A Visual History of Man's Best Friend ⁠64%

6/9/2026, 3:37:09 PM

BS Summary: This article contains 13 faulty reasoning types, including Framing Effect, Appeal to Emotion, and Attempt to Sell a Product or Service, with Halo Effect as the most egregious example at 42.4% saturation with 53 hits. Analysis detected 356 faulty-reasoning hits from 125 analyzed words, generating a BS Score of 58.4% and a BS Rank of ⁠64% (7,777 of 21,176 articles). This article is worse (more manipulative) than 63.30% of the article peer group.

Dogs have been ubiquitous in the visual arts since people began scrawling on cave walls, and they’ve remained a common element throughout the Western artistic tradition. 
Laqueur says that painters are interested in dogs seeing us because, well, we’re interested in dogs seeing us. 
There’s something about their gaze that draws us to them, and that in turn draws us to drawing them with us. 
Laqueur's book is called "The Dog's Gaze", and he joins us to discuss the remarkable bond between dogs and people and what we learn by trying to see ourselves through their eyes. 
Thomas W. 
Laqueur | Helen Fawcett Distinguished Professor of History Emeritus at the University of California, Berkeley 
Airdate: June 10, 2026 
Article reasoning-pattern comparisonThis article: 0.0%KUER: 2.8%Confirmation Bias0.0%This article: 0.0%KUER: 1.3%Anchoring Bias0.0%This article: 20.8%KUER: 3.4%Availability Heuristic20.8%This article: 20.8%KUER: 1.2%Representativeness Heuristic20.8%This article: 0.0%KUER: 0.5%Hindsight Bias0.0%This article: 0.0%KUER: 1.9%Overconfidence Bias0.0%This article: 40.0%KUER: 7.4%Framing Effect40.0%This article: 0.0%KUER: 1.3%Loss Aversion0.0%This article: 0.0%KUER: 1.2%Status Quo Bias0.0%This article: 0.0%KUER: 0.3%Sunk Cost Effect0.0%This article: 16.8%KUER: 4.4%Optimism Bias16.8%This article: 0.0%KUER: 2.4%Pessimism Bias0.0%This article: 0.0%KUER: 6.3%Negativity Bias0.0%This article: 0.0%KUER: 2.2%Self-Serving Bias0.0%This article: 0.0%KUER: 0.9%Fundamental Attribution Error0.0%This article: 0.0%KUER: 0.2%Actor-Observer Bias0.0%This article: 0.0%KUER: 1.9%In-Group Bias0.0%This article: 0.0%KUER: 0.6%Out-Group Homogeneity Bias0.0%This article: 42.4%KUER: 2.3%Halo Effect42.4%This article: 0.0%KUER: 0.1%Horn Effect0.0%This article: 0.0%KUER: 0.0%Dunning-Kruger Effect0.0%This article: 0.0%KUER: 1.2%Recency Bias0.0%This article: 0.0%KUER: 0.3%Primacy Effect0.0%This article: 0.0%KUER: 0.1%Blind-Spot Bias0.0%This article: 0.0%KUER: 0.6%Ad Hominem0.0%This article: 0.0%KUER: 0.4%Straw Man0.0%This article: 12.0%KUER: 4.7%Appeal to Authority12.0%This article: 0.0%KUER: 1.7%False Dilemma0.0%This article: 0.0%KUER: 1.1%Slippery Slope0.0%This article: 14.4%KUER: 0.2%Circular Reasoning14.4%This article: 20.8%KUER: 4.1%Hasty Generalization20.8%This article: 0.0%KUER: 0.2%Red Herring0.0%This article: 0.0%KUER: 0.7%Bandwagon0.0%This article: 25.6%KUER: 5.6%Appeal to Emotion25.6%This article: 0.0%KUER: 0.7%Begging the Question0.0%This article: 16.8%KUER: 2.4%Post Hoc (False Cause)16.8%This article: 0.0%KUER: 0.1%Tu Quoque0.0%This article: 0.0%KUER: 0.4%Burden of Proof0.0%This article: 0.0%KUER: 0.2%Appeal to Nature0.0%This article: 0.0%KUER: 0.3%Composition/Division0.0%This article: 0.0%KUER: 3.1%Anecdotal0.0%This article: 0.0%KUER: 0.1%No True Scotsman0.0%This article: 14.4%KUER: 1.5%Ambiguity (Equivocation)14.4%This article: 0.0%KUER: 0.0%Gambler’s Fallacy0.0%This article: 0.0%KUER: 0.2%Middle Ground0.0%This article: 0.0%KUER: 0.1%Personal Incredulity0.0%This article: 0.0%KUER: 0.1%Special Pleading0.0%This article: 0.0%KUER: 0.2%Genetic Fallacy0.0%This article: 14.4%KUER: 0.8%Unattributed Quote14.4%This article: 0.0%KUER: 0.7%Quote-first Misdirection0.0%This article: 0.0%KUER: 2.2%Biased Writer Voice0.0%This article: 0.0%KUER: 1.6%Indoctrination0.0%This article: 0.0%KUER: 0.8%Politically Left Leaning Bias0.0%This article: 0.0%KUER: 0.3%Politically Right Leaning Bias0.0%This article: 25.6%KUER: 1.0%Attempt to Sell a Product or S…25.6%

125 words analyzed.

Speakers

2speakers52%attributed speech60writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 7 words • 0.0% coverageWriter's voice • 26 words • 0.0% coverageLaqueur • 18 words • 100.0% coverageWriter's voice • 21 words • 0.0% coverageLaqueur • 32 words • 100.0% coverageWriter's voice • 2 words • 0.0% coverageHelen Fawcett Distinguished Professor of History Emeritus at the University of California, Berkeley • 15 words • 0.0% coverageWriter's voice • 4 words • 0.0% coverage
Selected voice

Laqueur

100%flagged-word coverage
50 attributed words77% of attributed speech78% writer coverage
0%32.5%65.0%Attempt to Sell a Product +64.0 ptsWriter: 0.0%Laqueur: 64.0%64.0%Unattributed Quote+36.0 ptsWriter: 0.0%Laqueur: 36.0%36.0%

Attribution is sentence-level. Pattern percentages are calculated only from words assigned to that voice.

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Analysis

Hover over highlighted words in the article to view the associated bias or fallacy analysis.