The human brain can generate new ideas, hypotheses and candidate solutions to difficult tasks wit... more The human brain can generate new ideas, hypotheses and candidate solutions to difficult tasks with surprising ease. We argue that this process has evolutionary dynamics, with multiplication, inheritance and variability all implemented in neural matter. This inspires our model, whose main component is a population of recurrent attractor networks with palimpsest memory that can store correlated patterns. The candidate solutions are represented as output patterns of the attractor networks and they are maintained in implicit working memory until they are evaluated by selection. The best patterns are then multiplied and fed back to attractor networks as a noisy version of these patterns (inheritance with variability), thus generating a new generation of candidate hypotheses. These components implement a truly Darwinian process which is more efficient than both natural selection on genetic inheritance or learning, on their own. We argue that this type of evolutionary search with learning can be the basis of high-level cognitive processes, such as problem solving or language.
We used a simple artificial neural network model, drawn from the domain of language development, ... more We used a simple artificial neural network model, drawn from the domain of language development, to begin the work of understanding what principles underlie effective interventions for developmental disorders of language and cognition, from the perspective of neurocomputational mechanisms of development. The work aims to complement a clinical perspective of the principles of effective intervention. Our study explored the effectiveness of different types of intervention modeled as items added to the normal training set. We assessed whether best interventions were specific to problem domains, specific to deficit types, and/or dependent on when in development they take place. While the model was highly simplified, it represents a first step in seeking to understand how atypical internal representations may be reshaped by alternative training regimes. The next step is to scale up the simulations to more realistic models of specific task domains within language acquisition.
We investigate reaction times for classification of visual stimuli composed of combinations of sh... more We investigate reaction times for classification of visual stimuli composed of combinations of shapes, to distinguish between parallel and serial processing of stimuli. Reaction times in a visual XOR task are slower than in AND/OR tasks in which pairs of shapes are categorised. This behaviour is explained by the time needed to perceive shapes in the various tasks, using a parallel drift diffusion model. The parallel model explains reaction times in an extension of the XOR task, up to 7 shapes. Subsequently, the behaviour is explained by a combined model that assumes perceptual chunking, processing shapes within chunks in parallel, and chunks themselves in serial. The pure parallel model also explains reaction times for ALL and EXISTS tasks. An extension to the perceptual chunking model adds time taken to apply a logical rule. We are able to improve the fit to the data by including this extra parameter, but using model selection the extra parameter is not supported. We further simulate the behaviour exhibited using an echo state network, successfully recreating the behaviour seen in humans.
Replication is an important “credibility control” mechanism for clarifying the reliability of pub... more Replication is an important “credibility control” mechanism for clarifying the reliability of published findings. However, replication is costly, and it is infeasible to replicate everything. Accurate, fast, lower cost alternatives such as eliciting predictions from experts or novices could accelerate credibility assessment and improve allocation of replication resources for important and uncertain findings. We elicited judgments from experts and novices on 100 claims from preprints about an emerging area of research (COVID-19 pandemic) using a new interactive structured elicitation protocol and we conducted 35 new replications. Participants’ average estimates were similar to the observed replication rate of 60%. After interacting with their peers, novices updated both their estimates and confidence in their judgements significantly more than experts and their accuracy improved more between elicitation rounds. Experts’ average accuracy was 0.54 (95% CI: [0.454, 0.628]) after interac...
The human brain can generate new ideas, hypotheses and candidate solutions to difficult tasks wit... more The human brain can generate new ideas, hypotheses and candidate solutions to difficult tasks with surprising ease. We argue that this process has evolutionary dynamics, with multiplication, inheritance and variability all implemented in neural matter. This inspires our model, whose main component is a population of recurrent attractor networks with palimpsest memory that can store correlated patterns. The candidate solutions are represented as output patterns of the attractor networks and they are maintained in implicit working memory until they are evaluated by selection. The best patterns are then multiplied and fed back to attractor networks as a noisy version of these patterns (inheritance with variability), thus generating a new generation of candidate hypotheses. These components implement a truly Darwinian process which is more efficient than both natural selection on genetic inheritance or learning, on their own. We argue that this type of evolutionary search with learning can be the basis of high-level cognitive processes, such as problem solving or language.
We used a simple artificial neural network model, drawn from the domain of language development, ... more We used a simple artificial neural network model, drawn from the domain of language development, to begin the work of understanding what principles underlie effective interventions for developmental disorders of language and cognition, from the perspective of neurocomputational mechanisms of development. The work aims to complement a clinical perspective of the principles of effective intervention. Our study explored the effectiveness of different types of intervention modeled as items added to the normal training set. We assessed whether best interventions were specific to problem domains, specific to deficit types, and/or dependent on when in development they take place. While the model was highly simplified, it represents a first step in seeking to understand how atypical internal representations may be reshaped by alternative training regimes. The next step is to scale up the simulations to more realistic models of specific task domains within language acquisition.
We investigate reaction times for classification of visual stimuli composed of combinations of sh... more We investigate reaction times for classification of visual stimuli composed of combinations of shapes, to distinguish between parallel and serial processing of stimuli. Reaction times in a visual XOR task are slower than in AND/OR tasks in which pairs of shapes are categorised. This behaviour is explained by the time needed to perceive shapes in the various tasks, using a parallel drift diffusion model. The parallel model explains reaction times in an extension of the XOR task, up to 7 shapes. Subsequently, the behaviour is explained by a combined model that assumes perceptual chunking, processing shapes within chunks in parallel, and chunks themselves in serial. The pure parallel model also explains reaction times for ALL and EXISTS tasks. An extension to the perceptual chunking model adds time taken to apply a logical rule. We are able to improve the fit to the data by including this extra parameter, but using model selection the extra parameter is not supported. We further simulate the behaviour exhibited using an echo state network, successfully recreating the behaviour seen in humans.
Replication is an important “credibility control” mechanism for clarifying the reliability of pub... more Replication is an important “credibility control” mechanism for clarifying the reliability of published findings. However, replication is costly, and it is infeasible to replicate everything. Accurate, fast, lower cost alternatives such as eliciting predictions from experts or novices could accelerate credibility assessment and improve allocation of replication resources for important and uncertain findings. We elicited judgments from experts and novices on 100 claims from preprints about an emerging area of research (COVID-19 pandemic) using a new interactive structured elicitation protocol and we conducted 35 new replications. Participants’ average estimates were similar to the observed replication rate of 60%. After interacting with their peers, novices updated both their estimates and confidence in their judgements significantly more than experts and their accuracy improved more between elicitation rounds. Experts’ average accuracy was 0.54 (95% CI: [0.454, 0.628]) after interac...
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