Abstract
Context plays a crucial role in learning and memory, but a satisfactory characterization of this role in models of memory remains elusive. Classical and recent studies show that context cannot be meaningfully treated as either the figure or the ground, the noise or the signal, in memory models. This impasse belies certain cognitivist assumptions common to traditional cognitive science. A number of postcognitivist movements in philosophy and cognitive science have offered effective critiques of the basic framework, often borrowed in memory science, that depicts the human cognitive system as a dimensionless executive control unit receiving and transforming signals as input from the environment. These revisionary movements have also offered up alternative dynamic approaches to cognitive modeling and explanation, which can and should be deployed in memory science in order to resolve the impasse surrounding the modeling of context and memory.
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Notes
- 1.
It is true that there has been some revisionary language in memory science lately: ‘persistence’ rather than ‘storage’, reconsolidation instead of ‘consolidation’, but I have argued elsewhere that these changes–although indicative of an admirable spirit–have so far been little other than cosmetic [19].
- 2.
Matthew Sanders and colleagues make this explicit, writing: “In order for context conditioning to proceed, the various stimuli in the environment must be associated with one another as the context. Therefore, we have proposed that one function of the hippocampus is to assemble a contextual representation...” [6, pp. 220].
- 3.
Connectionism, of the medium-strength variety, is the endorsement of neural network architectures as robustly informative for those who would study minds. That is, there are few who doubt that neural network architectures have something to do with cognition, a least at the “subsymbolic” level, and there are few who think that neural network architectures have already solved all of our puzzles about cognition, but many among the postcognitivist vanguard stand on the middle ground here, urging that cognitive scientists and philosophers of mind have real and revisionary lessons to learn by paying close attention to the way that neural networks actually function.
- 4.
Although I am here taking my philosophical cue from Wittgenstein, this point brings to mind some particularly Heideggerian critiques of standard cognitive science, given Heidegger’s insistence that we are always already involved in the world, as Dreyfus often points out [23]. An excellent treatment of this Heideggerian/Wittgensteinian point is a recent monograph by Lee Braver [16].
- 5.
This is not to say that informational encapsulation has no place in models, or that a given explanans may not fruitfully be broken up into component “systems”–rather it is something like a shift in the burden of proof, a consistently suspicious attitude toward over-modularizing that which can be done in continuous and dynamic terms.
- 6.
Indeed, adequately characterizing just what explanation consists in is a notorious enough difficulty that I take it that any identification of explanation and information processing-style models can only be a tacit, uncritical move.
- 7.
This is not to suggest that language-involving cognitive phenomena are necessarily beyond the ken of dynamic systems modeling techniques, only that there is good reason to expect successful models of these to be among the late-comers to the dynamic systems table.
- 8.
As characterized above, much of these experiments take the form of tracking the help and hindrance of “context cues” for declarative memory–simple context cue experiments such as those that study positive or negative intereference in semantic memory may be amenable, for example, in the same way that “cognitive control” was found to be in a recent study by Todd Braver [35].
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O’Loughlin, I. (2015). Neither Noise nor Signal. In: Christiansen, H., Stojanovic, I., Papadopoulos, G. (eds) Modeling and Using Context. CONTEXT 2015. Lecture Notes in Computer Science(), vol 9405. Springer, Cham. https://doi.org/10.1007/978-3-319-25591-0_29
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