From Heuristic to Analytic: Cognitively Motivated Strategies for Coherent Physical Commonsense Reasoning (2023)

Abstract: Pre-trained language models (PLMs) have shown impressive performance in various language tasks. However, they are prone to spurious correlations, and often generate illusory information. In real-world applications, PLMs should justify decisions with formalized, coherent reasoning chains, but this challenge remains under-explored. Cognitive psychology theorizes that humans are capable of utilizing fast and intuitive *heuristic* thinking to make decisions based on past experience, then rationalizing the decisions through slower and deliberative *analytic* reasoning. We incorporate these interlinked dual processes in fine-tuning and in-context learning with PLMs, applying them to two language understanding tasks that require coherent physical commonsense reasoning. We show that our proposed Heuristic-Analytic Reasoning (HAR) strategies drastically improve the coherence of rationalizations for model decisions, yielding state-of-the-art results on Tiered Reasoning for Intuitive Physics (TRIP). We also find that this improved coherence is a direct result of more faithful attention to relevant language context in each step of reasoning. Our findings suggest that human-like reasoning strategies can effectively improve the coherence and reliability of PLM reasoning.

## introduction pre-trained language models (plms) have recently shown strong performance on an increasing variety of challenging reasoning problems @xcite @xcite @xcite . while their capabilities are impressive, their tendency to make artificial mistakes in reasoning, including exploiting spurious correlations @xcite and hallucinating factual information @xcite in order to reach conclusions, makes them difficult to rely on in practice. this possibility of incoherence in reasoning with plms creates a demand for more reliable, logical, and transparent reasoning strategies compatible with differentiable architectures like plms @xcite . meanwhile, in theories of cognitive psychology, drawing conclusions in reasoning problems and coherently rationalizing them have long been thought to come from dual processes of human cognition @xcite @xcite ; tsujii and watanabe, 2009; evans, 2011): fast, associative heuristic thinking based on experience, and slower, deliberative analytic thinking, which requires more working memory. specifically, prior work theorizes that heuristic processes enable us to extract the most relevant information from the context and provide quick intuition for decisions, which can then inform analytic processes that operate on this information to perform inference and rationalize when needed @xcite @xcite . in this work, inspired by the synergy between these dual processes in humans, we propose analogous heuristic-analytic reasoning (har) strategies for plms, which bootstrap lower-level (analytic) rationalization from higher-level (heuristic) decision-making. using physical commonsense as an example, we implemented har for both finetuning and in-context learning. through evaluation on two benchmarks, tiered reasoning for intuitive physics (trip; @xcite and a novel reframing of propara @xcite requiring multiple levels of reasoning, we find that har consistently enables more coherent reasoning, achieving state-of-the-art results on coherence metrics in trip. more importantly, our analysis shows this improved coherence in in-context learning is enabled by more faithful and transparent rationalization through stronger attention to relevant language context in reasoning. this demonstrates that similar to humans, plms' reasoning is strengthened by linking heuristic and analytic processes, and such strategies may be applied in other reasoning tasks for more trustworthy decision-making. ## related work dual process theory in ai. dual process theories of cognitive psychology have recently attracted interest in some areas of ai. @xcite apply them to augment reinforcement learning algorithms with deliberative planning of policies through tree search. @xcite combine them for more efficient navigation with ai agents that evolve from slow to fast decision-making while navigating. similarly inspired by dual process theories, hua and zhang (2022) apply logical reasoning over representation learning for more accurate commonsense knowledge base completion. @xcite use dual-process inspired associative selection combined with evidence generation to perform question answering on scientific articles. @xcite propose additional research questions and directions around the application of dual process theories of human cognition in ai. unlike these past works, we apply dual process theories of human cognition in coherent physical commonsense reasoning with plms, both through fine-tuning and in-context learning. ## coherent commonsense reasoning we study the problem of coherent commonsense reasoning in natural language understanding. here, coherent reasoning refers to the ability of a language understanding system to justify a high-level decision on a reasoning task with valid low-level evidence from or beyond the language context that supports the decision @xcite . we specifically focus on the domain of physical commonsense: humans' intuitive set of rules and concepts that explain how the world physically works. since humans build their physical commonsense intuition from a young age @xcite , this knowledge may be considered obvious to most humans @xcite , creating a challenging problem for plms which cannot directly interact with the physical world, and may not encounter much of this knowledge in pre-training. while humans rarely communicate about physical commonsense in language or carefully rationalize their intuitions in terms of low-level physical states, it is essential for ai systems to do so for safety, transparency, and trustworthiness. in the context of coherence, we define physical commonsense reasoning (pcr) as the application of this complex background knowledge to understand language describing physical situations and procedures. pcr may include inferring lowlevel, concrete physical states of participants in text @xcite , or reasoning over them to perform higher-level tasks, e.g., answering questions about procedures @xcite ## har for plm fine-tuning fine-tuning plms is one popular approach for adapting them to downstream tasks, applied in recent work toward coherent commonsense reasoning @xcite , and suitable for applications with compute or privacy restrictions. as shown in figure 2 , we can build explicit heuristic-analytic reasoning structure into plm fine-tuning for our target tasks by deleting parts of the language context that are no longer relevant as the model makes predictions for each step of reasoning, both during training and inference. while our approach provides just one example, this reasoning trick can apply to plm finetuning for any multi-step reasoning problem where the most relevant language context to support reasoning changes with each step. ## har for plm in-context learning while har can benefit domain-specific applications when integrated into plm fine-tuning, it requires expensive training on in-domain data that may sacrifice generalizability. to alleviate the limitations of fine-tuning, we also integrated har into in-context learning, taking advantage of emergent abilities of large plms to perform coherent reasoning through free-form language generation. prompting techniques like chain-of-thought (cot; wei et al., 2022) can be useful in in-context learning to demonstrate valid reasoning strategies to reach conclusions. however, cot has traditionally been used to improve performance on complex high-level tasks by breaking them down into simpler low-level steps and reasoning from the bottom up. meanwhile, the most complex sub-task and bottleneck in our problem is low-level physical state prediction @xcite , which is impossible to further break down, as descriptions of actions directly invoke a world state based on physical commonsense rules of how the world works. since it would thus be difficult to use traditional cot to generate a useful explanation to support physical state prediction, we apply har through a reverse cot method where tiered tasks are demonstrated and predicted in a top-down sequence from high-level decisions to low-level rationalization. 4 in trip, as shown in figure 4 , plms are conditioned to first predict plausibility, a relatively easy heuristic process. to further refine the relevant conthe donut is now inedible." ## conclusion and future work in this work, we took inspiration from the synergy between heuristic and analytic processes in human reasoning to explore how high-level decisionmaking tasks in commonsense reasoning can condition and drive lower-level rationalization tasks supporting them in plms. we proposed two general strategies to integrate heuristic-analytic reasoning (har) into plm fine-tuning and in-context learning, and found that har sharply improved reasoning coherence, outperforming competitive baselines on two benchmark tasks. in fine-tuning, we saw that har enabled plms to learn to reason not only more coherently, but also faster. meanwhile, in in-context learning, we found that improvements were enabled by more faithful attention to the language context within each step of reasoning, shedding light on the nature of incoherence in language generation. while this human-inspired approach shows promising strides toward more trustworthy reasoning from plms, future work should continue to dive deeper into cognitively motivated strategies to further strengthen coherent reasoning in ai systems and improve human-machine alignment. 13