44 −64 48 2.60 23 
PCC −4 −40 24 2.33 25 
 
Negative Modulation 
Occipital fusiform gyrus 24 −66 −6 3.48 116 
Lingual gyrus −16 −54 −10 3.26 49 
Angular gyrus 54 −52 20 2.85 147 
Inferior frontal gyrus −38 44 −18 2.78 24 
Inferior frontal gyrus 48 26 2.76 26 
Hippocampus 28 −14 −16 2.76 27 
RSC 16 −40 2.70 40 
Temporal pole 40 −34 2.68 82 
Lingual gyrus −14 −74 2.55 26 
Cuneus −10 −88 26 2.54 90 
Inferior frontal gyrus 34 18 −22 2.38 20 
Cuneus 22 −70 14 2.38 39 
Lingual gyrus 26 −52 2.30 39 
Inferior frontal gyrus −46 16 −2 2.28 21 
Cuneus 22 −88 24 2.25 25 
Regionxyztk
Positive Modulation 
Inferior temporal gyrus −42 −10 −36 3.25 31 
Superior lateral occipital cortex 44 −64 48 2.60 23 
PCC −4 −40 24 2.33 25 
 
Negative Modulation 
Occipital fusiform gyrus 24 −66 −6 3.48 116 
Lingual gyrus −16 −54 −10 3.26 49 
Angular gyrus 54 −52 20 2.85 147 
Inferior frontal gyrus −38 44 −18 2.78 24 
Inferior frontal gyrus 48 26 2.76 26 
Hippocampus 28 −14 −16 2.76 27 
RSC 16 −40 2.70 40 
Temporal pole 40 −34 2.68 82 
Lingual gyrus −14 −74 2.55 26 
Cuneus −10 −88 26 2.54 90 
Inferior frontal gyrus 34 18 −22 2.38 20 
Cuneus 22 −70 14 2.38 39 
Lingual gyrus 26 −52 2.30 39 
Inferior frontal gyrus −46 16 −2 2.28 21 
Cuneus 22 −88 24 2.25 25 

DISCUSSION

In the last several years, substantial attention has been given to the cognitive and neural processes associated with episodic future simulation—the capacity to draw on the constructive nature of memory to flexibly reconstitute past experiences into coherent simulations of a personal future event (Schacter & Addis, 2007). In contrast, researchers are beginning to investigate the processes involved in guiding our actions toward realizing this personal future, what we have termed “autobiographical planning.” Constructing future simulations is closely associated with default network activity, as details of personal episodic events are recombined into an imagined future. We predicted that planning for that future would additionally engage frontoparietal control regions commonly associated with performance on standard laboratory-based measures of planning. Consistent with previous studies investigating the specific qualities of personal episodic simulation (e.g., Addis et al., 2007), we further hypothesized that contributions of specific brain regions in both default and frontoparietal control networks would be modulated by plan detail, novelty, ease of plan formulation, perceived difficulty, confidence in goal completion, and temporal proximity.

Consistent with predictions and previous multivariate analyses (Spreng et al., 2010, 2013; Spreng & Schacter, 2012), whole-brain analyses revealed robust engagement of default and frontoparietal control network regions during autobiographical planning relative to a counting control task. Results of the hierarchical regression analyses provided further support for this default executive model of autobiographical planning. Novel plans that were richly detailed, comparatively easy to construct in mind, and judged to be readily and confidently achievable and were targeted toward attainment of a shorter term goal robustly and concurrently engaged regions of both the default and executive control networks. In contrast, repeated plans or those that were sparsely detailed, difficult to formulate and accomplish, and directed toward a more distant goal state engaged regions of the default network but failed to reliably engage brain regions associated with executive control processing.

Detailed autobiographical planning for novel goals that were perceived to be easier and more readily achievable was associated with a broad pattern of cortical activity encompassing both default and frontoparietal control regions bilaterally. This finding is consistent with evidence linking default network brain regions with episodic simulation (Schacter et al., 2012) and imagining personal, versus nonpersonal, future events (D'Argembeau et al., 2010). Similarly, engagement of frontoparietal control network regions, including dorsolateral pFC as well as lateral parietal cortices, is consistent with activation patterns observed in an independent sample of participants engaged in autobiographical planning (Gerlach et al., 2014) during goal-directed future thinking (Stawarczyk & D'Argembeau, 2015) and during standardized, in-laboratory planning tasks (van den Huevel et al., 2003; Owen, 1997).

Coactivation of default and frontoparietal control brain regions during detailed autobiographical planning is consistent with a recent report demonstrating that prospective mind-wandering, commonly associated with default network activity, often involves future planning. Critically, the extent of this association between mind-wandering and planning was dependent on individual differences in cognitive control capacity (Baird, Smallwood, & Schooler, 2011). Providing additional behavioral evidence for a link between future thinking and executive function, individual differences in working memory predicted future episodic specificity, even after controlling for autobiographical memory specificity (Hill & Emery, 2013). A recent meta-analytic review directly investigating patterns of brain activity during mind-wandering observed activation in both default network and frontoparietal control regions (Fox, Spreng, Ellamil, Andrews-Hanna, & Christoff, 2015). Taken together, these findings are consistent with our observations that autobiographical planning requires both default network engagement to project into one's personal future and frontoparietal control network involvement to construct a viable way forward. Furthermore, these patterns of brain activity, linking personal relevance to planning, extend beyond the neocortex to subcortical brain structures. Detailed plans for novel versus rehearsed goals were associated with robust hippocampal activation, consistent with reports demonstrating that MTL structures are actively engaged during detailed and novel episodic simulations (Gaesser, Spreng, McLelland, Addis, & Schacter, 2013; Addis et al., 2011; Martin, Schacter, Corballis, & Addis, 2011; Addis & Schacter, 2008). Similarly, detailed planning in novel contexts was also associated with activity in the caudate nucleus, a region commonly associated with future planning for novel actions on standardized planning tasks (e.g., Wunderlich et al., 2012; Jankowski, Scheef, Hüppe, & Boecker, 2009; Monchi et al., 2006).

