Periodic Reporting for period 4 - PIVOTAL (Predictive Memory Systems Across the Human Lifespan)
Période du rapport: 2022-08-01 au 2023-07-31
Imagine unlocking your office in the morning. Within milliseconds you will be able to tell whether the furniture, computer, and papers on the desk are still where they are expected to be. You will also quickly detect if something unexpected is in the room, for example a box of chocolates an appreciative colleague has left for you. The mechanism proposed to underlie our mind’s efficient ability to grasp the environment converges on the notion of predictive brain. Put forward as a universal principle of the brain, the crux of the notion is that brains are essentially prediction machines that constantly attempt to match incoming inputs with top-down predictions. This provides us a powerful means to streamline the massive amount of continuous incoming information from the environment. Furthermore, when actual input is discrepant from the predicted input, a prediction error (PE) is elaborated to drive learning, i.e. updating internal models that will help to improve future predictions.
If the predictive brain is indeed a unifying principle, two critical issues need to be resolved. First, the predictive processing framework has not yet delineated the nature of internal models on which predictions are based (e.g. memory of prior experience) and how our actual experiences shape them in turn. Second, how does such a universal brain principle play out in diverse brains (e.g. young versus old brains)? Addressing these knowledge gaps is important in order to make a breakthrough in our understanding of the fundamental nature of the human mind and brain and to test the adaptivity of the predictive brain principle in accommodating inherent diversities of human brains.
By connecting three separate strands of research (i.e. predictive processing, memory systems, and lifespan development), the PIVOTAL research program aims to unravel the cognitive and neural mechanisms that enable the brain to (i) generate predictions based on memory of prior experience (episodic memory) and knowledge about the world (semantic memory); (ii) verify its predictions given the actual event, and (iii) engage in subsequent processes that in turn modify the memory representation. Using cognitive neuroscience methodology (functional magnetic resonance imaging), experimental research designs, and computational modelling, these mechanisms are being systematically examined in children, younger adults, and older adults, whose neurocognitive landscapes are highly different from each other. The gain in knowledge will characterize the cognitive architectures that allow the human brain to perform predictive processing as a fundamental operation in its interaction with the environment.
If the predictive brain is indeed a unifying principle, two critical issues need to be resolved. First, the predictive processing framework has not yet delineated the nature of internal models on which predictions are based (e.g. memory of prior experience) and how our actual experiences shape them in turn. Second, how does such a universal brain principle play out in diverse brains (e.g. young versus old brains)? Addressing these knowledge gaps is important in order to make a breakthrough in our understanding of the fundamental nature of the human mind and brain and to test the adaptivity of the predictive brain principle in accommodating inherent diversities of human brains.
By connecting three separate strands of research (i.e. predictive processing, memory systems, and lifespan development), the PIVOTAL research program aims to unravel the cognitive and neural mechanisms that enable the brain to (i) generate predictions based on memory of prior experience (episodic memory) and knowledge about the world (semantic memory); (ii) verify its predictions given the actual event, and (iii) engage in subsequent processes that in turn modify the memory representation. Using cognitive neuroscience methodology (functional magnetic resonance imaging), experimental research designs, and computational modelling, these mechanisms are being systematically examined in children, younger adults, and older adults, whose neurocognitive landscapes are highly different from each other. The gain in knowledge will characterize the cognitive architectures that allow the human brain to perform predictive processing as a fundamental operation in its interaction with the environment.
(1) What is the nature of the internal models on which predictions are generated?
Our study with functional magnetic resonance imaging (fMRI) focusing on the early visual cortex (V1 and V2) have revealed that a key aspect of these internal models is that they carry information about our time-distant memories. Moreover, we have also shown that this time-distant (i.e. mnemonic) information co-exists with information from our current environment. The extent to which mnemonic information is reinstated in V1 and V2 depends on whether the information is retrieved episodically or semantically. The visual cortex also differed in its effective connectivity with distant brain regions as a function of retrieved information type. It is the integration of these distinct pieces of information that render our meaningful experience of the world.
(2) How does prediction error (PE) shape our memories?
