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Impaired spatial and non-spatial configural learning in patients with hippocampal pathology.

Kumaran D, Hassabis D, Spiers HJ, Vann SD, Vargha-Khadem F, Maguire EA - Neuropsychologia (2007)

Bottom Line: Our data also provide evidence that residual configural learning can occur in the presence of significant hippocampal dysfunction.Moreover, evidence obtained from a post-experimental debriefing session suggested that patients acquired declarative knowledge of the underlying task contingencies that corresponded to the best-fit strategy identified by our strategy analysis.In summary, our findings support the notion that the hippocampus plays an important role in both spatial and non-spatial configural learning, and provide insights into the role of the medial temporal lobe (MTL) more generally in incremental reinforcement-driven learning.

View Article: PubMed Central - PubMed

Affiliation: Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London WC1N 3BG, UK. d.kumaran@fil.ion.ucl.ac.uk

ABSTRACT
The hippocampus has been proposed to play a critical role in memory through its unique ability to bind together the disparate elements of an experience. This hypothesis has been widely examined in rodents using a class of tasks known as "configural" or "non-linear", where outcomes are determined by specific combinations of elements, rather than any single element alone. On the basis of equivocal evidence that hippocampal lesions impair performance on non-spatial configural tasks, it has been proposed that the hippocampus may only be critical for spatial configural learning. Surprisingly few studies in humans have examined the role of the hippocampus in solving configural problems. In particular, no previous study has directly assessed the human hippocampal contribution to non-spatial and spatial configural learning, the focus of the current study. Our results show that patients with primary damage to the hippocampus bilaterally were similarly impaired at configural learning within both spatial and non-spatial domains. Our data also provide evidence that residual configural learning can occur in the presence of significant hippocampal dysfunction. Moreover, evidence obtained from a post-experimental debriefing session suggested that patients acquired declarative knowledge of the underlying task contingencies that corresponded to the best-fit strategy identified by our strategy analysis. In summary, our findings support the notion that the hippocampus plays an important role in both spatial and non-spatial configural learning, and provide insights into the role of the medial temporal lobe (MTL) more generally in incremental reinforcement-driven learning.

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Performance of amnesic patients divided into two groups according to strategy (optimal or sub-optimal). (A) Average performance of optimal strategy patients (P01 and P04). These patients adopted a configural associative strategy (see Section 3/Section 2 for details of strategy analysis) and performed relatively well on the task (average performance during last three blocks: 80%, S.D. 2.9). (B) Average performance of sub-optimal strategy patients (P02 and P03). These patients performed relatively poorly (average performance during last three blocks: 62.7%, S.D. 5.6), failing to adopt a configural strategy and using at best an elemental (i.e. single shape) strategy. The use of this elemental strategy naturally results in superior performance in the non-spatial, as compared to the spatial, condition, although this difference was not statistically significant.
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fig5: Performance of amnesic patients divided into two groups according to strategy (optimal or sub-optimal). (A) Average performance of optimal strategy patients (P01 and P04). These patients adopted a configural associative strategy (see Section 3/Section 2 for details of strategy analysis) and performed relatively well on the task (average performance during last three blocks: 80%, S.D. 2.9). (B) Average performance of sub-optimal strategy patients (P02 and P03). These patients performed relatively poorly (average performance during last three blocks: 62.7%, S.D. 5.6), failing to adopt a configural strategy and using at best an elemental (i.e. single shape) strategy. The use of this elemental strategy naturally results in superior performance in the non-spatial, as compared to the spatial, condition, although this difference was not statistically significant.

Mentions: We next examined the performance of the four patients in greater detail (see Figs. 5 and 6). This revealed that the patients could be split into two groups according to their overall performance on the task. Moreover, a subsequent strategy analysis (see below) demonstrated that the two patients who significantly improved their overall performance across the experiment had adopted an optimal associative strategy, whereas the other two patients had adopted inferior (e.g. elemental) strategies. A repeated measures ANOVA considering the P01 and P04 as a group (see Figs. 5A, and 6A and D: hereafter termed “optimal strategy patients”—see below for strategy analysis), demonstrated a significant learning effect over the experiment: i.e. significant effect of block (F(6,6) = 5.98, p = 0.02), with no effect of condition (F(1,1) = 6.5, p = 0.24) or condition–block interaction (F(6,6) = 1.1, p = 0.45). As can be appreciated from Fig. 5A, the two optimal strategy patients improved over the first three blocks to a level not significantly different from control subjects (performance in third block: control subjects 82.4% (S.D. 16.1), patients 80% (S.D. 6.4): t(6) = 0.25, p = 0.81). However, after this point, these two patients failed to improve over the next 200 trials or four blocks: (r = 0.15, p = 0.8). In contrast, the performance of controls continued to improve (r = 0.98, p = 0.001).