Planning for more temporally remote goals, which was considered by our participants to be more difficult and had lower probability of success, engaged default network brain regions but was not associated with activity in the frontoparietal control network. Planning for less immediate and more intangible personal goals engaged the right hippocampus, which has also been associated with episodic simulation of low-probability events (Weiler et al., 2010). Planning for personal goals that were considered more difficult to accomplish, that were harder to plan in the scanner, or where confidence in completion was low, which were also associated with temporal distance, preferentially engaged posterior default network regions, visual cortices, as well as affective and reward processing regions, including the amygdala and OFC. This observation suggests that autobiographical planning for a personally distant future may involve more affective and perceptually based projections as opposed to the controlled construction of detailed plans to a more proximal and tangible goal state.

On the basis of these findings, we suggest that autobiographical planning requires access to detailed representations of one's personal past, mediated by default network brain regions, as well as the control processes necessary to update, reconfigure, inhibit, and flexibly recombine these representations to forge a mental pathway toward personal goal attainment. However, the analyses of qualitative plan features also hint at a more complex model of autobiographical planning—one in which there may be multiple paths to plan our personal futures. In our recently proposed taxonomy of future thinking (Szpunar et al., 2014), we argued that planning (as well as other modes of future thinking) varies along a gradient from specific, episodic planning that involves organizing steps needed to achieve a particular autobiographical future outcome to abstract, semantic planning needed for some general state of the world to arise in the future. In a similar spirit, we suggest that specific autobiographical planning describes a detailed, achievable, and actionable planning process for attaining a clearly envisioned future, whereas abstract autobiographical planning refers to plans that may be constructed from more generalized semantic or affective representations of a less tangible and distant future. In this model, abstract autobiographical plans would be associated with default network structures as well as posterior and limbic brain regions linked to perceptually or affectively based holistic representations of a more generalized future self.

This distinction between specific and abstract autobiographical planning mirrors recent evidence that future simulations may vary by level of construal (Trope & Liberman, 2010). Construal theory predicts that prospection may consist of both richly detailed and more abstract representations of the future. Our data suggest that autobiographical planning may reflect a similar distinction. Specific autobiographical planning may require engagement and interaction among default and frontoparietal brain regions to both instantiate the goal state and to shape the detailed means to its attainment. In contrast, abstract autobiographical planning engages default network regions to instantiate the desired, albeit more distal, goal state; however, specific control processes may give way to more sensory and affective responses to a less detailed or determined future.

We observed the hippocampus to be involved in facets of both abstract and specific autobiographical planning. We observed predominantly right anterior hippocampus activity associated with more abstract features of autobiographical planning, including greater temporal distance (y = −10), low confidence in plan completion (y = −14), and high novelty (y = −18). In contrast, specific planning features were associated with left anterior hippocampal activity, including high detail plans (y = −14) and close temporal proximity (y = −18). However, laterality does not cleanly separate abstract and specific planning processes: Detailed planning was also associated with the right anterior hippocampus (y = −18), and left posterior hippocampus (y = −36) was engaged for higher novelty plans. Parametric modulation of the hippocampus for both greater detail and temporal distance has been observed previously in research on future episodic event simulation effects (Addis & Schacter, 2008), likely reflecting the contribution of the hippocampus to both generating specific details for simulated experiences and recombining those details into a coherent event. Addis and Schacter (2008) proposed that detail recombination may be more difficult for distant, more abstract future events than for proximal, more concrete events and require greater hippocampal contributions; this account fits with our observations concerning autobiographical planning. Overall, the modulation effects we observed here may reflect the multifaceted functions of the hippocampus, including the accessing of episodic detail, recombination of details, and/or encoding of plans (cf. Addis & Schacter, 2012, for a review).

In this study, we were able to demonstrate that autobiographical planning involves coactivation of default and frontoparietal brain regions, consistent with our hypothesis that envisioning a personal future and devising a means to its attainment are critically dependent on cross-talk between these two brain networks. Furthermore, our analysis of the qualitative features of autobiographical planning suggests that it is not a unitary process but may be differentiated, both in its phenomenology and neural instantiation, depending on the psychological distance that must be bridged between the present and an imagined future.

Acknowledgments

We thank Adrian Gilmore and Clifford Robbins for assistance with stimulus preparation and data collection, Tom Miller and David Orama for help with data processing, and the Harvard Center for Brain Science Neuroimaging Core and the Harvard Neuroinformatics Research Group for imaging support. This work was supported by NIMH grant MH060941 to D. L. S.

Reprint requests should be sent to R. Nathan Spreng, Department of Human Development, Cornell University, Ithaca, NY 14853, or via e-mail: nathan.spreng@gmail.com.

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