In order to understand the world around us and to predict upcoming events, our brain is sensitive to regularities in the environment. When one encounters a familiar event, a prediction can usually be made about the next event that would follow. But what happens when prediction is not matched, that is, one experiences a PE? In a series of experiments, we found that the effect of PE on episodic memory is subtle and highly dependent on several moderators. First, our memories are updated in the presence of uncertainty more than when faced with a very predictable context. Therefore, it is important to consider the prior state of the internal model. Another key moderator of the influence of PE on episodic memory is whether prediction is made explicitly or not. The is due to a bias for choice confirmation, which reflect stronger learning from choice-confirming compared to disconfirming outcomes. Furthermore, the effect of PE on episodic memory can specifically be linked to increased recollection (being able to retrieve specific episodic details) and not mere familiarity (general subjective feeling of having experienced something).
(3) How do prediction processes play out in developing and aging brains?
We found that prediction processes play out differently in developing and aging brains depending on the types of memory involved. For prediction generation, both concurrent contextual and time-distant mnemonic feedback can be detected in older adults’ V1 and V2 regions (as they do in younger adults’). Interestingly, only contextual feedback suffers from age-related dedifferentiation (i.e. reduced specificity of neural representations). On the contrary, episodic feedback remains well-differentiated and semantic feedback is even better differentiated in older compared to younger adults. These results highlight the increased importance of semantic knowledge across the lifespan in generating feedback. However, when looking at the relationship between PE and memory formation, we found that children and younger adults, but not older adults, show enhanced memory for events that violate semantic predictions. This supports the postulation that children and younger adults being more flexible to encode and potentially update their semantic memory in face of unexpected events.
Our study with functional magnetic resonance imaging (fMRI) focusing on the early visual cortex (V1 and V2) have revealed that a key aspect of these internal models is that they carry information about our time-distant memories. Moreover, we have also shown that this time-distant (i.e. mnemonic) information co-exists with information from our current environment. The extent to which mnemonic information is reinstated in V1 and V2 depends on whether the information is retrieved episodically or semantically. The visual cortex also differed in its effective connectivity with distant brain regions as a function of retrieved information type. It is the integration of these distinct pieces of information that render our meaningful experience of the world.
(2) How does prediction error (PE) shape our memories?
In order to understand the world around us and to predict upcoming events, our brain is sensitive to regularities in the environment. When one encounters a familiar event, a prediction can usually be made about the next event that would follow. But what happens when prediction is not matched, that is, one experiences a PE? In a series of experiments, we found that the effect of PE on episodic memory is subtle and highly dependent on several moderators. First, our memories are updated in the presence of uncertainty more than when faced with a very predictable context. Therefore, it is important to consider the prior state of the internal model. Another key moderator of the influence of PE on episodic memory is whether prediction is made explicitly or not. The is due to a bias for choice confirmation, which reflect stronger learning from choice-confirming compared to disconfirming outcomes. Furthermore, the effect of PE on episodic memory can specifically be linked to increased recollection (being able to retrieve specific episodic details) and not mere familiarity (general subjective feeling of having experienced something).
(3) How do prediction processes play out in developing and aging brains?
We found that prediction processes play out differently in developing and aging brains depending on the types of memory involved. For prediction generation, both concurrent contextual and time-distant mnemonic feedback can be detected in older adults’ V1 and V2 regions (as they do in younger adults’). Interestingly, only contextual feedback suffers from age-related dedifferentiation (i.e. reduced specificity of neural representations). On the contrary, episodic feedback remains well-differentiated and semantic feedback is even better differentiated in older compared to younger adults. These results highlight the increased importance of semantic knowledge across the lifespan in generating feedback. However, when looking at the relationship between PE and memory formation, we found that children and younger adults, but not older adults, show enhanced memory for events that violate semantic predictions. This supports the postulation that children and younger adults being more flexible to encode and potentially update their semantic memory in face of unexpected events.
Understanding how the human brain operates as a prediction machine continues to be a hot topic. However, to our knowledge we remain the the only group systematically examining the mechanisms that underlie predictive processing in relation to episodic memory and semantic memory, and doing so within a lifespan context. Our findings have elucidated the nature of internal models in the predictive processing framework with an explicit established link to human memory systems. By doing this we expand the horizons of both the predictive coding and memory fields. We also have gained a better understanding of how these mechanisms operate in children, younger adults, and older adults, who differ from each other in important ways due to divergence in developmental orientation (progression vs. conservation) and neurocognitive landscape (structural and functional integrity of memory neural circuits). We contribute a more dynamic perspective to the predictive brain theory, which may inspire future studies to better understand developmental disorders and pathological aging where predictive processing become aberrant, addressing why certain disorders tend to emerge in particular time windows (e.g. schizophrenia during adolescence).