Impaired spatial and non-spatial configural learning in patients with hippocampal pathology.

Kumaran D, Hassabis D, Spiers HJ, Vann SD, Vargha-Khadem F, Maguire EA - Neuropsychologia (2007)

Performance of amnesic patients divided into two groups according to strategy (optimal or sub-optimal). (A) Average performance of optimal strategy patients (P01 and P04). These patients adopted a configural associative strategy (see Section 3/Section 2 for details of strategy analysis) and performed relatively well on the task (average performance during last three blocks: 80%, S.D. 2.9). (B) Average performance of sub-optimal strategy patients (P02 and P03). These patients performed relatively poorly (average performance during last three blocks: 62.7%, S.D. 5.6), failing to adopt a configural strategy and using at best an elemental (i.e. single shape) strategy. The use of this elemental strategy naturally results in superior performance in the non-spatial, as compared to the spatial, condition, although this difference was not statistically significant.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2572909&req=5

fig5: Performance of amnesic patients divided into two groups according to strategy (optimal or sub-optimal). (A) Average performance of optimal strategy patients (P01 and P04). These patients adopted a configural associative strategy (see Section 3/Section 2 for details of strategy analysis) and performed relatively well on the task (average performance during last three blocks: 80%, S.D. 2.9). (B) Average performance of sub-optimal strategy patients (P02 and P03). These patients performed relatively poorly (average performance during last three blocks: 62.7%, S.D. 5.6), failing to adopt a configural strategy and using at best an elemental (i.e. single shape) strategy. The use of this elemental strategy naturally results in superior performance in the non-spatial, as compared to the spatial, condition, although this difference was not statistically significant.
Mentions: We next examined the performance of the four patients in greater detail (see Figs. 5 and 6). This revealed that the patients could be split into two groups according to their overall performance on the task. Moreover, a subsequent strategy analysis (see below) demonstrated that the two patients who significantly improved their overall performance across the experiment had adopted an optimal associative strategy, whereas the other two patients had adopted inferior (e.g. elemental) strategies. A repeated measures ANOVA considering the P01 and P04 as a group (see Figs. 5A, and 6A and D: hereafter termed “optimal strategy patients”—see below for strategy analysis), demonstrated a significant learning effect over the experiment: i.e. significant effect of block (F(6,6) = 5.98, p = 0.02), with no effect of condition (F(1,1) = 6.5, p = 0.24) or condition–block interaction (F(6,6) = 1.1, p = 0.45). As can be appreciated from Fig. 5A, the two optimal strategy patients improved over the first three blocks to a level not significantly different from control subjects (performance in third block: control subjects 82.4% (S.D. 16.1), patients 80% (S.D. 6.4): t(6) = 0.25, p = 0.81). However, after this point, these two patients failed to improve over the next 200 trials or four blocks: (r = 0.15, p = 0.8). In contrast, the performance of controls continued to improve (r = 0.98, p = 0.001).

Bottom Line: Our data also provide evidence that residual configural learning can occur in the presence of significant hippocampal dysfunction.Moreover, evidence obtained from a post-experimental debriefing session suggested that patients acquired declarative knowledge of the underlying task contingencies that corresponded to the best-fit strategy identified by our strategy analysis.In summary, our findings support the notion that the hippocampus plays an important role in both spatial and non-spatial configural learning, and provide insights into the role of the medial temporal lobe (MTL) more generally in incremental reinforcement-driven learning.

View Article: PubMed Central - PubMed

Affiliation: Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London WC1N 3BG, UK. d.kumaran@fil.ion.ucl.ac.uk

ABSTRACT
The hippocampus has been proposed to play a critical role in memory through its unique ability to bind together the disparate elements of an experience. This hypothesis has been widely examined in rodents using a class of tasks known as "configural" or "non-linear", where outcomes are determined by specific combinations of elements, rather than any single element alone. On the basis of equivocal evidence that hippocampal lesions impair performance on non-spatial configural tasks, it has been proposed that the hippocampus may only be critical for spatial configural learning. Surprisingly few studies in humans have examined the role of the hippocampus in solving configural problems. In particular, no previous study has directly assessed the human hippocampal contribution to non-spatial and spatial configural learning, the focus of the current study. Our results show that patients with primary damage to the hippocampus bilaterally were similarly impaired at configural learning within both spatial and non-spatial domains. Our data also provide evidence that residual configural learning can occur in the presence of significant hippocampal dysfunction. Moreover, evidence obtained from a post-experimental debriefing session suggested that patients acquired declarative knowledge of the underlying task contingencies that corresponded to the best-fit strategy identified by our strategy analysis. In summary, our findings support the notion that the hippocampus plays an important role in both spatial and non-spatial configural learning, and provide insights into the role of the medial temporal lobe (MTL) more generally in incremental reinforcement-driven learning.

Show MeSH
Related in: MedlinePlus